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

Palumbo P, Palmerini L, Bandinelli S, Chiari L - PLoS ONE (2015)

Bottom Line: Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi"-DEI, University of Bologna, Bologna, Italy.

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.

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Related in: MedlinePlus

Representative distributions of the Lasso model.Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).
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pone.0146247.g003: Representative distributions of the Lasso model.Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).

Mentions: Fig 3 shows an example of the output of the Lasso model for four representative samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score, compared with the distribution of the observed number of falls in the InCHIANTI dataset. The expected number of falls for the four selected samples is 0.21, 0.23, 0.66, 1.08, respectively. The fall rate in the InCHIANTI population (baseline data) is 0.42 falls/(person · year).


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

Palumbo P, Palmerini L, Bandinelli S, Chiari L - PLoS ONE (2015)

Representative distributions of the Lasso model.Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696849&req=5

pone.0146247.g003: Representative distributions of the Lasso model.Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).
Mentions: Fig 3 shows an example of the output of the Lasso model for four representative samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score, compared with the distribution of the observed number of falls in the InCHIANTI dataset. The expected number of falls for the four selected samples is 0.21, 0.23, 0.66, 1.08, respectively. The fall rate in the InCHIANTI population (baseline data) is 0.42 falls/(person · year).

Bottom Line: Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi"-DEI, University of Bologna, Bologna, Italy.

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.

Show MeSH
Related in: MedlinePlus