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Renal parameters, particularly when combined with pulmonary factors, significantly improve COVID-19 mortality prediction in hospitalized patients. Optimal combinations vary by medical setting, aiding clinical decision-making.

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Area of Science:

  • Nephrology
  • Pulmonology
  • Infectious Diseases
  • Critical Care Medicine

Background:

  • Identifying predictors of COVID-19 mortality is crucial for hospitalized patients.
  • Comparing the predictive power of pulmonary versus renal parameters is essential for risk stratification.
  • Optimizing admission parameters can enhance patient management and outcomes.

Purpose of the Study:

  • To determine the best combination of admission parameters for predicting COVID-19 mortality.
  • To compare the predictive capacity of pulmonary and renal parameters for COVID-19 mortality.
  • To identify optimal predictive parameter combinations across different hospital settings.

Main Methods:

  • Retrospective analysis of 619 hospitalized COVID-19 patients (September-December 2020).
  • Data collection included laboratory results, comorbidities, chest X-ray (CXR) scores, and SpO2.
  • Renal function assessed using RIFLE, KDIGO criteria, and estimated glomerular filtration rate (eGFR).

Main Results:

  • Higher radiological scores and deteriorating renal function (AKI&CKD, eGFR) correlated with increased mortality (p<0.01 and p<0.001).
  • Renal parameters (AUC=0.8068) showed superior predictive ability for mortality compared to pulmonary parameters (AUC=0.7667).
  • Combining renal and pulmonary parameters yielded the highest AUC (0.8813), with optimal combinations identified for EMS, ED, and Internal Medicine.

Conclusions:

  • Renal parameters are superior to pulmonary parameters in predicting COVID-19 mortality in hospitalized patients.
  • Combining renal and pulmonary factors significantly enhances mortality prediction accuracy.
  • Specific parameter combinations are optimal for predicting mortality in Emergency Medical Services, Emergency Department, and Internal Medicine settings.