The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

中文翻译：

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1152 名 COVID-19 患者机械通气期间不良结果的风险因素：一项多中心机器学习研究，使用来自荷兰数据仓库的高粒度数据

确定 COVID-19 患者不良结局和延长重症监护病房 (ICU) 住院时间的风险因素对于预后、确定治疗强度和资源分配至关重要。以前的研究仅在入院时确定了风险因素，并且包括了有限数量的预测因素。因此，使用来自高度细化和多中心的荷兰数据仓库的数据，我们开发了机器学习模型来识别 COVID-19 有创机械通气 (IMV) 过程中 ICU 死亡率、无呼吸机天数和无 ICU 天数的风险因素耐心。DDW 是一个不断增长的荷兰 COVID-19 危重患者电子健康记录数据库。所有接受 IMV 的成年 ICU 患者均符合纳入条件。转诊，住院时间少于 24 小时的患者，并且在数据提取时仍然入院的患者被排除在外。根据文献选择预测因子，包括药物剂量和体液平衡。在第 1、7 和 14 天，使用五重嵌套交叉验证对每个患者最多三组观察进行了多种算法的训练和验证，将单个患者的观察保持在同一分组中。该模型共包括 1152 名患者。XGBoost 模型对所有结果都表现最佳，并用于计算预测变量的重要性。使用 Shapley 附加解释 (SHAP)，年龄是 IMV 开始和整个过程中最重要的人口统计学风险因素。部分依赖图 (PDP) 显示了不同年龄值的相对死亡概率，从 54 岁开始增加。除了年龄，酸血症、低 P/F 比和高驱动压力表明死亡概率较高。驱动压力的 PDP 显示从 12 cmH2O 开始的相对概率增加。在危重 COVID-19 患者的有创机械通气过程中，年龄是 ICU 死亡率、无 ICU 天数和无呼吸机天数的最重要的人口统计学风险因素。在机械通气过程中应密切监测 pH、P/F 比和驱动压力，作为预测这些结果的危险因素。重症 COVID-19 患者在整个有创机械通气过程中的无 ICU 天数和无呼吸机天数。在机械通气过程中应密切监测 pH、P/F 比和驱动压力，作为预测这些结果的危险因素。重症 COVID-19 患者在整个有创机械通气过程中的无 ICU 天数和无呼吸机天数。在机械通气过程中应密切监测 pH、P/F 比和驱动压力，作为预测这些结果的危险因素。