Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Journal: Scientific reports
PMID:

Abstract

Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate predictors encompassed demographics, comorbidities, laboratory findings, and therapy strategies. A stepwise logistic regression model was employed to select the features included in the final model. Six machine learning algorithms-logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN)-were employed to construct predictive models for DIC in critically ill children. Models were then evaluated by using area under the curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), precision, recall and decision curve analysis (DCA). Interpretation of the optimal model was conducted using shapley additive explanations (SHAP). A total of 6093 critically ill children were encompassed in this study, of whom 681 (11.2%) developed DIC. The RF model exhibited the highest levels of accuracy (0.856), sensitivity (0.866), Kappa (0.472), NPV (0.423), and recall (0.866). However, the XGB model outperformed RF, LR, SVM, DT, and KNN in terms of AUC (0.908), specificity (0.859), PPV (0.978), and precision (0.969). Decision curve analysis (DCA) confirmed the superior clinical utility of the XGB model. Overall, the XGB model demonstrated superior clinical utility compared to RF, LR, SVM, DT, and KNN. We named the final model Alfalfa-PICU-DIC. SHAP analysis identified D-dimer, INR, PT, TT, and PLT count as the top predictors of DIC. Machine learning models can be a reliable tool for predicting DIC in critically ill children, which will facilitate timely intervention, thereby reducing the burden of DIC on patients in the pediatric intensive care unit (PICU).

Authors

  • Jintuo Zhou
    Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
  • Yongjin Xie
    Department of Obstetrics and Gynecology, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Maternity and Child Health Hospital, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Peiguang Niu
    Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
  • Huajiao Chen
    Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, #18 Daoshan Road, Fuzhou, China.
  • Xiaoping Zeng
    College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China.
  • Jinhua Zhang
    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, People's Republic of China.