Department of Emergency Medicine, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Department of Critical Care Medicine, The Second People's Hospital of Lianyungang, Lianyungang, Jiangsu, China.
BMJ Open. 2022 Dec 15;12(12):e066894. doi: 10.1136/bmjopen-2022-066894.
To develop and validate a mechanical power (MP)-oriented prediction model of weaning failure in mechanically ventilated patients.
A retrospective cohort study.
Data were collected from the large US Medical Information Mart for Intensive Care-IV (MIMIC-IV) V.1.0, which integrates comprehensive clinical data from 76 540 intensive care unit (ICU) admissions from 2008 to 2019.
A total of 3695 patients with invasive mechanical ventilation for more than 24 hours and weaned with T-tube ventilation strategies were enrolled from the MIMIC-IV database.
Weaning failure.
All eligible patients were randomised into development cohorts (n=2586, 70%) and validation cohorts (n=1109, 30%). Multivariate logistic regression analysis of the development cohort showed that positive end-expiratory pressure, dynamic lung compliance, MP, inspired oxygen concentration, length of ICU stay and invasive mechanical ventilation duration were independent predictors of weaning failure. Calibration curves showed good correlation between predicted and observed outcomes. The prediction model showed accurate discrimination in the development and validation cohorts, with area under the receiver operating characteristic curve values of 0.828 (95% CI: 0.812 to 0.844) and 0.833 (95% CI: 0.809 to 0.857), respectively. Decision curve analysis indicated that the predictive model was clinically beneficial.
The MP-oriented model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.
开发和验证一种基于机械功率(MP)的机械通气患者撤机失败预测模型。
回顾性队列研究。
数据来自大型美国医疗信息集市重症监护-IV(MIMIC-IV)V.1.0,该数据库整合了 2008 年至 2019 年间来自 76540 例重症监护病房(ICU)入住患者的综合临床数据。
从 MIMIC-IV 数据库中纳入了 3695 例接受超过 24 小时有创机械通气并采用 T 管通气策略撤机的患者。
撤机失败。
所有符合条件的患者被随机分配到开发队列(n=2586,70%)和验证队列(n=1109,30%)。开发队列的多变量逻辑回归分析显示,呼气末正压、动态肺顺应性、MP、吸入氧浓度、ICU 住院时间和有创机械通气时间是撤机失败的独立预测因素。校准曲线显示预测结果与观察结果之间具有良好的相关性。该预测模型在开发和验证队列中均具有准确的区分能力,其受试者工作特征曲线下面积值分别为 0.828(95%CI:0.812 至 0.844)和 0.833(95%CI:0.809 至 0.857)。决策曲线分析表明,该预测模型具有临床获益。
基于 MP 的撤机失败模型能够准确预测机械通气患者撤机失败的风险,为临床医生在撤机决策方面提供有价值的信息。