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Risk factors for in-hospital mortality in laboratory-confirmed COVID-19 patients in the Netherlands: A competing risk survival analysis

Author

Listed:
  • Gerine Nijman
  • Maike Wientjes
  • Jordache Ramjith
  • Nico Janssen
  • Jacobien Hoogerwerf
  • Evertine Abbink
  • Marc Blaauw
  • Ton Dofferhoff
  • Marjan van Apeldoorn
  • Karin Veerman
  • Quirijn de Mast
  • Jaap ten Oever
  • Wouter Hoefsloot
  • Monique H Reijers
  • Reinout van Crevel
  • Josephine S van de Maat

Abstract

Background: To date, survival data on risk factors for COVID-19 mortality in western Europe is limited, and none of the published survival studies have used a competing risk approach. This study aims to identify risk factors for in-hospital mortality in COVID-19 patients in the Netherlands, considering recovery as a competing risk. Methods: In this observational multicenter cohort study we included adults with PCR-confirmed SARS-CoV-2 infection that were admitted to one of five hospitals in the Netherlands (March to May 2020). We performed a competing risk survival analysis, presenting cause-specific hazard ratios (HRCS) for the effect of preselected factors on the absolute risk of death and recovery. Results: 1,006 patients were included (63.9% male; median age 69 years, IQR: 58–77). Patients were hospitalized for a median duration of 6 days (IQR: 3–13); 243 (24.6%) of them died, 689 (69.9%) recovered, and 74 (7.4%) were censored. Patients with higher age (HRCS 1.10, 95% CI 1.08–1.12), immunocompromised state (HRCS 1.46, 95% CI 1.08–1.98), who used anticoagulants or antiplatelet medication (HRCS 1.38, 95% CI 1.01–1.88), with higher modified early warning score (MEWS) (HRCS 1.09, 95% CI 1.01–1.18), and higher blood LDH at time of admission (HRCS 6.68, 95% CI 1.95–22.8) had increased risk of death, whereas fever (HRCS 0.70, 95% CI 0.52–0.95) decreased risk of death. We found no increased mortality risk in male patients, high BMI or diabetes. Conclusion: Our competing risk survival analysis confirms specific risk factors for COVID-19 mortality in a the Netherlands, which can be used for prediction research, more intense in-hospital monitoring or prioritizing particular patients for new treatments or vaccination.

Suggested Citation

  • Gerine Nijman & Maike Wientjes & Jordache Ramjith & Nico Janssen & Jacobien Hoogerwerf & Evertine Abbink & Marc Blaauw & Ton Dofferhoff & Marjan van Apeldoorn & Karin Veerman & Quirijn de Mast & Jaap , 2021. "Risk factors for in-hospital mortality in laboratory-confirmed COVID-19 patients in the Netherlands: A competing risk survival analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    • Handle: RePEc:plo:pone00:0249231
    DOI: 10.1371/journal.pone.0249231
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    References listed on IDEAS

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    1. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
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    1. Gabriela M. Rodrigues & Edwin M. M. Ortega & Gauss M. Cordeiro & Roberto Vila, 2022. "An Extended Weibull Regression for Censored Data: Application for COVID-19 in Campinas, Brazil," Mathematics, MDPI, vol. 10(19), pages 1-17, October.
    2. Mohammad Anamul Haque & Giuliana Cortese, 2023. "Cumulative Incidence Functions for Competing Risks Survival Data from Subjects with COVID-19," Mathematics, MDPI, vol. 11(17), pages 1-16, September.

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