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      Developing and Validating a Primary Care EMR-based Frailty Definition using Machine Learning

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          Abstract

          Introduction

          Individuals 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.

          Objective

          The 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.

          Methods

          This 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.

          Results

          Of 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.

          Conclusions

          The 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.

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          Most cited references 21

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          Conceptualizations of frailty in relation to older adults.

          The aim of this article is to discuss the concept of frailty and its adequacy in identifying and describing older adults as frail. Despite the dramatic increase in use of the term 'frailty' over the past two decades, there is a lack of consensus in the literature about its meaning and use, and no clear conceptual guidelines for identifying and describing older adults as frail. Differences in theoretical perspectives will influence policy decisions regarding eligibility for, and allocation of, scarce health care resources among older adults. The article presents a literature review and synthesis of definitions and conceptual models of frailty in relation to older adults. The first part of the paper is a summary of the synonyms, antonyms and definitions of the term frailty. The second part is a critical evaluation of conceptual models of frailty. Six conceptual models are analysed on the basis of four main categories of assumptions about: (1) the nature of scientific knowledge; (2) the level of analysis; (3) the ageing process; (4) the stability of frailty. The implications of these are discussed in relation to clinical practice, policy and research. The review gives guidelines for a new theoretical approach to the concept of frailty in older adults: (1) it must be a multidimensional concept that considers the complex interplay of physical, psychological, social and environmental factors; (2) the concept must not be age-related, suggesting a negative and stereotypical view of ageing; (3) the concept must take into account an individual's context and incorporate subjective perceptions; (4) the concept must take into account the contribution of both individual and environmental factors.
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            Frailty in elderly people: an evolving concept.

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              Validating the 8 CPCSSN case definitions for chronic disease surveillance in a primary care database of electronic health records.

              The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy. Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated. We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism). The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases. © 2014 Annals of Family Medicine, Inc.
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                Author and article information

                Journal
                Int J Popul Data Sci
                Int J Popul Data Sci
                IJPDS
                International Journal of Population Data Science
                Swansea University
                2399-4908
                1 September 2020
                2020
                : 5
                : 1
                Affiliations
                [1 ] Department of Community Health Sciences, Cumming School of Medicine, University of Calgary
                [2 ] O’Brien Institute for Public Health and Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary
                [3 ] Centre for Health Informatics, Cumming School of Medicine, University of Calgary
                [4 ] Clinical Research Unit, Cumming School of Medicine, University of Calgary
                [5 ] Department of Family Medicine, Cumming School of Medicine, University of Calgary
                [6 ] Departments of Family Medicine and Community Health Sciences, Manitoba Centre for Health Policy, University of Manitoba
                [7 ] School of Nursing, University of British Columba
                [8 ] Centre for Health Services and Policy Research, University of British Columbia
                Author notes
                Corresponding author: Tyler Williamson PhD. tyler.williamson@ 123456ucalgary.ca

                Conflicts of Interest.: None to declare.

                Article
                5:1:32 S2399490820013440
                10.23889/ijpds.v5i1.1344
                7477778

                This work is licenced under a Creative Commons Attribution 4.0 International License.

                Categories
                Population Data Science

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