Titre
Risk Prediction Models for Cardiotoxicity of Chemotherapy Among Patients With Breast Cancer: A Systematic Review.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Kaboré, E.G.
Auteure/Auteur
Macdonald, C.
Auteure/Auteur
Kaboré, A.
Auteure/Auteur
Didier, R.
Auteure/Auteur
Arveux, P.
Auteure/Auteur
Meda, N.
Auteure/Auteur
Boutron-Ruault, M.C.
Auteure/Auteur
Guenancia, C.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
2574-3805
Statut éditorial
Publié
Date de publication
2023-02-01
Volume
6
Numéro
2
Première page
e230569
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Systematic Review ; Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
Cardiotoxicity is a serious adverse effect that can occur in women undergoing treatment for breast cancer. Identifying patients who will develop cardiotoxicity remains challenging.
To identify, describe, and evaluate all prognostic models developed to predict cardiotoxicity following treatment in women with breast cancer.
This systematic review searched the Medline, Embase, and Cochrane databases up to September 22, 2021, to include studies developing or validating a prediction model for cardiotoxicity in women with breast cancer. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess both the risk of bias and the applicability of the prediction modeling studies. Transparency reporting was assessed with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) tool.
After screening 590 publications, we identified 7 prognostic model studies for this review. Six were model development studies and 1 was an external validation study. Outcomes included occurrence of cardiac dysfunction (echocardiographic parameters), heart failure, and composite clinical outcomes. Model discrimination, measured by the area under receiver operating curves or C statistic, ranged from 0.70 (95% IC, 0.62-0.77) to 0.87 (95% IC, 0.77-0.96). The most common predictors identified in final prediction models included age, baseline left ventricular ejection fraction, hypertension, and diabetes. Four of the developed models were deemed to be at high risk of bias due to analysis concerns, particularly for sample size, handling of missing data, and not presenting appropriate performance statistics. None of the included studies examined the clinical utility of the developed model. All studies met more than 80% of the items in TRIPOD checklist.
In this systematic review of the 6 predictive models identified, only 1 had undergone external validation. Most of the studies were assessed as being at high overall risk of bias. Application of the reporting guidelines may help future research and improve the reproducibility and applicability of prediction models for cardiotoxicity following breast cancer treatment.
To identify, describe, and evaluate all prognostic models developed to predict cardiotoxicity following treatment in women with breast cancer.
This systematic review searched the Medline, Embase, and Cochrane databases up to September 22, 2021, to include studies developing or validating a prediction model for cardiotoxicity in women with breast cancer. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess both the risk of bias and the applicability of the prediction modeling studies. Transparency reporting was assessed with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) tool.
After screening 590 publications, we identified 7 prognostic model studies for this review. Six were model development studies and 1 was an external validation study. Outcomes included occurrence of cardiac dysfunction (echocardiographic parameters), heart failure, and composite clinical outcomes. Model discrimination, measured by the area under receiver operating curves or C statistic, ranged from 0.70 (95% IC, 0.62-0.77) to 0.87 (95% IC, 0.77-0.96). The most common predictors identified in final prediction models included age, baseline left ventricular ejection fraction, hypertension, and diabetes. Four of the developed models were deemed to be at high risk of bias due to analysis concerns, particularly for sample size, handling of missing data, and not presenting appropriate performance statistics. None of the included studies examined the clinical utility of the developed model. All studies met more than 80% of the items in TRIPOD checklist.
In this systematic review of the 6 predictive models identified, only 1 had undergone external validation. Most of the studies were assessed as being at high overall risk of bias. Application of the reporting guidelines may help future research and improve the reproducibility and applicability of prediction models for cardiotoxicity following breast cancer treatment.
PID Serval
serval:BIB_D67552AA27C0
PMID
Open Access
Oui
Date de création
2023-03-02T15:50:29.792Z
Date de création dans IRIS
2025-05-21T01:29:42Z
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Nom
Risk Prediction Models for Cardiotoxicity of Chemotherapy Among Patients With Breast Cancer.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
1.06 MB
Format
Adobe PDF
PID Serval
serval:BIB_D67552AA27C0.P001
URN
urn:nbn:ch:serval-BIB_D67552AA27C04
Somme de contrôle
(MD5):d20c26671a73eaae1c12d1f20ff7b8c2