Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data

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 gradientboosted 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.

How severe COVID-19 infection is changing ARDS management

“…ARDS is known to be a heterogeneous syndrome with different sub-phenotypes that are characterized by different clinical features, inflammatory cytokine profiles, physiology and differential response to interventions [6,7]. COVID-19 is no exception to this rule.…”

Antiplatelet Therapy for Acute Respiratory Distress Syndrome

“…First, there is considerable heterogeneity in ARDS itself. Several recent research directions are exploring whether different treatments can be given according to different phenotypes of ARDS [114,115]. Therefore, future research should be based on ARDS patients whose phenotype pathology is more aligned with the mechanisms of aspirin.…”

Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping?

“…Sinha and colleagues examined whether an ML approach, specifically a variant of the gradient-boosting machine (GBM) algorithm ( 9 ), could accurately identify inflammatory subtypes of ARDS ( 8 ). They used standard clinical and laboratory parameters to categorize patients from three prior clinical trials into inflammatory subtypes.…”

“…In this issue of the Journal , Sinha and colleagues (pp. 996–1004 ) present data demonstrating the potential of an ML approach using readily available clinical data to identify ARDS subphenotypes ( 8 ).…”