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- Title
Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.
- Authors
Sinha, Pratik; Churpek, Matthew M.; Calfee, Carolyn S.
- Abstract
Rationale: Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analysis. Plasma biomarkers, key components in phenotype identification, currently lack point-of-care assays and represent a barrier to the clinical implementation of phenotypes.Objectives: The objective of this study was to develop models to classify ARDS phenotypes using readily available clinical data only.Methods: Three randomized controlled trial cohorts served as the training data set (ARMA [High vs. Low Vt], ALVEOLI [Assessment of Low Vt and Elevated End-Expiratory Pressure to Obviate Lung Injury], and FACTT [Fluids and Catheter Treatment Trial]; n = 2,022), and a fourth served as the validation data set (SAILS [Statins for Acutely Injured Lungs from Sepsis]; n = 745). A gradient-boosted machine algorithm was used to develop classifier models using 24 variables (demographics, vital signs, laboratory, and respiratory variables) at enrollment. In two secondary analyses, the ALVEOLI and FACTT cohorts each, individually, served as the validation data set, and the remaining combined cohorts formed the training data set for each analysis. Model performance was evaluated against the latent class analysis-derived phenotype.Measurements and Main Results: For the primary analysis, the model accurately classified the phenotypes in the validation cohort (area under the receiver operating characteristic curve [AUC], 0.95; 95% confidence interval [CI], 0.94-0.96). Using a probability cutoff of 0.5 to assign class, inflammatory biomarkers (IL-6, IL-8, and sTNFR-1; P < 0.0001) and 90-day mortality (38% vs. 24%; P = 0.0002) were significantly higher in the hyperinflammatory phenotype as classified by the model. Model accuracy was similar when ALVEOLI (AUC, 0.94; 95% CI, 0.92-0.96) and FACTT (AUC, 0.94; 95% CI, 0.92-0.95) were used as the validation cohorts. Significant treatment interactions were observed with the clinical classifier model-assigned phenotypes in both ALVEOLI (P = 0.0113) and FACTT (P = 0.0072) cohorts.Conclusions: ARDS phenotypes can be accurately identified using machine learning models based on readily available clinical data and may enable rapid phenotype identification at the bedside.
- Subjects
ADULT respiratory distress syndrome; PHENOTYPES; BIOMARKERS; SECONDARY analysis; LUNG injuries; ADULT respiratory distress syndrome treatment; OXYGEN metabolism; INTERLEUKINS; VASOCONSTRICTORS; BLOOD pressure; BLOOD proteins; MORTALITY; INFLAMMATION; AGE distribution; VITAL signs; CELL receptors; PROGNOSIS; RESPIRATORY measurements; PHARMACOKINETICS; SERUM albumin; BICARBONATE ions; LEUKOCYTE count; PLATELET count; CARBON dioxide; RESEARCH funding; RESPIRATION; BLOOD coagulation factors; BILIRUBIN; CREATININE; PARTIAL pressure; ANTIGENS
- Publication
American Journal of Respiratory & Critical Care Medicine, 2020, Vol 202, Issue 7, p996
- ISSN
1073-449X
- Publication type
journal article
- DOI
10.1164/rccm.202002-0347OC