Development and External Validation of a Lymphoma-Specific Venous Thromboembolism Risk Assessment Model

Introduction:Venous thromboembolism (VTE) significantly affects cancer patients undergoing systemic therapy. Existing risk models developed in the pan-cancer population, such as the Khorana score, offer limited discrimination in cancer-specific setting. We aimed to develop and validate a risk assessment model (RAM) tailored for lymphoma patients, accounting for the clinical and sociodemographic heterogeneity across three distinct healthcare systems in the United States. Methods: Electronic health records (EHR) linked to cancer registry 2006-2021 from the Veterans Affairs national healthcare system (VA) with a total of 10,313 lymphoma patients were randomly divided into an 80% derivation cohort and a 20% internal validation cohort. Further external validation was carried out using data from two other healthcare systems - Harris Health System (HHS, N= 854, 2011-2020) and MD Anderson Cancer Center (MDACC, N=1,858, 2017-2020). Patients were included if they had newly diagnosed lymphoma requiring first-line systemic therapy within 1 year of diagnosis. Patients were excluded if they had recent diagnosis of acute VTE within the last 6 months or were prescribed anticoagulant within 1 month before index date. The index date was the time of systemic therapy, and all covariates were extracted on or before the index date. Incident VTE was defined using our published computable phenotype algorithm (PMID 36626707; 37067102). For RAM derivation (Figure 1), 32 candidate predictors were initially chosen based on clinical relevance and EHR data availability. Missing values were imputed via chain random forest. Parsimonious variable categories were selected by a combination of Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), and random forest to generate the final logistic regression model. For external validation, complete case analysis was performed using the derived beta coefficients without model refitting. Bootstrapped time-dependent c statistics and calibration curves for 6-month VTE were used to assess discrimination and fit. A pre-determined threshold of 8% VTE at 6-month from the predicted probability was used to stratify high vs. low-risk groups. Competing risk models were used to assess cumulative incidence. Results: At 6 months, the VTE incidence were 5.75% (n=469) in VA derivation and 6.12% (n=124), 8.24% (n=69), and 6.55% (n=112) in the VA, HHS, and MDACC validation cohorts, respectively. The three healthcare systems