Modeling for Prediction of Mortality Based on past Medical History in Hospitalized COVID-19 Patients: A Secondary

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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Seyyed Amir Yasin Ahmadi1, Yeganeh Karimi2, Arash Abdollahi3

  • 1Preventive Medicine and Public Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences, Tehran, Iran.

The Canadian Journal of Infectious Diseases & Medical Microbiology = Journal Canadien Des Maladies Infectieuses Et De La Microbiologie Medicale
|July 10, 2024
PubMed

Insights

Identifying past medical history is crucial for predicting COVID-19 mortality in Iranian patients. A history of infectious diseases significantly increases mortality risk, highlighting the need for targeted interventions.

Area of Science:

  • Medical research
  • Public health
  • Epidemiology

Background:

  • COVID-19 remains a concern for susceptible individuals post-emergency.
  • Identifying risk factors for COVID-19 mortality is essential for targeted interventions.

Purpose of the Study:

  • To develop a predictive model for COVID-19 mortality in Iranian patients.
  • To identify at-risk groups based on past medical history (PMHx) and other factors.

Main Methods:

  • Secondary analysis of hospitalized COVID-19 adult patients' data.
  • Logistic regression used to predict mortality based on PMHx (ICD-10 codes) and covariates (e.g., ICU admission).
  • Calculation of population attributable fraction (PAF) and odds ratios (OR) with 95% confidence intervals (CI).

Main Results:

  • Overall mortality rate was 19.68% among 8879 patients.
  • History of infectious and parasitic diseases showed the highest association with mortality (OR = 5.72).
  • Cardiovascular system diseases had the greatest population attributable fraction (20.46%).
  • Logistic regression identified infectious disease history as a significant predictor (OR = 3.089), aside from ICU admission and age.
  • The model achieved good performance (Area Under Curve = 0.875).

Conclusions:

  • A significant portion of COVID-19 mortality is linked to underlying conditions, particularly cardiovascular diseases.
  • History of infectious diseases is a key predictor of mortality in hospitalized COVID-19 patients.
  • Further research is needed for policy development to reduce COVID-19 mortality in vulnerable populations.
Abstract

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