Détails Publication
Risk Prediction Models for Cardiotoxicity of Chemotherapy Among Patients With Breast Cancer A Systematic Review,
Auteur(s): Elisé G. Kaboré, MD; Conor Macdonald, PhD; Ahmed Kaboré, PhD; Romain Didier, MD; Patrick Arveux, MD, PhD; Nicolas Meda, MD, PhD; Marie-Christine Boutron-Ruault, MD, PhD; Charles Guenancia, MD, PhD
Auteur(s) tagués: KABORE Ahmed
Renseignée par : KABORE Ahmed
Résumé

IMPORTANCE Cardiotoxicity is a serious adverse effect that can occur in women undergoing
treatment for breast cancer. Identifying patients who will develop cardiotoxicity remains challenging.
OBJECTIVE To identify, describe, and evaluate all prognostic models developed to predict
cardiotoxicity following treatment in women with breast cancer.
EVIDENCE REVIEW 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.
FINDINGS 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.
CONCLUSIONS AND RELEVANCE 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.

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