EGFR Inhibitor Enhances Cisplatin Sensitivity of Oral Squamous Cell Carcinoma Cell Lines

Epidermal growth factor receptor (EGFR) is involved in multiple aspects of cancer cell biology. EGFR has already been identified as an important target for cancer therapy, with various kinds of EGFR inhibitors currently used in treatment of several human cancers. Recently, EGFR and its downstream signaling pathways were identified as being associated with cisplatin sensitivity. In addition, EGFR inhibitors have shown significant promise for patients who failed cisplatin-based therapy. In this study, we investigated whether treatment with an EGFR inhibitor improves cisplatin sensitivity in oral squamous cell carcinoma (OSCC) cell lines. The effects of a combination of AG1478, a specific EGFR tyrosine kinase inhibitor, with cisplatin were evaluated in cultured OSCC cell lines and cisplatin-resistant sublines. Higher expression of EGFR and p-EGFR was found in the two cisplatin-resistant cell lines compared with the corresponding parental cell lines. In addition, augmented inhibition of OSCC cell growth by the combination of AG1478 with cisplatin was found in both cell lines. These results suggest that the combination of an EGFR inhibitor and cisplatin may be useful as a rational strategy for the treatment of patients with oral cancer with acquired cisplatin resistance.
Introduction
Epidermal growth factor receptor (EGFR) is expressed at high levels in a variety of solid tumors including oral cancers [1, 2]. EGFR and its downstream signaling pathways are involved in multiple aspects of cancer cell biology, including tumor cell proliferation, inhibition of apoptosis, invasion, metastasis, and angiogenesis [1–4]. EGFR has already been identified as an important target for cancer therapy, and various kinds of EGFR inhibitors are currently used in the treatment of several human cancers [5–10].
Cisplatin-based combination chemotherapy displays significant anti-tumor activity against solid tumors of oral squamous cell carcinoma (OSCC). However, the effectiveness of cisplatin in the treatment of recurrent/metastatic tumors is limited because of acquired or intrinsic resistance. EGFR and its signaling pathways are involved in the mechanism of cisplatin resistance. Cells that are resistant to cisplatin have an altered response to the EGF ligand and enhanced activation of the protein kinase [11]. In addition, several studies have suggested that enhanced expression of EGFR may be associated with cisplatin resistance in a variety of solid tumors including oral cancers [12, 13]. Increased EGFR expression may be a survival response by some tumors exposed to chemotherapeutic agents [14].
Increased availability of EGFR inhibitors in cisplatin-resistant cells has also been reported previously [13]. EGFR inhibitors have shown significant activity in cases failing cisplatin-based therapy [15, 16]. Therefore, EGFR blockade may be a useful therapeutic tool in the treatment of patients with acquired cisplatin resistance.
In this study, we established a cisplatin-resistant cell line from an OCSS-derived cell line and investigated the differential EGFR and phosphorylated EGFR (p-EGFR) expression between OSCC cell lines and the cisplatin-resistant sublines. In addition, we examined the effect of combination therapy with an EGFR inhibitor and cisplatin on the growth of OSCC cells.
Materials and Methods
Cell Lines
Two human OSCC cell lines have been established at Wakayama Medical University, Wakayama, Japan. The H-1 line was derived from a biopsy specimen of moderately differentiated OSCC in the lower gingiva. The Sa-3 line was derived from a biopsy specimen of well-differentiated OSCC in the upper gingiva. Both cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Nissui, Tokyo, Japan) supplemented with 10% fetal bovine serum (FBS; Equitech-Bio, Kerrville, TX, USA), 100 units/ml penicillin, and 100 μg/ml streptomycin (Gibco BRL, Grand Island, NY, USA) in a highly humidified atmosphere of 5% CO2 at 37°C.
In accordance with previously described methods [17, 18], the cisplatin (CDDP)-resistant sublines H-1/CDDP and Sa-3/CDDP were established by repeated subculture in the presence of increasing concentrations of cisplatin (Nippon Kayaku Corporation, Tokyo, Japan), from 0.1 μg/ml until cells became fully resistant to cisplatin and could grow exponentially; in each case the final cisplatin concentration was 0.5 μg/ml. The drug-resistant cell lines were passed in drug-free medium, and there was no loss of resistance during the two-month testing period.
Cell Growth Analysis with MTT Assay
Cells were seeded in 96-well plates at 2000 cells per well in DMEM containing 10% FBS. After 24 h, cells were exposed to one of nine concentrations (0.05, 0.1, 0.25, 0.5, 1, 1.25, 2.5, 5 and 10 μg/ml) of cisplatin or five concentrations (1, 5, 10, 20 and 30 μM) of AG1478 (Calbiochem San Diego CA, USA). After cells were incubated with cisplatin or AG1478 for 24 h, medium was changed to drug-free DMEM and cells were incubated for an additional 72 h. Thereafter, the number of cells per well was quantified with a MTT cell growth assay kit (Funakoshi, Tokyo, Japan) according to the manufacturer’s instructions. Briefly, after 10 μl MTT solution was added to each well, the well was incubated for 4 h and scanned at 550–630 nm by a MTP-300 microplate reader (Corona, Tokyo, Japan). Six wells were used for each drug concentration, and the experiment was repeated three times. The 50% inhibitory concentration (IC50) was calculated from the survival curve. In another experiment, to test whether the combination of cisplatin and AG1478 would achieve higher growth inhibition than the single agent at a concentration lower than the IC50, fixed concentrations of each drug were then tested in combination treatment of OSCC cell lines. All cells were exposed to AG1478 for 1 h before cisplatin. The statistical significance of differences was analyzed by applying Mann–Whitney U-test. The level of significance was set at p < 0.05.
Western Blot Analysis
Subconfluent cells were scraped from culture dishes, washed twice with phosphate buffered saline (PBS), and suspended in 700 μl Western blot lysis buffer containing 62.5 mM Tris–HCl (ph 6.8), 25% glycerol, and 2% sodium dodecyl sulfate (SDS). Samples were centrifuged at 15,000 rpm for 20 min at 4°C, after which supernatants were collected. After heating at 95°C for 5 min, equal amounts of proteins were separated with the use of 10% SDS-PAGE. After electrophoresis, proteins were transferred to a PVDF membrane in Tris-glycine buffer containing 20% methanol. The membrane was blocked with 3% skim milk containing 0.01% polyoxyethlene sorbitan monolaurate for 60 min, and incubated overnight with the corresponding primary antibodies {a 1:750 dilution of anti-rabbit polyclonal EGFR antibody [Santa Cruz Biotechnology, Santa Cruz, CA, USA], and a 1:750 dilution of anti-goat polyclonal p-EGFR (Tyr 1173) antibody [Santa Cruz Biotechnology]} at 4°C. The membrane was washed three times for 5 min each with PBS containing 0.05% polyoxyethlene sorbitan monolaurate and horseradish peroxidase-conjugated anti-rabbit or anti-goat antibody for 1 h at room temperature, respectively. Protein signals were visualized by enhanced chemiluminescence using ECL Western blotting detection reagents (Amersham, Arlington Heights, IL, USA) for 1 min and exposed to Kodak Biomax XAR film.
Results
Single Agent Effects
The sensitivities of all cell lines to various concentrations of cisplatin and AG1478 were determined by MTT assay and plotted on a survival curve (Fig. 1). The IC50 values of cisplatin and AG1478 are shown in Table 1. The IC50 values of the H-1/CDDP and Sa-3/CDDP lines to cisplatin were about 10-fold greater than the values for the parental H-1 and Sa-3 lines. Resistance of all cell lines to AG1478 was similar, but the IC50 values for parental cell lines tended to be slightly higher.
Fig. 1Inhibitory effects of cisplatin and AG1478 on growth of OSCC cell lines. The four lines, H-1, H-1/CDDP, Sa-3, and Sa-3/CDDP, were treated with cisplatin (0.05–10 μg/ml; a) or AG1478 (1–30 μM; b) for 24 h as described in the Materials and methods section. Cell viability was measured by MTT assay and is expressed as a percentage relative to control cells. The results showed that cisplatin and AG1478 inhibited OSCC cell growth in a dose-dependent mannerTable 1Cisplatin and AG1478 sensitivity of OSCC cell linesCell lineIC50IC50Cisplatin (μg/ml)AG1478 (μM)H-10.4925.3Sa-30.4328.5H-1/CDDP6.523.3Sa-3/CDDP5.921.2
Combination Effects
All cell lines were treated with AG1478 (10 μM) for 1 hour before cisplatin (0.25 μg/ml) for 24 h. The combination of AG1478 with cisplatin inhibited cell growth >60% in parental cell lines > 50% in cisplatin-resistant cell lines (Fig. 2). In both cell lines, we showed augmented effects of inhibition and showed a large effect of augmentation in cisplatin-resistant cell lines.
Fig. 2Effect of a combination of AG1478 with cisplatin on growth of OSCC cell lines. The four lines, H-1, H-1/CDDP, Sa-3, and Sa-3/CDDP, were treated with AG1478 (10 μM) for 1 h before addition of cisplatin (0.25 μg/ml) for 24 h. Cell viability was measured by MTT assay and is expressed as a percentage relative to control cells. In both cell lines, we showed augmented effects of inhibition and showed large-effect augmentation in cisplatin-resistant cell lines. *p < 0.05, compared with cisplatin-treated cells
Expression of EGFR and p-EGFR
To investigate the molecular basis for the increased sensitivity of chemoresistant tumors to cisplatin, we used Western blot analysis to determine the level of EGFR and p-EGFR expression in parental and cisplatin-resistant cell lines. Higher EGFR and p-EGFR expression was found in cisplatin-resistant cell lines compared with the corresponding parental cell lines (Fig. 3).
Fig. 3Total expression of epidermal growth factor receptor (EGFR) and phosphorylated receptor (p-EGFR). Total expression of EGFR and p-EGFR was determined by Western blot analysis. Ten micrograms of total cellular lysates prepared from parental and cisplatin (CDDP)-resistant cells were resolved by electrophoresis, transferred to a PVDF membrane, and blotted with anti-EGFR and anti-p-EGFR antibodies as described in the Materials and methods section
Discussion
Our studies suggest that changes in the expression of EGFR play an important role in regulating the drug-resistant phenotype of cells in response to cisplatin. Cisplatin is widely used for chemotherapy of many malignancies, especially of oral squamous cell carcinoma (OSCC). However, the effectiveness of cisplatin in the treatment of recurrent/metastatic tumors is limited because of acquired or intrinsic resistance. Increased EGFR expression may be a survival response by some tumors exposed to chemotherapeutic agents [14]. In our study, higher EGFR expression was found in two cisplatin-resistant cell lines compared with the corresponding parental cell lines (Fig. 3).
