Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network

Huang, Sarah D.; Bamba, Vaneeta; Bothwell, Samantha; Fechner, Patricia Y.; Furniss, Anna; Ikomi, Chijioke; Nahata, Leena; Nokoff, Natalie J.; Pyle, Laura; Seyoum, Helina; Davis, Shanlee M.

doi:10.1002/ajmg.a.63495

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The systematic anamnesis and exploration of morpho-functional dental, oral and craniofacial alterations and the use of a common taxonomy [31] has enabled our team to report and assess the relevant signs of TS previously overlooked. These data can facilitate individual diagnosis and should be included in future multicentric studies [34] and databases that aim to develop advanced algorithm-based diagnostic systems [19,35].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Oral Diagnosis and Management for Women with Turner Syndrome

Tallón-Walton,

Sánchez-Molins,

et al. 2024

Diagnostics

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Turner Syndrome (TS) is a rare genetic disorder that affects females when one of the X chromosomes is partially or completely missing. Due to high genetic and phenotypic variability, TS diagnosis is challenging and is often delayed until adolescence, resulting in poor clinical management. Numerous oral, dental and craniofacial anomalies have been associated with TS, yet a comprehensive description is still lacking. This study addresses this gap through a detailed analysis of oral health and craniofacial characteristics in a cohort of 15 females with TS and their first-degree relatives. Subjects with TS ranged from 3 to 48 years old, none showed evidence of periodontal disease and only the youngest was in mixed dentition. Using the Multifunction System, we identified an aggregation of multiple signs and symptoms in each TS subject, including tooth anomalies (supernumerary molars, agenesis, microdontia, enamel defects, alterations in eruption patterns -advanced and delayed for chronological age-, crowding, rotations and transpositions), malocclusion (class II/1 and II/2) and Class II facial profile, while relatives exhibited fewer manifestations. The early detection of these signs and symptoms is crucial for appropriate referral and the optimal clinical management of TS, especially during the critical period of 9 to 10 years when congenital dental anomalies appear. The use of an established taxonomy to describe these phenotypic features is essential for early detection. Multidisciplinary teams are required to ensure holistic care management in rare diseases like TS.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Oral Diagnosis and Management for Women with Turner Syndrome

Tallón-Walton,

Sánchez-Molins,

et al. 2024

Diagnostics

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“…Readily available electronic health record (EHR) data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients [36,60]. With a high accuracy of 87%-95% [36], machine learning models using EHR data have been used to profile patients in various areas, for example, to develop a phenotype for patients with Turner syndrome [61], identify low medication adherence profiles [62], find variable COVID-19 treatment response profiles [63], and predict hypertension treatment response [64]. Yet, while machine learning has helped find various asthma profiles [65][66][67][68][69][70][71][72], no prior study has predicted ICS response.…”

Section: Introductionmentioning

confidence: 99%

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Nkoy,

Stone,

Zhang

et al. 2024

JMIR Med Inform

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Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

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Development and validation of a self-updating gout register from electronic health records data

Bürgisser,

Mongin,

Mehouachi

et al. 2024

RMD Open

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ObjectiveTo develop an automatic gout register from electronic health records (EHRs) data.MethodsWe analysed the EHR of all patients >18 years old from a tertiary academic hospital (2013–2022) based on six criteria: International Classification of Diseases 10 gout diagnosis, urate-lowering therapy prescription, monosodium urate crystals in joint aspiration and gout-related terms in problem lists, clinical or imaging reports. We assessed the positive and negative predictive value (PPV and NPV) of the query by chart reviews.ResultsOf 2 110 902 outpatients and inpatients, 10 289 had at least one criterion for gout. The combination of joint aspiration OR diagnostic in the problem list OR≥2 other criteria created a register of 5138 patients, with a PPV of 92.4% (95% CI 88.5% to 95.0%) and an NPV of 94.3% (95% CI 91.9% to 96.0%). PPV and NPV were similar among outpatients and inpatients. Incidence was 2.9 per 1000 person-year and dropped by 30% from the COVID-19 pandemic onward. Patients with gout were on average 71.2 years old (SD 14.9), mainly male (76.5%), overweight (69.5%) and polymorbid (mean number of comorbidities of 3, IQR 1–5). More than half (57.4%) had received a urate-lowering treatment, 6.7% had a gout that led to a hospitalisation or ≥2 flares within a year and 32.9% received a rheumatology consultation.ConclusionAn automatic EHR-based gout register is feasible, valid and could be used to evaluate and improve gout management. Interestingly, the register uncovered a marked underdiagnosis or under-reporting of gout since the COVID-19 pandemic.

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Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network

Cited by 4 publications

References 15 publications

Comprehensive Oral Diagnosis and Management for Women with Turner Syndrome

Comprehensive Oral Diagnosis and Management for Women with Turner Syndrome

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Development and validation of a self-updating gout register from electronic health records data

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