Development of Predictive Models Based on Biochemical Parameters to Triage COVID-19 Patients: A Study Conducted in a Tertiary Care Hospital

• Published in: Curēus (Palo Alto, CA) Vol. 16; no. 3; p. e56197
• Main Authors: Sinha, Mainak, Banerjee, Ayan, Kumar, Sushil, Mahto, Mala, Kumari, Bandana, Ranjan, Alok, Bansal, Akash
• Format: Journal Article
• Language: English
• Published: United States Cureus Inc 14.03.2024; Cureus
• Subjects: Business metrics; COVID-19; Creatinine; Dehydrogenases; Emergency Medicine; Infectious Disease; Lymphocytes; Mortality; Neutrophils; Pandemics; Patients; Probability; Proteins; Regression analysis; Sodium; Variables; United States > US; procalcitonin; python; covid 19; predictive models; machine learning; interleukin (il)-6
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.56197

Background The COVID-19 disease continues to cause severe mortality and morbidity. Biochemical parameters are being used to predict the severity of the infection. This study aims to predict disease severity and mortality to help reduce mortality through timely intervention in a cost-effective way. Methods A total of 324 COVID-19 cases admitted at our hospital (All India Institute of Medical Sciences, Patna, BR, India) between June 2020 to December 2020 (phase 1: 190 patients) and April 2021 to May 2021 (phase 2: 134 patients) were recruited for this study. Statistical analysis was done using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA) and model prediction using Python (The Python Software Foundation, Wilmington, DE, USA). Results There were significant differences in biochemical parameters at the time of admission among COVID-19 patients between phases 1 and 2, ICU and non-ICU admissions, and expired and discharged patients. The receiver operating characteristic (ROC) curves predicted mortality solely based on biochemical parameters. Using multiple logistic regression in Python, a total of four models (two each) were developed to predict ICU admission and mortality. A total of 92 out of 96 patients were placed into the correct management category by our model. This model would have allowed us to preserve 17 of the 21 patients we lost. Conclusions We developed predictive models for admission (ICU or non-ICU) and mortality based on biochemical parameters at the time of admission. A predictive model with a significant predictive capability for IL-6 and procalcitonin values using normal biochemical parameters was proposed. Both can be used as machine learning tools to prognosticate the severity of COVID-19 infections. This study is probably the first of its kind to propose triage for admission in the ICU or non-ICU at the medical emergency department during the first presentation for the necessary optimal treatment of COVID-19 based on a predictive model.