Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery.

Xiaolei Jing,Hao Xu,Xueqi Wang,Hongxia Zhuang,Xiang Fang

doi:10.3389/fsurg.2021.797872

Xiaolei Jing, Hao Xu + Show 3 more

Open Access

https://doi.org/10.3389/fsurg.2021.797872

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Abstract

ObjectiveThis study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery.MethodsPatients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data.ResultsThree hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance.ConclusionsAccording to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications.

Highlights

Complications after major surgery occur frequently and are an important cause of mortality and morbidity, especially when they affect the lungs [1]
The incidence of pulmonary complication (PPC) in emergency intracerebral hemorrhage (ICH) patients is much higher than that of conventional surgery, and the occurrence of complications often leads to poor prognosis, even directly related to patient death
The characteristics of the patients included in this study were sex, age, education, medical history, respiratory history, whether a current smoker, Glasgow coma scale (GCS), glucose, Albumin (Alb), WBC, lymphocyte count, leukocyte, RBC, platelet, clotting time, early enteral nutrition, preventive tracheotomy respirator use, operative time, anesthesia time, the blood loss, ASA classification, and craniotomy

Summary

Introduction

Complications after major surgery occur frequently and are an important cause of mortality and morbidity, especially when they affect the lungs [1]. Identification of patients at risk of developing PPCs could enable the use of preventive measures as well as timely treatment. Patients with severe craniocerebral surgery often suffer from coma, lack of spontaneous breathing for a period of time, or need to be assisted breathing by the ventilator, and often combined with multiple severe multi-system symptoms. The incidence of PPC in emergency intracerebral hemorrhage (ICH) patients is much higher than that of conventional surgery, and the occurrence of complications often leads to poor prognosis, even directly related to patient death. There is a paucity of literature that investigates the deleterious effects of PPCs in neurosurgical patients, in those requiring emergency ICH surgery which could face up to the highest rate of surgical complications rate. We believe that better prediction of patients’ PPC and taking preventive measures can greatly improve the prognosis of patients. The model of PPC in patients with ICH was established by multiple machine learning methods

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