EGF receptors initiate cytoplasmic signaling through autophosphorylation of their intracellular domains [19]. Phosphorylated EGFR (p-EGFR) cytoplasmic tyrosine residues initiate the activated protein kinase pathway [20]. This mitogen-activated protein kinase (MAPK) pathway is involved in the mechanism of cisplatin resistance [21–23]. Activation of JNK and p38 by cisplatin has been shown to promote apoptotic cell death [22, 23]. In addition, activation of the ERK pathway by cisplatin was reported to promote induction of cell death [21]. In our study, higher p-EGFR expression was found in two cisplatin-resistant cell lines compared with the corresponding parental cell lines. These findings offer further evidence that activation of EGFR downstream signaling pathways is involved in cisplatin sensitivity.
The effect of combining EGFR inhibitors with cytotoxic agents has been studied in various types of cancer [16, 24]. The quinazoline derivative AG1478 is a specific reversible inhibitor of EGFR. The cancer cell growth depression effect of AG1478 has been reported in various carcinomas [24–26]. In our study, the combination of AG1478 and cisplatin showed augmented effects of inhibition in both cell lines and showed large-effect augmentation in cisplatin-resistant cell lines. These results suggest that acquired resistance to cisplatin is associated with enhanced sensitivity to an EGFR tyrosine kinase inhibitor, which correlates with increased EGFR and p-EGFR expression.
Augmented effects of inhibition of OSCC cell growth through the combination of AG1478 and cisplatin provide a potential and novel strategy for patients with oral cancer with acquired cisplatin resistance. In addition, it would be very advantageous if an equal chemotherapeutic effect could be obtained with a smaller dosage of cisplatin. The combination of AG1478 and cisplatin against OSCC definitely deserves additional in vivo and clinical studies.

egfr inhibitor
epidermal growth factor receptor (egfr)
oral squamous cell carcinoma (oscc)
cisplatin-resistant oscc cell line
cisplatin sensitivity and resistance


The role of PET/CT in Cogan’s syndrome

We report on the case of a 60-year-old woman with complaints of fatigue, coughing, anorexia, atypical chest pain, recurrent fever, and also ear pain and hearing loss. A test for anti-neutrophil cytoplasmic antibody (ANCA) was myeloperoxidase positive with p-ANCA specificity. Laboratory acute phase parameters were increased. A 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography investigation showed pathological uptake in the aorta ascendens, with no other involvement of the large vessels. After therapy with methylprednisolon intravenously and later prednisolon orally with methothrexate, her general condition and hearing loss improved both subjectively and objectively. “Atypical” Cogan’s syndrome was diagnosed on the basis of sensorineural deafness with improvement on steroids and large-vessel vasculitis of the aortic arch.
Introduction
The association of nonsyphilitic interstitial keratitis and audiovestibular involvement was first reported in 1934 by Mogan and Baumgartner. Their description was extended several years later by Cogan [1].
In “typical” Cogan’s syndrome, the presence of interstitial keratitis is necessary, the term “atypical” Cogan’s syndrome is used when other types of inflammatory eye disease, including conjunctivitis, uveitis, scleritis, and choroiditis are associated with the vestibuloauditory abnormalities [2]. In many cases, the symptomatology is not only restricted to the eyes and the ears but also other organs, thus resembling systemic vasculitis in one third of the patients. The most common symptoms are cardiovascular, musculoskeletal, neurological, gastrointestinal, and mucocutaneous [3].
Positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-d-glucose (FDG) is becoming increasingly important in diagnosis, staging, and therapy monitoring in clinical oncology and has recently been used in the diagnosis of infectious diseases with elevated intracellular glucose metabolism. Activated inflammatory cells have been shown to overexpress glucose transporters and to accumulate increased amounts of glucose and structurally related substances such as F18-FDG [4, 5].
Therefore FDG-PET is also introduced as a diagnostic means to assess involvement in large vessel vasculitis [6]. In this report, we report the use of FDG-PET/computed tomography (CT) scanning in the diagnosis of Cogan’s syndrome.
Case report
A 60-year-old Caucasian woman was admitted to the hospital with a 4-month history of excessive fatigue, coughing, anorexia and weight loss, night sweats, and atypical chest pain. She also experienced short periods of fever. She experienced headaches and ear pain and hearing loss for over the last month, mainly on the left side, and felt sometimes dizzy. No blurred vision complaints or eye problems were noted. She was not known with any allergies.
For her hypothyroidism (multinodular goiter), she used Thyrax (l-thyroxine)150 mcg once a day. She did not smoke and consumed alcohol only moderately.
The family history revealed a daughter with systemic lupus erythematosus.
Physical examination revealed a pulse of 104, and bloodpressure was 125/85 mmHg and the temperature 37.1°C. Heart sounds were normal, and the lungs were clear. The outer ears were normal. No lymphadenopathy was detected and no scalp tenderness or decreased pulsation at the temporal arteries was noted.
Laboratory tests revealed an erythrocyte sedimentation rate (ESR) of 51 mm/h and C-reactive protein (CRP) of 53 mg/L. Test for rheumatoid factor was 42 kU/L (<10), and tests for antinuclear facor and double-stranded DNA antibodies were negative.
A test for anti-neutrophil cytoplasmic antibody (ANCA) appeared to be myeloperoxidase positive with p-ANCA specificity.
Serum electrolytes and creatinine were normal. Her differential blood count, alkaline phosphatase, and transaminases were normal. The urine gave a negative test for protein, and the sediment contained no white cells, red cells, or casts in the urine.
Initially arteritis temporalis was suspected, but Doppler ultrasonography of the temporal arteries showed no abnormalities. A chest X-ray revealed no interstitial or focal abnormalities. Subsequently, a PET/CT was performed, which showed pathological uptake in the wall of the aortic arch. More intens pathological uptake was seen at the beginning of the aorta descendens in the lateral wall, most likely a sign of perivascular inflammation. Maximum standard uptake value (SUV max) measured 11.9. No other involvement of large vessels was noted (Fig. 1). A magnetic resonance imaging (MRI) scan of the cerebrum showed abnormalities suspicious for bilateral mastoiditis, possibly as a consequence of bilateral otitis media.
Fig. 1
a Transverse fused PET/CT slice showing pathological uptake in the wall of the aortic arch and particularly in the lateral wall and perivascular space adjacent to the truncus pulmonalis (SUV max 11.9). b Follow-up PET/CT 3 weeks later, after treatment with methylprednisolon i.v. and prednisolon orally, showing clearly decreased uptake in the aortic arch (SUV max 4.3). c Second follow-up PET/CT 6 months later, while patient was in a stable condition with methotrexate and low-dose prednisone. Again, high pathological uptake in the aortic arch with higher intensity in the lateral wall and perivascular space adjacent to the truncus pulmonalis (SUV max 12.9)
After placing inner ear tubes, her hearing loss improved only little. Culture of the ear secretion was negative for pathogenic microorganisms. Audiograms confirmed sensorineural hearing loss, particularly in the left ear.
Investigation of the eyes revealed no abnormalities, especially no keratitis.
Therapy was started with three cycles of 1,000 mg methylprednisolon intravenously and later 60 mg prednisolon daily orally. Her general condition and hearing loss improved subjectively. Audiograms 6 weeks later showed also objective improvement of hearing. A control CT scan showed improvement of the abnormalites in both mastoid regions.
As a consequence of the high doses of steroids, risedronate and calcium supplementation were started, and also methothrexate was added for the reason of its steroid-sparing effect. “Atypical” Cogan’s syndrome was diagnosed on the basis of sensorineural deafness with improvement on steroids and large-vessel vasculitis of the aortic arch.
A follow-up PET/CT investigation 3 weeks later showed clearly decreased uptake in the aortic arch, especially a dramatic decrease at the location of the focal uptake in the lateral wall, compared to the first PET/CT. The SUV max was calculated 4.3. ESR and CRP after 2 weeks of therapy were, respectively, 11 mm/h and <2 mg/L. White cell count was still elevated (16.9.109 L).
Six months later, she was in a stable condition with methotrexate and low-dose prednisone, and her ESR, CRP, and white cell count were now, respectively, 24 mm/h, 14 mg/L, and 13.7.109 L.
A third PET/CT showed higher pathological activity, compared to the first PET/CT, in the wall of the aortic arch and in the perivascular space adjacent to the truncus pulmonalis (the SUV max was12.9 compared to the SUV max of 11.9 of the first pretherapy PET/CT investigation). Consequently, the dose of methothrexate and prednisone were both increased to 20 mg/day.
Discussion
The etiology of Cogan’s syndrome is unknown; a minority of patients have rheumatoid factor, antinuclear bodies, and diminished complement levels. Histology of biopsies shows often vasculitis and perivascular inflammation. Giant cells may be present [7]. Specific involvement of large arteries has been reported in Cogan’s syndrome [8]. Aortitis in Cogan’s syndrome is indistinguishable from Takayasu’s arteritis. Cardiac involvement during the course of Cogan’s syndrome is, above all, aortic insufficiency. It is a severe complication that may require valve replacement, without which left ventricle involvement insufficiency develops, which can be fatal [9, 10].
Standard diagnostic modalities such as biopsy, angiography, ultrasound, and MRI are commonly unable to demonstrate the full extent of vascular involvement in large-vessel vasculitis. PET investigations might play an important role here as large-vessel F18-FDG uptake is positively correlated with the level of acute phase reactant markers in patients with large vessel vasculitis. In a study of 18 patients with Takayasu’s arteritis, the F18-FDG-PET examination showed a sensitivity of 92% and a specificity of 100% [11].
This is, to the best of our knowledge, the first report of Cogan’s syndrome diagnosed by the use of FDG-PET/CT scanning. This case report is also supportive in the hypothesis that with PET, the inflammatory activity of large-vessel vasculitis is more accurately assessed compared to laboratory acute phase parameters [12]. The CT component of PET/CT is useful in the precise anatomical localization of the PET abnormalities and may provide information about changes in the wall structure or luminal flow [13].
The conclusion seems justified that F18-FDG-PET/CT is helpful in risk assessment of large-vessel vasculitis, as it provides intrinsically fused morphologic and functional data in a single examination.

cogan’s syndrome
hearing loss
large-vessel vasculitis
f18-fdg pet/ct


High-Volume versus Low-Volume for Esophageal Resections for Cancer: The Essential Role of Case-Mix Adjustments based on Clinical Data

Background Most studies addressing the volume–outcome relationship in complex surgical procedures use hospital mortality as the sole outcome measure and are rarely based on detailed clinical data. The lack of reliable information about comorbidities and tumor stages makes the conclusions of these studies debatable. The purpose of this study was to compare outcomes for esophageal resections for cancer in low- versus high-volume hospitals, using an extensive set of variables concerning case-mix and outcome measures, including long-term survival.
