ArticleInternational journal of population data science2020
Developing and Validating a Primary Care EMR-based Frailty Definition using Machine Learning.
Article in International journal of population data science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Pooled it
- Use of Conventional Artificial Intelligence Methods in the Identification of Frailty: A Scoping Review.Journal of the American Geriatrics Society · 2026Article
- Use of machine learning for chronic disease case identification in primary care settings: A scoping review of methods, validation, and clinical relevance.The Journal of international medical research · 2026Article
- External validation of an AI-based preoperative frailty index using real-world data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Capturing factors associated with frailty using routinely collected electronic medical record data in British Columbia, Canada, primary care settings.Primary health care research & development · 2025Article
- Article
- The application of machine learning for identifying frailty in older patients during hospital admission.BMC medical informatics and decision making · 2024Article
- Article
- Perspectives on the representation of frailty in the electronic frailty index.BMC primary care · 2024Article
- Using electronic consultation (eConsult) to identify frailty in provider-to-provider communication: a feasibility and validation study.BMC geriatrics · 2023Review
- Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty.Frontiers in public health · 2023Observational
- Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions.Frontiers in public health · 2022Review
- Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty.BMC family practice · 2021Article
- Machine learning for identification of frailty in Canadian primary care practices.International journal of population data science · 2021Article
- Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?International journal of population data science · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIndividuals who have been identified as frail have an increased state of vulnerability, often leading to adverse health events, increased health spending, and potentially detrimental outcomes.
objectiveThe objective of this work is to develop and validate a case definition for frailty that can be used in a primary care electronic medical record database.
methodsThis is a cross-sectional validation study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Southern Alberta. 52 CPCSSN sentinels assessed a random sample of their own patients using the Rockwood Clinical Frailty scale, resulting in a total of 875 patients to be used as reference standard. Patients must be over the age of 65 and have had a clinic visit within the last 24 months. The case definition for frailty was developed using machine learning methods using CPCSSN records for the 875 patients.
resultsOf the 875 patients, 155 (17.7%) were frail and 720 (84.2%) were not frail. Validation metrics of the case definition were: sensitivity and specificity of 0.28, 95% CI (0.21 to 0.36) and 0.94, 95% CI (0.93 to 0.96), respectively; PPV and NPV of 0.53, 95% CI (0.42 to 0.64) and 0.86, 95% CI (0.83 to 0.88), respectively.
conclusionsThe low sensitivity and specificity results could be because frailty as a construct remains under-developed and relatively poorly understood due to its complex nature. These results contribute to the literature by demonstrating that case definitions for frailty require expert consensus and potentially more sophisticated algorithms to be successful.
Identifiers
What OpenQuestion holds
Registered trials
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.