FALLPREDICT: Predicting Fall-Related Health Care Encounters in Persons With Vascular Lower Limb Amputation Using...

To develop a novel preliminary prediction model (FALLPREDICT) that predicts the risk of falls in patients who have undergone a lower limb amputation (LLA) during the 12 months following their first definitive lower limb prosthesis (LLP) prescription. The model is designed for patients who have undergone an initial transtibial (TT) or transfemoral (TF) amputation due to sequelae of diabetes and/or peripheral artery disease. The overall goal of this research is to identify individuals at relatively increased risk of falls to help inform targeted fall prevention strategies.

Retrospective cohort study of patients who underwent an incident dysvascular LLA at the TT or TF level who received a definitive LLP (considered time zero for the model). Patients were tracked 12 months forward to identify incident falls.

The VA Corporate Data Warehouse and the National Prosthetics Patient Database.

A total of 1690 Veterans who underwent an initial TT or TF amputation because of diabetes and/or peripheral artery disease and received a qualifying LLP between March 1, 2018, and November 30, 2020.

Not applicable MAIN OUTCOME MEASURE: A health care encounter that documented a fall code. Incident falls were designated as any record of an ICD-10 external cause code for falls (range, W00-W19; eg, W01.0XXA) identified after the prosthesis prescription date (time zero).

Variable selection, using backward stepwise logistic regression, led to a final model of 9 predictors (amputation level, age, body mass index, a fall before amputation, a fall after amputation prior to prosthesis prescription, depression, peripheral neuropathy, kidney dialysis, and chronic obstructive pulmonary disease) that demonstrated good discrimination (c-statistic 0.71) and satisfactory calibration across predicted risk groups, supporting its ability to provide meaningful risk stratification and reliable absolute risk estimates.

The FALLPREDICT model can be used to identify patients at high risk of a future fall, helping guide targeted fall-intervention strategies.