Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza.
Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza.
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DOI：
10.1097/cce.0000000000000175
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发表时间：
2020-08-01
影响因子：
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通讯作者：
Randolph, Adrienne G
中科院分区：
文献类型：
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作者：
Sauthier, Michael S;Jouvet, Philippe A;Randolph, Adrienne G
流感病毒是急性低氧性呼吸衰竭的主要原因。早期识别的患者谁会遭受严重的并发症，可以帮助分层患者的临床试验和计划的资源使用的情况下，pandemia.Objective：我们的目的是确定哪些临床变量最好的预测长期急性低氧性呼吸衰竭流感感染的重症儿童。急性低氧血症性呼吸衰竭的定义采用国际公认的急性呼吸窘迫综合征定义中的低氧血症临界值。根据PICU第7天仍存在的急性低氧性呼吸衰竭标准定义长期急性低氧性呼吸衰竭。推导队列：在2009年11月至2018年4月的34个PICU的前瞻性多中心研究中，我们纳入了无严重疾病共病风险因素的儿童（< 18岁）。我们使用蒙特卡罗交叉验证方法，每个模型以70-30%的比例进行N 2次随机训练-测试分割。使用入院时（第1天）和最接近PICU第2天上午8点的临床数据，我们使用随机森林机器学习算法和逻辑回归计算受试者工作特征曲线下面积。我们纳入了258名儿童（中位年龄= 6.5岁），其中11名（4.2%）死亡。到第2天，65%（n = 165）的患者发生急性低氧性呼吸衰竭，到第7天下降至26%（n = 67），并出现长期急性低氧性呼吸衰竭。长期急性低氧性呼吸衰竭患者的ICU住院时间更长（16.5 vs 4.0 d; p < 0.001），死亡率更高（13.4% vs 1.0%）。一个多变量模型使用随机森林与10入院和8天2变量表现最好（0.93受试者工作特征曲线下面积; 95 CI%：0.90-0.95），其中第2天的呼吸频率、Fio 2和pH是最重要的因素。在这项前瞻性多中心研究中，大多数感染流感病毒的儿童-与长期急性低氧性呼吸衰竭相关的呼吸衰竭可以在其住院过程的早期将机器学习应用于常规临床数据中来识别。在床边实施之前需要进一步验证。
Influenza virus is a major cause of acute hypoxemic respiratory failure. Early identification of patients who will suffer severe complications can help stratify patients for clinical trials and plan for resource use in case of pandemic.OBJECTIVE: We aimed to identify which clinical variables best predict prolonged acute hypoxemic respiratory failure in influenza-infected critically ill children. Acute hypoxemic respiratory failure was defined using hypoxemia cutoffs from international consensus definitions of acute respiratory distress syndrome in patients with ventilatory support. Prolonged acute hypoxemic respiratory failure was defined by acute hypoxemic respiratory failure criteria still present at PICU day 7.DERIVATION COHORT: In this prospective multicenter study across 34 PICUs from November 2009 to April 2018, we included children (< 18 yr) without comorbid risk factors for severe disease.VALIDATION COHORT: We used a Monte Carlo cross validation method with N 2 random train-test splits at a 70-30% proportion per model.PREDICTION MODEL: Using clinical data at admission (day 1) and closest to 8 am on PICU day 2, we calculated the area under the receiver operating characteristic curve using random forests machine learning algorithms and logistic regression.RESULTS: We included 258 children (median age = 6.5 yr) and 11 (4.2%) died. By day 2, 65% (n = 165) had acute hypoxemic respiratory failure dropping to 26% (n = 67) with prolonged acute hypoxemic respiratory failure by day 7. Those with prolonged acute hypoxemic respiratory failure had a longer ICU stay (16.5 vs 4.0 d; p < 0.001) and higher mortality (13.4% vs 1.0%). A multivariable model using random forests with 10 admission and eight day 2 variables performed best (0.93 area under the receiver operating characteristic curve; 95 CI%: 0.90-0.95) where respiratory rate, Fio2, and pH on day 2 were the most important factors.CONCLUSIONS: In this prospective multicentric study, most children with influenza virus-related respiratory failure with prolonged acute hypoxemic respiratory failure can be identified early in their hospital course applying machine learning onto routine clinical data. Further validation is needed prior to bedside implementation.