DOI: 10.1093/ejhf/xuag193.1183 ISSN: 1388-9842

Validating prediction models for hypertrophic cardiomyopathy genotype positivity

P Kopp, K Sadigh

Abstract

Background

Genotype testing in hypertrophic cardiomyopathy (HCM) is a key tool for determining familial risk. However, diagnostic genetic tests are costly and identify a causative gene variant in only a minority of cases. The phenotype-based Toronto and Mayo scoring systems were developed to predict a patient’s likelihood of a positive genetic test in HCM. Evaluations of these models have shown promising results in several international cohorts.

Purpose

The purpose of this study is to evaluate the validity of the Toronto and Mayo scores for predicting HCM genotype positivity in a novel cohort.

Methods

Medical record review was performed for patients with a HCM diagnosis between January 2015 and December 2024. Demographic, clinical, and imaging data were collected for all patients who received a diagnostic HCM genetic test, and these data were used to calculate Toronto and Mayo scores for each patient. Performance of the Toronto and Mayo scores was evaluated by area under the receiver operating characteristic curve (AUC). Model calibration was evaluated by the Hosmer-Lemeshow goodness-of-fit test and predictive abilities of Toronto and Mayo scores were compared using DeLong’s Test.

Results

Of 586 patients identified with a HCM diagnosis, 78 received a diagnostic genotype test. Of these 78, 20 (26%) were found to be genotype positive. AUC was 0.76 (p<0.001) for Toronto score and 0.73 (p<0.001) for Mayo score. Toronto and Mayo scores had p-values of 0.620 and 0.922, respectively, for goodness-of-fit tests. There was no statistically significant difference in predictive ability between Toronto and Mayo scores (p=0.195).

Conclusions

These findings suggest that both the Toronto and Mayo scores are useful in predicting a positive HCM genetic test. These scoring systems may help to guide shared physician-patient decision-making regarding genetic testing and foster cost-effective usage of genetic testing resources.For image description, please refer to the figure legend and surrounding text.

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