Enhancing the diagnosis of fabry disease in cardiology with a targeted information: a before-after control-impact study

A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm

Background

Fabry disease (FD) is a rare genetic disorder characterized by glycosphingolipid accumulation and progressive damage across multiple organ systems. Due to its heterogeneous presentation, the condition is likely significantly underdiagnosed. Several approaches, including provider education efforts and newborn screening, have attempted to address underdiagnosis of FD across the age spectrum, with limited success. Artificial intelligence (AI) methods present another option for improving diagnosis. These methods isolate common health history patterns among patients using longitudinal real-world data, and can be particularly useful when patients experience nonspecific, heterogeneous symptoms over time. In this study, the performance of an AI tool in identifying patients with FD was analyzed. The tool was calibrated using de-identified health record data from a large cohort of nearly 5000 FD patients, and extracted phenotypic patterns from these records. The tool then used this FD pattern information to make individual-level estimates of FD in a testing dataset. Patterns were reviewed and confirmed with medical experts.

Results

The AI tool demonstrated strong analytic performance in identifying FD patients. In out-of-sample testing, it achieved an area under the receiver operating characteristic curve (AUROC) of 0.82. Strong performance was maintained when testing on male-only and female-only cohorts, with AUROCs of 0.83 and 0.82 respectively. The tool identified small segments of the population with greatly increased prevalence of FD: in the 1% of the population identified by the tool as at highest risk, FD was 23.9 times more prevalent than in the population overall. The AI algorithm used hundreds of phenotypic signals to make predictions and included both familiar symptoms associated with FD (e.g. renal manifestations) as well as less well-studied characteristics.

Conclusions

The AI tool analyzed in this study performed very well in identifying Fabry disease patients using structured medical history data. Performance was maintained in all-male and all-female cohorts, and the phenotypic manifestations of FD highlighted by the tool were reviewed and confirmed by clinical experts in the condition. The platform’s analytic performance, transparency, and ability to generate predictions based on existing real-world health data may allow it to contribute to reducing persistent underdiagnosis of Fabry disease.

The value of electrocardiography and echocardiography in distinguishing Fabry disease from sarcomeric hypertrophic cardiomyopathy

BACKGROUND

Screening for Fabry disease is sub-optimal in non-specialised centres.

AIM

To assess the diagnostic value of electrocardiographic scores of left ventricular hypertrophy and a combined electrocardiographic and echocardiographic model in Fabry disease.

METHODS

We retrospectively reviewed the electrocardiograms and echocardiograms of 61 patients (mean age 55.6±11.5 years; 57% men) with Fabry disease and left ventricular hypertrophy, and compared them with those from 59 patients (mean age 44.8±18.3 years; 66% men) with sarcomeric hypertrophic cardiomyopathy. Six electrocardiography criteria for left ventricular hypertrophy were specifically analysed: Sokolow-Lyon voltage index; Cornell voltage index; Gubner index; Romhilt-Estes score; Sokolow-Lyon product (voltage index×QRS duration); and Cornell product (voltage index×QRS duration).

RESULTS

Right bundle branch block was more frequent in patients with Fabry disease (54% vs. 22%; P=0.001). QRS duration, Gubner score and Sokolow-Lyon product were significantly higher in patients with Fabry disease. Maximal wall thickness was higher in patients with sarcomeric hypertrophic cardiomyopathy (21.9±5.1 vs. 15.5±2.9mm; P<0.001). Indexed sinus of Valsalva diameter was larger in patients with Fabry disease. After multivariable analysis, right bundle branch block, Sokolow-Lyon product, maximal wall thickness and aortic diameter were independently associated with Fabry disease. A model including these four variables yielded an area under the receiver operating characteristic curve of 0.918 (95% confidence interval 0.868-0.968) for Fabry disease.

CONCLUSION

Our model combining easy-to-assess electrocardiographic and echocardiographic variables may be helpful in improving screening and reducing diagnosis delay in Fabry disease.