Simple demographic characteristics and laboratory findings on admission may predict in-hospital mortality in patients with SARS-CoV-2 infection: development and validation of the covid-19 score

Abstract

Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) constitutes a major health burden worldwide due to high mortality rates and hospital bed shortages. SARS-CoV-2 infection is associated with several laboratory abnormalities. We aimed to develop and validate a risk score based on simple demographic and laboratory data that could be used on admission in patients with SARS-CoV-2 infection to predict in-hospital mortality. Methods Three cohorts of patients from different hospitals were studied consecutively (developing, validation, and prospective cohorts). The following demographic and laboratory data were obtained from medical records: sex, age, hemoglobin, mean corpuscular volume (MCV), platelets, leukocytes, sodium, potassium, creatinine, and C-reactive protein (CRP). For each variable, classification and regression tree analysis were used to establish the cut-off point(s) associated with in-hospital mortality outcome based on data from developing cohort and before they were used for analysis in the validation and prospective cohort. The covid-19 score was calculated as a sum of cut-off points associated with mortality outcome. Results The developing, validation, and prospective cohorts included 129, 239, and 497 patients, respectively (median age, 71, 67, and 70 years, respectively). The following cut of points associated with in-hospital mortality: age > 56 years, male sex, hemoglobin < 10.55 g/dL, MCV > 92.9 fL, leukocyte count > 9.635 or < 2.64 103/µL, platelet count, < 81.49 or > 315.5 103/µL, CRP > 51.14 mg/dL, creatinine > 1.115 mg/dL, sodium < 134.7 or > 145.4 mEq/L, and potassium < 3.65 or > 6.255 mEq/L. The AUC of the covid-19 score for predicting in-hospital mortality was 0.89 (0.84–0.95), 0.850 (0.75–0.88), and 0.773 (0.731–0.816) in the developing, validation, and prospective cohorts, respectively (P < 0.001The mortality of the prospective cohort stratified on the basis of the covid-19 score was as follows: 0–2 points,4.2%; 3 points, 15%; 4 points, 29%; 5 points, 38.2%; 6 and more points, 60%. Conclusion The covid-19 score based on simple demographic and laboratory parameters may become an easy-to-use, widely accessible, and objective tool for predicting mortality in hospitalized patients with SARS-CoV-2 infection.

Analysing COVID treatment outcomes in dedicated wards at a large university hospital in northern Poland. A result-based observational study

“…The copyright holder for this preprint this version posted July 9, 2022. ; https://doi.org/10.1101/2022.07.07.22277395 doi: medRxiv preprint however, differences in methods and data sources used by researchers that also affect these estimations. Published reports on in-hospital mortality based on electronic health records (EHRs), unlike statistical registries, correspond with our outcomes, with mortality ranging from 18,8 to 34,9 % [23,24].…”

Admission Predictors of Mortality in Hospitalized COVID-19 Patients—A Serbian Cohort Study

“…Despite continuous efforts and research on this topic, the exact mechanisms and the circumstances in which they trigger the disease progression and lethal outcome are not fully understood [ 2 , 4 ]. More than thirty parameters associated with COVID-19 mortality can be found in the literature, including older age, male gender, presence of certain comorbidities, gas exchange impairment, and various laboratory analyses (abnormalities of blood count and leukocyte formula, impaired coagulation status, and elevated biomarkers of inflammation, myocardial, renal, and hepatic impairment) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, the selection of significant predictors, their cut-off, and adjusted odds ratio (aOR) values differ across the literature.…”

“…These diversities can partially be explained by frequent SARS-CoV-2 mutations, differences in methodological approach and variable selection, heterogeneity of the studied population, and other study variations. In addition, data regarding analyses conducted on admission laboratory findings are limited [ 9 , 12 , 19 , 20 ].…”

Spatial Variability of COVID-19 Hospitalization in the Silesian Region, Poland

“…On the other hand, the observed decreasing mortality ratio in COVID-19 patients hospitalized in New York from March to August 2020 could be an effect of the increasingly younger age of patients with fewer comorbidities [ 28 ]. Results of other observations suggest that the COVID-19 scoring based on simple demographic (sex, age) and laboratory parameters (e.g., hemoglobin, platelets, leukocytes, creatinine, C-reactive protein CRP), and the occurrence of chronic diseases may become a widely accessible and objective tool for predicting mortality in hospitalized patients [ 40 , 41 , 42 ]. However, the spatial variability of hospitalized morbidity in the Silesian voivodeship was observed even after ratio standardization.…”