RESUMEN
Background: Research on venous thromboembolism (VTE) that relies only on the International Classification of Diseases (ICD) can misclassify outcomes. Our study aims to discover and validate an improved VTE computable phenotype for people with cancer. Methods: We used a cancer registry electronic health record (EHR)-linked longitudinal database. We derived three algorithms that were ICD/medication based, natural language processing (NLP) based, or all combined. We then randomly sampled 400 patients from patients with VTE codes (n = 1111) and 400 from those without VTE codes (n = 7396). Weighted sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated on the entire sample using inverse probability weighting, followed by bootstrapped receiver operating curve analysis to calculate the concordance statistic (c statistic). Results: Among 800 patients sampled, 280 had a confirmed acute VTE during the first year after cancer diagnosis. The ICD/medication algorithm had a weighted PPV of 95% and a weighted sensitivity of 81%, with a c statistic of 0.90 (95% confidence interval [CI], 0.89-0.91). Adding Current Procedural Terminology codes for inferior vena cava filter removal or early death did not improve the performance. The NLP algorithm had a weighted PPV of 80% and a weighted sensitivity of 90%, with a c statistic of 0.93 (95% CI, 0.92-0.94). The combined algorithm had a weighted PPV of 98% at the higher cutoff and a weighted sensitivity of 96% at the lower cutoff, with a c statistic of 0.98 (95% CI, 0.97-0.98). Conclusions: Our ICD/medication-based algorithm can accurately identify VTE phenotype among patients with cancer with a high PPV of 95%. The combined algorithm should be considered in EHR databases that have access to such capabilities.
RESUMEN
The epidemiology of cancer-associated thrombosis (CAT) among uninsured and vulnerable populations in the US is not well-characterized. We performed a retrospective cohort study for patients with newly diagnosed cancer from 2011 to 2020 at Harris Health System, which cares for uninsured residents in the Houston metropolitan area. Patient demographics, NCI comorbidity index, area of deprivation index (ADI), cancer histology, staging, and systemic therapy data were extracted. CAT included overall venous thromboembolism (VTE) or pulmonary embolism +/- lower extremity deep vein thrombosis (PE/LE-DVT) within 1 year of diagnosis. We used multivariable Fine-Gray models to assess the associations with CAT accounting for death as a competing risk. Among 15 342 patients, 74% were uninsured and 84% lived in socioeconomically disadvantaged neighborhoods. There were 16% Non-Hispanic White (NHW), 28% Non-Hispanic Black (NHB), 50% Hispanic (27% Mexican), and 6% Asian/Pacific Islanders (API). The 1-year CAT incidence rate was 14.6%. Overall VTE was lower for Hispanics versus NHW (SHR 0.87 [0.76-0.99]) and API versus NHW (SHR 0.58 [0.44-0.77]). PE/LE-DVT was higher for NHB versus NHW (SHR 1.18 [1.01-1.39]). CAT was also associated with chemotherapy-based regimens (+/- immunotherapy), age, obesity, cancer type/staging, VTE history, and recent hospitalization. NCI comorbidity and ADI scores were associated with mortality but not CAT. In a large cohort of underserved patients with cancer, we identified an elevated incidence of CAT with known and novel risk predictors. Hispanics had lower adjusted rates of CAT and mortality. Our findings highlight the need to investigate and incorporate vulnerable populations in clinical trials.