Since Luft published his study about the inverse relationship between surgical volume and hospital mortality in 1979, a plethora of studies has demonstrated an improvement of clinical outcome with increased hospital volume.1 Most of these studies use hospital mortality as the sole outcome measure. Often, data are obtained from insurance company’s databases, and few studies use clinical data for risk-adjustment.2
The surgical treatment of esophageal cancer is often mentioned as one of the procedures for which concentration in high-volume centers might improve outcome.3,4 Nevertheless, a clear volume cut-off point at which a cancer center is justified to perform esophageal resections can hardly be defined.5,6 Also, the volume–outcome literature for esophageal resections is limited to postoperative mortality as the sole determinant of outcome.
Considering the growing evidence for this volume–outcome relationship for esophageal cancer surgery, we decided to investigate the outcome of these procedures in our region from 1990 until 1999. During this study period none of the 11 hospitals affiliated with the Comprehensive Cancer Center Leiden (CCCL) in the Netherlands performed more than seven esophageal resections a year; all are considered low-volume hospitals (LVH).
In contrast to most volume–outcome studies, we decided to use clinical data obtained from the original patients’ files. We retrieved information about comorbid diseases, tumor characteristics, treatment, and outcome. Next to hospital mortality, several determinants of outcome were examined, such as the number of tumor-free margins and complication rates. Assuming that survival is an essential indicator for quality in cancer surgery, we included a 5-year follow-up. To put our data in the right perspective, we compared these outcomes to the results of the topographically nearest high-volume referral center (HVH).
PATIENTS AND METHODS
All surgically treated esophageal carcinomas in the period 1990–1999 were retrospectively identified through the Leiden Cancer Registry (LCR) of the Comprehensive Cancer Center Leiden (CCCL), in which all cancer patients treated in the midwestern part of the Netherlands are registered (1.7 million inhabitants). All 11 hospitals gave consent to participate in this audit and were visited by two investigators to retrieve the original patient files. Patient demographics, pathological notes, data on the surgical and (neo)adjuvant treatments, comorbidity as well as postoperative morbidity, mortality, length of stay, radicality of the resection, and long-term survival could all be retrieved from the patient’s files.
All tumors were staged according to the UICC TNM classification of 1997. This was done by two independent researchers. The obtained pTNM stages were checked with the pTNM stages registered in the LCR. Any discrepancies were discussed between the researchers and a trained data manager from the CCCL. If consensus could not be reached, the pTNM stage was registered as “unknown.”
To make a comparison with the outcomes of the nearest high-volume center, data were categorized according to the database of this center. In this hospital, data of patients operated on for an esophageal carcinoma are prospectively collected by a trained data manager.
Differences in patient, tumor, and treatment characteristics as well as outcome measurements were assessed using the Kruskal–Wallis test for continuous variables and the chi-square test for categorical variables. Logistic regression was used to determine prognostic factors of in-hospital mortality. Variables were entered in the multivariate model as a prognostic factor when P values <.10.
Survival was calculated as the difference between date of surgery and either the date of death or the date of last patient follow-up. For both groups, follow-up of the patients was completed until December 31,, 2005. Observed survival rates were estimated by using the Kaplan–Meier method. The log-rank test was used to assess differences in survival between patients who were operated in LVHs and the HVH. All analyses were conducted using SPSS software (version 12.0; SPSS Inc., Chicago. IL).
RESULTS
Hospital Volume
In 1990–1999 the evaluation and treatment of patients with an esophageal carcinoma were performed in 11 hospitals in the region of the CCCL (one university hospital, five teaching hospitals, and five general hospitals). In 342 patients the tumor was resected with curative intent. Figures 1A and B illustrate the distribution of surgical procedures within the studied time period and between the different hospitals. None of the CCCL hospitals performed more than seven esophageal resections a year, which makes them low-volume hospitals (LVHs).7 In the same period, 561 esophageal resections were performed in the nearest high-volume referral center (HVH); a mean volume of 56 resections a year.
FIG. 1. (A) Number of esophageal resections per year in HVH versus LVH group (1990–1999). (B) Total number of esophageal resections per hospital for HVH and LVHs (1990–1999).
Patient, Tumor, and Treatment Characteristics
Table 1 shows the patient, tumor, and treatment characteristics of both groups. More patients from the HVH had a squamous cell carcinoma and an advanced stage of the disease. Operative strategy as well as adjuvant or neoadjuvant treatment varied widely between the groups. The vast majority of resections in the HVH was performed according to the transhiatal technique, with a gastric tube reconstruction and anastomosis to the cervical remnant esophagus. In the LVH group a substantial number of anastomoses were located in the thoracic cavity, after a (partial) gastroesophagectomy with either a gastric tube reconstruction or esophagojejunostomy. In the pathology, clear surgical margins (R0) were reported in 72 % and 67 %, respectively for the LVHs and the HVH group.
TABLE 1.Patient, tumor, and treatment characteristics of esophageal resections in LVHa and HVHbCharacteristicsLVHHVHP valueNo. of patients%No. of patients%Age (years)6564.240  Range (years)33–8731–83Gender.072  Male2497343878  Female932712322Comorbidity.078  No1424227349  1 organ system1113217932  2 organ systems51158014  ≥3 organ systems113275  Unknown27820Histology.039  Adenocarcinoma2386934762  Squamous962819334  Barrett’s dysplasia4161  Other21143  Unknown2110Tumor localization.740  Cervical esophagus72143  Mid esophagus53158615  Distal esophagus1143320436  Gastroesophageal junction1664925145  Unknown2161Stage (pTNM)<.001  0 and I43126111  II1624721438  III1073118633  IV2169417 Unknown9361(Neo)-adjuvant treatment<.001  None3169246483  Chemotherapy1759317  Radiotherapy0020  Chemoradiation4100 Unknown5210Surgical approach<.001  Abdomino-cervical1504446683  Thoraco-abdominal97286011  Abdomino-thoraco-cervical4313173  Abdominal5215183Anastomoses<.001  Cervical1955754196  Thoracic912782  Abdominal561682  Unknown0040Total No. of patients342561a LVH, low-volume hospitals.b HVH, high-volume hospital.
Morbidity and Mortality
A significantly higher postoperative morbidity rate was found in the LVH group, which probably is also reflected by the longer hospital stay (Table 2). The clinical anastomotic leakage rate differed between both groups: LVHs 17% versus HVH 5%. The mortality rate was almost three times higher for patients treated in the LVHs than those who had their operation in the HVH: 13% vs 5%, respectively (P < .001). None of the LVHs had a mortality rate lower than the 5% of the HVH (Table 3). Univariate analysis showed that hospital volume, age, and comorbidity are prognostic factors for mortality (Table 4). The mortality risk increased with higher age and the number of organ systems affected. Especially cardiac (OR 3.22, CI 1.91–5.44), vascular (OR 2.49, CI 1.45–4.27), and respiratory (OR 1.90 CI 1.09–3.33) comorbidity were risk factors for postoperative mortality.
TABLE 2.Outcome after resection of esophagus for cancer in LVHa and HVHbOutcomeLVHHVHP valueNo. of patients%No. of patients%Margins.93  R02487237767  R1551616128  R23511214  Unknown4121Complications  Surgical complications1444220737.01  General complications1915620737<.001  No complications892624744<.001Hospital stayMedian (days)2114<.001In-hospital   Mortality4513285<.001  Survival  Median (months)2122.90  Range (months)(1–171)(1–158)Total No. of patients342561a LVH, low-volume hospitals.b HVH, high-volume hospital.TABLE 3.Mortality after resection of esophagus for cancer in LVHa and HVHbHospitalsmIn-hospital mortalityNo. of patientsNo. of deaths%HVH561285.0LVH 116212.5LVH 219210.5LVH 32827.1LVH 425312.0LVH 51417.1LVH 62827.1LVH 73425.9LVH 8641218.7LVH 9441022.7LVH 106233.3LVH 1164710.9Total No. of patients903738a LVH, low-volume hospitals.b HVH, high-volume hospital.TABLE 4.Univariate analysis of in-hospital mortalityUnivariate analysisOR95% CIP valueRegion<.001  HVH1.00Refa  LVH2.881.76–4.72Age (years).01  <500.190.04–0.79  50–590.510.25–1.04  60–691.00Refa  >701.200.70–2.04Gender.20  Male1.00Refa  Female0.670.36–1.24Comorbidity<.001  No1.00Refa  1 organ system2.021.06–3.86  2 organ systems4.512.30–8.85  ≥3 organ systems4.971.92–12.83Histology.97  Adenocarcinoma1.00Refa  Squamous0.990.60–1.65Stage.24  I1.00Refa  II0.500.24–1.04  III0.800.39–1.63  IV0.650.26–1.61Tumor localization.33  Cervical/mid esophagus1.00Refa  Distal esophagus/gastroesophageal junction1.410.71–2.80Neoadjuvant treatment.14  No1.00Refa  Yes0.490.20–1.25Surgical approach.31  Transhiatal1.00Refa  Transthoracic1.510.90–2.54.12Anastomosis.46  Cervical1.00Refa  Thoracic1.520.77–3.01  Abdominal1.260.52–3.04a Ref, reference category.
Multivariate analysis showed that both hospital volume and comorbidity were independent prognostic factors for hospital mortality (Table 5).
TABLE 5.Multivariate analysis of in-hospital mortalityMultivariate analysisOR95% CIP valueRegion<.001  HVH1.00Refa  LVHs3.051.82–5.11Age (years).10  <500.220.05–0.96  50–590.600.29–1.25  60–691.00Refa  >701.070.61–1.88Comorbidity.004  No1.00Refa  Yes2.341.30–4.19a Ref, Reference category.
Survival
Figure 2 shows the crude 10-year overall survival rate of all patients, in which an esophageal resection for cancer was performed. Survival rates for patients treated in the HVH are significantly better (P = .01). This survival benefit loses its statistical significance, after exclusion of patients who died postoperatively of complications of the surgical procedure (Fig. 3). Only, when we select patients with stage I and II disease do we see a better survival in the HVH (Fig. 4), meaning that its overall results are worsened by the poor survival in the higher stages of the disease, stage III and IV. This can be explained by the unfavorable tumor mix, with significantly more stage IV disease treated in the HVH, than in the LVHs (16.7 vs 6.1%).
