scholarly journals Promises and challenges of personalized medicine to guide ARDS therapy

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Katherine D. Wick ◽  
Daniel F. McAuley ◽  
Joseph E. Levitt ◽  
Jeremy R. Beitler ◽  
Djillali Annane ◽  
...  

AbstractIdentifying new effective treatments for the acute respiratory distress syndrome (ARDS), including COVID-19 ARDS, remains a challenge. The field of ARDS investigation is moving increasingly toward innovative approaches such as the personalization of therapy to biological and clinical sub-phenotypes. Additionally, there is growing recognition of the importance of the global context to identify effective ARDS treatments. This review highlights emerging opportunities and continued challenges for personalizing therapy for ARDS, from identifying treatable traits to innovative clinical trial design and recognition of patient-level factors as the field of critical care investigation moves forward into the twenty-first century.

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
E. Schwager ◽  
K. Jansson ◽  
A. Rahman ◽  
S. Schiffer ◽  
Y. Chang ◽  
...  

AbstractHeterogeneous 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.


2020 ◽  
Vol 46 (5) ◽  
pp. 930-942 ◽  
Author(s):  
Michael O. Harhay ◽  
Jonathan D. Casey ◽  
Marina Clement ◽  
Sean P. Collins ◽  
Étienne Gayat ◽  
...  

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