bluuebunny/crossref_metadata_2025_split huggingface dataset:
Home-based management of cancer patients (CPs) experiencing toxicities while on anticancer treatment: The impact of a nurse-led telephone triage (NTT).
Authors: Calvetti Lorenzo , Tealdo Marta , Cimenton Roberta , Gentile Angela , Pretto Rachele , Pavan Monica , Gasparin Barbara , Pagiusco Gaetana , De Vivo Rocco , Merlini Laura , Aprile Giuseppe 
Published: May 2020

2002 Background: Novel organization models are needed to ensure early management of new treatment-related toxicity of anticancer treatments. Aim of this prospective observational study was to evaluate the impact of the introduction of NTT in reducing hospitalization of CPs. Methods: CPs on active medical treatment at the Department of Oncology of San Bortolo Hospital (Vicenza, Italy) were given instructions to refer to NTT in case of treatment-related adverse events (TRAEs). The service was opened Mon to Fri from 8am to 8pm. Assessment of TRAEs was performed by trained oncology nurses according to the CTCAE scale and subsequent actions were taken according to the severity of the events. The assessment was made under supervision of a medical oncologist in charge of the service while on duty. Primary endpoint of the study was to compare the rate of hospitalization of CPs on anticancer treatment after the introduction of NTT compared to 2017-2018 period. Results: From September 2018 to September 2019 1,075 patients received systemic anticancer treatment (versus 936 patients in the equivalent 2017 – 2018 period). Total consultations at NTT were 429; 581 TRAEs were reported. 117 patients reported more than one TRAE. CTCAE were graded as G1 237 (40.8%), G2 231 (39.8%) or G3-G4 113 (19.4%). The most common grade ≥ 3 TRAE was fever (38 events (33.6%) that resulted a febrile neutropenia in 7 cases) followed by cancer pain (15 (13.3%)) and fatigue (9 (8%)). In the observation period, 109 CPs on treatment were hospitalized versus 138 in the 2017-2018 period with a normalized hospitalization rate of 10.1% versus 14.7 % (p = 0.002, chi-square) with a reduction of normalized number of hospitalizations of 44 (estimated cost savings of 380.160 euros). Conclusions: Our results provided evidence of successful implementation of the NTT system in reducing rates of hospitalization through emergency room in cancer patients receiving modern medical treatments.
Launch prices and price developments of cancer drugs in the United States and Europe.
Authors: Vokinger Kerstin Noëlle , Daniore Paola , Lee ChangWon C , Kesselheim Aaron S , Hwang Thomas J 
Published: May 2020

2006 Background: Cancer drug costs are rising in the US and Europe. While drug manufacturers set prices without restriction in the US, European countries have regulations that allow national authorities to directly negotiate drug prices at launch and over time. We analyzed and compared the launch prices and price developments of cancer drugs in the US, Germany, Switzerland and England. Methods: We identified new drugs indicated to treat solid tumors in adults that were FDA-approved between 2009 and 2019 and had also been approved by the EMA and Swissmedic by 31 December 2019. Launch prices and post-launch price changes as of 1 January 2020 were extracted and adjusted to average sales prices for monthly treatment costs in the US and compared to comparable currency-adjusted ex-factory monthly treatment costs in Germany, Switzerland, and England. A cross-sectional analysis was conducted to infer yearly trends in launch prices and post-launch price changes across the countries. Results: The study cohort included 42 drugs for solid tumors, of which 40 (95%) drugs were first approved in the US compared to Germany and England, and 41 (98%) to Switzerland. Average launch prices for monthly treatment costs per patient were $15,178 in the US vs $7,049 in Germany, $7,421 in Switzerland and $8,176 in England, i.e., 215% (interquartile range [IQR] 263%-187%), 205% (IQR 202%-185%) and 186% (IQR 166%-189%) higher in the US compared to Germany, Switzerland and England respectively. Post-launch prices of 36 (86%), 40 (95%), and 38 (90%) drugs decreased over time with total savings of monthly treatment costs for all drugs in the study cohort of $86,744, $44,936, and $1744 in Germany, Switzerland, and England respectively. By contrast, prices of 8 (19%) drugs decreased, while 34 (81%) increased post-launch in the US with total additional expenses of $128,192 for monthly treatment costs. Conclusions: Launch prices for cancer drugs are far higher in the US than in Germany, Switzerland, or England. These price disparities continue to increase substantially after market entry since cancer drug prices, in general, decrease over time in Europe and increase in the US. Spending on cancer drugs could be reduced in the US if it adopted the principles used to more effectively negotiate drug prices in Europe.
Prospective validation of a machine learning algorithm to predict short-term mortality among outpatients with cancer.
Authors: Manz Chris , Chivers Corey , Liu Manqing , Regli Susan B , Changolkar Sujatha , Evans Chalanda N. , Rareshide Charles A.L. , Draugelis Michael , Braun Jennifer , Navathe Amol S. , Kumar Pallavi , Bekelman Justin E. , Patel Mitesh S. , O'Connor Nina , Schuchter Lynn Mara , Shulman Lawrence N. , Parikh Ravi Bharat 
Published: May 2020

