Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

doi:10.1136/bmj-2022-073538

. 2023 May 3:381:e073538.

doi: 10.1136/bmj-2022-073538.

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Kym I E Snell¹, Brooke Levis², Johanna A A Damen^{3

4}, Paula Dhiman^{5

6}, Thomas P A Debray⁴, Lotty Hooft^{3

4}, Johannes B Reitsma^{3

4}, Karel G M Moons^{3

4}, Gary S Collins^{5

6}, Richard D Riley⁷

Affiliations

¹ Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK k.snell@bham.ac.uk.
² Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK.
³ Cochrane Netherlands, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
⁴ Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
⁵ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
⁶ NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁷ Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK.

PMID: 37137496
PMCID: PMC10155050
DOI: 10.1136/bmj-2022-073538

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Kym I E Snell et al. BMJ. 2023.

. 2023 May 3:381:e073538.

doi: 10.1136/bmj-2022-073538.

Authors

Affiliations

¹ Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK k.snell@bham.ac.uk.
² Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK.
³ Cochrane Netherlands, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
⁴ Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands.
⁵ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
⁶ NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁷ Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK.

PMID: 37137496
PMCID: PMC10155050
DOI: 10.1136/bmj-2022-073538

Abstract

Most clinical specialties have a plethora of studies that develop or validate one or more prediction models, for example, to inform diagnosis or prognosis. Having many prediction model studies in a particular clinical field motivates the need for systematic reviews and meta-analyses, to evaluate and summarise the overall evidence available from prediction model studies, in particular about the predictive performance of existing models. Such reviews are fast emerging, and should be reported completely, transparently, and accurately. To help ensure this type of reporting, this article describes a new reporting guideline for systematic reviews and meta-analyses of prediction model research.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the funders as described above; RDR receives royalties for sales from two published books. RDR and GSC are statistical editors for The BMJ but were not involved in The BMJ’s decision making process for this article.

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References

1. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. 2nd ed. Springer, 2019. 10.1007/978-3-030-16399-0. - DOI
1. Riley RD, van der Windt D, Croft P, Moons KGM, eds. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford University Press, 2019. 10.1093/med/9780198796619.001.0001. - DOI
1. Steyerberg EW, Moons KG, van der Windt DA, et al. PROGRESS Group . Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381. 10.1371/journal.pmed.1001381 - DOI - PMC - PubMed
1. van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KG. Clinical prediction models: diagnosis versus prognosis. J Clin Epidemiol 2021;132:142-5. 10.1016/j.jclinepi.2021.01.009 - DOI - PubMed
1. Gupta RK, Harrison EM, Ho A, et al. ISARIC4C Investigators . Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med 2021;9:349-59. 10.1016/S2213-2600(20)30559-2 - DOI - PMC - PubMed

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[1] Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. 2nd ed. Springer, 2019. 10.1007/978-3-030-16399-0. - DOI

[2] Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. 2nd ed. Springer, 2019. 10.1007/978-3-030-16399-0. - DOI

[3] Riley RD, van der Windt D, Croft P, Moons KGM, eds. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford University Press, 2019. 10.1093/med/9780198796619.001.0001. - DOI

[4] Riley RD, van der Windt D, Croft P, Moons KGM, eds. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford University Press, 2019. 10.1093/med/9780198796619.001.0001. - DOI

[5] Steyerberg EW, Moons KG, van der Windt DA, et al. PROGRESS Group . Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381. 10.1371/journal.pmed.1001381 - DOI - PMC - PubMed

[6] Steyerberg EW, Moons KG, van der Windt DA, et al. PROGRESS Group . Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381. 10.1371/journal.pmed.1001381 - DOI - PMC - PubMed

[7] van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KG. Clinical prediction models: diagnosis versus prognosis. J Clin Epidemiol 2021;132:142-5. 10.1016/j.jclinepi.2021.01.009 - DOI - PubMed

[8] van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KG. Clinical prediction models: diagnosis versus prognosis. J Clin Epidemiol 2021;132:142-5. 10.1016/j.jclinepi.2021.01.009 - DOI - PubMed

[9] Gupta RK, Harrison EM, Ho A, et al. ISARIC4C Investigators . Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med 2021;9:349-59. 10.1016/S2213-2600(20)30559-2 - DOI - PMC - PubMed

[10] Gupta RK, Harrison EM, Ho A, et al. ISARIC4C Investigators . Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med 2021;9:349-59. 10.1016/S2213-2600(20)30559-2 - DOI - PMC - PubMed

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Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Affiliations

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Authors

Affiliations

Abstract

Conflict of interest statement

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