Longer length of stay, days between discharge/first readmission, and pulmonary involvement ≥50% increase prevalence of admissions in ICU in unplanned readmissions after COVID-19 hospitalizations.

Hospital readmissions due to COVID-19 are one of the main concerns for the health system due to risks to the patient's life and increased use of health resources. Studies focusing on this issue are important to understand the risk factors and create strategies to avoid readmissions. We evaluated the readmission of patients with confirmed COVID-19 in a private hospital in southern Brazil, between March 2020 and 2021. Also, the characteristics and clinical outcomes of patients admitted to the intensive care unit (ICU) and nonadmitted were compared. Poisson regression models with prevalence ratio (PR) with 95% confidence intervals (95% CIs) were applied to confirm the association between variables and ICU admission. Of the 2084 hospitalized patients with COVID-19, 1806 were discharged alive. Among them, 106 were readmitted for unplanned reasons during one year. Early hospital readmission (≤30 days) occurred in 52.8% of the cases. The main reasons were respiratory, gastroenterological, kidney, and cardiac disease. The median age was 73.0 years old and women correspond to 52.8%. The presence of at least one comorbidity was detected in 87.7% of patients. Hypertension, diabetes, cardiac, and lung disease were more frequent. The ICU admitted patients (n = 43; 40.5%) mostly had 4-5 comorbidities, pulmonary involvement ≥50%, length of stay (LOS), and days between discharge and first readmission. Longer LOS (PR: 3.46; 95% CI: 1.24-5.67), days between discharge/first readmission (PR: 2.21; 95% CI: 1.15-5.88), and pulmonary involvement (≥50%; PR: 1.59; 95% CI: 1.11-3.54) were independently associated with ICU admission. Longer LOS, longer days between discharge/first readmission, and pulmonary involvement (≥50%) were associated with ICU admission in readmitted patients. Readmissions evaluation is pivotal and may help in ensuring safe care transition and postdischarge follow-up.

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

OBJECTIVES: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. METHODS: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. RESULTS: Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). CONCLUSIONS: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.