基于LASSO回归构建的肝硬化患者发生急性肾损伤的预测模型

肝脏 ›› 2026, Vol. 31 ›› Issue (7): 966-970.

基于LASSO回归构建的肝硬化患者发生急性肾损伤的预测模型

朱春兰, 曹雅琴, 陈旭峰

214044 无锡中国人民解放军联勤保障部队第九〇四医院消化内科(朱春兰,陈旭峰);
214044 无锡江南大学附属无锡第五人民医院消化内科(曹雅琴)

收稿日期:2026-02-07 出版日期:2026-07-31 发布日期:2026-08-21
通讯作者: 陈旭峰,Email:1056325695@qq.com
基金资助:
无锡市科技发展资金项目(N20240701)

A predictive model for acute kidney injury in liver cirrhotic patients based on LASSO regression

Zhu Chunlan¹, Cao Yaqin², Chen Xufeng¹

1. Department of Gastroenterology, the 904 Hospital of the Joint Logistics Support Force of the People′s Liberation Army, Wuxi 214044, China;
2. Department of Gastroenterology, Wuxi Fifth People′s Hospital Affiliated to Jiangnan University, Wuxi 214044, China

Received:2026-02-07 Online:2026-07-31 Published:2026-08-21
Contact: Chen Xufeng,Email:1056325695@qq.com

摘要/Abstract

摘要： 目的构建肝硬化患者发生急性肾损伤(AKI)的预测模型,筛查肝硬化患者并发AKI的独立风险因素。方法纳入 2023 年 1 月至 2025 年10月解放军联勤保障部队第九〇四医院收治的 157 例肝硬化患者,其中,住院 7 d内发生AKI 69 例。采用 LASSO 回归模型筛选疾病潜在风险因子,将筛选结果纳入多因素 logistic 回归分析,甄别影响疾病进展的独立风险因素。基于上述独立风险因素构建预测模型,以受试者工作特征曲线下面积(AUC)量化模型的预测效能。结果单因素比较显示,AKI组与非AKI组的年龄、Child-Pugh分级、合并腹水、合并自发性腹膜炎、使用利尿剂比例及白细胞、血肌酐、估算肾小球滤过率、降钙素原等指标差异有统计学意义(P<0.05)。LASSO回归模型成功筛选出年龄、血红蛋白、估算肾小球滤过率、血肌酐4个关键变量;多因素logistic回归分析显示,年龄(OR=1.180,95%CI:1.034~1.347)、血肌酐(OR=1.072,95%CI:1.015~1.132)为肝硬化患者发生AKI的独立危险因素(P<0.05),血红蛋白(OR=0.909,95%CI:0.841~0.982)、估算肾小球滤过率(OR=0.921,95%CI:0.871~0.974)为独立保护因素(P<0.05);联合预测模型AUC为0.972(95%CI:0.940~1.000),灵敏度97.1%、特异度95.5%,预测效能优于单一指标。结论基于LASSO回归构建的联合预测模型可高效、准确地预测肝硬化患者住院7 d内AKI的发生风险,筛选出的年龄、血肌酐、血红蛋白、估算肾小球滤过率为关键影响因素,可为临床早期预警和精准干预提供依据。

关键词: 肝硬化, 急性肾损伤, LASSO回归, 预测模型, 风险因素

Abstract: Objective To develop a predictive model for acute kidney injury (AKI) in liver cirrhosis patients within 7 days of hospitalization, and to identify independent risk factors for AKI, and offer evidence for early clinical identification of high-risk patients and intervention strategy development. Methods A total of 157 liver cirrhosis patients admitted from January 2023 to October 2025 were retrospectively enrolled in the 904 Hospital of the Joint Logistics Support Force of the People′s Liberation Army. They were divided into an AKI group (n=69) and a non-AKI group (n=88) based on AKI occurrence within 7 days of hospitalization. Clinical features and laboratory parameters were gathered and analyzed comparatively across groups. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify potential risk factors, and the screened variables were in turn analyzed through multivariate logistic regression to ascertain independent risk factors. A predictive model was further constructed, and the effectiveness of this model was evaluated by means of the receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC). Results Univariate analyses revealed marked intergroup disparities in age, Child-Pugh classification, incidence of ascites and spontaneous bacterial peritonitis, rate of diuretic administration, as well as multiple laboratory parameters (white blood cell count, serum creatinine, estimated glomerular filtration rate, procalcitonin), with all variations achieving statistical significance (P<0.05). LASSO regression identified four key variables including age, hemoglobin, estimated glomerular filtration rate, and serum creatinine. Multivariate logistic regression confirmed age (OR=1.180, 95%CI:1.034-1.347) and serum creatinine (OR=1.072, 95%CI:1.015-1.132) as independent AKI risk factors (P<0.05), while hemoglobin (OR=0.909, 95%CI:0.841~0.982) and estimated glomerular filtration rate (OR=0.921, 95%CI:0.871~0.974) were independent protective factors (P<0.05). The combined model achieved an AUC of 0.972 (95%CI:0.940~1.000) with 97.1% sensitivity and 95.5% specificity, outperforming individual indicators. Conclusion The LASSO-derived combined model effectively forecasts 7-day AKI risk among cirrhotic patients. Age, serum creatinine, hemoglobin and estimated glomerular filtration rate act as pivotal predictive indicators, facilitating timely clinical vigilance and tailored intervention to optimize patient prognosis.

Key words: Liver cirrhosis, Acute kidney injury, LASSO regression, Predictive model, Risk factor

朱春兰, 曹雅琴, 陈旭峰. 基于LASSO回归构建的肝硬化患者发生急性肾损伤的预测模型[J]. 肝脏, 2026, 31(7): 966-970.

Zhu Chunlan, Cao Yaqin, Chen Xufeng. A predictive model for acute kidney injury in liver cirrhotic patients based on LASSO regression[J]. Chinese Hepatolgy, 2026, 31(7): 966-970.

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