Background: We aimed to characterize patients hospitalized for coronavirus disease 2019 (COVID-19) and identify predictors of invasive mechanical ventilation (IMV).
Methods: We performed a retrospective cohort study in patients with COVID-19 admitted to a private network in Sao Paulo, Brazil from March to October 2020. Patients were compared in three subgroups: non-intensive care unit (ICU) admission (group A), ICU admission without receiving IMV (group B) and IMV requirement (group C). We developed logistic regression algorithm to identify predictors of IMV.
Results: We analyzed 1,650 patients, the median age was 53 years (42-65) and 986 patients (59.8%) were male. The median duration from symptom onset to hospital admission was 7 days (5-9) and the main comorbidities were hypertension (42.4%), diabetes (24.2%) and obesity (15.8%). We found differences among subgroups in laboratory values obtained at hospital admission. The predictors of IMV (odds ratio and 95% confidence interval [CI]) were male (1.81 [1.11-2.94], P=0.018), age (1.03 [1.02-1.05], P<0.001), obesity (2.56 [1.57-4.15], P<0.001), duration from symptom onset to admission (0.91 [0.85-0.98], P=0.011), arterial oxygen saturation (0.95 [0.92- 0.99], P=0.012), C-reactive protein (1.005 [1.002-1.008], P<0.001), neutrophil-to-lymphocyte ratio (1.046 [1.005-1.089], P=0.029) and lactate dehydrogenase (1.005 [1.003-1.007], P<0.001). The area under the curve values were 0.860 (95% CI, 0.829-0.892) in the development cohort and 0.801 (95% CI, 0.733-0.870) in the validation cohort.
Conclusions: Patients had distinct clinical and laboratory parameters early in hospital admission. Our prediction model may enable focused care in patients at high risk of IMV.
Keywords: COVID-19; SARSCoV-2; algorithm; coronavirus; intensive care units; mechanical ventilation.