FIG. 2.Overall survival after esophagus resection for cancer: LVHs vs HVH (in-hospital mortality included).FIG. 3.Overall survival after esophagus resection for cancer: LVHs vs HVH (in-hospital mortality excluded).FIG. 4.Overall survival after esophagus resection for stage I and II carcinoma: LVHs vs HVH (in-hospital mortality excluded).
DISCUSSION
Currently, there is extensive interest in comparing outcome of complex surgical procedures between high- and low-volume providers. Most of the studies are registry-based or relatively small. Our series offers additional proof to the volume–outcome relationship, because it is based on clinical data, retrieved from the original patient files. This allows us to make reliable comparisons for comorbidities and tumor stage, which proved to be important prognostic factors for in-hospital mortality and survival.
A review of the evidence for a volume–outcome relationship was published by Dudley in 20008 and Halm in 2002.2 In the latter publication 135 studies were reviewed, of which only five were not from the United States or Canada. The majority of reports were based on state or national hospital discharge databases, where only a few studies used clinical data for risk adjustment. The outcome measure was “death” in 79% of the studies, without analyzing other dimensions of “outcome,” such as morbidity, length of hospital stay, reoperations, et cetera. For cancer-related procedures, long-term survival was not mentioned. Higher-level methodological issues were rarely addressed. Only five studies concerning cancer treatment adjusted for (neo)-adjuvant therapies or the type of surgical resection, but without any adjustment for tumor stage.
Since 2002, more extensive studies on hospital or surgeon volume appeared in the international literature. Birkmeyer reported a total number of 2.5 million operations concerning 14 different surgical procedures derived from the MEDICARE database.9 Mortality was the only outcome measure. Even after risk adjustment, which decreased the outcome differences between high- and low-volume hospitals, the differences in results for esophageal and pancreatic resections were highly significant, favoring surgery in a high-volume center. Two more recently published reviews of the volume–outcome relationship for esophagectomies came up with 12 papers addressing this subject.4,5 Only two of these studies were based on clinical data. Although both showed a decrease in mortality, they failed to show a statistically significant relationship of operative mortality with hospital volume.10,11 In our own review of the literature we identified another study from the United Kingdom using clinical data, in which hospital case volume independently predicted operative mortality2 (Table 6).
TABLE 6.Volume–outcome articles for in-hospital mortality after esophagectomy 1998–2006AuthorJournal/YearDataVolume “cut-off”ConclusionDimick et al.32Ann Thorac Surg 2005Adm<6>SUrbach and Baxter33BMJ 2004Adm<9>NSMcCulloch et al.12BMJ 2003Clin<10–20>SChristian et al.34Ann Surg 2003Adm<22>SFinlayson et al.35Arch Surg 2003Adm<4–9>SUrbach et al.36CMAJ 2003AdmaSDimick et al.37Surgery 2003Adm<7>SBirkmeyer et al.9N Engl J Med 2002Adm<2–4–7–19>SGillison et al.11Br J Surg 2002Clin<19>NSBachmann et al.10Br J Surg 2002ClinaNSDimick et al.38Ann Thorac Surg 2001Adm<4–15>Svan Lanschot et al.7Cancer 2001Adm<10–20>SKuo et al.39Ann Thorac Surg 2001Adm<6>SSwisher et al.40J Thorac Cardiovasc Surg 2000Adm<5>SGordon et al.41J Am Coll Surg 1999Adm<10–20–50>SBegg et al.42JAMA 1998Mixed<5–10>SPatti et al.43J Gastrointest Surg 1998Adm<1–2–4–6>Sa Urbach and Bachmann used equally sized groups and reported only median volumes of these groups.Adm denotes administrative data; Clin denotes clinical data; S denotes significant; NS denotes not significant.
In the present study, independent data managers collected data retrospectively from the patient files. Not only the (in-hospital) mortality rate was obtained, but also a range of other outcome data, such as complication rates, resection margins, length of stay, and long-term survival. In our opinion the latter is an important performance indicator in surgical oncology, surprisingly sporadically mentioned in the volume–outcome literature.
The results of patients treated in 11 low-volume hospitals were compared with the results of patients treated in the nearest high-volume referral center. Significant differences in outcome could be revealed. In-hospital mortality was significantly higher in the low-volume hospitals. The retrieved information about comorbidity and stage of the disease made an extensive preoperative risk and tumor load comparison possible. Risk adjustment is an important issue in outcome research, because patients with severe comorbidity may be unequally distributed between (groups of) hospitals. Especially, when only administrative data are used to assess hospital performances, a selection-bias could lead to inadvertently penalizing those surgeons who provide excellent care to patients with more severe comorbid disease.7,13 Administrative data sets were never designed to predict risk and should probably not be used as such.14 Therefore, the validity of studies that fail to make case-mix adjustments based on clinical data, has to be questioned.
Nevertheless, a multivariate analysis of our data shows hospital volume to be an independent prognostic factor for in-hospital mortality. Although differences in surgical technique could be detected, with more transthoracic esophagectomies and intrathoracic anastomoses in the low-volume group, these factors are not significantly related to mortality. These findings are confirmed by earlier reports.15–18 Also, there is little evidence for a beneficial role of neoadjuvant therapies.19–22 However, above all, choices made concerning diagnostic strategy, neoadjuvant treatments, and surgical technique are related to the knowledge, experience, and judgment of the (team of) specialists.
After exclusion of in-hospital mortality, the survival of patients in the HVH was equal to those treated in the LVHs. However, the results of the HVH were negatively influenced by its case-mix. More patients with stage IV disease were treated in the HVH, corresponding with its status as a tertiary referral center. The very poor survival in this group of patients influences the overall results significantly. Only when we are informed about differences in tumor stage, we are able to detect real differences in survival between patients treated in different hospitals. Although in this study, all pathology reports were reviewed, and the number of lymph nodes resected was equal for both groups, we still have to be cautious suggesting a survival benefit for high-volume surgery. Only when a uniform pathologic evaluation is guaranteed, can we be sure that observed differences in tumor stages are truly characteristic for patient groups. This could be the reason that few studies have attempted to examine the influence of hospital volume on long-term survival in cancer surgery, only one of them concerning esophagectomies.23–27 A recent study from the Netherlands failed to show a survival benefit in high-volume hospitals (>20 resections a year), but did show an improved survival for esophagectomies performed in university compared to non-university hospitals.28 On the other hand, for pancreatectomies and hepatectomies registered in the MEDICARE-database, Fong showed a significantly better survival for procedures performed in high-volume centers.25 In his study, administrative data about age, gender, comorbidity, and extent of the resection were included in a univariate and multivariate analysis, but stages of the disease, radicality, and intent of the resection (palliative or curative) were not reported.
In conclusion, our study shows that hospital volume is an important determinant of perioperative morbidity and mortality in esophageal cancer surgery. Nevertheless, volume in itself is no guarantee for high quality of surgical care in a specific institution. Selecting (only) favorable patients can be the basis of superior results. Therefore, case-mix adjustments are essential in the assessment of surgical performance of different institutions.

case-mix
comorbidity
high-volume hospitals
esophageal cancer–esophagectomy
surgical outcomes


Effects of isoflavones (soy phyto-estrogens) on serum lipids: a meta-analysis of randomized controlled trials

Objectives To determine the effects of isoflavones (soy phyto-estrogens) on serum total cholesterol (TC), low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL) and triglyceride (TG).
Introduction
In recent years, phytoestrogens have attracted a great deal of interests in the medical and scientific literature. It also appears in the lay press for its effects on cardiovascular and menopausal health, and even cancer prevention. These compounds are present in large quantities in soybeans, clover and some legumes. Because of resemblances to human estrogen and the observations that Asian populations who consume more isoflavones compared with women in western countries have less menopausal symptoms, isoflavones are postulated as natural products that may be beneficial to postmenopausal women in cardiovascular health. Commercial products containing different quantities and mixtures of isoflavones are now widely available. A meta-analysis of soy protein in 1995 found significant cholesterols lowering effects when compared with animal proteins [1]. The authors suggest that isoflavones may be the principal physiologically active components responsible for the lipid lowering effects. However, it is still controversial [5]. Many reviews and editorials [2,3] have discussed the relevance of phytoestrogens on cardiovascular health and hypocholesterolemic effects but there was no meta-analysis up to our searching.
Methods
Searching strategy
We searched the databases from ACP Journal Club 1991 to Oct 2002, Cochrane Controlled Trials Register 3rd Quarter 2002, Cochrane Database of Systematic Reviews 4th Quarter 2002, Database of Abstracts of Reviews of Effectiveness 4th Quarter 2002, British Nursing Index (BNI) 1994 to Oct 2002, CANCERLIT 1975 to Oct 2002, CINAHL 1982 to Oct Week 4 2002, CSA – Life Sciences Collection 1982 to Oct 2002, EMBASE 1980 to 2002 Week 45, International Pharmaceutical Abstracts 1970 to Oct 2002, PREMEDLINE Oct 27, 2002, MEDLINE 1996 to Oct Week 4 2002. We searched the keywords with Ovid software version rel6.2.0: 'soy', 'soy protein', 'soybean', 'tofu', 'phytoestrogen', 'isoflavone', 'genistein', 'daidzein', 'formononectin' and 'biochanin A' by the method described by Dickersin 1994 [4]. We did not restrict any languages during the searching. Hand searching was made by retrieving relevant articles from the obtained studies and unpublished data were obtained through contacting experts. We identified on-going trials by searching ClinicalTrials.gov, the UK National Research Register and Meta-register of controlled trials on the internet.
Selection of eligible trials
We included both single and double blind randomized controlled trials with baseline and after treatment values for synthesizing risk (mean) differences. The outcome measures were differences of serum total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and triglycerides (TG) between post-randomization baselines and after treatments. Eligible interventions were isoflavones tablets of single isolated component or any mixtures of genisteins, daidzein, formononectin and biochanin A. Isoflavones interventions in forms of soy protein supplements or diets were also included as long as they compared isoflavones containing treatments with isoflavones depleting controls. Treatments with soy polysaccharides, fiber, and phytosterols as their primary interventions were excluded.
Validity assessment and data abstraction
Two independent investigators reviewed the articles obtained without masking. There were no scorings to the included trials. Data were entered twice to reduce input errors. Inter-rater reliability was not performed. Data were abstracted with a designed form before analysis. Duplicated trials or studies with the same population were counted once to reduce the duplicated publication bias. Data disagreement between the two reviewers was resolved by discussion.
