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; Press, Valerie G.; Robison, Rachel G.; Kho, Abel N.; Bandi, Sindhura; Biswas, Ashvini; Avila, Pedro C.; Kumar, Harsha; Yu, Byung; Naureckas, Edward T.; Nyenhuis, Sharmilee M.; Codispoti, Christopher D.

doi:10.1080/02770903.2017.1389952

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…Participating registries represented multiple purposes, patient populations, and care settings (Table I). [20][21][22][23][24][25][26][27][28][29][30][31][32] Five participating registries focus on severe asthma, whereas the others capture data on patients with mild, moderate, or severe asthma. Three participating registries enroll pediatric patients only, 5 enroll adults only, and 3 enroll adults and children.…”

Section: Resultsmentioning

confidence: 99%

Harmonized outcome measures for use in asthma patient registries and clinical practice

Gliklich

Castro

Leavy

et al. 2019

Journal of Allergy and Clinical Immunology

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Background: Asthma, a common chronic airway disorder, affects an estimated 25 million persons in the United States and 330 million persons worldwide. Although many asthma patient registries exist, the ability to link and compare data across registries is hindered by a lack of harmonization in the outcome measures collected by each registry. Objectives: The purpose of this project was to develop a minimum set of patient-and provider-relevant standardized outcome measures that could be collected in asthma patient registries and clinical practice. Methods: Asthma registries were identified through multiple sources and invited to join the workgroup and submit outcome measures. Additional measures were identified through literature searches and reviews of quality measures and consensus statements. Outcome measures were categorized by using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework. A minimum set of broadly relevant measures was identified. Measure definitions were harmonized through in-person and virtual meetings. Results: Forty-six outcome measures, including those identified from 13 registries, were curated and harmonized into a minimum set of 21 measures in the Outcome Measures Framework categories of survival, clinical response, events of interest, patient-reported outcomes, resource utilization, and experience of care. The harmonized definitions build on existing consensus statements and are appropriate for adult and pediatric patients. Conclusions: The harmonized measures represent a minimum set of outcomes that are relevant in asthma research and clinical practice. Routine and consistent collection of these measures in registries and other systems would support creation of a national research infrastructure to efficiently address new questions and improve patient management and outcomes. (J

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Section: Resultsmentioning

confidence: 99%

Harmonized outcome measures for use in asthma patient registries and clinical practice

Gliklich

Castro

Leavy

et al. 2019

Journal of Allergy and Clinical Immunology

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“…To date, there is no singular approach to asthma case identification from the EHR; previous efforts to do so demonstrate a range of findings, with PPVs from 27 to 100%. 12,23,[27][28][29][30][31][32][33][34][35] Computable phenotypes that rely on ICD codes are vulnerable due to dependence on correct diagnosis by a health care provider. Our findings demonstrate that the CAPriCORN and PheKB asthma computable phenotypes as applied to pediatric patients in the UCLA Health System had decreased performance.…”

Section: Discussionmentioning

confidence: 99%

“…The CAPriCORN algorithm was developed at the University of Chicago by Pacheco et al and later modified by Afshar et al with the goal of identifying asthma patient cohorts for research within the Patient-Centered Outcomes Research (PCORI) Clinical Data Research Network. 12,23 The computable phenotype was applied to a population aged 5 to 89 years. The PheKB algorithm was developed by Almoguera et al at CHOP within a study to identify genetic markers in asthma patients and the average age of participants was 11 years.…”

Section: Computable Phenotypesmentioning

confidence: 99%

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Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution

et al. 2020

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Objectives Asthma 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. Methods We 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. Results As 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. Conclusion The 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.

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“…The limitations of manual chart review have led to the emergence of phenotyping algorithms to automate the identification of patient cohorts from large data sets for a variety of conditions such as diabetes, heart disease, and asthma [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Phenotyping Algorithm to Identify People With HIV in Electronic Health Record Data (HIV-Phen): Development and Evaluation Study

May¹,

Giordano²,

Gottlieb³

2021

JMIR Form Res

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Background Identification of people with HIV from electronic health record (EHR) data is an essential first step in the study of important HIV outcomes, such as risk assessment. This task has been historically performed via manual chart review, but the increased availability of large clinical data sets has led to the emergence of phenotyping algorithms to automate this process. Existing algorithms for identifying people with HIV rely on a combination of International Classification of Disease codes and laboratory tests or closely mimic clinical testing guidelines for HIV diagnosis. However, we found that existing algorithms in the literature missed a significant proportion of people with HIV in our data. Objective The aim of this study is to develop and evaluate HIV-Phen, an updated criteria-based HIV phenotyping algorithm. Methods We developed an algorithm using HIV-specific laboratory tests and medications and compared it with previously published algorithms in national and local data sets to identify cohorts of people with HIV. Cohort demographics were compared with those reported in the national and local surveillance data. Chart reviews were performed on a subsample of patients from the local database to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the algorithm. Results Our new algorithm identified substantially more people with HIV in both national (up to an 85.75% increase) and local (up to an 83.20% increase) EHR databases than the previously published algorithms. The demographic characteristics of people with HIV identified using our algorithm were similar to those reported in national and local HIV surveillance data. Our algorithm demonstrated improved sensitivity over existing algorithms (98% vs 56%-92%) while maintaining a similar overall accuracy (96% vs 80%-96%). Conclusions We developed and evaluated an updated criteria-based phenotyping algorithm for identifying people with HIV in EHR data that demonstrates improved sensitivity over existing algorithms.

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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)

Cited by 14 publications

References 11 publications

Harmonized outcome measures for use in asthma patient registries and clinical practice

Harmonized outcome measures for use in asthma patient registries and clinical practice

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution

A Phenotyping Algorithm to Identify People With HIV in Electronic Health Record Data (HIV-Phen): Development and Evaluation Study

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