ArticleBMC medical informatics and decision making2020
Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure.Journal of healthcare engineering · 2021Trial
- Bronchodilator Response: Utility and implications in patients undergoing hematopoietic stem cell transplantation.Respiratory medicine · 2025Article
- Hemodynamic Effects of Guideline-Based Sedation in Mechanically Ventilated Adults: A Multicenter Observational Analysis.Critical care explorations · 2025Observational
- Acute Respiratory Distress Syndrome Phenotypes After Stem Cell Transplantation: A Latent Class Analysis.Critical care explorations · 2025Article
- Outcomes following catheter ablation for structural ventricular tachycardia stratified by left ventricular ejection fraction: A retrospective cohort study.Heart rhythm O2 · 2025Article
- Propofol, Triglycerides, and Acute Pancreatitis: A Multicenter Epidemiologic Analysis.Annals of the American Thoracic Society · 2025Observational
- Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information database network.Frontiers in cardiovascular medicine · 2025Article
- Temporal trends in critical care utilization and outcomes in allogeneic hematopoietic stem cell transplant recipients.Annals of hematology · 2024Article
- Development of a computable phenotype using electronic health records for venous thromboembolism in medical inpatients: the Medical Inpatient Thrombosis and Hemostasis study.Research and practice in thrombosis and haemostasis · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Septic shock definitions and associated outcomes in blood culture positive critically ill patients.Annals of translational medicine · 2023Article
- A multicenter program for electronic health record screening for patients with heart failure with preserved ejection fraction: Lessons from the DELIVER-EHR initiative.Contemporary clinical trials · 2022Review
- Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.Critical care explorations · 2021Article
- Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts.BMC medical informatics and decision making · 2021Article
- Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting.Applied clinical informatics · 2021Article
- Derivation and Validation of an Automated Search Strategy to Retrospectively Identify Acute Respiratory Distress Patients Per Berlin Definition.Frontiers in medicine · 2021Article
- Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.Methods of information in medicine · 2020Article
- Article
- Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure.Journal of the Society for Cardiovascular Angiography & InterventionsArticle
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6 authors.
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Abstract
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.
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