Study characteristics
The characteristics of the obtained studies were tabulated with subtypes of isoflavones interventions, subjects' serum lipids status, dosages of isoflavones and lengths of treatment. Sub-group analysis was performed with different forms of isoflavones interventions, such as isolated genistein or mixtures of isoflavones tablets versus placebo, isoflavones containing versus depleting soy protein diets. Sensitivity analysis was also made across different population characteristics and lengths of treatment. Funnel plots were used to detect possible publication bias or treatment heterogeneity across sample sizes.
Quantitative data synthesis
We obtained the risk differences (RD) from the post-randomization baselines and after-treatment values in each trial and calculated the pooled standard deviation of the RD as:
where n1 and SD1 were sample size and standard deviation from treatment and n2 and SD2 were from control. Inverse variance method was used to pool all trial results [6] with software 'Review Manager 4.2 [31]. Fixed-effects model was used as the method of combination and it was supplemented with random-effects model if necessary. Since log odds ratios were not available in the trials, funnel plots were plotted with standard errors against risk (mean) differences.
Results
Trial flow
The trial flow chart was illustrated in Figure 1. Seventeen studies [7-23] (21 comparisons) with 853 subjects were included in this meta-analysis. Reasons for exclusion were non-randomization, lack of control [24-27], insufficient original data and baseline values [28,29].
Figure 1
Trial flow chart of including isoflavones studies. Databases generated 3542 potential eligible randomized controlled trials (RCT). After careful review, seventeen RCT (reference [7-23]) were included in this meta-analysis.
Study characteristics
The characteristics of the trials included were shown in Table 1. The populations being studied were adults of age between 18 and 73. Soy phytoestrogens interventions varied from isoflavones tablets to isoflavones containing soy diets. Isoflavones tablets were introduced in 11 trials, soy capsule in 1 trial, isolated soy protein (ISP) in 3 trials, and soy foods in 2 trials. The average intake of isoflavones was 73 mg per day (ranging from 28.5 to 150 mg) and length of treatment was 10 weeks (ranging from 2 to 26 weeks). Three trials recruited hyperlipidemic subjects while 8 included participants with normolipidemia. Thirteen studies recruited female, two recruited male and two recruited subjects with both genders.
Table 1
Characteristics of the 17 included studies. There were 9 crossover and 8 parallel studies. The isoflavones tablets introduced were mainly in aglycone form. The abbreviations were listed below table.
Study
Treatment (Rx) †
Isoflavones content in Rx (mg/d) ‡
Control
Isoflavones content in control (mg/d)
No. of subjects
Lipid status §
Subject gender ||
Length of treatment (weeks)
*Calvert 1981 [7]
soy biscuits
NA
soy biscuits
0
10
H
male
4
Clifton-Bligh 2001(I) [8]
T
57
T
Clifton-Bligh 2001(II) [8]
T
85.5
28.5
46
B
female
26
Dewell 2002 [9]
T
150 G = 40; D = 50; glycosides = 60
placebo
0
36
H
female
24
Hale 2002 [10]
T
80 G = 40; D = 40
placebo
0
29
NS
post
2
Han 2002 [11]
soy protein capsule
100 G = 70; D = 18; glycitein = 12
soy protein capsule
0
78
N
peri
16
Hodgson 1998 [12]
T
55 G = 30; D = 1; B = 16; F = 8
placebo
0
59
N
male & female
8
Howes 2000 [13]
T
40 G = 1; D = 0.5; B = 26; F = 16
placebo
0
75
NS
post
5
Mackey 2000 [14]
ISP
65
ISP
< 4
49
H
post
12
*Merz-Demlow 2000(I) [15]
ISP
113–144
ISP
*Merz-Demlow 2000(II) [15]
ISP
55–74
9–11
13
N
female
3
*Nestel 1997 [16]
T
40–80 G= 22–43; D = 17–33; glycitein= 1–3
placebo
0
21
B
female
5
*Nestel 1999(I) [17]
T
80 G = 8; D = 7; B = 49; F = 16
placebo
*Nestel 1999(II) [17]
T
40 G = 4; D = 3.5; B = 24.5; F = 8
0
13
B
female
5
*Samman 1999 [18]
T
86 G = 8.6; D = 7.4; B = 51.4; F = 18.6
placebo
0
14
N
pre
16
*Sanders 2002 [19]
soy burger
56 G = 34.8; D = 21.2
soy burger
2
22
N
male & female
2
*Simons 2000 [20]
T
80
placebo
0
20
N
post
8
Squadrito 2002 [21]
T
54 G= 54
placebo
0
60
N
female
24
*Urban 2001 [22]
T
70 G = 42; D = 27
ISP
3
28
N
elderly men
6
*Wangen 2001(I) [23]
ISP
110–154
ISP
*Wangen 2001(II) [23]
ISP
54–76
6–8.2
18
NS
post
4
* crossover design † 'T' = isoflavone tablet; 'ISP' = isolated soy protein ‡ 'G' = Genistein; 'D' = Daidzein; 'B' = Biochanin A; 'F' = Formononectin §'H' = hyperlipidemic subjects, 'N' = normolipidemic subjects; 'B' = both hyper- and normolipidemic subjects; 'NS' = not specified || 'pre' = premenopausal women; 'peri' = perimenopausal women; 'post' = postmenopausal women
Quantitative data synthesis
We found that isoflavones tablets insignificantly increased serum TC by 0.01 mmol/L (95% CI = -0.17 to 0.18; heterogeneity p = 1.0) (Figure 2); LDL by 0.00 mmol/L (95% CI = -0.14 to 0.15; heterogeneity p = 0.9); HDL by 0.01 mmol/L (95% CI = -0.05 to 0.06; heterogeneity p = 1.0); and triglyceride by 0.03 mmol/L (95% CI = -0.06 to 0.12; heterogeneity p = 0.9). Isoflavones containing soy biscuits or burger had insignificant effects on serum TC with reference to isoflavones depleting soy biscuits or burger. Only isoflavones containing isolated soy protein (ISP) or soy protein (SP) capsule reduced serum TC when compared with isoflavones depleting ISP or SP capsule (Table 2). There was only one trial [11] introduced SP capsule as treatment. Mixtures of the four soy phytoestrogens (genistein, daidzein, formononectin and biochanin A) or isolated genistein did not have significant effects on serum TC. The significant result from intervening the mixtures of genistein and daidzein had shown to be statistically heterogeneous to combine (heterogeneity p = 0.06) as genistein and daidzein tablets were introduced among 3 studies [9,10,16] while Sanders [19] chose genistein and daidzein soy burger and Urban [22] used genistein and daidzein ISP. In subgroup analysis, the effect of genistein and daidzein tablets on serum TC from the 3 studies was shown to be insignificant, 0.1 mmol/L (95% CI = -0.25 to 0.45; heterogeneity p = 0.87). Dose-response effect was not found in any forms of interventions in this meta-analysis. The results remained insignificant when the length of treatment increased. Although one trial [22] (without washout period) produced significant effect, it seemed that study design had no influence on the serum TC. There were insignificant decreases of serum TC among pre- and postmenopausal women. Interestingly, cholesterol lowering effects could only be found among normolipidemic subjects but not participants with hyperlipidemia and men were shown to be benefited with isoflavones treatment. Isoflavones in the forms of tablets and ISP were all insignificant on serum low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL) and triglycerides (TG). These results were shown in Table 3. Funnel plots of isoflavones tablets were asymmetrical and the results of the published trials tended to favor treatment.
Figure 2
Funnel plot of risk difference of isoflavones tablets versus placebo on serum total cholesterol. Total treatment size was 274 and total control was 208. The pooled risk difference of isoflavones tablets versus placebo was 0.01 with 95% confidence interval between -0.17 and 0.18. Heterogeneity p-value was 1.00. Note: 'N' = sample size; 'SD' = standard deviation; 'RD' = risk difference; '95% CI' = 95% confidence interval
Table 2
Subgroup analysis of the effects of isoflavones on serum total cholesterol. Isoflavones were shown to have significant benefits when given in the form of ISP or soy protein capsule. Regardless the forms of prescription, isoflavones decreased serum total cholesterol among normolipidemic but not hyperlipidemic subjects.
Subgroup outcome
No. of comparisons
No. of subjects ||
Treatment effect on TC (mmol/L) ¶
Heterogeneity p-value §
Form of intervention †:
 a.) isoflavones tablets
12 [8-10,12,13,16-18,20,21]
482
0.01 (-0.17, 0.18)
1.00
 b.) ISP(+)
8 [14,15,22,23]
229
-0.11 (-0.21, -0.01)*
0.34
 c.) soy protein capsule
1 [11]
78
-0.69 (-1.19, -0.19)*
NA
 d.) soy biscuit
1 [7]
20
-0.05 (-0.47, 0.37)
NA
 e.) soy burger
1 [19]
44
0.09 (-0.38, 0.56)
NA
 f.) Overall
21 [7-23]
853
-0.09 (-0.18, -0.01)*
0.77
Isoflavones mixture ‡:
 G&D&B&F
5 [12,13,17,18]
214
-0.06 (-0.34, 0.21)
0.96
 G&D
6 [9-11,16,19,22]
285
-0.22 (-0.41, -0.03)*
0.06
 G
1 [21]
60
0.10 (-0.35, 0.55)
NA
Isoflavones intake (mg per day):
 < 50
3 [8,13,17]
132
-0.12 (-0.54, 0.29)
0.99
 51–100
12 [8,10,12,14,15,17-23]
484
-0.10 (-0.21,0.01)
0.74
 101 – 150
4 [9,11,15,23]
176
-0.10 (-0.24, 0.05)
0.09
Design :
 parallel crossover
9 [8-14,21]
447
-0.08 (-0.27, 0.10)
0.48
  a.) no washout
1 [22]
56
-0.43 (-0.73, -0.13)*
NA
  b.) washout mentioned
6 [15,19,20,23]
208
-0.07 (-0.17, 0.03)
0.91
Gender:
 male
2 [7,22]
76
-0.30 (-0.54, -0.05)*
0.15
 female
17 [8-11,13-18,20,21,23]
674
-0.07 (-0.17, 0.02)
0.89
Treatment length:
 2–10 weeks
14[7,10,12,13,15-17,19,20,22,23]
541
-0.09 (-0.18, 0.00)
0.84
 11–20 weeks
3 [11,14,18]
155
-0.22 (-0.52, 0.07)
0.07
 21–30 weeks
4 [8,9,21]
157
0.02 (-0.28, 0.31)
0.96
Menopausal status:
 pre-menopausal
3 [15,18]
80
-0.06 (-0.17, 0.06)
0.58
 peri-menopausal
1 [11]
78
-0.69 (-1.19, -0.19)*
NA
 post-menopausal
13 [8-10,13,14,16,17,20,21,23]
516
-0.05 (-0.20, 0.10)
1.00
Subjects with:
 normolipidemia
8 [11,12,15,18,19,21,22]
377
-0.11 (-0.21, -0.01)*
0.08
 hyperlipidemia
3 [7,9,14]
105
-0.05 (-0.36, 0.26)
1.00
* statistically significant † 'ISP (+)' = isoflavone containing isolated soy protein; 'ISP (-)' = isoflavone depleting isolated soy protein ‡ 'G' = Genistein; 'D' = Daidzein; 'B' = Biochanin A; 'F' = Formononectin § NA = not applicable || Since subjects acted as their own controls in a crossover trial, the 'calculated' total number was thus doubled in a single pair comparison. ¶ 95% confidence interval in parenthesis
Table 3
Effects of isoflavones on serum LDL-cholesterol, HDL-cholesterol and triglycerides levels. Isoflavones in the forms of tablets or isolated soy protein (ISP) did not show significant benefits over serum LDL-cholesterol, HDL-cholesterol and triglycerides levels. The results were not heterogeneous to combine.
