External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland. Issue 5 (15th November 2021)
- Record Type:
- Journal Article
- Title:
- External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland. Issue 5 (15th November 2021)
- Main Title:
- External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland
- Authors:
- Simpson, Colin R
Robertson, Chris
Kerr, Steven
Shi, Ting
Vasileiou, Eleftheria
Moore, Emily
McCowan, Colin
Agrawal, Utkarsh
Docherty, Annemarie
Mulholland, Rachel
Murray, Josie
Ritchie, Lewis Duthie
McMenamin, Jim
Hippisley-Cox, Julia
Sheikh, Aziz - Abstract:
- Abstract : Background: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. Results: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. Conclusions: Version 1 of theAbstract : Background: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. Methods: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. Results: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. Conclusions: Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively. … (more)
- Is Part Of:
- Thorax. Volume 77:Issue 5(2022)
- Journal:
- Thorax
- Issue:
- Volume 77:Issue 5(2022)
- Issue Display:
- Volume 77, Issue 5 (2022)
- Year:
- 2022
- Volume:
- 77
- Issue:
- 5
- Issue Sort Value:
- 2022-0077-0005-0000
- Page Start:
- 497
- Page End:
- 504
- Publication Date:
- 2021-11-15
- Subjects:
- COVID-19 -- clinical epidemiology
Chest -- Diseases -- Periodicals
Thorax
Chest -- Diseases
Periodicals
Periodicals
617.54 - Journal URLs:
- http://thorax.bmjjournals.com/contents-by-date.0.shtml ↗
http://www.bmj.com/archive ↗ - DOI:
- 10.1136/thoraxjnl-2021-217580 ↗
- Languages:
- English
- ISSNs:
- 0040-6376
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
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