JMIR Publications

ABSTRACT

Background:

Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338000 and 917000, respectively. In China, FNFs account for 48.22% of hip fractures.Currently, a large number of studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in such patients with severe FNF admitted to the ICU.

Objective:

Three machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.

Methods:

A retrospective analysis was conducted using femoral neck fracture patient information from the Medical Information Mart for Intensive Care (MIMIC) III. After balancing the dataset using the SMOTE algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, XGBoost, and BP neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.

Results:

A total of 366 patients with femoral neck fractures were selected, including 48 cases of in-hospital death. Data from 636 patients were obtained by balancing the dataset with the in-hospital death group: survival group as 1:1. The three machine learning models exhibited high predictive accuracy, and the AUC of the random forest, XGBoost, and BP neural networks were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top ten feature variables that were meaningful for predicting the risk of in-hospital death of patients were vitamin D, lactate, creatinine, the Simplified Acute Physiology Score（SAPS） II, calcium, loss in ICU, white blood cell, age, BMI and CK.

Conclusions:

Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of severe patients and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.