Type of intervention*
No. of trials
No. of comparisons
No. of subjects
Treatment effect (mmol/L) †
Heterogeneity p-value
1.) Isoflavones tablets
 LDL
9
11[8,10,12,13,16-18,20,21]
446
0.00 (-0.14, 0.15)
0.94
 HDL
10
12[8-10,12,13,16-18,20,21]
482
0.01 (-0.05, 0.06)
0.98
 TG
9
12[8-10,12,13,16,17,20,21]
482
0.03 (-0.06, 0.12)
0.93
2.) ISP (+)
 LDL
3
5 [14,15,23]
173
-0.06 (-0.16, 0.03)
0.84
 HDL
3
5 [14,15,23]
173
-0.01 (-0.07, 0.05)
0.97
 TG
3
5 [14,15,23]
173
0.02 (-0.05, 0.09)
1.00
* 'ISP (+)' = isoflavones containing isolated soy protein; 'LDL' = Low density lipoprotein cholesterol; 'HDL' = High density lipoprotein cholesterol; 'TG' = Triglycerides † 95% confidence interval in parenthesis
Discussion
Main findings
Phytoestrogens with either isolated genistein or isoflavones mixtures of genistein, daidzein, formononectin and biochanin A were found to be statistically insignificant in lowering serum total cholesterol (Table 2). Resolving the heterogeneity from pooling the mixtures of genistein and daidzein, we found that tablets prescription with these two isoflavones [9,10,16] yielded insignificant benefits over serum TC, 0.1 mmol/L (95% CI = -0.25 to 0.45; heterogeneity p-value = 0.87). One trial [19], introducing soy burger as the prescription of these 2 isoflavones, found insignificant benefits. Another trial [22], prescribing them in the form of isolated soy protein (ISP), had shown a decrease in serum TC by 0.43 mmol/L (95% CI = -0.73 to -0.13). It may be a result of bias as this trial lacked a washout period. When dose-response effect and treatment length were taken into account, phytoestrogens seemed to have insignificant effects over serum cholesterol (Table 2). Isoflavones in the forms of tablets, soy burger or biscuits seemed to have insignificant benefits over serum TC. Only Han [11] reported a significant benefit for isoflavones in the form of soy protein capsule. Isoflavones in the forms of tablets or ISP could not be shown to have significant benefits over serum low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol and triglycerides (TG) (Table 3).
We could not demonstrate significant benefits with all types of prescriptions when the dosages or treatment lengths increased. No dose-response effect was detected nor benefits on serum cholesterol with any forms of prescription among pre- and post-menopausal women and hyperlipidemic subjects. The benefit over normolipidemic subjects was heterogeneous (p = 0.08). It may be again the result of prescription diversity. Separated analysis found that isoflavones tablets [12,18,21] had insignificant effect on serum TC among normolipidemic subjects, 0.05 mmol/L (95% CI = -0.23 to 0.33; heterogeneity p = 0.9). Only isoflavones in the form of soy protein capsule [11] or ISP [22] reported benefits on participants with normolidemia. In fact, some suggest that the beneficial effects of soy protein require synergistic reactions between isoflavones and other soy components [32]. This hypothesis was supported by some animal experiments [33-35]. It seemed that isoflavones in the form of tablets, up to 150 mg per day (genistein about 50 mg, daidzein about 50 mg), had no significant benefits on serum cholesterols in this meta-analysis. Phytoestrogens treatments in forms of soy protein supplements or soy protein capsule may need further investigations, especially interactions between other chemically active components inside prescriptions.
Validity and limitation
In subgroup analysis, the sample sizes were inevitably reduced and there might be insufficient power to detect small significant differences. Isoflavones treatments with dosages beyond 150 mg per day or treatment lengths beyond 26 weeks could not be shown in this study. The effects on serum cholesterols among subjects with hypertension, diabetes mellitus or other cardiovascular risk factors were also beyond this meta-analysis. Generalizability over other phytoestrogens such as coumestans, lignans and resveratrol may need further trials to clarify the questions. No funnel plots were shown in this study because the choice of risk differences as the horizontal axes may lead to funnel plot asymmetry [36]. Publication bias was still possible although vigorous searching had been attempted.
Biological plausibility
It was suggested that isoflavones readily bind estrogen receptors – beta (ER-beta) which are an important receptors in both the central nervous and cardiovascular systems [37-39]. Isoflavones may also have anti-oxidant effects on blood vessels [40-42]. However, compared with estradiol, isoflavones bind estrogen receptors with 100 and 1,000 times less affinity [30]. Both conjugated and aglycone forms of daidzein and genistein are rapidly cleared from the plasma following a single dose of isoflavones [43] and urinary excretion is virtually nil within 48 hours after ingestion [44,45]. Biological effects of soy phytoestrogens are still controversial. The hypothesis from an influential meta-analysis [1] stating soy estrogens may be responsible for the lipid reduction effects was criticized for the fact that the trials involved largely isoflavone-free soy protein products [46]. Apart from phytoestrogens, in fact, some other components such as phytic acid, saponins and fibers were potential candidates responsible for the hypocholesterolemic effects [32,48,49].
Suggestion to future trials
Tablets of single isolated isoflavone with considerable dosages may be important in future randomized trials. Intervention with isoflavones mixtures may be helpful but interactions between components should be handled with extra cares. Introduction of phytoestrogens in forms of soy diets is not suggested because we found much heterogeneity inside the diets, ranging from soy biscuits to burger, which may be difficult to combine and analyze. Confounding factors such as fiber, fatty acids, amino acids and energy intake are especially hard to control. Complications associated with hyperlipidemia, such as coronary heart disease (CHD) or cardiovascular accident (CVA), may be selected as other endpoints in future trials. It may help constructing convincing funnel plots with less heterogeneity as trials accumulate.
Conclusions
Isoflavones tablets, up to 150 mg per day, had insignificant effects in lowering serum total cholesterol, LDL-cholesterol and triglyceride. There was also insignificant benefit over serum HDL-cholesterol. The results were consistent when tablets were introduced as isolated genistein, mixture of genistein and daidzein, or mixture of genistein, daidzein, formononectin and biochanin A. No significant effects were found among participants with normo- or hyperlipidemia and women with pre- or postmenopausal status. Isoflavones interventions in the forms of soy proteins, such as isolated soy protein (ISP), soy diets or soy protein capsule, were inconclusive due to inadequate sample size, heterogeneity and presence of potentially uncontrolled confounders.
List of abbreviations
'ISP' = isolated soy protein; 'TC' = total cholesterol; 'LDL' = Low density lipoprotein cholesterol; 'HDL' = High density lipoprotein cholesterol; 'TG' = Triglycerides
Competing interests
None declared.
Authors' contributions
JY participated in the design of this manuscript. JY and YTF participated in abstracted the data and performed statistical analysis. All authors read and approved the final manuscript.

isoflavones soy lipids meta-analysis phytoestrogens


SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

Objective Risk adjustment systems now in use were developed more than a decade ago and lack prognostic performance. Objective of the SAPS 3 study was to collect data about risk factors and outcomes in a heterogeneous cohort of intensive care unit (ICU) patients, in order to develop a new, improved model for risk adjustment.
Introduction
Following the publication in the early 1980s of the Acute Physiology and Chronic Health Evaluation Score (APACHE [1]), Simplified Acute Physiology Score (SAPS [2]), and-some years later—APACHE II [3] systems, outcome prediction became an important topic among European intensivists. Ten years later, a new generation of these instruments was published: APACHE III [4], SAPS II [5], and Mortality Probability Model (MPM) II [6]. All of these newer systems were developed by using sophisticated statistical techniques in large multinational databases, and were found to perform better than their predecessors [7, 8].
The availability of such sophisticated methods for risk adjustment facilitated outcome research in critically ill patients, which became increasingly important over time. Risk adjustment systems now have a fixed place in critical care research for various purposes. At the patient level, the reporting of severity of illness and the use of risk-adjusted mortality rates to draw inferences from their results are a prerequisite for any study to be published. At the intensive care unit (ICU) level, observed-to-expected mortality ratios (or the use of direct standardisation techniques based on severity scores) have become standard for assessing the impact of ICU-related factors on outcome, such as the effects of organisation and management [9, 10].
However, a series of studies assessing the performance of risk adjustment systems unveiled a lack of prognostic performance of these systems: In most cases, lack of calibration was evident over several subgroups of patients, often accompanied by an underestimation of mortality in low-risk patients and an overestimation in high-risk patients. This pattern was observed for all published outcome prediction models in several countries [11, 12, 13, 14, 15, 16, 17, 18] and seemed to be worsening over time [19].
For this reason, several researchers tried to improve the prognostic performance of various systems through recalibration, using one of two possible approaches. A level 1 customization requires calculation of a new equation for the prediction of hospital mortality (without changing the weights of the constituent variables). A level 2 customization involves a reweighting of each variable contained in the model. Although recalibration was able to improve prognostic accuracy in some cases [13, 14], it generally did not solve the various problems inherent in the models.
These problems can be classified as either user-, patient-, or model-dependent. User-dependent problems include differences in the definitions and application criteria [20, 21]. Patient-dependent problems are mainly shifts in the baseline characteristics of the populations over time [22]: age distribution, distribution of illnesses, and the development of new treatments, all of which affect prognosis. Model-dependent problems have many different causes, such as the lack of important prognostic variables (e.g., diagnostic information [4, 23]) or the presence, location and aetiology of infection [24, 25, 26]. Confounding variables and statistically wrong assumptions [9, 27] also distort performance results.
