Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records.
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ABSTRACT:
SUBMITTER: Bellows BK
PROVIDER: S-EPMC3957394 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
Publications
Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records.
Bellows Brandon K BK LaFleur Joanne J Kamauu Aaron W C AW Ginter Thomas T Forbush Tyler B TB Agbor Stephen S Supina Dylan D Hodgkins Paul P DuVall Scott L SL
Journal of the American Medical Informatics Association : JAMIA 20131107 e1
Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified ...[more]
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Project description:Binge eating disorder (BED) is characterized by regular binge eating episodes during which individuals ingest comparably large amounts of food and experience loss of control over their eating behaviour. The worldwide prevalence of BED for the years 2018-2020 is estimated to be 0.6-1.8% in adult women and 0.3-0.7% in adult men. BED is commonly associated with obesity and with somatic and mental health comorbidities. People with BED experience considerable burden and impairments in quality of life, and, at the same time, BED often goes undetected and untreated. The aetiology of BED is complex, including genetic and environmental factors as well as neuroendocrinological and neurobiological contributions. Neurobiological findings highlight impairments in reward processing, inhibitory control and emotion regulation in people with BED, and these neurobiological domains are targets for emerging treatment approaches. Psychotherapy is the first-line treatment for BED. Recognition and research on BED has increased since its inclusion into DSM-5; however, continuing efforts are needed to understand underlying mechanisms of BED and to improve prevention and treatment outcomes for this disorder. These efforts should also include screening, identification and implementation of evidence-based interventions in routine clinical practice settings such as primary care and mental health outpatient clinics.
| S-EPMC9793802 | biostudies-literature
Project description:Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p?=?0.015), 0.09 (p?=?0.064), 0.13 (p?=?0.003), 0.00 (p?=?0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p?=?0.004). These h2g were lower or similar to the h2g observed by the ICCBD?+?PGCBD (0.23, p?=?3.17E-80, total N?=?33,181). However, the rg between ICCBD?+?PGCBD and the EHR-based cases were high for 95-NLP (0.66, p?=?3.69?×?10-5), coded-strict (1.00, p?=?2.40?×?10-4), and coded-broad (0.74, p?=?8.11?×?10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
| S-EPMC5904248 | biostudies-literature
Project description:ObjectiveLarge electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data.Materials and methodsUsing deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors.ResultsOf 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors.Discussion and conclusionThis automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.
| S-EPMC7651915 | biostudies-literature
Project description:ObjectiveAlthough several effective behavioral treatments for binge-eating disorder (BED) exist, there are racial disparities in treatment access, with African-Americans and/or Black individuals having some of the lowest rates of access to care. Little is known about the experience and treatment of binge eating (BE) and BED among Black women.MethodThis systematic review, conducted according to PRISMA guidelines, synthesizes information related to BE and BED in Black women.ResultsA total of N = 38 studies met our eligibility criteria. We did not identify any systematic risk of bias across studies. The majority of included studies used cross-sectional survey methodology, and relied on interview (EDE) and self-report measures (particularly the Binge Eating Scale, BES) for the assessment of BE. Outcomes were inconsistently measured across trials, and there are limited data on the results of evidence-based treatments for BE/BED in Black women.DiscussionAlthough Black women have similar or higher rates of BE than White women, most research on BE and BED has focused on White women, with Black individuals underrepresented in clinical trials. Future research should examine evidence-based treatments to prevent and treat BED in this population.ObjetivoAunque existen varios tratamientos conductuales que son efectivos para el Trastorno de Atracones (BED, por sus siglas en inglés), existen disparidades raciales en el acceso a tratamiento, con individuos Afroamericanos y/o personas de color teniendo algunas de las tasas más bajas de acceso al cuidado de la salud. Se sabe muy poco acerca de la experiencia y tratamiento del comer en atracones (BE, por sus siglas en inglés) y BED entre mujeres afroamericanas y/o de color. MÉTODO: Esta revisión sistemática, realizada bajo lineamientos de las guías PRISMA, sintetiza información relacionada con BE y BED en mujeres afroamericanas y/o de color.ResultadosUn total de N = 38 estudios cumplieron con nuestros criterios de elegibilidad. No identificamos ningún riesgo sistemático de sesgo entre los estudios. La mayoría de los estudios incluidos utilizaron una metodología de encuesta transversal y se basaron en la entrevista (EDE) y las medidas de autoinforme (en particular, la Binge Eating Scale, BES) para la evaluación de BE. Los resultados se midieron de manera inconsistente entre los ensayos, y hay datos limitados sobre los resultados de los tratamientos basados en la evidencia para BE/BED en mujeres afroamericanas y/o de color. DISCUSIÓN: Aunque las mujeres afroamericanas y/o de color tienen tasas similares o más altas de BE que las mujeres blancas, la mayoría de las investigaciones sobre BE y BED se han centrado en las mujeres blancas, con individuos afroamericanos y/o de color subrepresentados en ensayos clínicos. La investigación futura debería examinar los tratamientos basados en la evidencia para prevenir y tratar el BED en esta población.
| S-EPMC8010989 | biostudies-literature
Project description:Adoption of electronic health records (EHRs) has increased dramatically since the 2009 implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act. The latest data from the Centers for Disease Control and Prevention (CDC) indicate that the majority of U.S. hospitals and nearly half of U.S. health care professionals have implemented an EHR with advanced functionality.(1) The goals of the HITECH act were not only to incentivize the adoption of EHRs, but also to increase the quality, safety, and efficiency of health care by promoting the concept of "meaningful use."(2,3) The stepwise implementation of "meaningful use" is now entering the latter stages with a focus on improving patient outcomes.(4).
