JMIR Publications

ABSTRACT

Background:

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a multi-systemic hereditary connective tissue disorder characterized by generalized joint hypermobility, chronic pain, and a complex spectrum of comorbidities. Diagnosis relies on complex clinical criteria, leading to poor recognition by clinicians and fragmented care. Consequently, patients navigate the healthcare system for an average of 22.1 years before receiving a diagnosis, which substantially delays appropriate management. Electronic health records (EHRs) contain rich longitudinal data that, if systematically analyzed, could identify patients whose clinical histories are highly suggestive of hEDS.

Objective:

This study protocol describes the development, validation, and usefulness assessment of a computable phenotype to identify patients with a high likelihood of undiagnosed hEDS from EHR data. The primary aims are: (1) to characterize the data-driven clinical signature of hEDS; (2) to develop and validate a machine learning algorithm to identify high-likelihood cases; and (3) to evaluate the algorithm’s practical utility and potential to reduce diagnostic delay.

Methods:

We will conduct a multi-phase study. First, a large-scale retrospective analysis of a national EHR database (Cosmos) will be used to define the clinical signature of hEDS, including comorbidity associations, temporal diagnostic patterns, and clinical sub-phenotypes. Second, using data from three academic health systems, we will develop a predictive machine learning model that integrates structured data with features extracted from clinical notes via natural language processing (NLP). The model's performance will be validated against an independent cohort adjudicated through expert chart review. Finally, we will use discrete-event simulation to conduct a usefulness assessment, quantifying the algorithm's potential to reduce diagnostic delay under realistic constraints on specialist availability.

Results:

As a study protocol, empirical results are not yet available. We have initiated Aim 1 (Cosmos data acquisition) and received the data export for Aim 2 from one site. Expected outputs include a data-driven clinical signature of hEDS (Q2 2026), a validated computable phenotype with performance metrics (Q1 2027), and a quantitative estimate of the achievable reduction in diagnostic delay with an optimal prediction threshold for clinical deployment (Q2 2027).

Conclusions:

This work will produce a validated, actionable algorithm for identifying patients with a high likelihood of undiagnosed hEDS. The clinical signature analysis will provide robust evidence to inform provider education and improve clinical practice. Ultimately, this protocol establishes a rigorous framework to develop and assess a tool with the potential to substantially reduce diagnostic delays and advance the application of computable phenotyping to other diagnostically challenging conditions. Clinical Trial: Not Applicable