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Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults

Lucinda Archer, Samuel D Relton, Ashley Akbari Orcid Logo, Kate Best, Milica Bucknall, Simon Conroy, Miriam Hattle, Joe Hollinghurst, Sara Humphrey, Ronan Lyons Orcid Logo, Suzanne Richards, Kate Walters, Robert West, Danielle van der Windt, Richard D Riley, Andrew Clegg, (The eFI+ investigators)

Age and Ageing, Volume: 53, Issue: 3

Swansea University Authors: Ashley Akbari Orcid Logo, Joe Hollinghurst, Ronan Lyons Orcid Logo

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DOI (Published version): 10.1093/ageing/afae057

Abstract

BackgroundFalls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed...

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Published in: Age and Ageing
ISSN: 0002-0729 1468-2834
Published: Oxford University Press (OUP) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65929
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Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.MethodsData comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.ResultsThe model’s discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration.ConclusionThe eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.</abstract><type>Journal Article</type><journal>Age and Ageing</journal><volume>53</volume><journalNumber>3</journalNumber><paginationStart/><paginationEnd/><publisher>Oxford University Press (OUP)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0002-0729</issnPrint><issnElectronic>1468-2834</issnElectronic><keywords>falls, prediction model, prognosis, proactive, prevention, older people</keywords><publishedDay>22</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-03-22</publishedDate><doi>10.1093/ageing/afae057</doi><url/><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This research was funded by the NIHR Health Technology Assessment (HTA) programme (unique award identifier NIHR127905). L.A. and R.R. are supported by funding from the NIHR Birmingham Biomedical Research Centre (BRC) at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. A.C. is part-funded by the National Institute for Health Research Applied Research Collaboration Yorkshire &amp; Humber, the NIHR Leeds BRC and Health Data Research UK, an initiative funded by UK Research and Innovation Councils, NIHR and the UK devolved administrations and leading medical research charities. J.H., A.A. and R.A.L. were supported by Health and Care research Wales [Projects: SCG-19-1654, SCF-18-1504] and Health Data Research UK [HDR-9006], which receives its funding from HDR UK Ltd funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. A.A. and R.A.L. are also funded by the Economic and Social Research Council through Administrative Data Research UK (ES/S007393/1).</funders><projectreference/><lastEdited>2024-04-15T15:33:47.9356840</lastEdited><Created>2024-03-29T13:03:20.8294225</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Lucinda</firstname><surname>Archer</surname><order>1</order></author><author><firstname>Samuel D</firstname><surname>Relton</surname><order>2</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>3</order></author><author><firstname>Kate</firstname><surname>Best</surname><order>4</order></author><author><firstname>Milica</firstname><surname>Bucknall</surname><order>5</order></author><author><firstname>Simon</firstname><surname>Conroy</surname><order>6</order></author><author><firstname>Miriam</firstname><surname>Hattle</surname><order>7</order></author><author><firstname>Joe</firstname><surname>Hollinghurst</surname><order>8</order></author><author><firstname>Sara</firstname><surname>Humphrey</surname><order>9</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>10</order></author><author><firstname>Suzanne</firstname><surname>Richards</surname><order>11</order></author><author><firstname>Kate</firstname><surname>Walters</surname><order>12</order></author><author><firstname>Robert</firstname><surname>West</surname><order>13</order></author><author><firstname>Danielle van der</firstname><surname>Windt</surname><order>14</order></author><author><firstname>Richard D</firstname><surname>Riley</surname><order>15</order></author><author><firstname>Andrew</firstname><surname>Clegg</surname><order>16</order></author><author><firstname>(The eFI+</firstname><surname>investigators)</surname><order>17</order></author></authors><documents><document><filename>65929__29877__38305a9e8b9c49039ba25725ca65e2d2.pdf</filename><originalFilename>65929.pdf</originalFilename><uploaded>2024-04-03T10:48:57.5876023</uploaded><type>Output</type><contentLength>1128954</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This is an Open Access ar ticle distributed under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 65929 2024-03-29 Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false d7c51b69270b644a11b904629fe56ab0 Joe Hollinghurst Joe Hollinghurst true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2024-03-29 HDAT BackgroundFalls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.MethodsData comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.ResultsThe model’s discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration.ConclusionThe eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems. Journal Article Age and Ageing 53 3 Oxford University Press (OUP) 0002-0729 1468-2834 falls, prediction model, prognosis, proactive, prevention, older people 22 3 2024 2024-03-22 10.1093/ageing/afae057 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University Another institution paid the OA fee This research was funded by the NIHR Health Technology Assessment (HTA) programme (unique award identifier NIHR127905). L.A. and R.R. are supported by funding from the NIHR Birmingham Biomedical Research Centre (BRC) at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. A.C. is part-funded by the National Institute for Health Research Applied Research Collaboration Yorkshire & Humber, the NIHR Leeds BRC and Health Data Research UK, an initiative funded by UK Research and Innovation Councils, NIHR and the UK devolved administrations and leading medical research charities. J.H., A.A. and R.A.L. were supported by Health and Care research Wales [Projects: SCG-19-1654, SCF-18-1504] and Health Data Research UK [HDR-9006], which receives its funding from HDR UK Ltd funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. A.A. and R.A.L. are also funded by the Economic and Social Research Council through Administrative Data Research UK (ES/S007393/1). 2024-04-15T15:33:47.9356840 2024-03-29T13:03:20.8294225 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Lucinda Archer 1 Samuel D Relton 2 Ashley Akbari 0000-0003-0814-0801 3 Kate Best 4 Milica Bucknall 5 Simon Conroy 6 Miriam Hattle 7 Joe Hollinghurst 8 Sara Humphrey 9 Ronan Lyons 0000-0001-5225-000X 10 Suzanne Richards 11 Kate Walters 12 Robert West 13 Danielle van der Windt 14 Richard D Riley 15 Andrew Clegg 16 (The eFI+ investigators) 17 65929__29877__38305a9e8b9c49039ba25725ca65e2d2.pdf 65929.pdf 2024-04-03T10:48:57.5876023 Output 1128954 application/pdf Version of Record true This is an Open Access ar ticle distributed under the terms of the Creative Commons Attribution License. true eng https://creativecommons.org/licenses/by/4.0/
title Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
spellingShingle Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
Ashley Akbari
Joe Hollinghurst
Ronan Lyons
title_short Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
title_full Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
title_fullStr Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
title_full_unstemmed Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
title_sort Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults
author_id_str_mv aa1b025ec0243f708bb5eb0a93d6fb52
d7c51b69270b644a11b904629fe56ab0
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
d7c51b69270b644a11b904629fe56ab0_***_Joe Hollinghurst
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Ashley Akbari
Joe Hollinghurst
Ronan Lyons
author2 Lucinda Archer
Samuel D Relton
Ashley Akbari
Kate Best
Milica Bucknall
Simon Conroy
Miriam Hattle
Joe Hollinghurst
Sara Humphrey
Ronan Lyons
Suzanne Richards
Kate Walters
Robert West
Danielle van der Windt
Richard D Riley
Andrew Clegg
(The eFI+ investigators)
format Journal article
container_title Age and Ageing
container_volume 53
container_issue 3
publishDate 2024
institution Swansea University
issn 0002-0729
1468-2834
doi_str_mv 10.1093/ageing/afae057
publisher Oxford University Press (OUP)
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description BackgroundFalls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.MethodsData comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.ResultsThe model’s discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration.ConclusionThe eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.
published_date 2024-03-22T15:33:44Z
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score 11.014537

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