Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis,The BMJ

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Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
The BMJ ( IF 105.7 ) Pub Date : 2022-07-12 , DOI: 10.1136/bmj-2021-069881
Valentijn M T de Jong _{1,

2,

3} , Rebecca Z Rousset ₄ , Neftalí Eduardo Antonio-Villa _{5,

6} , Arnoldus G Buenen _{7,

8} , Ben Van Calster _{9,

10,

11} , Omar Yaxmehen Bello-Chavolla ₅ , Nigel J Brunskill _{12,

13} , Vasa Curcin ₁₄ , Johanna A A Damen _{2,

4} , Carlos A Fermín-Martínez _{5,

6} , Luisa Fernández-Chirino _{5,

15} , Davide Ferrari _{14,

16} , Robert C Free _{17,

18} , Rishi K Gupta ₁₉ , Pranabashis Haldar _{17,

18,

20} , Pontus Hedberg _{21,

22} , Steven Kwasi Korang ₂₃ , Steef Kurstjens ₂₄ , Ron Kusters _{24,

25} , Rupert W Major _{12,

12} , Lauren Maxwell ₂₆ , Rajeshwari Nair _{27,

28} , Pontus Naucler _{21,

22} , Tri-Long Nguyen _{4,

29,

30} , Mahdad Noursadeghi ₃₁ , Rossana Rosa ₃₂ , Felipe Soares ₃₃ , Toshihiko Takada _{4,

34} , Florien S van Royen ₄ , Maarten van Smeden ₄ , Laure Wynants _{8,

35} , Martin Modrák ₃₆ , , Folkert W Asselbergs _{37,

38,

39} , Marijke Linschoten ₃₇ , , Karel G M Moons _{2,

4} , Thomas P A Debray _{2,

4}

Affiliation

Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Netherlands.
Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, Netherlands.
Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City, Mexico.
MD/PhD (PECEM) Program, Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
Maxima MC, Veldhoven, the Netherlands.
Bernhoven, Uden, Netherlands.
Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands.
EPI-centre, KU Leuven, Leuven, Belgium.
Department of Cardiovascular Sciences, College of Life Sciences, University of Leicester, Leicester, UK.
John Walls Renal Unit, University Hospitals of Leicester NHS Trust, Leicester, UK.
School of Population Health and Environmental Sciences, King's College London, London, UK.
Faculty of Chemistry, Universidad Nacional Autónoma de México, México City, Mexico.
Centre for Clinical Infection and Diagnostics Research, School of Immunology and Microbial Sciences, King's College London, London, UK.
Department of Respiratory Sciences, College of Life Sciences, University of Leicester, Leicester, UK.
NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
Institute for Global Health, University College London, London, UK.
Department of Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK.
Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden.
Copenhagen Trial Unit, Centre for Clinical Intervention Research, Department 7812, Rigshospitalet, Copenhagen University Hospital, Denmark.
Laboratory of Clinical Chemistry and Haematology, Jeroen Bosch Hospital, Den Bosch, Netherlands.
Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, Netherlands.
Heidelberger Institut für Global Health, Universitätsklinikum Heidelberg, Germany.
University of Iowa Carver College of Medicine, Iowa City, IA, USA.
Centre for Access and Delivery Research Evaluation Iowa City Veterans Affairs Health Care System, Iowa City, IA, USA.
Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Department of Pharmacy, University Hospital Centre of Nîmes, Nîmes, France.
Division of Infection and Immunity, University College London, London, UK.
Infectious Diseases Service, UnityPoint Health-Des Moines, Des Moines, IA, USA.
Industrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Department of General Medicine, Shirakawa Satellite for Teaching And Research (STAR), Fukushima Medical University, Fukushima, Japan.
Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands.
Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic.
Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
Health Data Research UK and Institute of Health Informatics, University College London, London, UK.
Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.

Objective To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design Two stage individual participant data meta-analysis. Setting Secondary and tertiary care. Participants 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality. Results Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care. The data from Tongji Hospital, China that support the findings of this study are available from [https://github.com/HAIRLAB/Pre\_Surv\_COVID_19][1]. Data collected within CAPACITY-COVID is available on reasonable request (see ). Data for the CovidRetro study are available on request from MM or the secretariat of the Institute of Microbiology of the Czech Academy of Sciences (contact via mbu@biomed.cas.cz) for researchers who meet the criteria for access to confidential data. The data are not publicly available owing to privacy restrictions imposed by the ethical committee of General University Hospital in Prague and the GDPR regulation of the European Union. We can arrange to run any analytical code locally and share the results, provided the code and the results do not reveal personal information. The remaining data that support the findings of this study are not publicly available. [1]: https://github.com/HAIRLAB/Pre_Surv_COVID_19

更新日期：2022-07-12

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