Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems.

PURPOSE: Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy. METHODS: Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS). RESULTS: Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set. CONCLUSION: The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months (v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies.[Media: see text].

Developing and optimizing a computable phenotype for incident venous thromboembolism in a longitudinal cohort of patients with cancer.

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.

Patient-Centered Outcomes of an Emergency Department Social and Medical Resource Intervention.

INTRODUCTION: Few studies have examined the impact of emergency department (ED) social interventions on patient outcomes and revisits, especially in underserved populations. Our objective in this study was to characterize a volunteer initiative that provided community medical and social resources at ED discharge and its effect on ED revisit rates and adherence to follow-up appointments at a large, county hospital ED. METHODS: We performed a cross-sectional analysis of ED patients who received medical and social resources and an educational intervention at discharge between September 2017-June 2018. Demographic information, the number of ED return visits, and outpatient follow-up appointment adherence within 30 and 90 days of ED discharge were obtained from electronic health records. We obtained data regarding patient utilization of resources via telephone follow-up communication. We used logistic regression analyses to evaluate associations between patient characteristics, reported resource utilization, and revisit outcomes. RESULTS: Most patients (55.3% of 494 participants) identified as Latino/Hispanic, and 49.4% received healthcare assistance through a local governmental program. A majority of patients (83.6%) received at least one medical or social resource, with most requesting more than one. Patients provided with a medical or social resource were associated with a higher 90-day follow-up appointment adherence (odds ratio [OR] 2.56; 95% confidence interval [CI] 1.05-6.25, and OR 4.75; 95% CI 1.49-15.20], respectively), and the provision of both resources was associated with lower odds of ED revisit within 30 days (OR 0.50; 95% CI 0.27-0.95). Males and those enrolled in the healthcare assistance program had higher odds of ED revisits, while Hispanic/Latino and Spanish-speaking patients had lower odds of revisits. CONCLUSION: An ED discharge intervention providing medical and social resources may be associated with improved follow-up adherence and reduced ED revisit rates in underserved populations.