Machine learning to predict venous thrombosis in acutely ill medical patients

Abstract

Background The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. Objectives To evaluate the performance of machine learning models compared to the IMPROVE score. Methods The APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A “reduced” model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance was assessed by discrimination and calibration to predict a composite VTE end point. The frequency of predicted risks of VTE were plotted and divided into tertiles. VTE risks were compared across tertiles. Results The ML and rML algorithms outperformed the IMPROVE score in predicting VTE (c‐statistic: 0.69, 0.68 and 0.59, respectively). The Hosmer‐Lemeshow goodness‐of‐fit P‐value was 0.06 for ML, 0.44 for rML, and <0.001 for the IMPROVE score. The observed event rate in the lowest tertile was 2.5%, 4.8% in tertile 2, and 11.4% in the highest tertile. Patients in the highest tertile of VTE risk had a 5‐fold increase in odds of VTE compared to the lowest tertile. Conclusion The super learner algorithms improved discrimination and calibration compared to the IMPROVE score for predicting VTE in acute medically ill patients.

Prevention of Venous Thromboembolism in 2020 and Beyond

“…Predicting an individual's risk of VTE is extremely challenging because no single variable is strongly predictive, and we are forced to rely on systems that incorporate multiple variables to produce meaningful predictive values for VTE. Machine learning has the capacity to overcome these challenges by recognizing patterns in complex sets of information that accurately predict the risk of VTE and bleeding [189]. When interpreted by such systems, continuous variables do not need to be reduced to categorical variables for ease of use.…”

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

“…A recent study examined the discriminative ability of the IMPROVE ensemble machine learning software to predict VTE onset in a subpopulation of acutely ill patients classified as high risk for VTE. 28 Though the research yielded high discriminative ability with an AUROC of 0.69 for the standard ML algorithm and .68 c-statistic for the reduced ML (rML) model, this study did not present results indicating the length of time prior to onset that VTE can be predicted, and also remains to be validated in a general inpatient population. 28 The results present a strong argument for the capability of ML to predict VTE.…”

“… 28 Though the research yielded high discriminative ability with an AUROC of 0.69 for the standard ML algorithm and .68 c-statistic for the reduced ML (rML) model, this study did not present results indicating the length of time prior to onset that VTE can be predicted, and also remains to be validated in a general inpatient population. 28 The results present a strong argument for the capability of ML to predict VTE. Expanding on these findings, our research presents results that may indicate how a ML may be useful in predicting DVT up to 24 hours in advance of onset while being used in more general clinical settings.…”

The potential of artificial intelligence to improve patient safety: a scoping review

“…One study applied a super learner ensemble approach to identify inpatients at higher risk of future VTEs with an AUC of 0.69 (ref. 30 ). Prediction can also be applied to manage at-risk populations in the outpatient setting; for example, a multiple kernel learning algorithm was developed to predict VTE risk among patients undergoing chemotherapy with a sensitivity of 89%, markedly outperforming the recommended Khorana score (sensitivity = 11%) 31 .…”