Dataset Information

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

ABSTRACT: Objective: Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. Methods: A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. Results: The inter-observer reliability between the physician reviewers had a ?-coefficient of 0.95 (95% CI 0.93-0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. Conclusions: The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.

SUBMITTER: Afshar M

PROVIDER: S-EPMC6203662 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

Afshar Majid M Press Valerie G VG Robison Rachel G RG Kho Abel N AN Bandi Sindhura S Biswas Ashvini A Avila Pedro C PC Kumar Harsha Vardhan Madan HVM Yu Byung B Naureckas Edward T ET Nyenhuis Sharmilee M SM Codispoti Christopher D CD

The Journal of asthma : official journal of the Association for the Care of Asthma 20171110 9

<i>Objective</i>: Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and a ...[more]

PMID: 29027824

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Project description:ObjectivesAsthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)-but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma.MethodsWe compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (n = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations.ResultsAs applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4-97.2%), sensitivity = 85.7% (95% CI: 83.9-87.5%), and harmonized F1 = 90.4% (95% CI: 89.2-91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5-85.7%), sensitivity = 69.4% (95% CI: 66.3-72.5%), and F1 = 75.4% (95% CI: 73.1-77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation.ConclusionThe performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.

Project description:BackgroundWith higher adoption of electronic health records at health-care centers, electronic search algorithms (computable phenotype) for identifying acute decompensated heart failure (ADHF) among hospitalized patients can be an invaluable tool to enhance data abstraction accuracy and efficacy in order to improve clinical research accrual and patient centered outcomes. We aimed to derive and validate a computable phenotype for ADHF in hospitalized patients.MethodsWe screened 256, 443 eligible (age > 18 years and with prior research authorization) individuals who were admitted to Mayo Clinic Hospital in Rochester, MN, from January 1, 2006, through December 31, 2014. Using a randomly selected derivation cohort of 938 patients, several iterations of a free-text electronic search were developed and refined. The computable phenotype was subsequently validated in an independent cohort 100 patients. The sensitivity and specificity of the computable phenotype were compared to the gold standard (expert review of charts) and International Classification of Diseases-9 (ICD-9) codes for Acute Heart Failure.ResultsIn the derivation cohort, the computable phenotype achieved a sensitivity of 97.5%, and specificity of 100%, whereas ICD-9 codes for Acute Heart Failure achieved a sensitivity of 47.5% and specificity of 96.7%. When all Heart Failure codes (ICD-9) were used, sensitivity and specificity were 97.5 and 86.6%, respectively. In the validation cohort, the sensitivity and specificity of the computable phenotype were 100 and 98.5%. The sensitivity and specificity for the ICD-9 codes (Acute Heart Failure) were 42 and 98.5%. Upon use of all Heart Failure codes (ICD-9), sensitivity and specificity were 96.8 and 91.3%.ConclusionsOur results suggest that using computable phenotype to ascertain ADHF from the clinical notes contained within the electronic medical record are feasible and reliable. Our computable phenotype outperformed ICD-9 codes for the detection of ADHF.

Project description:BackgroundElectronic health record (EHR)-based computable phenotype algorithms allow researchers to efficiently identify a large virtual cohort of Human Immunodeficiency Virus (HIV) patients. Built upon existing algorithms, we refined, improved, and validated an HIV phenotype algorithm using data from the OneFlorida Data Trust, a repository of linked claims data and EHRs from its clinical partners, which provide care to over 15 million patients across all 67 counties in Florida.MethodsOur computable phenotype examined information from multiple EHR domains, including clinical encounters with diagnoses, prescription medications, and laboratory tests. To identify an HIV case, the algorithm requires the patient to have at least one diagnostic code for HIV and meet one of the following criteria: have 1+ positive HIV laboratory, have been prescribed with HIV medications, or have 3+ visits with HIV diagnostic codes. The computable phenotype was validated against a subset of clinical notes.ResultsAmong the 15+ million patients from OneFlorida, we identified 61,313 patients with confirmed HIV diagnosis. Among them, 8.05% met all four inclusion criteria, 69.7% met the 3+ HIV encounters criteria in addition to having HIV diagnostic code, and 8.1% met all criteria except for having positive laboratories. Our algorithm achieved higher sensitivity (98.9%) and comparable specificity (97.6%) relative to existing algorithms (77-83% sensitivity, 86-100% specificity). The mean age of the sample was 42.7 years, 58% male, and about half were Black African American. Patients' average follow-up period (the time between the first and last encounter in the EHRs) was approximately 4.6 years. The median number of all encounters and HIV-related encounters were 79 and 21, respectively.ConclusionBy leveraging EHR data from multiple clinical partners and domains, with a considerably diverse population, our algorithm allows more flexible criteria for identifying patients with incomplete laboratory test results and medication prescribing history compared with prior studies.

Project description:BackgroundQuality assessment in pediatric care has recently gained momentum. Although many of the approaches to indicator development are similar regardless of the population of interest, few nationwide sets of indicators specifically designed for assessment of primary care of children exist. We performed an empirical analysis of the validity of "Pediatric Asthma Hospitalization Rate" indicator under the assumption that lower admission rates are associated with better performance of primary health care.MethodsThe validity of "Pediatric Asthma Hospitalization Rate" indicator proposed by the Agency for Healthcare Research and Quality in the Italian context was investigated with a focus on selection of diagnostic codes, hospitalization type, and risk adjustment. Seasonality and regional variability of hospitalization rates for asthma were analyzed for Italian children aged 2-17 years discharged between January 1, 2009, and December 31, 2011 using the hospital discharge records database. Specific rates were computed for age classes: 2-4, 5-9, 10-14, 15-17 years.ResultsIn the years 2009-2011 the number of pediatric hospitalizations for asthma was 14,389 (average annual rate: 0.52 per 1,000) with a large variability across regions. In children aged 2-4 years, the risk of hospitalization for asthma was 14 times higher than in adolescents, then it dropped to 4 in 5- to 9-year-olds and to 1.1 in 10- to 14-year-olds. The inclusion of diagnoses of bronchitis revealed that asthma and bronchitis are equally represented as causes of hospital admissions and have a similar seasonality in preschool children, while older age groups experience hospital admissions mainly in spring and fall, this pattern being consistent with a diagnosis of atopic asthma. Rates of day hospital admissions for asthma were up to 5 times higher than the national average in Liguria and some Southern regions, and close to zero in some Northern regions.ConclusionsThe patterns of hospitalization for pediatric asthma in Italy showed that at least two different indicators are needed to measure accurately the quality of care provided to children. The candidate indicators should also include day hospital admissions to better assess accessibility. Future evaluation by a structured clinical panel review at the national level might be helpful to refine indicator definitions and risk groupings, to determine appropriate application for such measures, and to make recommendations to policy makers.

Dataset Information

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

Publications

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

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