If recalibration is not sufficient to improve the performance of the prognostic model, the only alternative is to develop a new model that takes into account the results of studies done since the original model was developed. This means incorporating missing variables that have been shown to affect outcome, minimizing problems with the application of the model, and reducing the possibility of other confounders.
The objective of the SAPS 3 project was to cope with the above-stated problems by developing a new model for improved risk adjustment in critically ill patients. Another important goal was to make the new model available free of charge for use in the scientific community.
In the SAPS 3 study (which took place at the end of 2002), data about risk factors and outcomes in an international multicentric cohort of critically ill patients were prospectively collected so that a high-quality database would be available for further analysis of the associations between risks and outcomes in our patients.
Materials and methods
Project Organization
The SAPS 3 project was conducted by the SAPS 3 Outcomes Research Group. The project was endorsed by the European Society for Intensive Care Medicine (ESICM, http://www.esicm.org) and conducted in cooperation with the Section on Health Services Research and Outcome of the ESICM. The SAPS 3 Outcomes Research Group consists of a project coordinator and a steering group. The steering group was responsible for the scientific conduct and consistency of the project. An additional advisory board integrated further scientists with special expertise who were asked for comments on the scientific content and for help in conducting the project. The complete board lists can be found in Appendix D of the Electronic Supplementary Material (ESM).
During the data collection phase, a coordination and communications centre (CCC) was installed. The CCC was responsible for the management and control of the project. This included the administration of all project tasks and implementation of actions and activities as necessary; communication between project partners (e.g., centres, researchers and institutions) through sampling and distribution of necessary information; and pooling and administration of the data provided by project participants. In addition, the CCC provided almost around-the-clock service to answer urgent questions and resolve problems during the phase of data collection.
In each country, a country coordinator was responsible for operational management and direct communication with the participating ICUs in that country, including giving specific help when necessary. The country coordinator was responsible for ensuring completion of the various tasks required of the participating ICUs. The list of country coordinators can be found in Appendix E of the ESM.
At the ICU level, an ICU coordinator was responsible for local activities, such as obtaining approval from the local ethics or data-protection committees where applicable. In addition, the ICU coordinator was responsible for supervising the daily data collection, problem management, controlling the completeness of the data, data quality control, training medical and nonmedical staff for data collection, management of the data, and transmission of the data to the CCC or country coordinator. The list of ICU coordinators can be found in Appendix F of the ESM.
Data collection
Patient data were recorded by using either online data collection software (provided by iMDsoft, Tel Aviv, Israel) or the SAPS 3 stand-alone database system (provided by the CCC). The latter software used a Microsoft Access database (Microsoft Corporation, Redmond, WA, USA) for data storage and needed no Internet connection for data entry. Both systems maintained a variety of plausibility controls to ensure the quality of the recorded data. Each variable was precisely defined before the start of data collection (see Appendix C of the ESM). Detailed definitions of the variables were available to participants in both paper and electronic form. To facilitate plausibility checking, each variable was assigned a probability range, encompassing the range of probable values for that variable. In addition, a range of possible values (storage range) for that variable was defined (e.g., for FiO2, no values <21% or >100% could be accepted). Thus, formal plausibility controls in the software systems were used wherever possible and ensured the maximum of data quality checking during data collection.
Participants who could not use one of the two software options were allowed to record the data on paper forms and submit them to the CCC (n=26 ICUs). Patient data were then entered into the SAPS 3 stand-alone software system and thus checked for plausibility. In cases of uncertainty, ICU coordinators were contacted for clarification.
In addition, each ICU received a questionnaire with detailed questions about ICU structures and about resources available in other areas of the hospital.
Data were collected at ICU admission, on days 1, 2 and 3, and on the last day of the ICU stay. Data from the day of admission (aside from sociodemographic data such as age and sex) were categorized into different levels: (i) data about the condition of the patient before ICU admission, such as chronic conditions and medical diseases; (ii) data about the patient’s condition at ICU admission, such as the reason for admission, infection at admission, and surgical status; and (iii) data about the patient’s physiologic derangement at ICU admission. These data were collected within an hour before or after ICU admission.
On the following days of the ICU stay, further information was collected: severity of illness, as measured by the SAPS II [5]; number and severity of organ dysfunction, as measured by the Sequential Organ Failure Assessment (SOFA) [28]; length of ICU and hospital stay; and outcome data, including vital status at ICU and hospital discharge. All patients were subjected to mandatory follow-up until hospital discharge, but not longer than 90 days after ICU admission. Patients still remaining in the hospital at 90 days were at that time classified as being “still in the hospital”.
To record diagnoses, a three-level system was used. (i) An acute medical disease was recorded for all patients, independent of surgical status, i.e., the acute (or acute on chronic) disease that best explained the ICU admission. If the reason for ICU admission was infectious disease, then this was recorded. (ii) Surgical status at admission and the anatomic site of surgery were recorded for all patients undergoing surgery during the hospital stay before ICU admission. (iii) A concrete reason for admission had to be selected. At least one reason for admission was required, but several selections were possible (one within each organ system). If no other reason was present, at least “basic and observational care” had to be selected.
All participants received detailed documentation of patient- and ICU-based data items as well as a detailed description of the data collection process. Moreover, specific forms to check the completeness of the patient-based documentation were provided. Additionally, a training session for ICU coordinators was organised at the 15th Annual Congress of the ESICM in Barcelona, Spain, before the start of data collection. Throughout the project, the project website (http://www.saps3.org) provided all necessary information. In addition, the CCC was available to answer questions by email, fax and phone. Data were to be collected from all consecutively admitted patients between 14 October and 15 December 2002. ICUs with a high number of beds (and thus also admissions) could stop patient enrolment after contributing 100 patients.
Database
Data were collected and pooled by the CCC. The final database file was then imported into the SAS system, Version 8e (SAS Institute Inc., Cary, USA). Data cleaning was accomplished through the application of a variety of additional plausibility controls and cross-checking of information between redundant data fields.
A total of 22,791 admissions were recorded in the 309 participating ICUs during the study period. For patients who were admitted more than once (n=1,455), only the first admission was included, giving 21,336 admitted patients. Patients who were <16 years of age (n=628), those without ICU admission or discharge data (n=1,074), and those with records that lacked an entry in the field “ICU outcome” (n=57) were excluded. The Basic SAPS 3 Cohort thus comprises 19,577 patients from 307 ICUs.
For the development of a predictive model for hospital mortality as outcome, patients with a missing entry in the field of “vital status at hospital discharge” (n=2,540) or an entry of “still in the hospital” at the end of the follow-up period (n=253) were further excluded. The SAPS 3 Hospital Outcome Cohort thus comprises 16,784 patients from 303 ICUs.
Because the study was an observational study and no additional interventions were performed, the need for informed consent was waived by the institutional review board. Each ICU, however, was made responsible for obtaining local permissions as necessary.
Data quality
Recorded data were evaluated for completeness of the documentation and reliability. Interrater quality control was performed through rescoring of the data from day 0 (the day of ICU admission) for three randomly selected patients in each ICU. From the rescored data, kappa coefficients and intra-class correlation coefficients were calculated, as appropriate. Availability of the variables necessary to calculate the SAPS II was used as an indicator for the completeness of the data.
Statistical analysis
Statistical analysis was performed using the SAS system, version 8e (SAS Institute Inc., Cary, NC, USA). A P value of <0.05 was considered significant. Unless otherwise specified, results are expressed as median and interquartile ranges (quartile). Observed-to-expected (O/E) mortality ratios were calculated by dividing the number of observed deaths per group by the number of expected deaths per group (as predicted by the SAPS II). To test for statistical significance, we calculated 95% confidence intervals (CI) according to the method described by Hosmer and Lemeshow [29]. The Hosmer-Lemeshow goodness-of-fit Ĥ-statistic and Ĉ-statistic [30] were used to evaluate the calibration of the SAPS II. Discrimination was tested by measuring the area under the receiver operating characteristic (aROC) curve, as described by Hanley and McNeil [31]. Reliability of data collection was analysed using K-statistics or intra-class correlation coefficients, as appropriate. Statistical methods used for the development of the predictive model are described in Part 2 of this report.
Results
Data quality
Four hundred eighty-three rescored patients could be identified and linked to their original counterparts (2.5% of admitted patients). Data quality was found to be excellent, with the majority of coefficients being >0.85. Only two of the more than 50 tested variables had coefficients <0.80 (body weight, 0.79; positive end-expiratory pressure, 0.72), and only one was <0.70 (leukocytes [maximum], 0.57). For a detailed list of coefficients see Table E1 in the ESM. Data completeness was also found to be satisfactory, with 1 [0–3] SAPS II parameter missing per patient.
Description of ICUs
The Basic SAPS 3 cohort includes 307 ICUs from 35 countries. On average each ICU contributed 50 (27–78) patients to the cohort. To assess heterogeneity of results between different geographic regions, seven regions were defined: Australasia, Central and South America, Central and Western Europe, Eastern Europe, North America, Northern Europe, and Southern Europe and Mediterranean countries. The allocation of countries to these regions can be seen from Table E10 of the ESM.
Seventy percent of the participating ICUs identified themselves as mixed medical-surgical (Table E2, ESM). Roughly half of the ICUs (46%) were located in university-affiliated or teaching hospitals. Eighty-four percent of ICUs (n=258) reported having a full-time medical director, and 272 (88.6%) reported having a full-time nursing director. On weekdays, 76.6% of ICUs reported having an intensivist available on the ICU 24 hours per day, whereas 6.2% had an intensivist available in the hospital. In 12.1% of ICUs, the intensivist was at home, on-call, during the daytime. During weekends, this proportion did not change much (74.3%, 5.5%, and 15.0% on the ICU, in the hospital, and on-call, respectively). None of the participating ICUs reported having no intensivists available during night or weekend shifts.
