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Prediction of hospitalization time probability for COVID-19 patients with statistical and machine learning methods

Kiomars Motarjem1, Mahin Behzadifard2, Shahin Ramazi3

  • 1Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University.

Annals of Medicine and Surgery (2012)
|December 9, 2024
PubMed

Insights

Predicting COVID-19 outcomes is possible using admission blood tests. Key indicators include low calcium, low sodium, small red blood cells, low monocytes, high platelets, and age over 50.

Area of Science:

  • Medical Science
  • Clinical Medicine
  • Hematology

Background:

  • Coronavirus disease 2019 (COVID-19) can lead to severe complications and mortality.
  • Early identification of patients at high risk for severe outcomes is crucial for timely intervention.

Purpose of the Study:

  • To develop a predictive model for COVID-19 disease outcomes, specifically hospitalization length and mortality.
  • To identify key blood parameters and clinical factors at admission that correlate with disease severity.

Main Methods:

  • Analysis of data from 201 patients with confirmed COVID-19 infection.
  • Consideration of variables including age, sex, comorbidities, hospitalization duration, and 25 blood parameters at admission.
  • Utilization of an accelerated failure time model, specifically the log-normal model, for analysis.

Main Results:

  • Several factors significantly impacted hospitalization length and mortality (P<0.05).
  • These include hypocalcemia, hyponatremia, red blood cell microcytosis, monocytopenia, thrombocytosis, specific comorbidities (diabetes, cardiovascular diseases, hypertension), and age over 50.
  • The log-normal accelerated failure time model demonstrated the best fit for the data.

Conclusions:

  • Thrombocytosis, red blood cell microcytosis, monocytopenia, hypocalcemia, hyponatremia, comorbidities, and age over 50 are proposed as predictive markers for COVID-19 hospitalization length and mortality.
  • These laboratory and clinical factors at admission can aid in estimating patient prognosis.
  • The findings highlight the importance of these markers in predicting severe COVID-19 outcomes.
Abstract

Keywords:
COVID-19SARS-CoV-2analysiscomorbidityhospitalization days lengthmachine learning

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