Articles: phenotype.
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Using the Australiasian electronic Persistent Pain Outcomes Collaboration, a binational pain registry collecting standardized clinical data from paediatric ePPOC (PaedsePPOC) and adult pain services (AdultePPOC), we explored and characterized nationally representative chronic pain phenotypes and associations with clinical and sociodemographic factors, health care utilization, and medicine use of young people. Young people ≥15.0 and <25.0 years captured in PaedePPOC and AdultePPOC Australian data registry were included. Data from 68 adult and 12 paediatric pain services for a 5-year period January 2018 to December 2022 (first episode, including treatment information) were analysed. ⋯ From both services, 3 similar phenotypes emerged ("low," "moderate," "high"), characterized by an increasing symptom-severity gradient in multidimensional pain-related variables, showing meaningful differences across clinical and sociodemographic factors, health service utilization, and medicines use. Derived phenotypes point to the need for novel care models that differentially respond to the needs of distinct groups of young people, providing timely, targeted, age-appropriate care. To effectively scale such care, digital technologies can be leveraged to augment phenotype-informed clinical care.
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Persistent breast cancer treatment-related pain affects up to 40% of patients, decreasing their quality of life (QoL). While current research typically utilizes correlation and regression analysis to identify biopsychosocial phenotypes contributing to this pain, this study employs cluster analysis to identify qualitatively different phenotypes based on somatosensory and psychosocial characteristics both before and one week post-breast cancer surgery. Further, it investigates how these phenotypes are related to pain intensity one year post-surgery and examines the evolution of phenotype membership from pre- to post-surgery. ⋯ PERSPECTIVE: This secondary analysis, utilizing cluster analysis, reveals five distinct phenotype based on somatosensory and psychosocial characteristics both before and post-breast cancer surgery. Higher psychosocial distress and lower quality of life correlated with elevated pain intensity one year post-surgery, emphasizing the need to address patients' mental health perioperatively. TRIAL REGISTRATION: clinicaltrials.gov (NCT03351075).
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Primary aldosteronism (PA) is the most common cause of secondary hypertension, yet screening remains startlingly infrequent. We describe (1) PA screening practices in a large, diverse health system, (2) the development of a computable phenotype for PA screening, and (3) the design and pilot deployment of an electronic health record (EHR)-based active choice nudge to recommend PA screening. ⋯ PA screening rates are low. This pilot study suggests an EHR-based nudge leveraging a precise computable phenotype can dramatically increase appropriate PA screening.
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J. Thorac. Cardiovasc. Surg. · Jan 2025
Genetic variations in PTPN11 lead to a recurrent Left Ventricular Outflow Tract Obstruction phenotype in childhood hypertrophic cardiomyopathy.
Left ventricular septal myotomy provides a favorable prognosis for children with hypertrophic obstructive cardiomyopathy (HOCM). However, some children still suffer from recurrent left ventricular outflow tract obstruction (LVOTO) after surgery. Poor prognosis exists for HOCM caused by PTPN11 mutation. Therefore, the aim of this study was to determine the clinical features of recurrent obstruction in children with HOCM caused by pathogenic mutations in the PTPN11 gene. ⋯ Children with PTPN11 mutation-associated hypertrophic cardiomyopathy have a greater risk of recurrent LVOTO.
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Observational Study
Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters.
Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study's objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations. ⋯ We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest.