Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
Objectives To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.Design In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction o...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-09-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/9/e060026.full |
| _version_ | 1798036941858406400 |
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| author | Claire S Waddington Effrossyni Gkrania-Klotsas Jacobus Preller Martin Wiegand Sarah L Cowan David J Halsall Victoria L Keevil Brian D M Tom Vince Taylor Robert J B Goudie |
| author_facet | Claire S Waddington Effrossyni Gkrania-Klotsas Jacobus Preller Martin Wiegand Sarah L Cowan David J Halsall Victoria L Keevil Brian D M Tom Vince Taylor Robert J B Goudie |
| author_sort | Claire S Waddington |
| collection | DOAJ |
| description | Objectives To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.Design In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.Setting All data used in this study were obtained from a single UK teaching hospital.Participants We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.Primary and secondary outcome measures The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.Results Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).Conclusions Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.Trial registration The study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com). |
| first_indexed | 2024-04-11T21:19:53Z |
| format | Article |
| id | doaj.art-eb091d3671e94d05a62c26f6205586d8 |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-04-11T21:19:53Z |
| publishDate | 2022-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-eb091d3671e94d05a62c26f6205586d82022-12-22T04:02:42ZengBMJ Publishing GroupBMJ Open2044-60552022-09-0112910.1136/bmjopen-2021-060026Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort studyClaire S Waddington0Effrossyni Gkrania-Klotsas1Jacobus Preller2Martin Wiegand3Sarah L Cowan4David J Halsall5Victoria L Keevil6Brian D M Tom7Vince Taylor8Robert J B Goudie9Department of Medicine, University of Cambridge, Cambridge, UKDepartment of Infectious Diseases, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UKCambridge University Hospitals NHS Foundation Trust, Cambridge, UKFaculty of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UKCambridge University Hospitals NHS Foundation Trust, Cambridge, UKCambridge University Hospitals NHS Foundation Trust, Cambridge, UKDepartment of Medicine, University of Cambridge, Cambridge, UKMRC Biostatistics Unit, University of Cambridge, Cambridge, UKCancer Research UK, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UKMRC Biostatistics Unit, University of Cambridge, Cambridge, UKObjectives To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.Design In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.Setting All data used in this study were obtained from a single UK teaching hospital.Participants We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.Primary and secondary outcome measures The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.Results Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).Conclusions Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.Trial registration The study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com).https://bmjopen.bmj.com/content/12/9/e060026.full |
| spellingShingle | Claire S Waddington Effrossyni Gkrania-Klotsas Jacobus Preller Martin Wiegand Sarah L Cowan David J Halsall Victoria L Keevil Brian D M Tom Vince Taylor Robert J B Goudie Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study BMJ Open |
| title | Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study |
| title_full | Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study |
| title_fullStr | Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study |
| title_full_unstemmed | Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study |
| title_short | Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study |
| title_sort | development and validation of a dynamic 48 hour in hospital mortality risk stratification for covid 19 in a uk teaching hospital a retrospective cohort study |
| url | https://bmjopen.bmj.com/content/12/9/e060026.full |
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