Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.
Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.
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DOI：
10.1038/s41746-021-00505-5
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发表时间：
2021-09-09
影响因子：
15.2
通讯作者：
Frassica JJ
中科院分区：
文献类型：
--
作者：
Schwager E;Jansson K;Rahman A;Schiffer S;Chang Y;Boverman G;Gross B;Xu-Wilson M;Boehme P;Truebel H;Frassica JJ
不同的患者群体，复杂的药理学和重症监护室（ICU）的低招募率导致了许多临床试验的失败。最近，机器学习（ML）作为一种处理和识别大数据关系的新技术出现，为临床试验设计带来了新时代。在这项研究中，我们设计了一个ML模型，用于对急性呼吸窘迫综合征（ARDS）患者进行预测性分层，最终通过队列同质性增加统计功效来减少所需的患者数量。从Philips eICU研究所（eRI）数据库中，提取了不少于51，555例ARDS患者。我们根据结果定义了三个亚群：（1）快速死亡，（2）自发恢复，（3）长期住院患者。回顾性单变量分析确定了每个结果的高度预测变量。所有220个变量用于确定最准确和最普遍的模型来预测长期住院患者。多类梯度提升被认为是性能最好的ML模型。在单变量分析中，pH值、碳酸氢盐或乳酸盐的变化被证明是快速死亡的强预测因子，而只有多变量ML模型能够可靠地区分长期住院结局人群的病程（AUC为0.77）。我们证明了在迄今为止报道的最大的ARDS队列中使用ML算法进行前瞻性患者分层的可行性。我们的算法可以识别具有足够长的ARDS发作的患者，以允许患者有时间对治疗做出反应，从而增加统计功效。此外，早期登记警报可以提高招募率。
Heterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate.
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DOI：
10.1016/s2213-2600(18)30418-1
发表时间：
2019-03
期刊：
The Lancet. Respiratory medicine
影响因子：
--
作者：
Matthay MA;Calfee CS;Zhuo H;Thompson BT;Wilson JG;Levitt JE;Rogers AJ;Gotts JE;Wiener-Kronish JP;Bajwa EK;Donahoe MP;McVerry BJ;Ortiz LA;Exline M;Christman JW;Abbott J;Delucchi KL;Caballero L;McMillan M;McKenna DH;Liu KD
通讯作者：
Liu KD
影响因子：
81.5
作者：
Matthay, Michael A.;Zemans, Rachel L.;Calfee, Carolyn S.
通讯作者：
Calfee, Carolyn S.
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.
影响因子：
9.8
作者：
Pollard TJ;Johnson AEW;Raffa JD;Celi LA;Mark RG;Badawi O
通讯作者：
Badawi O
影响因子：
9.6
作者：
Bernard, GR;Wheeler, AP;Steinberg, KP
通讯作者：
Steinberg, KP