2021
DOI: 10.1101/2021.02.01.21250306
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ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores

Abstract: Objective: To develop and validate a rapid scoring system at hospital admission for predicting in-hospital mortality in patients hospitalized with coronavirus disease 19 (COVID-19), and to compare this score with other existing ones. Design: Cohort study Setting: The Brazilian COVID-19 Registry has been conducted in 36 Brazilian hospitals in 17 cities. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients that were admitted between March-… Show more

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Cited by 6 publications
(18 citation statements)
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“…[3,4] Underlying comorbidities have been widely associated with a worse prognosis for COVID-19 patients, since viral infections could act as triggers for worsening of chronic diseases. [5][6][7] Chagas disease (CD) is a multisystemic disorder, potentially affecting, cardiovascular, digestive, and neurological systems. It is the most common cause of infectious cardiomyopathy worldwide, and it may play a role in the clinical prognosis of COVID-19 patients.…”
Section: Introductionmentioning
confidence: 99%
“…[3,4] Underlying comorbidities have been widely associated with a worse prognosis for COVID-19 patients, since viral infections could act as triggers for worsening of chronic diseases. [5][6][7] Chagas disease (CD) is a multisystemic disorder, potentially affecting, cardiovascular, digestive, and neurological systems. It is the most common cause of infectious cardiomyopathy worldwide, and it may play a role in the clinical prognosis of COVID-19 patients.…”
Section: Introductionmentioning
confidence: 99%
“…It is well-known that COVID-19 mortality risk increases with increasing age and comorbidities [16,21,22]. In the present study, the small sample size precluded the assessment of specific risk factors for mortality in ECMO patients.…”
Section: Discussionmentioning
confidence: 80%
“…To adjust for potential confounding variables, we used propensity score matching technique. Propensity scores were estimated by logistic regression, and the model included gender, age, number of comorbidities (hypertension, diabetes mellitus, obesity, coronary artery disease, heart failure, atrial fibrillation or flutter, cirrhosis, chronic obstructive pulmonary disease, cancer and previous stroke) [16], pronation, ARDS and hospital. Genetic matching method (MatchIt package in R software) was performed in a 5:1 ratio using generalized Mahalanobis distance with scaling factors chosen based on the smallest p-value in covariate balance tests.…”
Section: Discussionmentioning
confidence: 99%
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