基于临床资料构建慢性阻塞性肺疾病急性加重期合并肺部感染的列线图预测模型

Nomogram prediction model construction of acute exacerbation of chronic obstructive pulmonary disease complicating pulmonary infection based on clinical data

目的基于临床资料分析慢性阻塞性肺疾病急性加重期(AECOPD)合并肺部感染的相关危险因素,并构建列线图预测模型。方法回顾性分析2020年11月至2022年10月于该院就诊的180例AECOPD患者临床资料,根据是否合并肺部感染分为合并感染患者76例(观察组)和未合并感染患者104例(对照组)。对两组的临床资料进行统计分析,通过受试者工作特征(ROC)曲线分析差异有统计学意义的连续变量;采用多因素Logistic回归分析AECOPD合并肺部感染的独立影响因素;通过R软件构建预测AECOPD合并肺部感染的列线图预测模型;通过校准曲线对预测模型进行内部验证;通过决策曲线评估预测模型的临床净收益。结果观察组年龄、合并糖尿病比例、吸烟比例、机械通气比例、住院时间≥2周比例均高于对照组(P<0.05),清蛋白水平低于对照组(P<0.05)。年龄、清蛋白诊断AECOPD合并肺部感染的曲线下面积(AUC)分别为0.888(95%CI:0.832~0.930)、0.882(95%CI:0.826~0.925),最佳截断值分别为61岁、30 g/L。患者年龄≥61岁、合并糖尿病、住院时间≥2周、清蛋白<30 g/L和吸烟是AECOPD合并肺部感染的独立危险因素(P<0.05)。构建了AECOPD合并肺部感染危险因素的列线图预测模型,该预测模型预测AECOPD合并肺部感染的校准曲线趋于理想曲线,C-index为0.786(95%CI:0.497~0.976);决策曲线显示当风险阈值>0.16时,此预测模型在预测AECOPD合并肺部感染风险因素方面可以提供额外的临床净收益。结论该研究构建了AECOPD合并肺部感染危险因素的列线图预测模型,有助于医护人员认识AECOPD合并肺部感染的相关因素,尽早制订个性化对策以改善患者预后。

Objective To analyze the related risk factors of complicating pulmonary infection in acute exacerbation of chronic obstructive pulmonary disease(AECOPD)based on clinical data,and to construct the nomogram prediction model.Methods The clinical data of 180 cases of AECOPD visited in this hospital from November 2020 to October 2022 were retrospectively analyzed.The patients were divided into 76 patients with pulmonary infection(observation group)and 104 patients without pulmonary infection(control group)according to whether or not complicating pulmonary infections.The clinical data of the two groups were statistically analyzed.The continuous variables with statistically significant differences were analyzed by the receiver operating characteristic(ROC)curve;the multiple Logistic regression was adopted to analyze the independent influencing factors of AECOPD complicating pulmonary infection;the nomogram prediction model for predicting AECOPD complicating pulmonary infection was constructed by using R software;the internal validation of the prediction model was conducted through calibration curves;the clinical net benefit of the prediction model was evaluated through decision curves.Results The age,proportion of complicating diabetes,smoking proportion,mechanical ventilation proportion,and proportion of hospitalization duration≥2 weeks in the observation group were higher than those in the control group(P<0.05),and the albumin level was lower than that in the control group(P<0.05).The area under the curve(AUC)of age and albumin in diagnosing AECOPD complicating pulmonary infection was 0.888(95%CI:0.832-0.930)and 0.882(95%CI:0.826-0.925)respectively,the optimal cutoff values were 61 years old and 30 g/L respectively.The age≥61 years old,complicating diabetes,hospitalization duration≥2 weeks,albumin<30 g/L and smoking were the independent risk factors of AECOPD complicating pulmonary infection(P<0.05).The nomogram prediction model of the risk factors for AECOPD complicating pulmonary infection was constructed,which predicted that the correction curve of AECOPD complicating pulmonary infection tended towards the ideal curve,the C-index was 0.786(95%CI:0.497-0.976);The decision curve showed that when the risk threshold value was>0.16,this predictive model could provide the additional clinical net benefits in predicting the risk factors of AECOPD complicating pulmonary infection.Conclusion The nomogram prediction model for risk factors of AECOPD complicating pulmonary infection is constructed in this study,which helps the medical staff to understand the related factors of AECOPD complicating pulmonary infection and formulate the personalized countermeasures as soon as possible to improve the prognosis.