IMPORTANCE Although several cancer drugs receive US Food and Drug Administration (FDA) approval each month, it is unclear how many of these cancer drugs transform the treatment landscape significantly by tumor group. Specifically, it remains unclear how many of these newly approved cancer drugs displace the existing standard-of-care therapies for their indication vs being added to existing therapies. OBJECTIVE To examine how many cancer drugs displace the standard-of-care therapies vs being added to existing therapy or filling breaks in systemic treatments in the metastatic setting, adjuvant setting, or maintenance setting. DESIGN, SETTING, AND PARTICIPANTSRetrospective cross-sectional study using landmark trials leading to FDA approval of cancer drugs between May 1, 2016, and May 31, 2021. The study evaluated all FDA approvals for cancer drugs between May 1, 2016, and May 31, 2021, using the FDA Oncology (Cancer)/Hematologic Malignancies Approval Notifications website. All clinical trials leading to FDA approval of cancer drugs during this period were examined. MAIN OUTCOMES AND MEASURESA drug was determined to have displaced the prior standardof-care therapy by evaluating the comparator arm (or lack thereof) in the clinical trial leading to the drug's approval and also by reviewing National Comprehensive Cancer Network Guidelines. Cancer drug approvals were categorized as first-line displacing if a drug was approved for use in the first-line setting and displaced the prior standard-of-care drug for an indication, first-line drug alternatives/new if a drug was approved for use in the first-line setting but did not displace the standard of care at the time of approval or was a new drug that was first of its class for an approved indication, add on if a drug was approved in combination with a previously approved therapy for a disease or if a drug was approved for use in the adjuvant or maintenance settings, and later line if a drug was approved for use in the second-, third-, or later-line settings.
PD-L1 expression is associated with differential response in cancers treated with checkpoint inhibitors. Clinical trials for Food and Drug Administration (FDA) approvals of programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors include limited subgroup analyses based on PD-L1 expression. We aimed to define the characteristics of PD-L1 defined subgroups of clinical trials leading to FDA approvals for new indications of PD-1/PD-L1 inhibitors. FDA approvals for PD-1/PD-L1 inhibitors from January 2014 to December 2020 were identified and the clinical trials leading to each drug approval were reviewed. We collected key variables from publicly available information on FDA website and peer-reviewed publications of clinical trials. We assessed regulatory characteristics (approval date, approved drug[s], cancer type, line of therapy and biomarker-restricted approval criteria) of each approval. Clinical trials leading to approvals were reviewed for trial design (RCT vs single arm study, primary endpoint) and PD-L1 defined subgroup design (no subgroup analysis, single threshold 2-group analysis, nested subgroups and adjacent subgroups). We then compared regulatory and trials characteristics (trial design, primary endpoint and biomarker approval criteria) between studies with nested and adjacent subgroups. There were 60 approvals for PD-1/PD-L1 inhibitors between January 2014 and December 2020. Twelve of 60 (20%) did not include any PD-L1 subgroups. Twenty-five of 60 (42%) approvals reported only two subgroups, 14 (23%) included adjacent subgroups and 9 (15%) had nested subgroups. Twentyfive of 60 trials (42%) are single arm studies. Comparison of characteristics between trials with nested subgroup design and adjacent subgroup design did not show differences. We conclude that approvals for new indications of PD-1/PD-L1 inhibitors are based on studies that do not include comprehensive reporting of outcomes by PD-L1 biomarker subgroups.
PURPOSE: There are no universal guidelines for blood product transfusions in patients with hematologic malignancies (HMs). Excess utilization of platelet and RBC transfusion in patients with HM increases the cost of care and likelihood of adverse events. We aim to decrease the total number of transfused units and multiunit orders of platelets and RBCs in the HM clinic by 25% from March 2020 to December 2020. METHODS: A multidisciplinary, interprofessional team was formed. Baseline rates of blood product utilization were determined using Qlik Analytic software. Strategies to improve utilization were developed, and three interventions were initiated. Data were collected on monthly intervals. Data for total number of platelet and RBC units ordered, total multiunit orders, average number of units ordered per encounter, and pretransfusion hemoglobin thresholds were collected from May 2019 to December 2020. RESULTS: Through our Plan-Do-Study-Act cycles from March 2020 to December 2020, the total number of platelet transfusion orders per month decreased from 164 to 98, multiunit platelet orders decreased from 63 to 2, and the average number of platelet transfusions per encounter decreased from 1.62 to 1.03. The total number of RBC transfusion orders decreased from 172 to 141, multiunit RBC orders decreased from 25 to 16, and the average number of RBC transfusions per encounter decreased from 1.21 to 1.18. CONCLUSION: Implementation of our multidisciplinary interventions led to more appropriate use of blood products in the outpatient setting. Ongoing efforts are underway to continue to improve utilization in the inpatient and outpatient setting.
