A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Geva, Alon; Gronsbell, Jessica; Cai, Tianxi; Cai, Tianrun; Murphy, Shawn N.; Lyons, Jessica C.; Heinz, Michelle M.; Natter, Marc D.; Patibandla, Nandan; Bickel, Jonathan; Mandl, Kenneth D.; Abman, Steven H.; Adatia, Ian; Austin, Eric D.; Feinstein, Jeffrey A.; Fineman, Jeff; Hanna, Brian D.; Hopper, Rachel K.; Ivy, D. Dunbar; Keller, Roberta L.; Krishnan, Usha; Kulik, Thomas J.; Mullen, Mary P.; Raj, Usha; Rosenzweig, Erika B.

doi:10.1016/j.jpeds.2017.05.037

Cited by 27 publications

(15 citation statements)

References 26 publications

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“…Individuals were excluded if they received the diagnosis of AF within a month of cardiac or abdominal surgery, or a thyroid-related diagnosis, and 12 months before or after prescription for methimazole or propylthioracil. Individuals in the PaTH AF Cohort were identified through electronic medical records using a computable phenotype [41] implementing the inclusion and exclusion criteria.…”

Section: Methodsmentioning

confidence: 99%

Association of sex, age and education level with patient reported outcomes in atrial fibrillation

Gleason

Himmelfarb

Ford

et al. 2019

BMC Cardiovasc Disord

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Background In atrial fibrillation (AF), there are known sex and sociodemographic disparities in clinical outcomes such as stroke. We investigate whether disparities also exist with respect to patient-reported outcomes. We explored the association of sex, age, and education level with patient-reported outcomes (AF-related quality of life, symptom severity, and emotional and functional status). Methods The PaTH AF cohort study recruited participants ( N = 953) with an AF diagnosis and age ≥ 18 years across 4 academic medical centers. We performed longitudinal multiple regression with random effects to determine if individual characteristics were associated with patient-reported outcomes. Results Women reported poorer functional status (β − 2.23, 95% CI: -3.52, − 0.94) and AF-related quality of life (β − 4.12, 95% CI: -8.10, − 0.14), and higher symptoms of anxiety (β 2.08, 95% CI: 0.76, 3.40), depression (β 1.44, 95% CI: 0.25, 2.63), and AF (β 0.29, 95% CI: 0.08, 0.50). Individuals < 60 years were significantly ( p < 0.05) more likely to report higher symptoms of depression, anxiety, and AF, and poorer AF-related quality of life. Lack of college education was associated with reporting higher symptoms of AF (β 0.42, 95% CI: 0.17, 0.68), anxiety (β 1.86, 95% CI: 0.26, 3.45), and depression (β 1.11, 95% CI: 0.15, 2.38), and lower AF-related quality of life (β − 4.41, 95% CI: -8.25, − 0.57) and functional status. Conclusion Women, younger adults, and individuals with lower levels of education reported comparatively poor patient-reported outcomes. These findings highlight the importance of understanding why individuals experience AF differently based on certain characteristics.

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Section: Methodsmentioning

confidence: 99%

Association of sex, age and education level with patient reported outcomes in atrial fibrillation

Gleason

Himmelfarb

Ford

et al. 2019

BMC Cardiovasc Disord

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“…We have listed several examples of how AI has boosted therapeutic development in RDs. These entail the identification of disease biomarkers [70][71][72], the increase of patient recruitment for CTs [67], and the discovery of drugs for repurposing [63]. However, much is still to be done to overturn the low rate of R&D for RDs.…”

Section: Discussionmentioning

confidence: 99%

“…The application of four data mining computable phenotype algorithms to EHR identified 413 new pediatric patients with PH. Other major advantages of this methodology are that (i) it allows for continuous patient recruitment and (ii) once validated, it is transferable to other settings [67].…”

Section: Patient Recruitment and Identificationmentioning

confidence: 99%

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Brasil

Pascoal

Francisco

et al. 2019

Genes

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The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs' challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs' AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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“…These data have previously been used for many studies, including costs of prematurity, genetic and environmental contributions to phenotypes among twins and sibling pairs, 17-OHP use to prevent preterm birth, and pediatric pulmonary hypertension phenotypes, among others. 4 , 19 – 21 …”

Section: Methodsmentioning

confidence: 99%

Medication utilization in children born preterm in the first two years of life

et al. 2021

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Objective: To compare medications dispensed during the first two years in children born preterm and full-term. Study Design: Retrospective analysis of claims data from a commercial national managed care plan 2008–2019. 329 855 beneficiaries were enrolled from birth through two years, of which 25 408 (7.7%) were preterm (<37 weeks). Filled prescription claims and paid amount over two years were identified. Results: In preterm children, the number of filled prescriptions was 1.4 times and cost was 3.8 times that of full-term children. Number and cost of medications were inversely related to gestational age. Differences peak at 4–9 months and resolve by 19 months after discharge. Palivizumab, ranitidine, albuterol, lansoprazole, budesonide, and prednisolone had the greatest differences in utilization. Conclusion: Prescription medication utilization among preterm children under two is driven by palivizumab, anti-reflux, and respiratory medications, despite little evidence regarding efficacy for many medications, and concern for harm with certain classes.

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A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Cited by 27 publications

References 26 publications

Association of sex, age and education level with patient reported outcomes in atrial fibrillation

Association of sex, age and education level with patient reported outcomes in atrial fibrillation

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

Medication utilization in children born preterm in the first two years of life

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