Developing and validating a primary care EMR-based frailty definition using machine learning

Williamson, Tyler; Aponte-Hao, Sylvia; Mele, Bria; Lethebe, Brendan Cord; Leduc, Charles; Thandi, Manpreet; Katz, Alan; Wong, Sabrina T.

doi:10.23889/ijpds.v5i1.1344

Cited by 15 publications

(14 citation statements)

References 32 publications

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“…The same XGBoost model achieved a sensitivity of 40.50% and a specificity of 93.97% using a decision threshold of 0.5. We can compare these results with what was achieved previously by Williamson et al, where the CPCSSN EMR data used were only from Alberta and decision threshold used was 0.5 [13]. We can see that by using more machine learning models and a larger dataset, sensitivity was able to improve from 28% to 40.50%, and specificity did not suffer a loss with both at 94%.…”

Section: Discussionsupporting

confidence: 67%

“…Data were gathered in two stages, with the initial data collection being restricted to Alberta only. Data from Alberta were gathered in 2015 for a previous study that focused on frailty identification [ 13 ], while the other provincial sites gathered data in 2019. A total of 5,466 patients were rated by 90 physicians in total across the five regional CPCSSN sites located across five Canadian provinces, with each patient receiving one CFS rating by their physician only.…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning has successfully been used in the creation of case definitions for other diseases such as hypertension and osteoarthritis in primary care that are being used for practice reporting, quality improvement, public health surveillance, and research [ 12 ]. Previous work using supervised machine learning for the identification of frailty in EMR data by Williamson et al used data from Alberta, Canada [ 13 ]. This study defined frailty using the Clinical Frailty Scale [ 14 ] showed fair performance, achieving a sensitivity of 0.28 and a specificity of 0.94.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for identification of frailty in Canadian primary care practices

Aponte-Hao

Wong

Thandi

et al. 2021

IJPDS

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IntroductionFrailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance. ObjectivesThe objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning. MethodsPhysicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n = 5,466), collected from 2015-2019. Frailty levels were dichotomized using a cut-off of 5. Extracted features included previously prescribed medications, billing codes, and other routinely collected primary care data. We used eight supervised machine learning algorithms, with performance assessed using a hold-out test set. A balanced training dataset was also created by oversampling. Sensitivity analyses considered two alternative dichotomization cut-offs. Model performance was evaluated using area under the receiver-operating characteristic curve, F1, accuracy, sensitivity, specificity, negative predictive value and positive predictive value. ResultsThe prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5. ConclusionSupervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.

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Section: Discussionsupporting

confidence: 67%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning for identification of frailty in Canadian primary care practices

Aponte-Hao

Wong

Thandi

et al. 2021

IJPDS

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“…AI is also credited with improving the predictive performance of a modified frailty index based on Hong Kong hospital data ( 79 ). However, in the primary care setting, Williamson et al used data from the Canadian Primary Care Sentinel Surveillance Network ( n = 875 adults aged over 65 years) to develop the EHR-based frailty definition by machine learning methods and showed that it had the poor predictive ability (sensitivity 28%, specificity 94%) compared with the CFS ( 80 ). However, another study based on the same database with a larger sample improved the predictive ability of this tool by using the XGBoost model (stands for eXtreme Gradient Boosting, is a gradient-boosted decision tree machine learning algorithm) and changing the decision threshold (sensitivity 78.14%, specificity 74.41%) ( 81 ).…”

Section: New Trendsmentioning

confidence: 99%

Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions

Luo

Liao

Zou

et al. 2022

Front. Public Health

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With the rapidly aging population, frailty, characterized by an increased risk of adverse outcomes, has become a major public health problem globally. Several frailty guidelines or consensuses recommend screening for frailty, especially in primary care settings. However, most of the frailty assessment tools are based on questionnaires or physical examinations, adding to the clinical workload, which is the major obstacle to converting frailty research into clinical practice. Medical data naturally generated by routine clinical work containing frailty indicators are stored in electronic health records (EHRs) (also called electronic health record (EHR) data), which provide resources and possibilities for frailty assessment. We reviewed several frailty assessment tools based on primary care EHRs and summarized the features and novel usage of these tools, as well as challenges and trends. Further research is needed to develop and validate frailty assessment tools based on EHRs in primary care in other parts of the world.

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“…The research team developed and validated a case definition for frailty screening using the Southern Alberta CPCSSN node, described in detail elsewhere [29]. Briefly, 52 family physicians applied the Canadian Study of Health and Aging Clinical Frailty Scale [9] to randomly selected charts of their own patients in order to form a reference set (n=875; n=150 considered frail).…”

Section: Frailty Case Definition -Emrmentioning

confidence: 99%

Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?

Wong¹,

Katz²,

Williamson³

et al. 2020

IJPDS

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Introduction. Frailty is a complex condition that affects many aspects of a patients’ wellbeing and health outcomes. Objectives. We used available Electronic Medical Record (EMR) and administrative data to determine definitions of frailty. We also examined whether there were differences in demographics or health conditions among those identified as frail in either the EMR or administrative data. Methods. EMR and administrative data were linked in British Columbia (BC) and Manitoba (MB) to identify those aged 65 years and older who were frail. The EMR data were obtained from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) and the administrative data (e.g. billing, hospitalizations) was obtained from Population Data BC and the Manitoba Population Research Data Repository. Sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare are described for those identified as frail. Results. Sociodemographic and utilization differences were found among those identified as frail from the EMR compared to those in the administrative data. Among those who were >65 years, who had a record in both EMR and administrative data, 5%-8% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC and MB, those identified as frail in both data sources have approximately twice the number of contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.2 vs. n=1.9 in BC; n=9.8 vs. n=2.8 in MB) compared to those who are not frail; 27% (BC) and 14% (MB) of those identified as frail in 2014 died in 2015. Conclusions/Implications. Identifying frailty using EMR data is particularly challenging since many functional deficits are not routinely recorded in structured data fields. Our results suggest frailty can be captured along a continuum using both EMR and administrative data.

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Developing and validating a primary care EMR-based frailty definition using machine learning

Cited by 15 publications

References 32 publications

Machine learning for identification of frailty in Canadian primary care practices

Machine learning for identification of frailty in Canadian primary care practices

Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions

Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?

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