Stratification of risk for hospital admissions for injury related to fall: cohort study

Abstract

Objective: To determine whether the ability to stratify an individual patient's hazard for falling could facilitate development of focused interventions aimed at reducing these adverse outcomes.

Design: Clinical and sociodemographic data from electronic health records were utilized to derive multiple logistic regression models of hospital readmissions for injuries related to falls. Drugs used at admission were summarized based on reported adverse effect frequencies in published drug labeling.

Setting: Two large academic medical centers in New England, United States.

Participants: The model was developed with 25,924 individuals age ≥ 40 with an initial hospital discharge. The resulting model was then tested in an independent set of 13,032 inpatients drawn from the same hospital and 36,588 individuals discharged from a second large hospital during the same period.

Main outcome measure: Hospital readmissions for injury related to falls.

Results: Among 25,924 discharged individuals, 680 (2.6%) were evaluated in the emergency department or admitted to hospital for a fall within 30 days of discharge, 1635 (6.3%) within 180 days of discharge, 2360 (9.1%) within one year, and 3465 (13.4%) within two years. Older age, female sex, white or African-American race, public insurance, greater number of drugs taken on discharge, and score for burden of adverse effects were each independently associated with hazard for fall. For drug burden, presence of a drug with a frequency of adverse effects related to fall of 10% was associated with 3.5% increase in odds of falling over the next two years (odds ratio 1.04, 95% confidence interval 1.02 to 1.05). In an independent testing set, the area under the receiver operating characteristics curve was 0.65 for a fall within two years based on cross sectional data and 0.72 with the addition of prior utilization data including age adjusted Charlson comorbidity index. Portability was promising, with area under the curve of 0.71 for the longitudinal model in a second hospital system.

Conclusions: It is potentially useful to stratify risk of falls based on clinical features available as artifacts of routine clinical care. A web based tool can be used to calculate and visualize risk associated with drug treatment to facilitate further investigation and application.

Fig 1 Process of generation of model to assess risk of falls in patients discharged from hospital

Fig 2 Calibration of models for prediction of falls within two years in patients discharged from hospital

Fig 3 Kaplan-Meier survival curves for time to readmission for fall, by fifths, in testing cohort

Fig 4 Illustration of risk visualization tool for risk of falls