Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.

BACKGROUND: Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). METHODS: De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention. RESULTS: In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. CONCLUSIONS: Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.

Characterizing sleep disorders in an autism-specific collection of electronic health records.

OBJECTIVE/BACKGROUND: Sleep problems are common in people on the autism spectrum. This study reviews one detailed approach to querying the electronic health record (EHR) in a large tertiary care center. PATIENTS/METHODS: We developed methods for identifying people on the autism spectrum and defined their sleep problems using the key words, "sleep" or "melatonin", or International Classification of Diseases (ICD) codes. We examined treatment responses of these individuals to melatonin supplementation. RESULTS: Sleep problems were documented in 86% of patients with ages ranging from 6 to 30 years old. Our specific keyword search yielded more patients with sleep diagnoses than ICD codes alone. About two-thirds of patients who received melatonin supplementation reported benefit from its use. CONCLUSIONS: Our study provides a framework for using deidentified medical records to characterize sleep, a common co-occurring condition, in people on the autism spectrum. Using specific keywords could be helpful in future work that queries the EHR.