RESUMO
BACKGROUND: Guidelines informing chemotherapy regimen selection are based on clinical trials with participants who do not necessarily represent general populations with breast cancer. Understanding who receives non-guideline regimens is important to understanding real-world chemotherapy administration and how it relates to patient outcomes. METHODS: Using data from the Optimal Breast Cancer Chemotherapy Dosing (OBCD) cohort study, based at Kaiser Permanente Northern California (2006-2019) and Kaiser Permanente Washington (2004-2015), we use logistic regression to examine the associations between patient characteristics and receipt of non-NCCN-guideline chemotherapy among 11,293 women with primary stage I-IIIA breast cancer receiving chemotherapy. RESULTS: Use of non-guideline regimens was strongly associated with several factors, including older age (OR≥80 vs 18-39: 5.25, 95%CI: 3.06-9.00)(p-trend=0.002) and human epidermal growth factor-2 status (ORHER2+ vs HER2-: 3.44; 95%CI: 3.06-3.87) and was less likely in women with larger tumor size (OR>5cm vs 0.1-≤0.5cm: 0.56; 95%CI: 0.36-0.87)(p-trend=0.01) and diagnosed in later years (OR2012-2019 vs 2005-2011: 0.80; 95%CI: 0.71-0.90). Factors associated varied by type of non-guideline regimen. For example, women with comorbidity and older age were more likely to receive non-guideline drug combinations in particular, while women with larger tumor size were less likely to receive non-guideline administration schedules. CONCLUSIONS: Non-guideline chemotherapy regimens are more likely in certain patient populations. IMPACT: These associations highlight that vulnerable patient populations may be less likely to receive guideline care and thus real-world studies are essential to understanding how the use of non-guideline regimens impacts patient outcomes in these groups.
RESUMO
BACKGROUND: We updated algorithms to identify breast cancer recurrences from administrative data, extending previously developed methods. METHODS: In this validation study, we evaluated pairs of breast cancer recurrence algorithms (vs. individual algorithms) to identify recurrences. We generated algorithm combinations that categorized discordant algorithm results as no recurrence [High Specificity and PPV (positive predictive value) Combination] or recurrence (High Sensitivity Combination). We compared individual and combined algorithm results to manually abstracted recurrence outcomes from a sample of 600 people with incident stage I-IIIA breast cancer diagnosed between 2004 and 2015. We used Cox regression to evaluate risk factors associated with age- and stage-adjusted recurrence rates using different recurrence definitions, weighted by inverse sampling probabilities. RESULTS: Among 600 people, we identified 117 recurrences using the High Specificity and PPV Combination, 505 using the High Sensitivity Combination, and 118 using manual abstraction. The High Specificity and PPV Combination had good specificity [98%, 95% confidence interval (CI): 97-99] and PPV (72%, 95% CI: 63-80) but modest sensitivity (64%, 95% CI: 44-80). The High Sensitivity Combination had good sensitivity (80%, 95% CI: 49-94) and specificity (83%, 95% CI: 80-86) but low PPV (29%, 95% CI: 25-34). Recurrence rates using combined algorithms were similar in magnitude for most risk factors. CONCLUSIONS: By combining algorithms, we identified breast cancer recurrences with greater PPV than individual algorithms, without additional review of discordant records. IMPACT: Researchers should consider tradeoffs between accuracy and manual chart abstraction resources when using previously developed algorithms. We provided guidance for future studies that use breast cancer recurrence algorithms with or without supplemental manual chart abstraction.