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PURPOSE Acute care events (ACEs), comprising emergency department visits and hospitalizations, are a priority area for reduction in oncology. Prognostic models are a compelling strategy to identify high-risk patients and target preventive services, but have yet to be broadly implemented, partly because of challenges with electronic health record (EHR) integration. To facilitate EHR integration, we adapted and validated the previously published PRediction Of Acute Care use during Cancer Treatment (PROACCT) model to identify patients at highest risk for ACEs after systemic anticancer treatment. METHODS A retrospective cohort of adults with a cancer diagnosis starting systemic therapy at a single center between July and November 2021 was divided into development (70%) and validation (30%) sets. Clinical and demographic variables were extracted, limited to those in structured format in the EHR, including cancer diagnosis, age, drug category, and ACE in prior year. Three logistic regression models of increasing complexity were developed to predict risk of ACEs. RESULTS Five thousand one hundred fifty-three patients were evaluated (3,603 development and 1,550 validation). Several factors were predictive of ACEs: age (in decades), receipt of cytotoxic chemotherapy or immunotherapy, thoracic, GI or hematologic malignancy, and ACE in the prior year. We defined high-risk as the top 10% of risk scores; this population had 33.6% ACE rate compared with 8.3% for the remaining 90% in the low-risk group. The simplest Adapted PROACCT model had a C-statistic of 0.79, sensitivity of 0.28, and specificity of 0.93. CONCLUSION We present three models designed for EHR integration that effectively identify oncology patients at highest risk for ACE after initiation of systemic anticancer treatment. By limiting predictors to structured data fields and including all cancer types, these models offer broad applicability for cancer care organizations and may offer a safety net to identify and target resources to this high risk. [ABSTRACT FROM AUTHOR]

The article discusses about the potential benefits of incorporating virtual visits in oncology care, the challenges faced and how virtual oncology programs can be designed to enhance equity. Topics of discussion includes the benefits includes increased patient access and convenience especially among rural patients or those with mobility, access, or financial challenges while access to technology remains a barrier to virtual visit adoption.

PURPOSE: Tyrosine kinase inhibitors (TKIs) have dramatically improved survival for patients with chronic myeloid leukemia (CML). No overall survival differences were observed between patients initiating first- and second-generation TKIs in trials; however, real-world safety and cost outcomes are unclear. We evaluated comparative safety and health care expenditures between first-line imatinib, dasatinib, and nilotinib among patients with CML. PATIENTS AND METHODS: Eligible patients had one or more fills for imatinib, dasatinib, or nilotinib in the MarketScan Commercial and Medicare Supplemental databases between January 1, 2011, and December 31, 2016 (earliest fill is the index date), 6 months pre-index continuous enrollment, CML diagnosis, and no TKI use in the pre-index period. Hospitalizations or emergency department visits (safety events) were compared across treatment groups using propensity-score-weighted 1-year relative risks (RRs) and subdistribution hazard ratios (HRs). Inflation-adjusted annual health care expenditures were compared using quantile regression. RESULTS: Eligible patients included 1,417 receiving imatinib, 1,067 receiving dasatinib, and 647 receiving nilotinib. The 1-year risk of safety events was high: imatinib, 37%; dasatinib, 44%; and nilotinib, 40%, with higher risks among patients receiving dasatinib (RR, 1.17; 95% CI, 1.06 to 1.30) and nilotinib (RR, 1.07; 95% CI, 0.93 to 1.23) compared with those receiving imatinib. Over a median of 1.7 years, the cumulative incidence of safety events was higher among patients receiving dasatinib (HR, 1.23; 95% CI, 1.10 to 1.38) and nilotinib (HR, 1.08; 95% CI, 0.95 to 1.24) than among those receiving imatinib. One-year health care expenditures were high (median, $125,987) and were significantly higher among patients initiating second-generation TKIs compared with those receiving imatinib (difference in medians: dasatinib v imatinib, $22,393; 95% CI, $17,068 to $27,718; nilotinib v imatinib, $19,463; 95% CI, $14,689 to $24,236). CONCLUSION: Patients receiving imatinib had the lowest risk of hospitalization or emergency department visits and 1-year health care expenditures. Given a lack of significant differences in overall survival, imatinib may represent the ideal first-line therapy for patients, on average. [ABSTRACT FROM AUTHOR]

Purpose The overuse of imaging, particularly for staging of low-risk prostate cancer, is well documented and widespread. The existing literature, which focuses on the elderly in fee-for-service settings, points to financial incentives as a driver of overuse and may not identify factors relevant to policy solutions within integrated health care systems, where physicians are salaried. Methods Imaging rates were analyzed among men with incident prostate cancer diagnosed between 2004 and 2011 within the Colorado and Northwest regions of Kaiser Permanente. The sample was stratified according to indication for imaging, ie, high risk for whom imaging was necessary versus low risk for whom imaging was discouraged. Logistic regression was used to model the association between imaging receipt and clinical/demographic patient characteristics by risk strata. Results Of the men with low-risk prostate cancer, 35% received nonindicated imaging at diagnosis, whereas 42% of men with high-risk prostate cancer did not receive indicated imaging. Compared with men diagnosed in 2004, those diagnosed in subsequent years were less likely to receive imaging across both risk groups. Men with high-risk cancer diagnosed at ≥ 65 years of age and those with clinical stage ≥ T2 were more likely to receive indicated imaging. Men with comorbidities were more likely to receive imaging across both risk groups. Men with low-risk prostate cancer who had higher median household incomes were less likely to receive nonindicated imaging. Conclusion Nonindicated imaging for diagnostic staging of patients with low-risk prostate cancer was common, but has decreased over the past decade. These findings suggest that factors other than financial incentives may be driving overuse of imaging. [ABSTRACT FROM AUTHOR]

Purpose: The Centers for Medicare & Medicaid Services (CMS) implemented chemotherapy measures (OP-35) to reduce potentially preventable emergency department visits (PPEDVs) and hospitalizations. This study evaluated the validity of the OP-35 measure in identifying PPEDVs among patients with cancer., Methods: This is a cross-sectional study, which used data from the 2012-2022 National Hospital Ambulatory Medical Care Survey. ED visits are assessed and compared on the basis of three measures: immediacy using Emergency Severity Index (ESI), disposition (discharge v hospitalization), and OP-35 criteria., Results: Between 2012 and 2022, a weighted sample of 46,723,524 ED visits were made by patients with cancer. Among reported ESI cases, 25.2% (8,346,443) was high urgency. In addition, 30.3% (14,135,496) of ED visits among patients with cancer led to hospitalizations. Using the OP-35 measure, it was found that 20.85% (9,743,977) was PPEDVs. A 21.9% (10,232,102) discrepancy between discharge diagnosis (CMS billing codes) and chief complaints was identified. Further analysis showed that 19.2% (1,872,556) of potentially preventable ED visits (CMS OP-35) were high urgency and 32.6% (3,181,280) resulted in hospitalization., Conclusion: The CMS approach to identifying PPEDVs has limitations. First, it may overcount preventable visits by including high-urgency or hospitalization-requiring cases. Second, relying on final diagnoses for retrospective preventability judgment can be misleading as they may not reflect the initial reason for the visit. In addition, differentiating causes for ED visits in patients with cancer undergoing various treatments is challenging as the approach does not distinguish between chemotherapy-related complications and others. Identification inconsistencies arise because of varying coding practices and chosen preventable conditions, lacking consensus and alignment with specific hospital or patient needs. Finally, the model fails to consider crucial nonclinical factors like social support, economic barriers, and alternative care access, potentially unfairly penalizing hospitals serving underserved populations.