Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients

Abstract

Purpose: Disease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect.

Methods: We applied consensus k-means clustering on 21 clinical parameters obtained within 24 h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022. Derived phenotypes were reproduced in 6225 COVID-19 ICU patients from Spain (February 2020 to December 2021). Longitudinal immunological characterization was performed in three COVID-19 ICU cohorts from the Netherlands and Germany, and associations between corticosteroid treatment and survival were assessed across phenotypes.

Results: We derived three phenotypes: COVIDICU1 (43% of patients) consisted of younger patients with the lowest Acute Physiology And Chronic Health Evaluation (APACHE) scores, highest body mass index (BMI), lowest PaO₂/FiO₂ ratio, and a 90-day in-hospital mortality rate of 18%. COVIDICU2 patients (37%) had the lowest BMI, were older and had higher APACHE scores and mortality rate (24%) than COVIDICU1. Patients with COVIDICU3 (20%) were the eldest with the most comorbidities, the highest APACHE scores, acute kidney injury and metabolic dysregulations, and the highest mortality rate (47%). These patients also displayed the most pronounced inflammatory response. Corticosteroid therapy started at day 5 [2-9] after ICU admission and administered for 5 [3-7] days was associated with an increased risk for 90-day mortality in patients with the COVIDICU1 and COVIDICU2 phenotypes (hazard ratio [HR] 1.59 [1.09-2.31], p = 0.015 and HR 1.79 [1.42-2.26], p < 0.001, respectively), but not in patients with the COVIDICU3 phenotype (HR 1.08 [0.76-1.54], p = 0.654).

Conclusion: Our multinational study identified three distinct clinical COVID-19 phenotypes, each exhibiting marked differences in demographic, clinical, and immunological features, and in the response to late and short-term corticosteroid treatment.

Keywords: COVID-19; Corticosteroids; Heterogeneity; Inflammation; Machine learning; Phenotypes.

Fig. 1

Comprehensive overview of the analyses conducted on the different cohorts in the present manuscript

Fig. 2

Phenotype characteristics and outcomes in the derivation cohort. a Distribution of the three clinical phenotypes, b Standardized mean difference (SMD) of all clustering variables per phenotype, illustrating how far each variable for that group is removed from the mean of the entire cohort. PF PaO₂/FiO₂ ratio, Creat creatinine, Temp temperature, Comor comorbidity, Bili bilirubin, WBC white blood cell count, HR heart rate, Gluc glucose, MAP mean arterial blood pressure, Thrombo thrombocytes, Resp respiratory, Bicarb bicarbonate, BMI body mass index, Alb albumin, c distribution for each phenotype of sex as well as requirement of vasopressive medication and mechanical ventilation, d density plots of the APACHE II and SAPS II severity scores and the PaO₂/FiO₂ ratio. The vertical lines indicate the median value for each phenotype, E) survival curve of in-hospital mortality for the three phenotypes

Fig. 3

Evolution of phenotype distribution and associated mortality over the course of the pandemic in the derivation cohort (n = 13.279). ICU admissions per week (dates provided as MM–YY), colored by phenotype. The phenotype proportions in each week are depicted at the bottom. Three major waves were identified, indicated by the colored lines underneath the admission number bars. The 90-day in-hospital mortality rate for each phenotype and the overall mortality rate are shown at the top of the plot

Fig. 4

Immunological characterization of the phenotypes in different cohorts. a Heatmap of all available cytokines and routine laboratory inflammation markers in the Radboudumc cohort (n = 194). Biomarker data were obtained during the first 3 days of ICU admission. If multiple measurements were available within this period, the mean value was used. Color indicates the scaled value of the plasma concentration of the different markers. CRP C-reactive protein, PCT procalcitonin, IL interleukin, TNF tumor necrosis factor, IFN interferon, IP interferon gamma-induced protein, MIP macrophage inflammatory protein, MCP monocyte chemoattractant protein, RA receptor antagonist, b principal component analysis (PCA) plot of patients of the Amsterdam UMC cohort (n = 202) based on the markers in the ‘coagulation’ panel measured within 48 h of ICU admission, c PCA plot of patients of the Amsterdam UMC cohort (n = 202) based on the markers in the ‘epithelial’ panel measured within 48 h of ICU admission, d PCA plot of patients of the Amsterdam UMC cohort (n = 202) based on the markers in the ‘inflammation and organ damage’ panel measured within 48 h of ICU admission, e PCA plot of patients of the Amsterdam UMC cohort (n = 202) based on the markers in the ‘pro-inflammatory cytokines and chemokines’ panel measured within 48 h of ICU admission; see Table S1 for composition of the panels, f plasma concentrations of the proinflammatory cytokines TNF, IL-8, and IP-10, as well as the anti-inflammatory cytokine IL-10 during the first 15 days of ICU admission in the Radboudumc cohort (n = 194). Data are presented as geometric mean (GM) and 95% confidence interval (CI), g plasma concentrations of the routine laboratory inflammatory markers CRP, D-dimer and PCT, and the pro-inflammatory cytokine IL-6 during the first 15 days of ICU admission in the Radboudumc cohort. Data are presented as GM and 95% CI, h plasma concentrations of CRP, D-dimer, PCT, and IL-6 during the first 14 days of ICU admission in the Jena cohort (n = 314). Data are presented as GM and 95%CI