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A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort

Ayse Ulgen1, Sirin Cetin2, Meryem Cetin3

  • 1Department of Biostatistics, Faculty of Medicine, Girne American University, Karmi, Cyprus.

Computational Biology and Chemistry
|April 29, 2022
PubMed

Insights

Identifying reliable blood test risk factors for COVID-19 severity is crucial. This study enhances survival analysis using machine learning to pinpoint key indicators for disease progression and recovery.

Area of Science:

  • Clinical Medicine
  • Biostatistics
  • Computational Biology

Background:

  • Reliable identification of routine laboratory blood test risk factors for COVID-19 severity and mortality is vital for patient care and hospital management.
  • Meta-analysis is commonly used to combine study results for reproducibility, but a more robust approach is needed.

Purpose of the Study:

  • To develop a more robust list of risk factors for COVID-19 severity and mortality using routine blood tests.
  • To extend standard survival analysis by incorporating machine learning, multivariable analysis, and analyzing both death and recovery events.

Main Methods:

  • Extended standard survival analysis on time-to-event data in three directions: machine learning prediction, multivariable analysis, and analyzing time-to-hospital release alongside time-to-death.
  • Generated ten ranking lists of risk factors based on these extended analyses.
  • Utilized stepwise variable selection in random survival forest to identify optimal prognostic factors.

Main Results:

  • Identified 20 out of 30 factors reliably associated with faster death or faster recovery from COVID-19.
  • Determined that 10-15 factors can achieve optimal prognosis performance when considering correlations and using stepwise variable selection.
  • The final list of significant risk factors includes calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia-related factors, sodium, potassium, eosinophils, and aspartate aminotransferase.

Conclusions:

  • The proposed extended survival analysis provides a robust method for identifying COVID-19 risk factors from routine blood tests.
  • A refined list of 10-15 key blood test parameters can effectively predict COVID-19 prognosis.
  • These findings can aid in clinical decision-making and hospital resource management for COVID-19 patients.

Keywords:
COVID-19Competing risksComposite rankingSurvival analysis

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