Description of patients
The Basic SAPS 3 Cohort comprises 19,577 patients admitted to participating ICUs during the study period. More than 70% of patients were admitted from the same hospital as the ICU, with operating rooms, emergency departments and normal wards contributing most of the patients (Table 1). Almost two thirds of the admissions were classified as unplanned. The mean age of patients was 60.0±17.7 years (Fig. 1), and 39.2% were female. Comorbidities were recorded in 65% of admitted patients, with arterial hypertension, chronic obstructive pulmonary disease, and chronic heart failure being the most frequent (Table E3, ESM).Table 1 ICU admission data for the two cohorts (Basic cohort: SAPS 3 basic cohort; HO cohort: SAPS 3 Hospital Outcome Cohort; n: number of patients)Basic cohortHO cohortn%n%Number of patients19,57716,784100.0Gender  Female7,67839.26,61039.4  Male11,88160.710,16160.5   Missing180.1130.1Age, years (median, quartiles)6349-746449-74Origin  Home2,81014.42,34314.0  Same hospital13,92671.112,06371.9  Chronic care facility740.4640.4  Public place5192.74322.6  Other hospital2,12510.91,79110.7  Other800.4590.4  Missing430.2320.2Intra-hospital location before ICU admission  Emergency room5,41927.74,63027.6  Intermediate care unit/High dependency unit5622.94752.8  Operating room7,53738.56,44938.4  Other5522.84132.5  Other ICU6983.66113.6  Recovery room4822.54002.4  Ward3,41117.43,03618.1  Missing9164.77704.6ICU admission status  Planned admission6,75034.55,59833.4  Unplanned admission12,33863.010,80164.4  Missing4892.53852.3Acute Infection at ICU admission  No infection15,25477.912,96877.3  Clinically improbable/colonization3421.72981.8  Clinically probable/documented2,76114.12,42214.4  Microbiologically documented12066.21,0836.5  Missing130.1130.1Surgical status  No surgical procedure8,43743.17,30543.5  Scheduled surgery6,80034.75,70034.0  Emergency surgery3,32117.02,93017.5  Missing1,0195.28495.1Fig. 1 Age distribution and associated mortality. The figure shows the age distribution of the Basic SAPS 3 Cohort (n=19,577) and the corresponding ICU mortality rates for each stratum. Columns: Number of patients as percentages of the whole cohort; squares: ICU mortality rates for the corresponding stratum
Cardiovascular, respiratory and neurologic diseases were the most frequent organ-specific reasons for admission (Table E4, ESM). The acute medical diseases necessitating ICU admission included a broad spectrum of diagnoses (Table E5, ESM). Approximately one half of the patients underwent surgery before ICU admission, with abdominal, cardiac and vascular surgery being the most frequent procedures (Table E6, ESM).
Regarding discharge details (Table 2), it is notable that a high percentage of patients were discharged unplanned (8.15%), i.e., without at least a 12-hour planning window. 15.2% of patients from the SAPS 3 Basic cohort died within the ICU. As can be seen from Table 3, patient cohorts differed significantly between regions. Both, ICU and hospital mortality rates exhibited a broad spectrum between ICUs: hospital mortality was on average 28% (17–42%) in the SAPS 3 Hospital outcome cohort.Table 2 ICU discharge and outcome data for the two cohorts (Basic cohort: SAPS 3 basic cohort; HO cohort: SAPS 3 Hospital Outcome Cohort; n: number of patients;
ICU LOS: ICU length of stay; IMCU/HDU: intermediate care unit/high dependency unit; Q1, Q3: lower and upper interquartile range, respectively)Basic cohortHO cohortn%n%Number of patients19,577100.016,784100.0ICU LOS, days (median, quartiles)21–621–6ICU discharge—destination  Home4382.23612.2  Same hospital14,94676.312,47774.3  Other hospital1,0295.38525.1  Missing3,16416.23,09418.4Intrahospital discharge  Emergency room580.3500.3  IMCU/HDU2,22211.41,87311.2  Other3031.52571.5  Other ICU5833.04792.9  Recovery room3061.62181.3  Ward12,25062.610,29161.3  Missing3,85519.73,61621.5ICU discharge—status  Planned discharge14,87276.012,26273.1  Unplanned discharge1,5958.11,4678.7  Missing3,11015.93,05518.2Risk adjustment  SAPS II score (median, Q1–Q3)3020–423121–43  SOFA score (median, Q1–Q3)96–1196–11Outcome  ICU mortality (%)15.217.7Table 3 ICU admission and discharge data for the seven defined geographic regions (SAPS 3 Basic Cohort; n=19,577)AustralasiaCentral & South AmericaEastern EuropeCentral and Western EuropeNorthern EuropeSouthern Europe and Mediterranean countriesNorth AmericaNumber of patients2,235 2,540 1,084 4,712 355 7,854 797 Females, %38.0 44.6 42.2 40.1 42.5 36.9 38.0 Age, years (median, quartiles)5945–713146–746248–726552–756651–766459–746449–75SAPS II score (median, Q1-Q3)2819–403020–422718–432921–403525–463222–442920–39SOFA score (median, Q1-Q3)86–1086–1197–1185–1197–1197–1196–11ICU mortality, %12.7 17.4 16.9 10.8 20.6 18.1 8.5
Performance of the SAPS II
The performance of the original SAPS II model [5] (using data from the first 24 hours) was tested in the SAPS 3 Hospital Outcome Cohort (n=16,784). Discrimination was good with an aROC of 0.83 (95% CI: 0.824–0.838). SAPS II showed underestimation of hospital mortality: The O/E ratio of the overall cohort was 1.08 (1.06–1.10). O/E ratios significantly differed between regions: from 0.86 (0.81–0.91) for Central and Western Europe to 1.32 (1.25–1.38) for Central and South America, with four out of the seven defined regions exhibited O/E ratios significantly different from 1 (Table E7, ESM). Calibration, as assessed by the Hosmer-Lemeshow Ĥ + Ĉ statistics, was poor for the overall cohort: Ĥ 227.21, Ĉ 184.70; both p<0.0001; This lack of calibration was present for all tested subgroups except for the region of North America (see Table E7, ESM).
Discussion
To the best of our knowledge, the SAPS 3 study is the largest prospective epidemiologic multicentre, multinational study conducted in health services and outcomes research in intensive care medicine to date.
The project was first intended to focus on Europe because it was believed such a strategy would produce a more homogeneous cohort of patients, which would in turn provide a more stable reference line for further comparisons. This idea was discussed during several investigator meetings and finally abandoned—first, because interest from outside Europe was enormous: 39% of ICUs that registered for the project were located outside Europe. The SAPS 3 board members thus agreed that such a high level of interest should not be ignored. Second, some investigators questioned whether a concentration on European ICUs would be successful in reducing heterogeneity anyway. Provision of intensive care in Europe is extremely variable, with enormous differences in severity of illness, provision of treatments and mortality from north to south and from west to east [32, 33].
For these reasons ICUs from regions outside Europe were invited to participate. Our results prove that we were right in our assumptions: First, one can easily see that the four European regions (as defined in our study) are hardly comparable: severity of illness as measured by the SAPS II varied from 27 to 35 points, and ICU mortality ranged from 10.8 to 20.6%—almost a doubling of mortality figures (Table 3). Second, almost a third of the patient cohort (28.5%) was contributed from regions outside Europe.
Although the decision to accept ICUs worldwide probably increased the heterogeneity of our sample, it also allowed the SAPS 3 database to better reflect important differences in patients’ and health care systems’ baseline characteristics that are known to affect outcome. These include, for example, different genetic makeups, different styles of living or a heterogeneous distribution of major diseases within different regions, as well as issues such as access to the health care system in general and to intensive care in particular, or differences in availability and use of major diagnostic and therapeutic measures within the ICUs [32, 34]. Although the integration of ICUs outside Europe and the U.S. surely increased it’s representativeness, it must be acknowledged, that the extent to which the SAPS 3 database reflects case-mix on ICUs worldwide cannot be determined yet.
It should additionally be noted that allocation of countries to regions does not always follow geographic borders (Table 3; see also Table E10 in the ESM). Partitioning of the sample was done to adjust for some of the above-stated differences between different populations and to develop a system that uses several different reference lines to compare ICUs on a similar level. Thus, patients are not necessarily representative of their respective regions.
To minimize possible seasonal influences, we chose late fall in the Northern Hemisphere for data collection. Thus, participants in both late fall/winter (Northern Hemisphere) and spring/summer (Southern Hemisphere) are represented in our cohort. A recent study [35] showed, moreover, that differences in seasonal mortality rates, at least in a sample of ICUs in the United Kingdom, were related to variations in case mix rather than to a specific impact of season on outcome.
Performance of the SAPS II was, not surprisingly, found to be similar to that in previous studies: acceptable discrimination but lack of calibration. Possible reasons for this have already been alluded to in the Introduction. In contrast to previous studies, however, we found an underestimation of hospital mortality, which contradicts the rationale that the shifting in calibration is due only to the development of new and possibly better therapies and thus to better ICU performance [19].
Analyzing the various geographic regions provides evidence that the underestimation of hospital mortality by the SAPS II might be partially attributable to the composition of the cohort: SAPS 3 is the first large international study on severity of illness systems to include patients from all continents. South America, for example, where provision of intensive care is much more limited than it is in Europe or North America, contributed extensively to the patient cohort. High O/E ratios have already been reported for this continent [36] and are probably linked to the limited availability of resources.
Data quality was one of our major concerns. Completeness of the documentation was found to be satisfactory: The amount of missing data is in fact smaller than reported from previous cohort studies on severity of illness systems [11, 12, 16]. With respect to reliability, intraclass-correlation coefficients and kappa coefficients were generally similar to or even better than those found in previous studies, showing a high degree of interrater agreement (see Table E1 in ESM) [37, 38].
We did, however, experience problems with the cohort of rescored patients: First, we had to exclude all rescored patients for whom the original counterpart was also excluded due to the application of any of the exclusion criteria. Second, in some cases the original patient identification was either missing or documented in such a way that a unique allocation was not possible. Both of these exclusions reduced the number of rescored patients available for analysis.
Two strategies to build up a cohort are available: first, to recruit only patients who meet well-documented inclusion criteria (such as documented vital status at hospital discharge) or, second, to document all patients and then exclude patients based on a predefined set of exclusion criteria. For the SAPS 3 study we chose the second option—to form two different cohorts—because we needed to provide a basic cohort for all further analyses of the SAPS 3 database. Since some studies will focus on different outcomes (e.g., ICU outcome rather than hospital outcome), we decided to use missing ICU outcome (and not hospital outcome) as an exclusion criterion for the basic cohort.
A possible limitation of the SAPS 3 database is that vital status at hospital discharge was not available for all admitted patients. Despite several efforts from the CCC and sufficient time to allow for a close follow-up, we did not succeed to receive all hospital outcomes documented. Recording of hospital outcome (or later outcomes) still poses major problems for ICUs in European and non-European hospitals, either because of technical problems or possibly because of data security algorithms in the hospitals. Exclusion of these patients did, however, not affect major criteria, such as geographic representation, ICU admission or discharge data, co-morbidities, or the distribution of the reasons for admission (Tables 1 and 2).
We conclude that the SAPS 3 database is within the above discussed limits of high quality and reflects the heterogeneity of current intensive care provision. As such, it provides an excellent basis for the development of a new risk adjustment system.
Electronic Supplementary Material
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intensive care unit
risk adjustment
severity of illness
hospital mortality
icu mortality