PURPOSE An important obstacle to cancer research is that nearly all academic cancer centers maintain substantial collections of highly duplicative, poorly quality-assured, nonintercommunicating, difficult-to-access data repositories. It is inherently clear that this state of affairs increases costs and reduces quality and productivity of both research and nonresearch activities. We hypothesized that designing and implementing a multipurpose cancer information system on the basis of the Biomedical Research Integrated Domain (BRIDG) model developed by the National Cancer Institute and its collaborators might lessen the duplication of effort inherent in capturing, quality-assuring, and accessing data located in multiple single-purpose systems, and thereby increases productivity while reducing costs. METHODS We designed and implemented a core data structure on the basis of the BRIDG model and incorporated multiple entities, attributes, and functionalities to support the multipurpose functionality of the system. We used the resultant model as a foundation upon which to design and implement modules for importing preexisting data, capturing data prospectively, quality-assuring data, exporting data to analytic files, and analyzing the quality-assured data to support multiple functionalities simultaneously. To our knowledge, our system, which we refer to as the Cancer Informatics Data System, is the first multipurpose, BRIDG-harmonized cancer research information system implemented at an academic cancer center. RESULTS We describe the BRIDG-harmonized system that simultaneously supports patient care, teaching, research, clinical decision making, administrative decision making, mandated volume-and-outcomes reporting, clinical quality assurance, data quality assurance, and many other functionalities. CONCLUSION Implementation of a highly quality-assured, multipurpose cancer information system on the basis of the BRIDG model at an academic center is feasible and can increase access to accurate data to support research integrity and productivity as well as nonresearch activities.
e19118 Background: For patients being treated for solid tumor malignancies, an unplanned hospitalization is a significant event that may help predict outcomes. Prior studies have demonstrated a 1 year mortality rate as high as 73% for oncology patients following an unplanned admission. As our therapies continue to improve, better understanding of the significance of acute hospitalizations is needed to help influence management decisions for oncology providers. Methods: We conducted a retrospective review of all patients admitted to a solid oncology inpatient service at Thomas Jefferson University Hospital (TJUH) who were discharged between January 1st and March 31st 2019. We excluded all patients who had not established care with a provider at TJUH prior to the index admission. We collected data on age, sex, length of stay (LOS), diagnosis, treatment activity, ECOG performance status (PS), 30 day and total re-admissions, survival and whether hospice was discussed during index admission. We censored data at 180 days after date of index discharge. We evaluated overall survival (OS) using the Kaplan-Meier method and compared survival to 30-day readmission and ECOG status using a log-rank test. Results: During our investigational time period there were 263 discharges, which yielded a study population of 182 patients. Median LOS was 5 days and 55% of patients were female. Gastrointestinal and genitourinary cancers were the most prevalent, observed in 32% and 28% of patients respectively. 75% of patients were on active treatment at time of admission. Survival data was available in 174 patients and at 6 months, OS was found to be 47%. 35% of patients were readmitted within 30 days of their index discharge. Patients readmitted within 30 days had a significantly lower OS than patients without a readmission (31% vs. 56%, P = 0.002). 70% of patients had a PS of 0 or 1 prior to index admission and OS in this group was significantly better in comparison to patients with a higher ECOG score (57% vs. 33%, P = 0.001). Hospice was discussed with 31% patients during index admission. Conclusions: We observed a 6 month overall survival of 47% following an unplanned admission and this was lower for patients that were re-admitted within 30 days. Higher PS scores predicted worse outcomes; however, the majority of patients had a favorable PS prior to index admission. This study suggests that following an unplanned admission, oncology patients are at elevated risk for poor outcomes and that providers should reassess goals of care at follow up clinic visits.
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