Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study

Abstract

Background Previously identified phenotypes of acute respiratory distress syndrome (ARDS) have been limited by a disregard for temporal dynamics. We aimed to identify longitudinal phenotypes in ARDS to test the prognostic and predictive enrichment of longitudinal phenotypes, and to develop simplified models for phenotype identification. Methods We conducted a multi-database study based on the Chinese Database in Intensive Care (CDIC) and four ARDS randomized clinical trials (RCTs). We employed latent class analysis (LCA) to identify longitudinal phenotypes using 24-hourly data from the first four days of invasive ventilation. We used the Cox regression model to explore the association between time-varying respiratory parameters and 28-day mortality across phenotypes. Phenotypes were validated in four RCTs, and the heterogeneity of treatment effect (HTE) was investigated. We also constructed two multinomial logistical regression analyses to develop the probabilistic models. Findings A total of 605 ARDS patients in CDIC were enrolled. The three-class LCA model was identified and had the optimal fit, as follows: Class 1 (n = 400, 66.1% of the cohort) was the largest phenotype over all study days, and had fewer abnormal values, less organ dysfunction and the lowest 28-day mortality rate (30.5%). Class 2 (n = 102, 16.9% of the cohort) was characterized by pulmonary mechanical dysfunction and had the highest proportion of poorly aerated lung volume, the 28-day mortality rate was 47.1%. Class 3 (n = 103, 17% of the cohort) was correlated with extra-pulmonary dysfunction and had the highest 28-day mortality rate (56.3%). Time-varying mechanical power was more significantly associated with 28-day mortality in Class 2 patients compared to other phenotypes. Similar phenotypes were identified in four RCTs. A significant HTE between phenotypes and treatment strategies was observed in the ALVEOLI (high PEEP vs. low PEEP) and the FACTT trials (conservative vs. liberal fluid management). Two parsimonious probabilistic models were constructed to identify longitudinal phenotypes. Interpretation We identified and validated three novel longitudinal phenotypes for ARDS patients, with both prognostic and predictive enrichment. The phenotypes of ARDS can be accurately identified with simple classifier models, except for Class 3.

Lung morphology impacts the association between ventilatory variables and mortality in patients with acute respiratory distress syndrome

“…ARDS is a heterogeneous syndrome involving different phenotypes with distinct clinical and outcome characteristics. We previously identified three novel ARDS phenotypes with different severity of pulmonary mechanics and found that the association between pulmonary parameters and mortality varied within phenotypes [ 7 ]. Lung morphology is another crucial aspect in distinguishing specific ARDS phenotypes.…”

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“…[3] At the same time, the large pulmonary embolism load caused a sharp increase in the patient's pulmonary artery pressure leading to right Both of the two theories may lead to the tachycardia, rapid pulse, high fever, sudden platelet reduction, oliguria and other clinical symptoms in our patient. [4] The clinical manifestations of FES are respiratory distress, nervous system changes, and petechial rash. This patient met the diagnostic criteria proposed by Gurd, [5] with the respiratory symptoms and positive signs of lung imaging changes.…”