针灸治疗干预下脑梗死恢复期患者并发肺部感染的列线图预测模型构建及验证^*

Development and Validation of a Nomogram Prediction Model for Pulmonary Infection in Cerebral Infarction Patients During the Recovery Phase After Acupuncture Treatment

摘要

目的 分析针灸治疗干预下的脑梗死恢复期患者并发肺部感染的危险因素，并构建列线图模型。方法 按照建模集与验证集7∶3比例，选择2022年1月-2024年1月期间在院内治疗的350例脑梗死患者为建模集。同时选择2024年2月至2024年12月收治的150例脑梗死患者作为验证集。收集患者一般资料、实验室指标及针灸干预变量，在建模集中使用LASSO回归筛选变量后进行多因素logistic回归，基于多因素回归结果建立列线图预测模型，分别在建模集和验证集中进行模型评价。结果 脑梗死恢复期患者肺部感染发生率26.20%（131/500），其中建模集患者95例，共检出147株病原菌，以革兰阴性菌为主。建模集中，LASSO回归及logistic多因素向后逐步回归法筛选出肺部感染的危险因素为年龄增加（OR=1.043，95%CI：1.010～1.077）、进食评估问卷调查工具-10（EAT-10）评分升高（OR=1.249，95%CI：1.125～1.387）、有侵入性操作（OR=2.715，95%CI：1.340～5.501）、美国国立卫生研究院卒中量表（NIHSS）评分升高（OR=2.477，95%CI：1.951～3.144）、白蛋白降低（OR=0.856，95%CI：0.786～0.931）、前白蛋白降低（OR=0.992，95%CI：0.987～0.997）、留针时间为30 min（OR=2.439，95%CI：1.146～5.191）、针灸疗程<14 d（OR=2.463，95%CI：1.157～5.243）以及发病后>3 d介入针灸治疗（OR=2.660，95%CI：1.190～5.942）。据此构建列线图模型，该模型在建模集和验证集中的曲线下面积（AUC）为0.916（95%CI：0.877～0.954）、0.923（95%CI：0.874～0.973），校正曲线证实模型预测概率与实际概率吻合度较高，应用可产生正向临床净效益。结论 基于Lasso-Logistic回归构建的列线图模型能够识别针灸干预下的脑梗死恢复期患者肺部感染风险，通过量化风险因素可指导患者优化针灸治疗方案、实现精准干预，降低感染风险。

Abstract

Objective To analyze the risk factors for pulmonary infection in convalescent patients with cerebral infarction under acupuncture and moxibustion intervention, and to develop a nomogram model. Methods According to a modeling-to-verification set ratio of 7∶3, 350 patients with cerebral infarction treated in the hospital from January 2022 to January 2024 were selected as the modeling set. At the same time, 150 patients with cerebral infarction admitted from February 2024 to December 2024 were selected as the verification set. General patient information, laboratory indicators, and acupuncture intervention variables were collected. After screening variables using LASSO regression in the modeling set, multivariate logistic regression was performed. Based on the results of the multivariate regression, a nomogram prediction model was established, and the models were evaluated in the modeling and validation set, respectively. Results The incidence of pulmonary infection in patients with cerebral infarction during convalescence was 26.20%(131/500), including 95 patients in the model group. A total of 147 pathogen strains were detected, mainly Gram-negative bacteria. Modeling concentration, LASSO regression, and multivariate stepwise logistic regression were used to identify factors influencing pulmonary infection: increased age (OR = 1.043, 95%CI: 1.010-1.077), higher Eating Assessment Tool 10 (EAT-10) score (OR = 1.249, 95%CI: 1.125-1.387), invasive procedures (OR = 2.715, 95%CI: 1.340-5.501), higher National Institutes of Health Stroke Scale (NIHSS) score (OR = 2.477, 95%CI: 1.951-3.144), lower albumin (OR = 0.856, 95%CI: 0.786-0.931), lower prealbumin (OR = 0.992, 95%CI: 0.987-0.997), needle retention time of 30 min (OR = 2.439,95%CI: 1.146-5.191), acupuncture course less than 14 d (OR = 2.463,95%CI: 1.157-5.243), and initiation of intervention acupuncture treatment more than >3 d after onset (OR = 2.660, 95%CI: 1.190-5.942). Based on these factors, a nomogram model was constructed, with areas under the curve (AUC) of 0.916 (95%CI: 0.877-0.954) and 0.923 (95%CI: 0.874-0.973). The calibration curve showed that the predicted probabilities of the model were in close agreement with the observed probabilities, and its application could yield positive net clinical benefits. Conclusion The nomogram model constructed used Lasso-logistic regression can identify the risk of pulmonary infection in patients with cerebral infarction during convalescence after acupuncture treatment. Quantifying risk factors can guide patients in optimizing acupuncture treatment plans, achieving precise intervention, and reducing the risk of infection.

关键词

针灸 / 脑梗死恢复期 / 肺部感染 / 列线图 / 预测模型

Key words

Acupuncture / Recovery period of cerebral infarction / Pulmonary infection / Nornogram prediction model

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基金资助

*安徽省临床医学研究转化专项项目(202304295107020103)

储浩然全国名老中医药专家传承工作室项目(国中医药人教函（2022）75号)