358 Development and validation of inpatient fall prediction models using digital hospital systems. (20th November 2022)

• Record Type: Journal Article
• Authors: Parsons, Rex; Cramb, Susanna; Abdel-Hafez, Ahmad; McPhail, Steven
• Is Part Of: Injury prevention. Volume 28(2022)Supplement 2
• Page Start: A53
• Page End: A53
• Publication Date: 2022-11-20
• Subjects: Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/injuryprev-2022-safety2022.159
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Abstract: Background: The recent digitisation and uptake of integrated electronic medical records (ieMR) within hospital systems has provided opportunities to improve the performance and usefulness of inpatient fall risk prediction models, including more frequently updated risk estimates for patients throughout their admission. Aims: We aimed to develop and internally validate a prediction model for inpatient falls using data obtained from the ieMR. Methods: We extracted data from the Princess Alexandra Hospital ieMR and associated fall events from RiskMan for all admissions in 2019 with a length of stay greater than 24 hours. Data extracted included ADL and cognitive assessments, vital signs, procedures, medications, hospital ward, and patient demographics. Internal validation was performed by cross validation. Predictions were made every 24 hours throughout the admission and the prediction target were falls that occurred within the 24 hours after the time of prediction. Three modelling approaches were assessed: random forest, LightGBM, and logistic regression. Results: There were 1, 160 falls that occurred during 34, 434 admissions (280, 898 patient-days). Fallers were older than non-fallers (mean ages of 65.4 and 58.8 years, respectively). Preliminary findings indicate the LightGBM model had the best discrimination with an area under the receiver operator characteristic curve of 0.631 (95% CI: [0.617 to 0.645]). Conclusion: This novel Australian study demonstrated that falls are hard to predict accurately, even with detailed data. Further refinements are planned. Learning Outcomes: This presentation will provide practical insights into the use of ieMR data for the development of contemporary fall prediction models.