Derivation and Validation of a Risk Factor Model to Identify Medical Inpatients at Risk for Venous Thromboembolism

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Thrombosis and haemostasis


BACKGROUND: Venous thromboembolism (VTE) prophylaxis is recommended for hospitalized medical patients at high risk for VTE. Multiple risk assessment models exist, but few have been compared in large data sets.

METHODS: We constructed a derivation cohort using 6 years of data from 13 hospitals to identify risk factors associated with developing VTE within 14 days of admission. VTE was identified using a complex algorithm combining administrative codes and clinical data. We developed a multivariable prediction model and applied it to 2 validation cohorts: a temporal cohort, including two additional years and a cross-validation, in which we refit the model excluding one hospital at a time, and applied the refitted model to the holdout hospital. Performance was evaluated using the C-statistic.

RESULTS: The derivation cohort included 160,928 patients with a 14-day VTE rate of 0.79%. The final multivariable model contained 13 patient risk factors. The model had an optimism corrected C-statistic of 0.80 and good calibration. The temporal validation cohort included 55,301 patients, with a VTE rate of 0.74%. Based on the c-statistic, the Cleveland Clinic Model (CCM) outperformed the Padua model (0.76 vs. 0.72, p<0.01). The CCM was more sensitive (65.8% vs. 60.4%, p=0.05) and more specific (74.9% vs. 71.4%, p<.001), with higher positive (1.9% vs. 1.5%, p<.001) and negative predictive values (99.7% vs. 99.6%, p=0.01). C-statistics for the CCM at individual hospitals ranged from 0.64 to 0.76.

CONCLUSION: A new VTE risk assessment model outperformed the Padua model. After further validation it could be recommended for widespread use.

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ePub ahead of print