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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
Age and Ageing, Volume: 53, Issue: 3
Swansea University Authors:
Ashley Akbari , Joe Hollinghurst, Ronan Lyons
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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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ISSN: | 0002-0729 1468-2834 |
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Oxford University Press (OUP)
2024
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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> |
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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) |
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Journal article |
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Age and Ageing |
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53 |
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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 |
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|
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
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Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science |
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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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1796411620773068800 |
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11.021648 |