OBJECTIVE: To develop and internally validate a falls prediction tool for people being discharged from inpatient aged care rehabilitation. STUDY DESIGN AND SETTING: Prospective cohort study. Possible predictors of falls were collected for 442 aged care rehabilitation inpatients at two hospitals. RESULTS: One hundred fifty participants fell in the 3 months after discharge from rehabilitation (34% of 438 with follow-up data). Predictors of falls were male gender (odds ratio [OR] 2.32, 95% confidence interval [CI]=1.00-4.03), central nervous system medication prescription (OR 2.04, 95% CI=1.00-3.30), and increased postural sway (OR 1.93, 95% CI=1.00-3.26). This three-variable model was adapted for clinical use by unit weighting (i.e., a score of 1 for each predictor present). The area under the receiver operating characteristic curve (AUC) for this tool was 0.69 (95% CI=0.64-0.74, bootstrap-corrected AUC=0.69). There was no evidence of lack of fit between prediction and observation (Hosmer-Lemeshow P=0.158). CONCLUSION: After external validation, this simple tool could be used to quantify the probability with which an individual will fall in the 3 months after an aged care rehabilitation stay. It may assist in the discharge process by identifying high-risk individuals who may benefit from ongoing assistance or intervention.
OBJECTIVE: To develop and internally validate a falls prediction tool for people being discharged from inpatient aged care rehabilitation. STUDY DESIGN AND SETTING: Prospective cohort study. Possible predictors of falls were collected for 442 aged care rehabilitation inpatients at two hospitals. RESULTS: One hundred fifty participants fell in the 3 months after discharge from rehabilitation (34% of 438 with follow-up data). Predictors of falls were male gender (odds ratio [OR] 2.32, 95% confidence interval [CI]=1.00-4.03), central nervous system medication prescription (OR 2.04, 95% CI=1.00-3.30), and increased postural sway (OR 1.93, 95% CI=1.00-3.26). This three-variable model was adapted for clinical use by unit weighting (i.e., a score of 1 for each predictor present). The area under the receiver operating characteristic curve (AUC) for this tool was 0.69 (95% CI=0.64-0.74, bootstrap-corrected AUC=0.69). There was no evidence of lack of fit between prediction and observation (Hosmer-Lemeshow P=0.158). CONCLUSION: After external validation, this simple tool could be used to quantify the probability with which an individual will fall in the 3 months after an aged care rehabilitation stay. It may assist in the discharge process by identifying high-risk individuals who may benefit from ongoing assistance or intervention.
Authors: Catherine Sherrington; Stephen R Lord; Constance M Vogler; Jacqueline C T Close; Kirsten Howard; Catherine M Dean; Gillian Z Heller; Lindy Clemson; Sandra D O'Rourke; Elisabeth Ramsay; Elizabeth Barraclough; Robert D Herbert; Robert G Cumming Journal: PLoS One Date: 2014-09-02 Impact factor: 3.240
Authors: Leani Souza Máximo Pereira; Catherine Sherrington; Manuela L Ferreira; Anne Tiedemann; Paulo H Ferreira; Fiona M Blyth; Jacqueline C T Close; Morag Taylor; Stephen R Lord Journal: Clin Interv Aging Date: 2014-02-05 Impact factor: 4.458
Authors: Deepa Sumukadas; Rosemary Price; Marion E T McMurdo; Petra Rauchhaus; Allan Struthers; Stephen McSwiggan; Graham Arnold; Rami Abboud; Miles Witham Journal: Age Ageing Date: 2018-01-01 Impact factor: 10.668