4.6 Article

Predicting Need for Escalation of Care or Death From Repeated Daily Clinical Observations and Laboratory Results in Patients With Severe Acute Respiratory Syndrome Coronavirus 2

Journal

AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 191, Issue 11, Pages 1944-1953

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwac126

Keywords

coronavirus disease 2019; COVID-19; critical care; mortality; SARS-CoV-2; severe acute respiratory syndrome coronavirus 2; survival analysis

Funding

  1. Nottingham University Hospitals NHS Trust
  2. University of Nottingham
  3. MRC Clinician Scientist Award [MR/P008348/1]
  4. NIHR Nottingham Biomedical Research Centre

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This study compared the performance of prognostic tools for SARS-CoV-2 using parameters fitted at the time of hospital admission or throughout the hospital stay. The researchers developed a dynamic risk prediction model for SARS-CoV-2 prognosis using clinical data from a single hospital center in the UK. The study found that a customized daily SARS-CoV-2 escalation risk prediction score performed better than generic early warning scores or single risk estimations calculated at admission in predicting the need for clinical escalation.
We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n = 1,141; C statistic: C = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission.

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