# Development of a smartphone-based app to support the differential diagnosis in patients with primary left ventricular hypertrophy

## TL;DR

A smartphone app called Thick-Heart was developed to help doctors quickly and accurately diagnose the cause of left ventricular hypertrophy in patients.

## Contribution

The development and validation of a digital decision support tool for differential diagnosis of primary left ventricular hypertrophy.

## Key findings

- The app correctly classified 93% of cases into the most likely diagnosis category with high sensitivity and specificity.
- Non-sarcomeric phenocopies showed a higher red flag burden than sarcomeric HCM, with systemic and extracardiac features being strong predictors.
- The app achieved a 100% positive predictive value for identifying Friedrich’s ataxia and 92% for TTR amyloidosis.

## Abstract

Patients with primary left ventricular hypertrophy (LVH) often experience a diagnostic delay of several years, largely related to fragmented knowledge among different specialties and the rarity of the conditions. We developed and validated a digital support tool to guide the physician in the differential diagnostic process of patients presenting with primary LVH.

A total of 818 patients with definitive diagnosis of sarcomeric hypertrophic cardiomyopathy (HCM) or one of its phenocopies [479 (62%) males, 48 ± 24 years] were included. Pre-specified disease-specific red flags (RFs) were categorized into five domains: family history, signs/symptoms, electrocardiography, echocardiographic, and laboratory. Each patient’s characteristics were inserted by two independent and blind investigators into the app. The diagnostic outcome, based on the presence/absence of RF, was categorized as follows: (i) most likely diagnosis, (ii) possible diagnosis, and (iii) less likely diagnosis. A total of 2979 RFs were identified and non-sarcomeric phenocopies exhibited a higher RF burden than sarcomeric HCM (3.9 vs. 2.7 RFs per patient, P = 0.007), with systemic features and extracardiac findings being strong predictors of non-sarcomeric disease. Thick-Heart App correctly classified 93% of cases into the most likely diagnosis category (sensitivity of 88–100%, specificity 97%). The positive predictive value (PPV) for TTR amyloidosis reached 92%, while Friedrich’s ataxia was correctly identified in all cases (PPV = 100%).

The Thick-Heart App correctly classified 93% of cases into the most-likely diagnosis category (sensitivity 88–100%, specificity 97%). Our study underscores the potential clinical value of digital decision support tools to enable timelier identification of specific cardiomyopathies, by promoting awareness in non-reference settings.

Graphical Abstract