A Real-time Prognostic Model for Venous Thromboembolic Events among Hospitalized Adults

Background Hospital-acquired venous thromboembolism (HA-VTE) is a leading cause of morbidity and mortality among hospitalized adults. Guidelines recommend use of a risk-prediction model to estimate HA-VTE risk for individual patients. Extant models do not perform well for broad patient populations and are not conducive to automation in clinical practice. Objective To develop an automated, real-time prognostic model for VTE during hospitalization among all adult inpatients using readily available data from the electronic health record (EHR). Methods The derivation cohort included inpatient hospitalizations (‘encounters’) for patients ≥16 years old at Vanderbilt University Medical Center between 2018-2020 (n=132,330). HA-VTE events were identified using ICD-10 codes. The prognostic model was developed using LASSO regression. Temporal external validation was performed in a validation cohort of encounters between 2021-2022 (n=62,546). Prediction performance was assessed by discrimination accuracy (C statistic) and calibration (integrated calibration index, ICI). Results There were 1,187 HA-VTEs in the derivation cohort (9.0 per 1000 encounters) and 864 in the validation cohort (13.8 per 1000 encounters). The prognostic model included 25 variables, with placement of a central line among the most important predictors. Prediction performance of the model was excellent (C statistic: 0.891, 95% CI: 0.882-0.900; ICI: 0.001). The model performed similarly well across subgroups of patients defined by age, sex, race, and type of admission. Conclusion This fully automated prognostic model uses readily available data from the EHR, exhibits superior prediction performance compared to existing models, and generates granular risk stratification in the form a predicted probability of HA-VTE for each patient.