Risk assessment for venous thromboembolism in chemotherapy treated ambulatory cancer patients: a machine learning approach

Objective: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). Design: Multiple kernel learning (MKL) based on support vector machines (SVM) and random optimization (RO) models were used to produce VTE risk predictors [referred as Machine-Learning (ML)-RO] yielding the best classification performance over a training (3-fold cross validation) and testing set. Results: Attributes of the patient dataset (n=1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in two ML-RO approaches not including the Khorana score (ML-RO-2: +LR=1.68, -LR=0.24; ML-RO-3: +LR=1.64, -LR=0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), which were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR=0.212; ML-RO-3-K: AUCPR=0.146) compared to the Khorana score (AUCPR=0.096). Of interest, the best fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. Conclusions: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors.