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

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 datasets.
Methods:  We constructed a derivation cohort using 6 years of data from 12 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 three validation cohorts: a temporal cohort, including two additional years, a cross-validation, in which we refit the model excluding one hospital each time, applying the refitted model to the holdout hospital, and an external cohort. Performance was evaluated using the C-statistic.
Results:  The derivation cohort included 155,026 patients with a 14-day VTE rate of 0.68%. The final multivariable model contained 13 patient risk factors. The model had an optimism corrected C-statistic of 0.79 and good calibration. The temporal validation cohort included 53,210 patients, with a VTE rate of 0.64%; the external cohort had 23,413 patients and a rate of 0.49%. Based on the C-statistic, the Cleveland Clinic Model (CCM) outperformed both the Padua (0.76 vs. 0.72, p  = 0.002) and IMPROVE (0.68, p  < 0.001) models in the temporal cohort. C-statistics for the CCM at individual hospitals ranged from 0.68 to 0.78. In the external cohort, the CCM C-statistic was similar to Padua (0.70 vs. 0.66, p  = 0.17) and outperformed IMPROVE (0.59, p  < 0.001).
Conclusion:  A new VTE risk assessment model outperformed recommended models.
Competing Interests: None decalared.
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