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Background <p>There are few data on the longer-term illness trajectory of patients following hospitalisation for COVID-19.</p> Methods <p>We prospectively enrolled 267 adults hospitalised for COVID-19. Longer-term follow up was available for 260 participants. Event rates for death or unplanned hospitalisation were calculated using a Poisson model. Univariate and multivariable analyses identified baseline predictors, with a backward selection process for the best fitting model.</p> Results <p>The mean age of COVID-19 participants was 54.9±12.1 years, and 41% were female. During median follow-up of 1028 days (IQR:1000,1085), 112 individuals (43.1%) had at least one event including 6 deaths (2.3%). There were 252 events in total. The first event rate was 18.9 per 100 person-years (95%CI: 15.7, 22.8). Multivariable predictors included healthcare worker status (HR 0.59, 95%CI: 0.34, 1.02, p=0.046), Charlson Comorbidity Index (HR 1.13, 95%CI: 1.02, 1.24, p=0.020), current smoking (HR 2.49, 95%CI: 1.21, 5.11, p=0.010), and haemoglobin (HR 0.93, 95%CI: 0.88, 0.99, p=0.020). The WHO Clinical Severity Score was not a significant predictor (p=0.187).</p> Conclusion <p>Comorbidity, current smoking status and haemoglobin predict illness trajectory following hospitalisation for COVID-19, rather than illness severity during hospitalisation. Further research is needed to explore interventions targeting these factors to improve prognosis.</p> Trial registration <p>CISCO-19; http://NCT04403607. Registration date; 23/05/2020</p>

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Illness trajectory in the longer term after hospitalisation for COVID-19: a prospective, multicentre cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Anna Kamdar,
  • Andrew J. Morrow,
  • Robert Sykes,
  • Alasdair McIntosh,
  • Catherine Bagot,
  • Hannah K. Bayes,
  • Kevin G. Blyth,
  • Colin Church,
  • Lynsey Gillespie,
  • Giles Roditi,
  • David Stobo,
  • Sarah Weeden,
  • Paul Welsh,
  • Kenneth Mangion,
  • Alex McConnachie,
  • Colin Berry

摘要

Background

There are few data on the longer-term illness trajectory of patients following hospitalisation for COVID-19.

Methods

We prospectively enrolled 267 adults hospitalised for COVID-19. Longer-term follow up was available for 260 participants. Event rates for death or unplanned hospitalisation were calculated using a Poisson model. Univariate and multivariable analyses identified baseline predictors, with a backward selection process for the best fitting model.

Results

The mean age of COVID-19 participants was 54.9±12.1 years, and 41% were female. During median follow-up of 1028 days (IQR:1000,1085), 112 individuals (43.1%) had at least one event including 6 deaths (2.3%). There were 252 events in total. The first event rate was 18.9 per 100 person-years (95%CI: 15.7, 22.8). Multivariable predictors included healthcare worker status (HR 0.59, 95%CI: 0.34, 1.02, p=0.046), Charlson Comorbidity Index (HR 1.13, 95%CI: 1.02, 1.24, p=0.020), current smoking (HR 2.49, 95%CI: 1.21, 5.11, p=0.010), and haemoglobin (HR 0.93, 95%CI: 0.88, 0.99, p=0.020). The WHO Clinical Severity Score was not a significant predictor (p=0.187).

Conclusion

Comorbidity, current smoking status and haemoglobin predict illness trajectory following hospitalisation for COVID-19, rather than illness severity during hospitalisation. Further research is needed to explore interventions targeting these factors to improve prognosis.

Trial registration

CISCO-19; http://NCT04403607. Registration date; 23/05/2020