Rodríguez Villamizar Patricia, Thille Arnaud W, Márquez Doblas Margarita, Frat Jean-Pierre, Leal Sanz Pilar, Alonso Elena, País Victoria, Morales Guillermo, Colinas Laura, Propín Alicia, Fernández Olivares Aida, Martínez Balaguer María, Alvaredo Rodrigo Diego, Hernández Gonzalo
Critical Care Medicine, Complejo Hospitalario Universitario de Toledo, Toledo, Spain.
Centre Hospitalier Universitaire de Poitiers, Médecine Intensive Réanimation, Poitiers, France.
Intensive Care Med. 2025 Jan;51(1):106-114. doi: 10.1007/s00134-024-07758-0. Epub 2025 Jan 7.
Predicting extubation failure remains a clinical challenge. This study aimed to determine diagnostic accuracy of models used at the bed side.
Post hoc analysis of 2341 patients at all risk included in five multicenter randomized trials. Diagnostic accuracy of three clinical prediction models was compared: 3-factors model including age > 65y, chronic heart or pulmonary disease; 4-factors model adding prolonged mechanical ventilation; and 11-factors model including age > 65 years, ≥ 2 comorbidities, prolonged mechanical ventilation, acute heart failure as the primary indication for mechanical ventilation, moderate-to-severe chronic obstructive pulmonary disease, APACHE II score > 12 on extubation day, airway patency problems, inability to deal with respiratory secretions, not simple weaning, obesity, or hypercapnia at the end of the spontaneous breathing trial. Crude and adjusted for spontaneous breathing trial (SBT) models were compared for all-cause reintubation at 7 days using Youden and Kappa indexes.
The 3-factors model had a very low global prediction capability (Youden index 0.08 and Kappa index 0.04); the 4-factors and 11-factors models had low global prediction capability (Youden index 0.12 and 0.16, and Kappa index 0.06 and 0.07, respectively). Aggressive SBT strategies (pressure support ≥ 7 cm HO with or without positive end-expiratory pressure) were associated with extubation failure risk (p < 0.001). All adjusted models had low diagnostic capability (0.08/0.03, 0.07/0.03, and 0.06/0.02 respectively).
Based on these results, the 3-factors model reported a very low diagnostic accuracy, and the 4 or 11-factors models showed similar low accuracy. No improvement was observed after adjusting for other aspects of weaning.
预测拔管失败仍然是一项临床挑战。本研究旨在确定床旁使用的模型的诊断准确性。
对五项多中心随机试验中纳入的所有风险的2341例患者进行事后分析。比较了三种临床预测模型的诊断准确性：三因素模型，包括年龄>65岁、慢性心脏或肺部疾病；四因素模型，增加了机械通气时间延长；以及十一因素模型，包括年龄>65岁、≥2种合并症、机械通气时间延长、急性心力衰竭作为机械通气的主要指征、中度至重度慢性阻塞性肺疾病、拔管日急性生理与慢性健康状况评分系统（APACHE II）>12、气道通畅问题、无法处理呼吸道分泌物、非简单撤机、肥胖或自主呼吸试验结束时高碳酸血症。使用约登指数和kappa指数比较了粗模型和针对自主呼吸试验（SBT）调整后的模型在7天时全因再插管情况。
三因素模型的整体预测能力非常低（约登指数0.08，kappa指数0.04）；四因素和十一因素模型的整体预测能力较低（约登指数分别为0.12和0.16，kappa指数分别为0.06和0.07）。积极的SBT策略（压力支持≥7cm H₂O，有或无呼气末正压）与拔管失败风险相关（p<0.001）。所有调整后的模型诊断能力均较低（分别为0.08/0.03、0.07/0.03和0.06/0.02）。
基于这些结果，三因素模型的诊断准确性非常低，四因素和十一因素模型的准确性也同样较低。在对撤机的其他方面进行调整后未观察到改善。
