Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Abstract

Background: The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae.

Methods: Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months.

Findings: Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01).

Interpretation: Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae.

Funding: ISCIII, UNESPA, CIBERES, FEDER, ESF.

Keywords: COVID-19; Critical Care; Prognosis.

Figure 1

Identification of comorbidity phenotypes in the whole CIBERESUCICOVID cohort using Latent Class Analysis. A) Prevalence of comorbidities according to phenotypes. B) Disease severity parameters at ICU admission according to phenotypes. C) Laboratory parameters according to phenotypes. The values in B and C have been standardized.

Figure 2

Hospital prognosis according to morbidity phenotypes in the whole CIBERESUCICOVID cohort. A) Comparison of the risk of having in-hospital complications between phenotypes. B) In-hospital mortality according to morbidity phenotypes using Cox model. Cox regression model with phenotypes as predictor, and age and sex as confounding factors. Low-morbidity phenotype was used as reference group. Cox model showed a c-statistic of 0.65. 43 patients were excluded from this analysis because of mismatches in the dates of ICU admission and hospital discharge. A total of 808, 784 and 200 patients died during hospitalization in the low-morbidity, high-morbidity and renal-morbidity phenotypes, respectively.

Figure 3

Multimorbidity patterns in patients with high comorbid burden. A) Prevalence of comorbidities according to subphenotypes. B) Impact of subphenotypes on in-hospital mortality. Cox regression model with subphenotypes as predictor, and age and sex as confounding factors. Significance levels were indicate as * if p value<0.05, ** if p value<0.01 and *** if p value<0.001. Cox model showed a c-statistic of 0.63.

Figure 4

Impact of individual comorbidities of patients with low comorbid burden on in-hospital mortality and invasive mechanical ventilation (IMV). Logistic regression models were used to assess the association between comorbidities and IMV risk. Cox proportional hazards models were used to assess mortality risk. All models were adjusted for age and sex. The n (%) of subjects having each comorbidity is reported.

Figure 5

Graphical abstract. Identification of phenotypes and impact on prognosis. Phenotypes identified in the whole CIBERESUCICOVID cohort by means of Latent Class Analysis based on 17 potentially relevant comorbidities and validated internally using training and test sub-cohorts. The width of the flow lines in each of the phenotypes is proportional to the number of subjects in each time point (all lines are proportional within each phenotype). The width of the flow lines is not proportional between phenotypes (for instance, the renal-morbidity phenotype flow has been over-represented in the sake of a better data visualization). Key characteristics of each phenotype: Low-morbidity (n=3385; 58%), younger patients with few comorbidities; High-morbidity (n=2074; 35%), patients with high comorbid burden; Renal-morbidity (n=407; 7%), patients with chronic kidney disease, high comorbidity burden and the worst oxygenation profile.