2009 Background: Oncologists accurately identify only 35% of patients with cancer who will die in six months. There is an urgent need for automated, accurate prognostic systems to inform treatment and advance care planning in oncology. We assessed the prospective performance of a previously described ML algorithm (Parikh et al, JAMA Netw Open, 2019) to predict short-term mortality in a cohort of general oncology outpatients. Methods: Our prospective cohort consisted of patients aged ≥18 years who had a medical or gynecologic oncology encounter between March 1 and April 30, 2019 in either a tertiary academic practice or one of twelve community practices within a large academic cancer system. We used a retrospectively validated gradient-boosting ML algorithm, based on 559 structured electronic health record (EHR) variables, to predict 180-day mortality prior to each oncology encounter. For patients with multiple encounters, we selected the last encounter to assess performance. We assessed several performance metrics, including area under the receiver operating curve (AUC), area under the precision-recall curve (AUPRC), scaled Brier score (sBrier; a measure of calibration ranging from 0 [random] to 1 [perfect]), and positive predictive value (PPV). Results: Of 25,537 unique patients, median age was 64.4 (interquartile range 53.3 – 73.0), 76.8% were White, 56.5% were treated at a community center, and 4.1% died within 180 days. The ML algorithm had an AUC of 0.89 (95% confidence interval [CI] 0.88-0.90), AUPRC 0.34, and sBrier 0.29. At a prespecified threshold of 40%, observed 180-day mortality was 44.5% (95% CI 40.7 – 48.4%) in the high-risk group vs. 3.0% (95% CI 2.8% – 3.3%) in the low-risk group. There was an 85-fold difference in mortality (13.6% vs. 0.16%) in the top vs. bottom risk quartiles. The model was well-calibrated for mortality risks ≤40% and slightly under-calibrated for mortality risks > 40%. Performance varied across cancer types in the tertiary hospital but did not vary by race or practice type (Table). Conclusions: In this prospective cohort study among outpatients with cancer, a ML prognostic algorithm based on EHR data had better discrimination and calibration that published cancer-specific models. This is one of the first ML prognostic models to be prospectively validated in oncology. [Table: see text]
Four distinct patient-reported outcome (PRO) trajectories in longitudinal responses collected before, during, and after chemotherapy.
Authors: Blayney Douglas W. , Azad Amee , Yilmaz Melih , Bozkurt Selen , Brooks James D. , Hernandez-Boussard Tina 
Published: May 2020

