Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias.

Humans Bias Head and Neck Neoplasms Outcome Assessment, Health Care Prognosis Squamous Cell Carcinoma of Head and Neck
Head and neck cancer Methodology Outcome prediction Systematic review

Journal

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192

Informations de publication

Date de publication:
06 2023
Historique:
received: 28 10 2022
revised: 20 02 2023
accepted: 10 03 2023
medline: 5 6 2023
pubmed: 20 3 2023
entrez: 19 3 2023
Statut: ppublish

Résumé

Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

Identifiants

DOI: 10.1016/j.radonc.2023.109629 PMID: 36934895
pubmed: 36934895
pii: S0167-8140(23)00167-6
doi: 10.1016/j.radonc.2023.109629
pii:
doi:

Types de publication

Journal Article Review Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

109629

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Auteurs

Farhannah Aly (F)

Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia. Electronic address: Farhannah.Aly@health.nsw.gov.au.

Christian Rønn Hansen (CR)

Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.

Daniel Al Mouiee (D)

Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.

Purnima Sundaresan (P)

Sydney West Radiation Oncology Network, Western Sydney Local Health District, Sydney, Australia; Sydney Medical School, The University of Sydney, Sydney, Australia.

Ali Haidar (A)

Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia.

Shalini Vinod (S)

Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.

Lois Holloway (L)

Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Outcome prediction models incorporating...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Facteurs épidémiologiques › Biais (épidémiologie) › Outcome prediction models incorporating...
questionsmedicales.fr › Tumeurs › Tumeurs par siège › Tumeurs de la tête et du cou › Outcome prediction models incorporating...
questionsmedicales.fr › Qualité, accès, évaluation des soins de santé › Qualité des soins de santé › Mécanismes d'évaluation des soins de santé › Évaluation des résultats et des processus en soins de santé › Évaluation de résultat (soins) › Outcome prediction models incorporating...
questionsmedicales.fr › Diagnostic › Pronostic › Outcome prediction models incorporating...
questionsmedicales.fr › Tumeurs › Tumeurs par siège › Tumeurs de la tête et du cou › Carcinome épidermoïde de la tête et du cou › Outcome prediction models incorporating...