Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

Paula Dhiman; Jie Ma; Victoria N Gibbs; Alexandros Rampotas; Hassan Kamal; Sahar S Arshad; Shona Kirtley; Carolyn Doree; Michael F Murphy; Gary S Collins; Antony J R Palmer

doi:10.1016/j.jclinepi.2023.05.002

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

J Clin Epidemiol. 2023 Jul:159:10-30. doi: 10.1016/j.jclinepi.2023.05.002. Epub 2023 May 6.

Authors

Affiliations

¹ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: paula.dhiman@csm.ox.ac.uk.
² Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
³ Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
⁴ Systematic Review Initiative, NHS Blood & Transplant, John Radcliffe Hospital, Oxford, UK.
⁵ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; School of Medicine, University of Dundee, Ninewells Hospital & Medical School, Dundee, Scotland DD1 9SY.
⁶ NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Systematic Review Initiative, NHS Blood & Transplant, John Radcliffe Hospital, Oxford, UK; NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
⁷ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
⁸ Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Oxford University Hospitals, Nuffield Orthopaedic Centre, Windmill Road, Headington, Oxford OX3 7HE, UK.

PMID: 37156342
DOI: 10.1016/j.jclinepi.2023.05.002

Abstract

Background: Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice.

Study design and setting: We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST).

Results: We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.

Conclusion: Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.

Keywords: Blood transfusion; Meta-analysis; Prediction model; Risk of bias; Surgery; Systematic review.

Publication types

Systematic Review
Review
Research Support, Non-U.S. Gov't

MeSH terms

Blood Transfusion* / methods
Hemorrhage
Humans
Models, Statistical*
Prognosis

Abstract

Publication types

MeSH terms

Grants and funding