Accepting Critically Ill Transfer Patients: Adverse Effect on a Referral Center's Outcome and Benchmark Measures

Context Benchmarking compares performance of providers or systems with a standard. Are such comparisons fair, even if they are adjusted for varying case mix and severity of illness of patients? Contribution This prospective study showed that patients who were transferred to an intensive care unit from other hospitals had worse outcomes than those who were directly admitted. In modeling analyses, benchmarking adjusted with sophisticated case-mix and severity-of-illness information, but not admission source, penalized units with a 25% transfer rate (versus a 0% rate) by 14 excess deaths per 1000 admissions. Implications Benchmarking of intensive care unit performance should account for transfer patients. The Editors Payers and consumers of health care are increasingly seeking information on quality of care (1-3). Since the Centers for Medicare & Medicaid Services (formerly the Health Care Financing Administration) began reporting hospital mortality rates in the mid-1980s (4), an increasing number of health care providers, health plans, third-party payers, and regulatory agencies are collecting and disseminating information used to judge and compare quality of care (5-8). The converging influences of an expanding use of expensive technology in medicine and the resulting rapid growth in health care costs are driving these initiatives (9-14). Purchasers, providers, and the government are relying on various benchmarking methods to compare outcomes, quality, and costs of health care (15-18). Benchmarking, the process by which the performance of an individual, group, hospital, or health system is compared with a standard, is the cornerstone of many efforts to measure and compare the quality and efficiency of care (19-21). Principally, benchmarking accounts for variation in outcomes, processes, or costs of care (22, 23). The results of these benchmarks are often used to prepare profiles (report cards) that compare a provider's, hospital's or health system's outcomes and costs (24, 25). Frequently, this type of profiling is performed by using limited administrative data, such as age, sex, and diagnosis-related group categories that are primarily collected for billing purposes (26). These profiling methods have met with substantial criticism (24, 27-29). Most commonly, critics cite the lack of sufficiently accurate case-mix adjustment and severity-of-illness measures on which to compare actual and predicted outcomes for different types of care and different groups of patients (30, 31). These considerations are especially important for tertiary referral centers that care for patients with complex or severe illnesses, many of whom have been referred or transferred from another hospital or area (26, 32, 33). Several previous studies have shown that transfer patients often require more resources (33-35) but have worse outcomes than nontransferred patients (36-38). The level of severity adjustment, however, is often not detailed in these studies (39). How detailed must risk adjustment be to adequately control for case-mix differences? For several reasons, adjustment for the risk for the outcomes of transfer patients in the intensive care unit (ICU) should be the ideal setting in which to examine this question. Currently, the most sophisticated and highly validated set of clinical and physiologic case-mix measures available have been developed for ICU patients (40-43). This is partly because an extensive amount of physiologic information is available from the repetitive and often invasive monitoring unique to the ICU. Furthermore, lead-time and selection bias (44-47) (the reason why patient location before ICU admission has been included in some risk-adjustment models [43]) may account for some of the difficulty in accounting for ICU patients' outcomes. However, few studies have evaluated the ability to measure the severity of illness among patients admitted to an ICU after previously receiving care from a general medical ward or another hospital (43, 44, 46, 47) by using current, state-of-the-art ICU outcome prediction models (35, 48). Furthermore, among the most rigorously developed and tested ICU prediction models, there has been no detailed evaluation of the transfer effect at an individual hospital level. Even small bias in comparisons of observed versus expected deaths, as well as other outcomes, can substantially affect how high-quality institutions compare with peer hospitals. Therefore, we studied the incremental improvement that increasingly more detailed clinical and physiologic case-mix adjustment could provide in evaluating the greater severity of illness of transfer patients. We sought to examine whether full case-mix adjustment could adequately account for the increased risk for poor outcomes among transfer patients (that is, patients who received medical care before transfer to a referral center medical intensive care unit [MICU]). In addition, we