Predictors of in-hospital mortality in critically ill patients with COVID-19: a large dual tertiary centre study

Chiara Cirillo1, Eleni Nakou2, Georgios Georgiopoulos1

  • 1Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, UK.

BMJ Open
|December 1, 2022
PubMed

Insights

Older age, impaired left ventricular ejection fraction, and elevated C-reactive protein are key predictors of in-hospital mortality in COVID-19 patients admitted to the ICU. Risk stratification requires a combination of echocardiographic, laboratory, and clinical data.

Area of Science:

  • Cardiology
  • Intensive Care Medicine
  • Infectious Diseases

Background:

  • COVID-19 significantly impacts critically ill patients, necessitating identification of mortality predictors.
  • Intensive Care Unit (ICU) admission for COVID-19 patients requires robust prognostic tools.

Purpose of the Study:

  • To investigate the relationship between echocardiographic parameters, laboratory findings, and clinical characteristics with in-hospital mortality in adult COVID-19 patients admitted to the ICU.
  • To identify key factors for risk stratification in severe COVID-19 cases.

Main Methods:

  • Observational retrospective study involving 300 adult COVID-19 patients admitted to ICUs in two UK tertiary centers.
  • Transthoracic echocardiogram performed on ICU admission; data analyzed using multivariable Cox regression and random forest analysis.

Main Results:

  • Older age (HR: 1.027), left ventricular ejection fraction <35% (HR: 5.908), and peak C-reactive protein (CRP) (HR: 1.002) were independently correlated with mortality.
  • Random forest analysis highlighted RV dysfunction, troponin, peak CRP, creatinine, BMI, and age as important predictors.

Conclusions:

  • Severely impaired left and right ventricular function in COVID-19 ICU patients carry adverse prognostic implications.
  • Older age and inflammatory markers (like CRP) have a substantial impact on mortality.
  • A combined approach using echocardiographic, laboratory, demographic, and clinical data is appropriate for risk stratification.
Abstract

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