Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

Abstract

Background: Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs.

Objectives and methods: In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy.

Results: The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy-parsimony analysis revealed that tools with a small number of predictors (~1-5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20-30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20-30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

Fig 1. 10-fold cross validation scheme.

Illustrative scheme of the 10-fold cross validation procedure. During the 10 iterations each sample receives a probabilistic prediction. All these predictions are then used to assess the Lasso model.

Fig 2. ROC curves.

ROC curves of the Lasso model and the other risk indicators (number of previous falls, gait speed, SPPB, and FRAT-up) when predicting for single (left panel) and multiple (right panel) falls.

Fig 3. Representative distributions of the Lasso model.

Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5^th, 10^th, 90^th, and 97.5^th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).

Fig 4. Assessment of Lasso model calibration.

(A) Reliability diagram: observed vs predicted fall rate, obtained from grouping samples according to deciles on the risk score; error bars indicate 95% confidence intervals. (B) Marginal calibration plot: observed and predicted number of samples vs number of falls. (C) Histogram of the probability integral transform.

Fig 5. Accuracy-parsimony analysis.

Performance of the model when constraining the maximum number of variables to be included in the model. Left panel, left axis: AUC for single falls (black empty circles), AUC for multiple falls (black filled circles). Right panel, left axis: MSE (black filled circles). Both panels, right axes: mean number of variables that were actually included in the models (blue circles).