Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

中文翻译：

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使用机器学习方法预测 COVID-19 患者拔管失败的因素

在重症监护室中，确定拔管的最佳时机可能具有挑战性。在本研究中，我们的目的是确定 COVID-19 危重患者拔管失败的预测因素。我们使用了荷兰多中心数据仓库中 3464 名成年重症新冠肺炎患者的高度精细数据，包括人口统计、临床观察、药物、体液平衡、实验室值、生命体征和来自生命支持设备的数据。所有至少尝试过一次拔管的插管患者均符合分析条件。排除转院患者、入院时间少于24小时的患者以及在数据提取时仍在入院的患者。潜在的预测因子是由重症监护医生团队选出的。主要和次要结局分别是拔管而不重新插管或在接下来的 7 天内和 48 小时内死亡。我们使用五重嵌套交叉验证来训练和验证多种机器学习算法。使用 Shapley 附加解释来估计预测变量的重要性，而拔管失败的相对概率的截止值则通过部分依赖图来估计。模型推导中总共包括 883 名患者。48小时内再插管率为13.4%，第7天再插管率为18.9%，死亡率分别为0.6%和1.0%。Grandient-Boost 模型表现最佳（曲线下面积为 0.70），用于计算预测变量的重要性。通气特征和设置是最重要的预测因素。更具体地说，受控模式持续时间长于 4 天，最后吸入氧气分数高于 35%，拔管前一天每公斤理想体重的平均潮气量高于 8 ml/kg，以及辅助模式持续时间较短（< 2 天）与其中值相比。此外，与中位数相比，较高的 C 反应蛋白和白细胞计数、较低的血小板计数、较低的格拉斯哥昏迷评分和较低的体重指数与拔管失败相关。危重 COVID-19 患者拔管失败的最重要预测因素包括通气设置、炎症参数、神经系统状态和体重指数。因此，这些预测因素应定期记录在电子健康记录中。