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Original research
Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score
  1. Felix Chua1,2,
  2. Rama Vancheeswaran3,
  3. Adrian Draper4,
  4. Tejal Vaghela5,
  5. Matthew Knight3,
  6. Rahul Mogal3,
  7. Jaswinder Singh6,
  8. Lisa G Spencer7,
  9. Erica Thwaite8,
  10. Harry Mitchell3,
  11. Sam Calmonson3,
  12. Noor Mahdi3,
  13. Shershah Assadullah3,
  14. Matthew Leung3,
  15. Aisling O'Neill3,
  16. Chhaya Popat3,
  17. Radhika Kumar3,
  18. Thomas Humphries7,
  19. Rebecca Talbutt7,
  20. Sarika Raghunath7,
  21. Philip L Molyneaux1,2,
  22. Miriam Schechter5,
  23. Jeremy Lowe5,
  24. Andrew Barlow3
  1. 1 Interstitial Lung Disease Unit, Department of Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London, UK
  2. 2 National Heart and Lung Institute, Imperial College London, London, UK
  3. 3 Respiratory Medicine, West Hertfordshire Hospitals NHS Trust, Watford, UK
  4. 4 Respiratory Medicine, St. George's Hospital, London, UK
  5. 5 Information Governance, West Hertfordshire Hospitals NHS Trust, Watford, UK
  6. 6 Radiology, West Hertfordshire Hospitals NHS Trust, Watford, UK
  7. 7 Respiratory Medicine, Aintree site, Liverpool Hospitals NHS Foundation Trust, Liverpool, UK
  8. 8 Radiology, Aintree site, Liverpool Hospitals NHS Foundation Trust, UK, Liverpool, UK
  1. Correspondence to Dr Felix Chua, Interstitial Lung Disease Unit, Department of Respiratory Medicine, Royal Brompton and Harefield NHS Foundation Trust, London SW3 6NP, UK; f.chua{at}rbht.nhs.uk

Abstract

Introduction Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.

Methods Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).

Results 983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).

Conclusion The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.

  • respiratory infection
  • viral infection

Data availability statement

Data are available upon reasonable request. Deidentified participant data may be requested from the corresponding author following publication of the study (ORCID identifier: 0000-0001-7845-0173)

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

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Data availability statement

Data are available upon reasonable request. Deidentified participant data may be requested from the corresponding author following publication of the study (ORCID identifier: 0000-0001-7845-0173)

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Footnotes

  • Twitter @mjknight0380, @Andyatfrogmore

  • FC, RV and AD contributed equally.

  • Contributors RV, AB, MK and RM developed the clinical algorithm and supervised patient management. FC, RV and AD conceived and designed the investigational plan. FC drafted the manuscript with contributions of intellectual content from RV, AD, LGS, PLM and AB. RV, TV, MK, RM, JS, LS, ET, HM, SC, NM, SA, ML, AO, CP, RK, TH, RT, SR, MS and JL collected the data at respective sites. AD, FC and RV examined the data and undertook statistical analyses. All authors approved the final version of the manuscript for submission. RV is guarantor and attests that all named authors and contributors meet authorship criteria and that no others meeting such criteria have been omitted.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned, externally peer reviewed. No part of this work has been written by a medical writer or published in printed or electronic form. Some of the findings of this study have been accepted for presentation at the British Thoracic Society Winter Meeting 2020 to be held in February 2021, at which point the abstract will be published in printed format in a supplement of Thorax. A copy of the originally submitted manuscript was uploaded to the medRixv preprint website; https://doi.org/10.1101/2020.10.19.20215426.

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