investigated the potential bias in benchmarking outcomes produced by a transfer effect even with the use of state-of-the-art case-mix measures. Methods Study Design and Patient Information We obtained the data for this study from a prospectively developed cohort of 4579 consecutive admissions to the MICU at a tertiary care university hospital (800 beds, 24 of which are MICU beds) from 1 January 1994 to 1 April 1998. A study group of 4208 patients was available after previously published exclusion criteria were met (49) (Appendix). This MICU primarily admits noncardiac care, medical patients with various conditions and is a referral center for patients with acute respiratory distress syndrome and hepatic failure. Surgical and trauma patients are admitted to other, specialized ICUs. Measurements of all dependent and independent variables were collected prospectively, whereas hypotheses were generated before data analyses but after the data were collected (secondary or post hoc analyses of a prospective cohort study). The data were obtained from the university's ICU clinical database, APACHE Medical Systems (APACHE Medical Systems, Inc., Vienna, Virginia). The data quality was ensured by using previously established practices (40). The need for informed consent was waived by the institutional review board. Standard Acute Physiology, Age, and Chronic Health Evaluation (APACHE) methods were used to construct case-mix adjustment and severity-of-illness scores (40). A daily Acute Physiology Score (APS) based on the most abnormal values of 17 specific physiologic variables during the previous 24-hour period measured disease severity. The age and comorbidity score was derived from previously validated weights for the patients' ages and one of seven severe comorbid conditions (hepatic failure, cirrhosis, immunosuppressive conditions, hematologic malignant conditions, lymphoma, metastatic cancer, and AIDS). The type and amount of monitoring and treatment received over each 24-hour period were recorded by using the Therapeutic Intervention Scoring System (TISS) (50). Patients were characterized as receiving either monitored care or active treatment, such as ventilator management (51). Each patient's primary reason for MICU admission (MICU admission diagnosis) was recorded as 1 of 430 diseases, injuries, surgical procedures, or events most immediately threatening to the patient and requiring the services of the MICU. Variables Our outcome measures were MICU and hospital lengths of stay, MICU and hospital mortality rates, and MICU readmission rates. Intensive care unit and hospital length of stay is also an established proxy measure of overall resource use (52, 53). Our primary predictor variables included age, comorbid conditions, diagnosis variables, and admission APS. General ICU severity models are primarily developed to predict mortality rate and length of stay. Although we did not use the APACHE model for most of our analyses, we did use the APS component of the APACHE score as an essential variable to control for severity of illness. Data collected for all admissions to the MICU also included patient demographic characteristics, hospital and MICU admission dates and times, location from which a patient was admitted to the MICU, and the time from admission at the original source to the MICU admission at the study hospital (hours to MICU). Patients admitted directly to the MICU from the emergency department or an ambulatory clinic were categorized as direct admissions. Patients admitted from any of the non-ICU general nursing floors or telemetry units were categorized as floor admissions. Finally, patients transferred from another hospital were categorized as transfer admissions, 98% of whom were from another hospital's ICU. Quantifying Risk Adjustment Offered by More Precise Biophysiologic Information We used three predictive models to develop benchmarks to study the incremental effect of increasingly more precise risk adjustment on predicting outcomes for the directly admitted, floor, and transfer patients. For these analyses, we consolidated the admission diagnoses to 19 categories to have sufficient sample size for each group. First, we examined the effect of admission source on outcomes without any diagnostic or physiologic information in an unadjusted model. Second, in the partial-adjustment model, we controlled for age, sex, comorbid conditions, and diagnosis only. This traditional adjustment is similar to or even more precise than many general hospital case-mix and severity-adjustment methods that rely on administratively derived diagnosis-related group information. Finally, we added more precise acute physiology clinical information (APS) to adjust for physiologic derangements yielding a full case-mix and severity-adjustment model. We also used the predicted mortality rates and length of stay information from the actual APACHE III model itself, which include an average ICU admission source correction. The results of this last model are reported in the Results section