2012 Background: Cancer chemotherapy, whether given with curative or palliative intent, is toxic. Toxicity is routinely captured in clinical trials by investigator observation and increasingly by PRO. The ability to capture PRO in the routine treatment workflow has been standard at Stanford since 2015 (Roy et al ASCO 2020). Analysis of longitudinally captured, real world PRO and prospectively identifying patients (pts) whose quality of life (QOL) is at risk of deteriorating either permanently or temporarily is needed. Routine serial PRO measurement should enhance precision care delivery, precision toxicity detection and management. Methods: We identified patients undergoing chemotherapy at Stanford and analyzed PROMIS (PRO Measurement Information System) responses. Pts with PROMIS survey information at three intervals—pre-treatment, during chemotherapy and post chemotherapy—were identified. We evaluated global physical health (GPH) and global mental health (GMH). Pts with a clinically significant decrease (CSD) in GPH or GMH scores were identified. A k-median cluster analysis was used to identify patient trajectory clusters and a machine-learning model was applied to identify risk factors for CSD and predict CSD. Results: We identified 670 adult oncology patients undergoing chemotherapy who completed at least one PROMIS survey in each interval. GPH scores were 48.4 ± 9.1 before, 47.1 ± 8.5 during, and 48.5 ± 8.9 after chemotherapy and GMH scores were 50.5 ± 8.2, 49.1 ± 8.5, and 50.7 ± 9.0, respectively. The majority of patients did not have a CSD in GPH or GMH post treatment compared to pretreatment scores. Pretreatment scores were the strongest predictor of a CSD in GPH and GMH. Trajectory clustering identified four distinct trajectories: Temporary Improver, Temporary Deteriorator, Improver, Inexorable Deteriorators. We were not able to predict any cluster based on pre-treatment features. Conclusions: Using routinely collected PROMIS surveys in a real-world setting, we are able to predict patients with post-treatment decreases in their physical and mental well-being. We further defined four novel patient trajectories during chemotherapy, which could guide personalized supportive interventions to improve patient’s chemotherapy experience. Identification of patients at risk for deterioration and the patterns of deterioration could help guide efficient deployment of toxicity mitigating and supportive care interventions to patients most in need.
Absence of optimism bias in industry-sponsored cancer trials.
Authors: Noticewala Sonal S , Patel Roshal R , Ludmir Ethan B. 
Published: May 2020

2020 Background: Randomized controlled trials (RCTs) in oncology power their studies to detect expected effect sizes. Prior studies have shown that there is optimism bias, the a priori overestimation of treatment effect size among cooperative-group-supported RCTs. However, it is unknown whether such bias is present among industry-supported trials. Methods: All published phase 3 clinical oncology RCTs were identified through ClinicalTrials.gov. Only superiority-design RCTs assessing a therapeutic intervention to improve disease-related outcomes were included. We compared the ratio of observed to expected hazard ratio (OEHRR) between trial subgroups using the Mann-Whitney U-test; comparisons of median OEHRR to a hypothetical median of 1 was performed using the Wilcoxon Signed Rank test. Results: We identified 140 phase 3 trials with available hazard ratio (HR) data. Of these, 123 trials (88%) were industry-sponsored, and 38 trials (27%) were cooperative-group-supported. For all trials, the median OEHRR was 1.099 (IQR = 0.855-1.291), demonstrating evidence of optimism bias when compared to a hypothetical median OEHRR of 1 (p = 0.018). In the subgroup analysis, compared to non-industry-sponsored trials (median OEHRR 1.253, IQR 1.061-1.334), industry-supported trials (median OEHRR 1.061, IQR 0.829-1.274) had a significantly lower OEHRR (p = 0.022) and did not demonstrate optimism bias (p = 0.15). Similarly non-cooperative group trials (median OEHRR 1.208, IQR 1.019-1.317) had a significantly lower OEHRR (p = 0.005) and did not demonstrate optimism bias (p = 0.562) compared to cooperative group trials (median OEHRR 1.208, IQR 1.019-1.317), which did demonstrate optimism bias (p < 0.001). Conclusions: Cooperative group trials, which represent a minority of trials, suffer from optimism bias. In contrast, industry-funded trials, which account for the majority of trials, do not demonstrate evidence of optimism bias, and have very close concordance between observed and expected effect size. These findings suggest that the powering and design of industry-funded trials better models the outcomes eventually observed. The reasons for this are likely complex and multifactorial, but may include financial constraint considerations, as industry-supported trials may not be as financially-limited as cooperative group studies. Therefore, industry-supported studies may be able to power trials with sufficient participants to reflect the estimated effect size.
Distress screening through PROMIS at an academic cancer center and network site: Implementation of a hybrid model.
Authors: Roy Mohana , Neal Joel W. , Bugos Kelly , Sharp Christopher , Falconer Patricia , Rosenthal Eben Lloyd , Blayney Douglas W. , Modaressi Shiva , Robinson Ashley , Ramchandran Kavitha 
Published: May 2020

