Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data

• Published in: BMJ open Vol. 13; no. 8; p. e071321
• Main Authors: Sharma, Vishal, Kulkarni, Vinaykumar, Joon, Tanya, Eurich, Dean T, Simpson, Scot H, Voaklander, Don, Wright, Bruce, Samanani, Salim
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group LTD 22.08.2023; BMJ Publishing Group
• Series: Original research
• Subjects: Algorithms; Ambulatory care; Cost control; Emergency medical care; Epidemiology; Falls; Health services; Hospitals; Injury prevention; Machine learning; Older people; Population; Prevention; Public health; Alberta Canada; Canada
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2022-071321

Objective To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions. Design This prognostic study used population-level administrative health data to develop an ML prediction model. Setting This study took place in Alberta, Canada during 2018–2019. Participants Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded. Exposure Unit of analysis was the individual person. Main outcomes/measures We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data. Results Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%–30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings. Conclusion ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.