Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning

Haike Lei; Mengyang Zhang; Zeyi Wu; Chun Liu; Xiaosheng Li; Wei Zhou; Bo Long; Jiayang Ma; Huiyi Zhang; Ying Wang; Guixue Wang; Mengchun Gong; Na Hong; Haixia Liu; Yongzhong Wu

doi:10.3389/fcvm.2022.845210

Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning

Front Cardiovasc Med. 2022 Mar 7:9:845210. doi: 10.3389/fcvm.2022.845210. eCollection 2022.

Authors

Haike Lei¹, Mengyang Zhang², Zeyi Wu², Chun Liu², Xiaosheng Li¹, Wei Zhou¹, Bo Long¹, Jiayang Ma², Huiyi Zhang², Ying Wang¹, Guixue Wang³, Mengchun Gong², Na Hong², Haixia Liu¹, Yongzhong Wu¹

Affiliations

¹ Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China.
² Digital Health China Technologies, Co., Ltd., Beijing, China.
³ MOE Key Laboratory for Biorheological Science and Technology, State and Local Joint Engineering Laboratory for Vascular Implants, College of Bioengineering, Chongqing University, Chongqing, China.

Abstract

Background: There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients.

Methods: Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model.

Results: In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision-recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% CI: 0.893-0.926) and an AUPRC of 0.43 (95% CI: 0.363-0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802-0.917) and AUPRC of 0.30 (95% CI: 0.265-0.358), respectively.

Conclusion: According to the study results, there was no conspicuous decrease in the model's performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.

Keywords: lung cancer; machine learning; random forest; risk prediction model; venous thromboembolism.