Antibiotics, Vital Signs and Comorbidities as Predictors of COVID-19 Mortality: An Unadjusted and Adjusted Logistic Regression Analysis

Authors

  • Muhammad Islam Crop Reporting Service (Director Bahawalpur), Agriculture Department, Punjab, Pakistan
  • Imtiaz Ahmed Punjab Land Records Authority (Deputy Director Bahawalpur), Board of Revenue Punjab, Pakistan
  • Hashaam Akhtar Global Health Department, Health Services Academy, Islamabad, 44000, Pakistan
  • Muhammad Amin Department of Statistics, University of Sargodha, Sargodha, Pakistan
  • Samar Akhtar Department of Clinical Pharmacy, Yusra Institute of Pharmaceutical Sciences, Rawalpindi, 44000, Pakistan
  • Shahzad Ali Khan Health Services Academy, Islamabad, 44000, Pakistan
  • Muhammad Faisal Centre for Digital Innovations in Health and Social Care, Institute of Health and Social Care, University of Bradford, Bradford, UK

DOI:

https://doi.org/10.53560/PPASA(63-1)703

Keywords:

COVID, Antibiotic, Vital Signs, Comorbidities, Unadjusted and Adjusted Models, Mortality Risk

Abstract

This study explores the association between COVID-19 patients and antibiotic usage (Azithromycin, Ceftriaxone, Tanzo, Tienum) concerning vital signs and diseases to enhance treatment efficacy, emphasizing mortality risk. Logistic regression is used for unadjusted model (M0) links antibiotics, while adjusted models (M1, M2, M3) incorporate vital signs and diseases, etc. Antibiotics are categorized into low (L1) and high (L2) doses. Models are classified as ILRM (insignificant with low risk of mortality), SLRM (significant with low risk of mortality), IHRM (insignificant with high risk of mortality), and SHRM (significant with high risk of mortality). Azithromycin exhibits IHRM and SHRM at L1/L2 for M0, M1/M2 show IHRM at L1 and ILRM at L2. M3 shows IHRM. Ceftriaxone transitions from ILRM at L1 to IHRM at L2 (except M3). Tanzo shows SLRM at L1 (M0) and ILRM in other models (except M3 at L2). Tienum indicates IHRM at both levels (M0), while adjusted models associate it with ILRM at L1 and IHRM at L2. This research provides crucial insights for healthcare professionals to predict and manage vital signs and disease variations and to underline the mortality risk in COVID-19 patients.

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Published

2026-03-23

How to Cite

Muhammad Islam, Imtiaz Ahmed, Hashaam Akhtar, Muhammad Amin, Samar Akhtar, Shahzad Ali Khan, & Muhammad Faisal. (2026). Antibiotics, Vital Signs and Comorbidities as Predictors of COVID-19 Mortality: An Unadjusted and Adjusted Logistic Regression Analysis. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 63(1), 47–59. https://doi.org/10.53560/PPASA(63-1)703

Issue

Vol. 63 No. 1 (2026)

Section

Research Articles

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