天津医药 ›› 2024, Vol. 52 ›› Issue (5): 486-489.doi: 10.11958/20231011

• 临床研究 • 上一篇    下一篇

重症监护病房获得性衰弱风险预测模型的构建

王灵(), 龙登炎   

  1. 黔东南苗族侗族自治州人民医院重症医学科(邮编556000)
  • 收稿日期:2023-07-10 修回日期:2023-11-24 出版日期:2024-05-15 发布日期:2024-05-09
  • 作者简介:王灵(1979),男,主任医师,主要从事重症康复、脓毒症方面的研究。E-mail:463082910@qq.com
  • 基金资助:
    黔东南州科技支撑计划(黔东南科合支撑[2021]12号);贵州省科技支撑计划(黔科合支撑[2020]4Y139号);贵州省高层次创新型人才培养计划(黔千层人才[2022]201701号)

Construction of acquired weakness risk prediction model in intensive care unit

WANG Ling(), LONG Dengyan   

  1. Department of Medical Intensive Care Unit, People's Hospital of Qiandongnan Miao and Dong Autonomous Prefecture, Kaili 556000, China
  • Received:2023-07-10 Revised:2023-11-24 Published:2024-05-15 Online:2024-05-09

摘要:

目的 构建重症监护病房获得性衰弱(ICU-AW)的风险预测模型,指导临床预防及治疗。方法 纳入并分析重症医学科收治的1 063例患者的性别、年龄、年龄校正查尔森合并症指数(aCCI)、是否输注白蛋白、心力衰竭、是否行康复治疗、咪达唑仑用量、去甲肾上腺素用量、机械通气时间与ICU-AW的相关性,筛查独立危险因素并建立预测模型,分析模型的预测能力。结果 1 063例患者中发生ICU-AW 370例,Logistic回归分析显示高龄、高aCCI、长机械通气时间、高咪达唑仑用量、高去甲肾上腺素用量、心力衰竭为ICU-AW的独立危险因素,康复治疗及输注白蛋白为独立保护性因素;预测模型的回归方程为:Logit(P)=0.017×年龄+0.008×机械通气时间+0.006×去甲肾上腺素用量-0.832×康复治疗-0.648×输注白蛋白+1.224×aCCI+0.017×咪达唑仑用量+1.834×心力衰竭-6.806。模型的受试者工作特征曲线下面积(AUC)为0.908(0.890~0.925),敏感度为82.20%,特异度为82.40%。结论 利用上述变量构建的模型具有较好的预测效能,可为临床防治提供新思路。

关键词: 重症监护病房获得性衰弱, 临床预防及治疗, 独立危险因子, 预测模型

Abstract:

Objective To construct a risk prediction model for intensive care unit-acquired weakness (ICU-AW) to guide clinical prevention and treatment strategies. Methods The correlation between gender, age, age-adjusted Charlson Comorbidity Index (aCCI), injecting albumin, heart failure, rehabilitation treatment, midazolam dosage, norepinephrine dosage and mechanical ventilation duration in 1 063 patients admitted to the intensive care unit was analyzed. Independent risk factors were identified to establish the prediction model, and the predictive ability of the model was analyzed. Results Among 1 063 patients, 370 developed ICU-AW. Logistic regression analysis identified advanced age, higher aCCI, prolonged mechanical ventilation duration, increased midazolam and norepinephrine dosages, and heart failure as independent risk factors for ICU-AW, while rehabilitation treatment and injecting albumin were identified as independent protective factors. The regression equation of the prediction model was: Logit (P) = 0.017 × age + 0.008 × mechanical ventilation duration + 0.006 × norepinephrine dosage - 0.832 × rehabilitation treatment - 0.648 × injecting albumin + 1.224 × aCCI + 0.017 × midazolam dosage + 1.834 × heart failure - 6.806. The area under the curve (AUC) of the model was 0.908 (0.890-0.925), with the sensitivity of 82.20% and specificity of 82.40%. Conclusion The model constructed using these variables demonstrates good predictive efficiency and can provide new insights for clinical prevention and treatment of ICU-AW.

Key words: intensive care unit-acquired weakness, clinical prevention and treatment, independent risk factors, predictive model

中图分类号: