Perme评分和机器学习检测重症监护病房获得性虚弱:一项前瞻性观察队列研究。

IF 1.5 Q3 CRITICAL CARE MEDICINE
Lilian Elisabete Bernardes Delazari, Lígia Dos Santos Roceto Ratti, Adria Cristina da Silva, Melissa Sibinelli, Aline Maria Heidemann, Higor Luiz Marconi Montedioca, Emanuella Feitoza Dos Santos, Antonio Luís Eiras Falcão
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引用次数: 0

摘要

背景与目的:重症监护病房获得性虚弱(ICU -acquired weakness, ICUAW)是危重患者长期机械通气(MV)的常见并发症,影响康复并延长重症监护病房(ICU)的住院时间。像握力测试(HGST)这样的标准诊断工具需要患者的配合,在关键情况下可能会受到限制。本研究评估了Perme ICU活动能力评分(一种多维功能评估)是否与ICUAW相关,并可作为一种筛查工具,以HGST作为诊断参考。患者和方法:我们于2021年5月至2023年10月在巴西三级ICU进行了一项前瞻性观察研究。我们评估了临床稳定(压力支持7 cm H2O, PEEP 5 cm H2O, RASS -1至+1)且接受MV≥7天的成人(≥18岁)。根据性别特异性HGST截断值定义ICUAW(结果:在97例患者中,78.4%的患者被确定为ICUAW。较低的Perme评分与ICUAW显著相关(p < 0.001)。截断值≤9时,敏感性76.3%,特异性71.4%,OR = 8.06 (95% CI: 2.72 ~ 23.8)。在多变量分析中,Perme评分(OR = 0.86;p = 0.0004)和SAPS 3仍然是独立预测因子。机器学习模型证实Perme Score是最重要的变量。结论:Perme评分是一种可行的、辅助的ICUAW筛查工具。临界值≤9支持早期功能风险分层,但由于特异性有限和阴性预测值(NPV),应结合临床情况进行解释。如何引用本文:Delazari LEB, Ratti LSR, da Silva AC, Sibinelli M, Heidemann AM, Montedioca HLM等。Perme评分和机器学习检测重症监护病房获得性虚弱:一项前瞻性观察队列研究。中华检验医学杂志;2015;29(7):562-568。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Perme Score and Machine Learning for Detecting ICU-acquired Weakness: A Prospective Observational Cohort Study.

Perme Score and Machine Learning for Detecting ICU-acquired Weakness: A Prospective Observational Cohort Study.

Perme Score and Machine Learning for Detecting ICU-acquired Weakness: A Prospective Observational Cohort Study.

Perme Score and Machine Learning for Detecting ICU-acquired Weakness: A Prospective Observational Cohort Study.

Background and aims: Intensive care unit-acquired weakness (ICUAW) is a common complication in critically ill patients on prolonged mechanical ventilation (MV), impairing recovery and prolonging intensive care unit (ICU) stays. Standard diagnostic tools like the handgrip strength test (HGST) require patient cooperation and may be limited in critical settings. This study evaluated whether the Perme ICU Mobility Score, a multidimensional functional assessment, is associated with ICUAW and can serve as a screening tool using HGST as the diagnostic reference.

Patients and methods: We conducted a prospective observational study in a Brazilian tertiary ICU from May 2021 to October 2023. We assessed adults (≥18 years) undergoing MV for ≥7 days who were clinically stable (pressure support 7 cm H2O, PEEP 5 cm H2O, RASS -1 to +1). ICUAW was defined using sex-specific HGST cutoffs (<11 kg men, <7 kg women). Logistic regression, least absolute shrinkage and selection operator (LASSO), and Random Forest models assessed the association between ICUAW and Perme Score. ROC curves and the Youden index determined the optimal cutoff.

Results: Among 97 patients, ICUAW was identified in 78.4%. Lower Perme Scores were significantly associated with ICUAW (p < 0.001). A cutoff ≤9 showed 76.3% sensitivity, 71.4% specificity, and OR = 8.06 (95% CI: 2.72-23.8). In multivariate analysis, the Perme Score (OR = 0.86; p = 0.0004) and SAPS 3 remained independent predictors. Machine learning models confirmed Perme Score as the most significant variable.

Conclusions: The Perme Score is a feasible, complementary screening tool for ICUAW. A cutoff ≤9 supports early functional risk stratification but should be interpreted alongside clinical context due to limited specificity and negative predictive value (NPV).

How to cite this article: Delazari LEB, Ratti LSR, da Silva AC, Sibinelli M, Heidemann AM, Montedioca HLM, et al. Perme Score and Machine Learning for Detecting ICU-acquired Weakness: A Prospective Observational Cohort Study. Indian J Crit Care Med 2025;29(7):562-568.

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来源期刊
CiteScore
3.50
自引率
10.00%
发文量
299
期刊介绍: Indian Journal of Critical Care Medicine (ISSN 0972-5229) is specialty periodical published under the auspices of Indian Society of Critical Care Medicine. Journal encourages research, education and dissemination of knowledge in the fields of critical and emergency medicine.
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