ArticleJournal of the American Heart Association2018
Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.
Article in Journal of the American Heart Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.Journal of the American Heart Association · 2024Trial
- Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.Circulation. Genomic and precision medicine · 2021Trial
- Associations of CMR-based pulse wave velocity and aortic distensibility with infrarenal ultrasound-based aortic parameters in the Hamburg City Health Study.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Learning From the Adoption of a Readmission Clinical Decision Support Tool: Group Model Building Approach.JMIR human factors · 2026Article
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- Holodiastolic Flow Reversal in the Descending Aorta: A Marker of Concomitant Acute Myocarditis and Aortitis in Children-A Case Series.Pediatric cardiology · 2026Article
- Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.International journal of molecular sciences · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment.Journal of the American Heart Association · 2023Article
- Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research.Journal of cardiovascular translational research · 2023Review
- Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review.Ethnicity & disease · 2023Review
- Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery.JAMA surgery · 2022Article
- Artificial Intelligence for Education, Proctoring, and Credentialing in Cardiovascular Medicine.Texas Heart Institute journal · 2022Review
- Usability of a Digital Registry to Promote Secondary Prevention for Peripheral Artery Disease Patients.Mayo Clinic proceedings. Innovations, quality & outcomes · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges.Frontiers in bioengineering and biotechnology · 2020Article
- Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.Circulation. Cardiovascular quality and outcomes · 2019Article
- Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.Journal of the American Heart Association · 2018Article
- Clinical Decision Support for Peripheral Artery Disease: Answering the Call.Journal of the American Heart Association · 2018Article
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17 authors.
Funding
Abstract
Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P<0.0001 versus the reference subgroup). An equation for risk calculation was derived from Cox model parameters and β estimates. Big data infrastructure enabled deployment of the real-time risk calculator to the point of care via the EHR . Conclusions This study demonstrates that electronic tools can be deployed to EHR s to create automated real-time risk calculators to predict survival of patients with peripheral arterial disease. Moreover, the prognostic model developed may be translated to patient care as an automated and individualized real-time risk calculator deployed at the point of care.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.