2021 Background: The NCCN guidelines recommend routine distress screening of patients with cancer, but the implementation of such programs is inconsistent. Up to one in three such patients experience distress, however fewer than half of them are identified and referred for supportive services. Methods: We implemented a hybrid (electronic and paper) distress screening tool, using a modified version of the PROMIS-Global Health questionnaire. Patients received either an electronic or in-clinic paper questionnaire to assess overall health and distress at the Stanford Cancer Center and its associated integrated network site. Iterative changes were made including integration with the electronic health record (EHR) to trigger questionnaires for appointments every 60 days. A consensus “positive screen” threshold was defined, with data collected on responses and subsequent referrals placed to a supportive care services platform. Results: Between June 2015 and December 2017, 53,954 unique questionnaires representing 12,744 distinct patients were collected, with an average completion rate of 58%. Approximately 30% of the questionnaires were completed prior to the visit electronically through a patient portal. The number of patients meeting the positive screen threshold remained ~ 40% throughout this period. Following assessment by the clinical team, there were 3763 referrals to cancer supportive services. Among the six most common referral categories, those with a positive screen were more likely to have a referral placed (OR 6.4, 95% CI 5.8-6.9 p- < 0.0001), with a sensitivity of 80% and a specificity of 61%. However, 89% of responses with a positive screen did not have a referral to supportive care services. Conclusions: The hybrid electronic and paper use of a commonly available patient reported outcome tool, as a high throughput distress screening tool, is feasible at a multi-site academic cancer center. Our positive screen rate for referrals was sensitive and consistent, but with a low positive predictive value. This screening also resulted in variable clinical response and overall increased clinical burden. Future directions for our group have included refining the threshold for a positive screen and implementation of a real-time response system, especially to address acute concerns.
The impact of early integrated supportive care on length of stay at an NCI-designated cancer center.
Authors: Kaltman Jessica , Sun Can-Lan , Loscalzo Matthew J. , Kronstadt Erik , Goodspeed Elizabeth , Qamar Samina , Glaser Christine , Zachariah Finly , Leitner Andrew , Biller Les , Dale William 
Published: May 2020

2024 Background: With movement towards value-based care, institutions seek ways to reduce costs by decreasing inpatient stays. A multidisciplinary approach to supportive care, especially when provided early, is one way to realize value-based care. We assess the impact of pre-admission versus post-admission involvement of an Integrated Supportive Care Model (ICSM) on inpatient length of stay (LOS) at a NCI-designated cancer center. Methods: Data was collected from 2014 to 2016 at City of Hope. The Integrated Supportive Care Model at City of Hope includes: palliative care, psychiatry, psychology, interventional pain, social work, child-life, distress screening, and couples program. “Pre-admission” was defined as seeing at least one service prior to hospital admission; “Post-admission” defined as seeing at least one service during admission. “Short LOS” for hematology patients was categorized as ≤ 14 days and for oncology patients as ≤ 3 days. Continuous LOS between patients receiving an ISCM intervention pre- and post-admission was compared using Kruskal-Wallis test. Univariate and multivariable logistic regression was done to examine association between involvement of ISCM pre- and post-admission and categorical LOS. P-values < 0.05 were considered statistically significant. Results: 1,627 (809 with hematologic malignancy, 818 with oncologic malignancy) patients with only one hospitalization during the study time were included. For hematology patients, involvement with the ISCM pre-admission was associated with shorter LOS ( ≤ 14 days) compared with involvement post-admission (29.3 vs 11.1%, multivariable OR = 4.08, P < 0.001). Median LOS for hematology patients who participated in the ISCM pre-admission was shorter than those who received ISCM services post-admission (21 vs. 22 days, p = 0.049). Similarly, for oncology patients, ISCM involvement pre-admission was associated with shorter LOS ( ≤ 3 days) compared to involvement post-admission (91.4% vs 8.6%, multivariable OR = 3.74, P < 0.001). Median LOS for oncology patients who received an ISCM intervention pre-admission was shorter than those who received an ISCM intervention post-admission (2 vs. 6 days, p < 0.001). Conclusions: In hematologic and oncologic malignancies, use of an ISCM prior to patient’s first hospitalization is associated with significantly shorter LOS compared with those who received ISCM services during the hospital stay. This suggests efforts should be made to include an ISCM early in the trajectory of illness, prior to first hospitalization.
Predicting the risk of VISIT emergency department (ED) in lung cancer patients using machine learning.
Authors: Rodriguez-Brazzarola Pablo , Ribelles Nuria , Jerez Jose Manuel , Trigo Jose , Cobo Manuel , Ramos Garcia Inmaculada , Gutierrez Calderon M Vanesa , Subirats Jose Luis , Galeote Miguel Ana María , Mesa Hector , Galvez Carvajal Laura , Franco Leo , Jimenez Rodriguez Begoña , Godoy Ana , Ruíz Sofía , Mesas Andres , Iglesias Campos Marcos , López Irene , Rueda Dominguez Antonio , Alba Emilio 
Published: May 2020

