Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.
Journal:
American journal of respiratory and critical care medicine
PMID:
32551817
Abstract
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. The objective of this study was to develop models to classify ARDS phenotypes using readily available clinical data only. 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]; = 2,022), and a fourth served as the validation data set (SAILS [Statins for Acutely Injured Lungs from Sepsis]; = 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. 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; < 0.0001) and 90-day mortality (38% vs. 24%; = 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 ( = 0.0113) and FACTT ( = 0.0072) cohorts. 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.
Authors
Keywords
Age Factors
Area Under Curve
Bicarbonates
Bilirubin
Biomarkers, Tumor
Blood Pressure
Carbon Dioxide
Creatinine
Humans
Inflammation
Intercellular Adhesion Molecule-1
Interleukin-6
Interleukin-8
Latent Class Analysis
Leukocyte Count
Machine Learning
Mortality
Oxygen
Partial Pressure
Phenotype
Plasminogen Activator Inhibitor 1
Platelet Count
Prognosis
Protein C
Pulmonary Ventilation
Randomized Controlled Trials as Topic
Receptors, Tumor Necrosis Factor, Type I
Respiratory Distress Syndrome
Serum Albumin
Tidal Volume
Vasoconstrictor Agents
Vital Signs