随机森林模型和Logistic回归模型预测髋部骨折患者住院时间延长的效能比较

Comparison of random forest model and logistic regression model in predicting the prolonged length of stay of hip fracture patients

背景:髋部骨折发生率与日俱增,由于此类人群身体状况较差,常需要长时间住院,而住院时间延长导致床位流通率下降和经济负担增加。目前针对髋部骨折延迟出院的预测模型较少,此次研究旨在寻找针对髋部骨折延迟出院的最佳模型,指导临床决策。目的:探究髋部骨折患者住院时间延长的危险因素,建立两种不同的风险预测模型,获得最佳风险预测工具,为临床干预及管理提供参考。方法:回顾性分析2019年1月至2021年12月天津市天津医院收治的老年髋部骨折患者683例,将全部患者按7∶3比例随机分成建模组(479例)和验证组(204例),以住院时间的第75百分位数为分界点(>28 d),分为住院时间延长组和正常组。基于建模组采用SPSS和R软件进行单因素分析和多因素Logistic回归分析和变量重要性排序筛选最佳预测变量,构建列线图及随机森林模型。基于验证组通过ROC曲线下面积、准确度、灵敏度、特异度、阳性预测值和阴性预测值评价并比较两种模型的预测效能。结果与结论:(1)Logistic回归分析显示,骨牵引、肺炎、再骨折、多发性创伤、静脉血栓、肺感染和年龄校正Charlson合并症指数(ageadjusted Charlson Comorbidity Index,ACCI)是髋部骨折患者住院时间延迟的危险因素。(2)随机森林模型根据基尼指数减少平均值排序显示,年龄、骨牵引、手术类型、ACCI、肺炎是前5个指标,对延迟出院的预测有重要影响。(3)Logistic回归模型和随机森林预测模型的ROC曲线下面积、准确度、灵敏度、特异度、阳性预测值、阴性预测值分别为0.774(95%CI:0.696-0.853)和0.708(95%CI:0.627-0.789)、60.78%和90.85%、80.39%和23.53%、50.82%和78.09%、86.01%和46.15%,结果显示Logistic模型具有较好的预测效能。(4)上述结果证实,Logistics回归模型和随机森林模型对髋部骨折患者住院时间延长均具有较高的预测价值,这对临床医护人员及时识别高危患者并采取有效干预措施降低髋部骨折患者住院时间具有重要意义。

BACKGROUND:The incidence of hip fracture patients is increasing day by day.Because of their poor physical condition,these people often need to stay in hospital for a long time.However,the prolonged length of stay leads to a decrease in bed circulation rate and an increase in economic burden.At present,there are few prediction models for delayed discharge of hip fractures.This study aims to find the best model for delayed discharge of hip fractures and guide clinical decision-making.OBJECTIVE:To explore the risk factors of prolonged length of stay in patients with hip fractures,establish two different risk prediction models,obtain the best risk prediction tools,and provide a reference for clinical intervention and management.METHODS:Data from 683 elderly patients with hip fractures in Tianjin Hospital from January 2019 to December 2021 were retrospectively analyzed.All patients were randomly divided into a modeling group(479 cases)and a verification group(204 cases)according to the ratio of 7:3.The 75th percentile of length of stay was taken as the cut-off point(>28 days),and they were divided into extended hospitalization group and normal group.Single-factor and multifactor Logistic regression analysis and variable importance ranking were used to screen the best prediction model;the nomogram and random forest models were constructed.The prediction efficiency of the two models was evaluated by the receiver operating characteristic curve area,accuracy,sensitivity,specificity,positive prediction value and negative prediction value.RESULTS AND CONCLUSION:(1)Logistic regression analysis showed that bone traction,pneumonia,refolding,multiple trauma,venous thrombosis,lung infection and age-adjusted Charlson Comorbidity Index were the risk factors of prolonged length of stay of hip fracture patients.(2)The random forest model showed that age,bone traction,surgical type,age-adjusted Charlson Comorbidity Index and pneumonia were the first five indexes according to the average reduction of the Gini index,which had an important influence on the prediction of delayed discharge.(3)The area under the receiver operating characteristic curve,accuracy,sensitivity,specificity,positive prediction value and negative prediction value of the Logistic regression model and random forest prediction model were 0.774(95%CI:0.696-0.853)and 0.708(95%CI:0.627-0.789),60.78%and 90.85%,80.39%and 23.53%,50.82%and 78.09%,86.01%and 46.15%,respectively.The results exhibited that the Logistic model had good prediction efficiency.(4)Above findings confirm that the Logistics regression model and random forest model have high predictive value for a prolonged length of stay in patients with hip fractures,which is of great significance for clinical medical staff to identify high-risk patients in time and take effective intervention measures to reduce the length of stay of patients with hip fractures.