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Project description:Interpersonal psychotherapy (IPT) is an effective specialty treatment for binge eating disorder (BED). Behavioral weight loss treatment (BWL) and guided self-help based on cognitive behavior therapy (CBTgsh) have both resulted in short-term reductions in binge eating in obese patients with BED.To test whether patients with BED require specialty therapy beyond BWL and whether IPT is more effective than either BWL or CBTgsh in patients with a high negative affect during a 2-year follow-up.Randomized, active control efficacy trial.University outpatient clinics.Two hundred five women and men with a body mass index between 27 and 45 who met DSM-IV criteria for BED. Intervention Twenty sessions of IPT or BWL or 10 sessions of CBTgsh during 6 months.Binge eating assessed by the Eating Disorder Examination.At 2-year follow-up, both IPT and CBTgsh resulted in greater remission from binge eating than BWL (P < .05; odds ratios: BWL vs CBTgsh, 2.3; BWL vs IPT, 2.6; and CBTgsh vs IPT, 1.2). Self-esteem (P < .05) and global Eating Disorder Examination (P < .05) scores were moderators of treatment outcome. The odds ratios for low and high global Eating Disorder Examination scores were 2.8 for BWL, 2.9 for CBTgsh, and 0.73 for IPT; for self-esteem, they were 2.4 for BWL, 1.9 for CBTgsh, and 0.9 for IPT.Interpersonal psychotherapy and CBTgsh are significantly more effective than BWL in eliminating binge eating after 2 years. Guided self-help based on cognitive behavior therapy is a first-line treatment option for most patients with BED, with IPT (or full cognitive behavior therapy) used for patients with low self-esteem and high eating disorder psychopathology.clinicaltrials.gov Identifier: NCT00060762.
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Project description:This report examined baseline affective response to binge eating as a predictor of binge-eating disorder (BED) treatment outcome. Baseline affective response was defined as: (1) each individual's average net change (i.e., area under the curve [AUC]) of positive affect (PA) or negative affect (NA) before and after binge-eating episodes and (2) post-binge eating slope of PA or NA across seven-days of ecological momentary assessment (EMA). Adults with BED completed Integrative Cognitive-Affective Therapy (ICAT-BED) or cognitive behavioral therapy guided self-help (CBTgsh). Individuals with greater net increases in PA (AUC) following binge eating at baseline exhibited better treatment response in ICAT-BED at end-of-treatment and follow-up. NA affective response was only significant at end-of-treatment; individuals with less rapid post-binge improvements in NA (slope) did better in ICAT-BED, while individuals with lower net improvements in NA (AUC) did better in CBTgsh. Affective response to binge eating may be a marker of BED treatment response.
| S-EPMC8579466 | biostudies-literature
Project description:The definition of severe acute respiratory infection (SARI) - a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission - has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.
| S-EPMC5997502 | biostudies-literature
Project description:IntroductionResearch on binge eating disorder continues to evolve and advance our understanding of recurrent binge eating.MethodsThis mixed-methods, cross-sectional survey aimed to collect information from experts in the field about clinical aspects of adult binge eating disorder pathology. Fourteen experts in binge eating disorder research and clinical care were identified based on receipt of relevant federal funding, PubMed-indexed publications, active practice in the field, leadership in relevant societies, and/or clinical and popular press distinction. Anonymously recorded semi-structured interviews were analyzed by ≥2 investigators using reflexive thematic analysis and quantification.ResultsIdentified themes included: (1) obesity (100%); (2) intentional/voluntary or unintentional/involuntary food/eating restriction (100%); (3) negative affect, emotional dysregulation, and negative urgency (100%); (4) diagnostic heterogeneity and validity (71%); (5) paradigm shifts in understanding binge eating disorder (29%); and (6) research gaps/future directives (29%).DiscussionOverall, experts call for a better understanding of the relationship between binge eating disorder and obesity, including a need for clarification around the extent to which the two health issues are separate vs. related/overlapping. Experts also commonly endorse food/eating restriction and emotion dysregulation as important components of binge eating disorder pathology, which aligns with two common models of binge eating disorder conceptualization (e.g., dietary restraint theory and emotion/affect regulation theory). A few experts spontaneously identified several paradigm shifts in our understanding of who can have an eating disorder (beyond the anorexi-centric "thin, White, affluent, cis-gendered neurotypical female" stereotype), and the various factors that can drive binge eating. Experts also identified several areas where classification issues may warrant future research. Overall, these results highlight the continual advancement of the field to better understand adult binge eating disorder as an autonomous eating disorder diagnosis.
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Project description:The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.
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