2042 Background: Lung cancer patients commonly need unplanned visits to ED. Many of these visits could be potentially avoidable if it were possible to identify patients at risk when the previous scheduled visit takes place. At that moment, it would be possible to perform elective actions to manage patients at risk to consult the ED in the near future. Methods: Unplanned visits of patients in active cancer therapy (i.e. chemo or immunotherapy) are attended in our own ED facilities. Our Electronic Health Record (EHR) includes specific modules for first visit, scheduled visits and unplanned visits. Lung cancer patients with at least two visits were eligible. The event of interest was patient visit to ED within 21 or 28 days (d) from previous visit. Free text data collected in the three modules were obtained from EHR in order to generate a feature vector composed of the word frequencies for each visit. We evaluate five different machine learning algorithms to predict the event of interest. Area under the ROC curve (AUC), F1 (harmonic mean of precision and recall), True Positive Rate (TPR) and True Negative Rate (TNR) were assessed using 10-fold cross validation. Results: 2,682 lung cancer patients treated between March 2009 and October 2019 were included from which 819 patients were attended at ED. There were 2,237 first visits, 47,465 scheduled visits (per patient: range 1-174; median 12) and 2,125 unplanned visits (per patient: range 1-20; median 2). Mean age at diagnosis was 64 years. The majority of patients had late stage disease (34.24 % III, 51.56 % IV). The Adaptive Boosting Model yields the best results for both 21 d or 28 d prediction. Conclusions: Using unstructured data from real-world EHR enables the possibility to build an accurate predictive model of unplanned visit to an ED within the 21 or 28 following d after a scheduled visit. Such utility would be very useful in order to prevent ED visits related with cancer symptoms and to improve patients care. [Table: see text]
Driving quality improvement: How clinical decision support can facilitate compliance with evidence-based pathways.
Authors: Patt Debra A. , He Bo , Garey Jody S. , Rowan Paul , Swartz Michael D , Linder Stephen , Brooks Barry Don , Neubauer Marcus A. 
Published: May 2020

2045 Background: Cancer care is changing rapidly with more detailed understanding of disease and more numerous therapeutic choices. As treatment choice is more complex, mechanisms to improve compliance with evidence based treatment can improve the quality of cancer care. Methods: A retrospective cohort study was conducted from January 2014-May 2016 evaluating the impact of a clinical decision support system (CDSS) on compliance with evidence based pathways (EBP) across 9 statewide community based oncology practices. These EBP are developed with physician input on efficacy toxicity and value and incorporated in to a CDSS that is used within the Electronic Health Record (EHR) at point of care to alter the choice architecture a clinician sees when prescribing therapy. A multi-level logistic regression model was used to adjust for group effects on physician or practice behavior. SAS 9.4 software was used and GLIMMIX was applied. Individual physician benchmark compliance was evaluated using McNemar's test. Results: Regimen compliance with EBP was measured pre- and post- implementation of the CDSS tool across a large network encompassing 9 statewide practices and 633 physicians who prescribed over 30,000 individual patient treatment regimens over a 6 month period. The CDSS that is incorporated within the EHR significantly improved compliance with EBP across the entire cohort of practices, and in individual practices (see Table). Individual oncologists reached a target of 75% compliance more often (58% vs 72%) after implementation of the tool (p < 0.001). Conclusions: CDSS is a tool that improves compliance with EBP that is effective at improving targets of compliance broadly, at the practice, and at the individual clinician level. Clinical informatics solutions that influence physician behavior can be inclusive of physicians in design, iterative in process, and nudge as opposed to force clinician behavior to drive quality improvement. These clinical informatics solutions grow in importance as the complexity of cancer care continues to increase and we seek to improve upon the quality and value of care delivery. [Table: see text]