Mortality Prediction For COVID-19 Patients Based on Demographic, Typical Laboratory Results, and Clinical Data
Keywords:death risk feature, hospital mortality prediction, comorbidities, bivariate analysis, prediction model
AbstractBackground:Timely identification of patients with a high risk of mortality from COVID-19 can make a big improvement in triage, bed placement, time saving, and maybe even outcome . Objectives: construct and evaluate individual mortality risk estimates based on anonymised demographic, clinical, and laboratory data at admission, as well as to find out the probability of death Materials and methods: Data included 681 patients, obtained from two Muhammadiyah Hospitals in Kebumen, Central Java, Indonesia. Data was collected between January 2020 to December 2022. The medical records were examined to identify the demographic data, vital signs, clinical data and typical laboratory test. In bivariate analysis, the Chi-square test was used.. Results: Patients were 48.02% males, and mortality was 18.05%. The five top predictors were Respiratory Failure( OR 7.420, 95% CI (1.169-47.103) , Myocardial Infarction (OR 1.639, 95% CI (0.881-3.050), D-dimer (OR 1.493, 95% CI (1.112-2.004), Chronic Kidney Disease (OR 1.493, 95% CI (1.112-2.004), Lymphocyte (OR 1.397, 95% CI ( 1.232-1.584). Conclusions: Comorbidities including chronic kidney disease, myocardial Infarction and DM 1 type; laboratory test results including D-dimer, lymphocyte, neutrophil, creatinine, leukocytes, glucose, hemoglobin; age, SPO2 and respiratory failure were associated with and can predict mortality in COVID-19 patients.
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