Outcomes Associated With Intensive Care and Organ Support Among Patients With COVID-19: A Systematic Review and...

Accurate accounting of coronavirus disease 2019 (COVID-19) critical care outcomes has important implications for health care delivery.

We aimed to determine critical care and organ support outcomes of intensive care unit (ICU) COVID-19 patients and whether they varied depending on the completeness of study follow-up or admission time period.

We conducted a systematic review and meta-analysis of reports describing ICU, mechanical ventilation (MV), renal replacement therapy (RRT), and extracorporeal membrane oxygenation (ECMO) mortality. A search was conducted using PubMed, Embase, and Cochrane databases.We included English language observational studies of COVID-19 patients, reporting ICU admission, MV, and ICU case fatality, published from December 1, 2019 to December 31, 2020. We excluded reports of less than 5 ICU patients and pediatric populations. Study characteristics, patient demographics, and outcomes were extracted from each article. Subgroup meta-analyses were performed based on the admission end date and the completeness of data.

Of 6,778 generated articles, 145 were retained for inclusion (n = 60,357 patients). Case fatality rates across all studies were 34.0% (95% CI = 30.7%, 37.5%, P < 0.001) for ICU deaths, 47.9% (95% CI = 41.6%, 54.2%, P < 0.001) for MV deaths, 58.7% (95% CI = 50.0%, 67.2%, P < 0.001) for RRT deaths, and 43.3% (95% CI = 31.4%, 55.4%, P < 0.001) for extracorporeal membrane oxygenation deaths. There was no statistically significant difference in ICU and organ support outcomes between studies with complete follow-up versus studies without complete follow-up. Case fatality rates for ICU, MV, and RRT deaths were significantly higher in studies with patients admitted before April 31st 2020.

Coronavirus disease 2019 critical care outcomes have significantly improved since the start of the pandemic. Intensive care unit outcomes should be evaluated contextually (study quality, data completeness, and time) for the most accurate reporting and to effectively guide mortality predictions.