Predictors of In-Hospital Mortality in Older Patients With COVID-19: The COVIDAge Study

Abstract

Objective: To determine predictors of in-hospital mortality related to COVID-19 in older patients.

Design: Retrospective cohort study.

Setting and participants: Patients aged 65 years and older hospitalized for a diagnosis of COVID-19.

Methods: Data from hospital admission were collected from the electronic medical records. Logistic regression and Cox proportional hazard models were used to predict mortality, our primary outcome. Variables at hospital admission were categorized according to the following domains: demographics, clinical history, comorbidities, previous treatment, clinical status, vital signs, clinical scales and scores, routine laboratory analysis, and imaging results.

Results: Of a total of 235 Caucasian patients, 43% were male, with a mean age of 86 ± 6.5 years. Seventy-six patients (32%) died. Nonsurvivors had a shorter number of days from initial symptoms to hospitalization (P = .007) and the length of stay in acute wards than survivors (P < .001). Similarly, they had a higher prevalence of heart failure (P = .044), peripheral artery disease (P = .009), crackles at clinical status (P < .001), respiratory rate (P = .005), oxygen support needs (P < .001), C-reactive protein (P < .001), bilateral and peripheral infiltrates on chest radiographs (P = .001), and a lower prevalence of headache (P = .009). Furthermore, nonsurvivors were more often frail (P < .001), with worse functional status (P < .001), higher comorbidity burden (P < .001), and delirium at admission (P = .007). A multivariable Cox model showed that male sex (HR 4.00, 95% CI 2.08-7.71, P < .001), increased fraction of inspired oxygen (HR 1.06, 95% CI 1.03-1.09, P < .001), and crackles (HR 2.42, 95% CI 1.15-6.06, P = .019) were the best predictors of mortality, while better functional status was protective (HR 0.98, 95% CI 0.97-0.99, P = .001).

Conclusions and implications: In older patients hospitalized for COVID-19, male sex, crackles, a higher fraction of inspired oxygen, and functionality were independent risk factors of mortality. These routine parameters, and not differences in age, should be used to evaluate prognosis in older patients.

Keywords: COVID-19; Mortality; older patients.

Fig. 1

Receiver operating characteristic (ROC) curve from the logistic regression models to predict mortality, including 4 predictors: Male sex; Crackles; FiO₂ %; FIM; and the 4 best predictors together.

Fig. 2

Kaplan-Meier survival curves.

Supplementary Fig. 1

Kaplan-Meier curves of survival. Tick marks represents patient discharged which were censored. (A) Dyspnea (P = .007); (B) heart failure signs (P = .01); (C) O₂ support mode (P < .001); (D) lymphocytes < 1 × 10⁹/L (P < .001); (E) pulmonary infiltrate (P < .001); (F) bilateral pulmonary infiltrate (P < .001); and (G) peripheral pulmonary infiltrate (P < .001). Kaplan-Meier curves were compared using log rank tests.