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. 2024 May 6;8(4):102433.
doi: 10.1016/j.rpth.2024.102433. eCollection 2024 May.

A real-time prognostic model for venous thromboembolic events among hospitalized adults

Benjamin F Tillman  1 , Henry J Domenico  2 , Ryan P Moore  2 , Daniel W Byrne  2 , Colleen T Morton  1 , Amanda S Mixon  3   4   5 , Benjamin French  2
Affiliations

A real-time prognostic model for venous thromboembolic events among hospitalized adults

Benjamin F Tillman et al. Res Pract Thromb Haemost. .

Abstract

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.

Objectives: To develop an automated, real-time prognostic model for venous thromboembolism during hospitalization among all adult inpatients using readily available data from the electronic health record.

Methods: The derivation cohort included inpatient hospitalizations ("encounters") for patients ≥16 years old at Vanderbilt University Medical Center between 2018 and 2020 (n = 132,330). HA-VTE events were identified using International Classification of Diseases, 10th Revision, codes. The prognostic model was developed using least absolute shrinkage and selection operator regression. Temporal external validation was performed in a validation cohort of encounters between 2021 and 2022 (n = 62,546). Prediction performance was assessed by discrimination accuracy (C statistic) and calibration (integrated calibration index).

Results: There were 1187 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; integrated calibration index, 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 electronic health record, exhibits superior prediction performance compared with existing models, and generates granular risk stratification in the form of a predicted probability of HA-VTE for each patient.

Keywords: inpatients; prognosis; risk; safety; venous thromboembolism.

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Figures

Figure 1
Figure 1
Relative importance of each variable in the final prognostic model, as quantified by its Wald chi-squared statistic along with odds ratios (ORs) and 95% CIs. BUN, blood urea nitrogen; CRP, C-reactive protein.
Figure 2
Figure 2
Prediction performance of the final prognostic model among patient subgroups in the derivation cohort, presented as C statistics with 95% CIs. Note: “Encounters” provides the number of encounters, “Events” provides the number of hospital-acquired venous thromboembolic events, and “Rate per 1000” provides the event rate per 1000 encounters.
See this image and copyright information in PMC

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