HALP指数与肌少症指数联合模型对AECOPD患者预后的预测价值

The Predictive Value of a Combined Model of the HALP Index and Sarcopenia Index for Prognosis in Patients with AECOPD

摘要: 目的：探讨联合血红蛋白–白蛋白–淋巴细胞–血小板(HALP)指数与肌少症指数(SI)构建的预测模型在评估慢性阻塞性肺疾病急性加重(AECOPD)患者预后中的临床应用价值。方法：回顾性收集2023年1月至2024年5月承德市中心医院收治的107例AECOPD患者的临床资料，以出院后1年内是否发生不良预后为研究终点。首先通过单因素分析筛选潜在影响因素，继而采用多因素Logistic回归模型明确独立的预后预测因子。最后，通过绘制受试者工作特征(ROC)曲线，对比评估HALP指数、SI及二者联合模型对终点事件的预测性能。结果：在1年随访期内，共70例(65.4%)患者出现不良预后。多因素回归分析确认，住院时间延长(OR = 1.691)、近1年内急性加重频次高(OR = 4.159)、较低的HALP指数(OR = 0.840)以及较低的SI (OR = 0.899)是AECOPD患者预后不良的独立危险因素(P < 0.05)。ROC曲线分析表明，HALP指数、SI及联合模型预测不良预后的曲线下面积(AUC)依次为0.919、0.739和0.930，统计比较证实，联合模型的预测效能显著优于任一单一指标(P < 0.001)。结论：基于HALP指数与SI构建的联合模型对AECOPD患者预后具有良好的预测能力，有助于早期识别高风险人群。

Abstract: Objective: To develop and validate a novel predictive model that integrates the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) index with the Sarcopenia Index (SI) for assessing the prognosis of patients hospitalized with Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD). Methods: A retrospective analysis was conducted on clinical data from 107 AECOPD patients admitted to Chengde Central Hospital between January 2023 and May 2024. The primary endpoint was the incidence of adverse outcomes within one year after discharge. Potential influencing factors were initially identified through univariate analysis. Subsequently, independent prognostic predictors were determined using multivariate logistic regression. The predictive performance of the HALP index, SI, and their combined model for the endpoint event was evaluated and compared by analyzing Receiver Operating Characteristic (ROC) curves. Results: During the one-year follow-up period, 70 patients (65.4%) experienced adverse outcomes. Multivariate regression analysis confirmed that longer hospital stay (OR = 1.691), higher frequency of acute exacerbations in the previous year (OR = 4.159), lower HALP index (OR = 0.840), and lower SI (OR = 0.899) were independent risk factors for poor prognosis in AECOPD patients (all P < 0.05). ROC curve analysis showed that the areas under the curve (AUC) for predicting adverse outcomes were 0.919 for the HALP index, 0.739 for the SI, and 0.930 for the combined model. Statistical comparisons confirmed that the predictive efficacy of the combined model was significantly superior to that of any single index (P < 0.001). Conclusion: The combined model based on the HALP index and SI demonstrates strong predictive capability for the prognosis of AECOPD patients, facilitating the early identification of high-risk individuals.