Social vulnerability index enhances FRAX prediction of hip fractures in fall patients

The Fracture Risk Assessment Tool (FRAX), widely used for predicting the 10-year likelihood of hip fractures, does not incorporate factors like prior falls and sociodemographic characteristics, notably the Social Vulnerability Index (SVI). Recognizing these limitations, we aim to evaluate the predictive accuracy of FRAX by integrating fall frequency, fall energy, and SVI into the model for assessing the risk of fall-induced hip fractures. A retrospective case-control study was conducted, and patients aged ≥ 40 years with a documented diagnosis of a fall-induced hip fracture were age-matched with controls with a history of falls without an associated hip fracture. Basic demographic data, along with information about the number of prior falls and the energy of the current falls, were collected. The FRAX and SVI were calculated accordingly. Logistic regression analysis was employed to identify significant predictors. The performance of the models was evaluated and reported using appropriate metrics. Baseline characteristics of the dataset were presented as medians with interquartile ranges (IQR) or as percentages, where applicable. The significance of the identified variables was quantified using Odds Ratio (OR) along with their 95% Confidence Interval (CI). A p-value threshold of 0.05 was set for statistical significance. A total of 261 patients per group were included with a median age of 74 (IQR 67-80) and 72 (IQR 62-83) years. The FRAX score was significantly associated with the likelihood of experiencing a fall-induced hip fracture, as indicated by an OR of 1.06 (CI: 1.03-1.09). Participants with a one-time history of falls had an OR of 1.58 (CI: 1.02-2.37), compared to 1.84 (CI: 1.09-3.1) for those with multiple falls. The White participants, along with the Housing Type and Transportation domain of the SVI, also demonstrated to play a role (OR = 2.85 (CI: 1.56-5.2) and OR = 0.3 (CI: 0.12-0.8), respectively). This study underscored the significance of factors such as fall frequency, SVI, and race in predicting fall-induced hip fractures. It also highlighted the need for further refinement of the FRAX tool. We recommend that future research should be focused on validating the impact of these sociodemographic and fall characteristics on a broader scale, along with exploring the implications of clinical surrogates related to falls.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-04-27
DOI
https://doi.org/10.1038/s41598-025-99373-2
Citations
1
Primary Topic
Hip and Femur Fractures
Type
article
Field-Weighted Citation Impact
2.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Social vulnerability index enhances FRAX prediction of hip fractures in fall patients

Mitchel B. Harris, Santiago A. Lozano‐Calderón, Soheil Ashkani‐Esfahani, Job Nicolaas Doornberg et al.
1 citations
Scientific Reports
Hip and Femur Fractures
2.01
article

Social vulnerability index enhances FRAX prediction of hip fractures in fall patients

Mitchel B. Harris, Santiago A. Lozano‐Calderón, Soheil Ashkani‐Esfahani, Job Nicolaas Doornberg, Atta Taseh, Evan Sirls, George Casey, Sarah Hearns
article en
1 citations

Abstract

The Fracture Risk Assessment Tool (FRAX), widely used for predicting the 10-year likelihood of hip fractures, does not incorporate factors like prior falls and sociodemographic characteristics, notably the Social Vulnerability Index (SVI). Recognizing these limitations, we aim to evaluate the predictive accuracy of FRAX by integrating fall frequency, fall energy, and SVI into the model for assessing the risk of fall-induced hip fractures. A retrospective case-control study was conducted, and patients aged ≥ 40 years with a documented diagnosis of a fall-induced hip fracture were age-matched with controls with a history of falls without an associated hip fracture. Basic demographic data, along with information about the number of prior falls and the energy of the current falls, were collected. The FRAX and SVI were calculated accordingly. Logistic regression analysis was employed to identify significant predictors. The performance of the models was evaluated and reported using appropriate metrics. Baseline characteristics of the dataset were presented as medians with interquartile ranges (IQR) or as percentages, where applicable. The significance of the identified variables was quantified using Odds Ratio (OR) along with their 95% Confidence Interval (CI). A p-value threshold of 0.05 was set for statistical significance. A total of 261 patients per group were included with a median age of 74 (IQR 67-80) and 72 (IQR 62-83) years. The FRAX score was significantly associated with the likelihood of experiencing a fall-induced hip fracture, as indicated by an OR of 1.06 (CI: 1.03-1.09). Participants with a one-time history of falls had an OR of 1.58 (CI: 1.02-2.37), compared to 1.84 (CI: 1.09-3.1) for those with multiple falls. The White participants, along with the Housing Type and Transportation domain of the SVI, also demonstrated to play a role (OR = 2.85 (CI: 1.56-5.2) and OR = 0.3 (CI: 0.12-0.8), respectively). This study underscored the significance of factors such as fall frequency, SVI, and race in predicting fall-induced hip fractures. It also highlighted the need for further refinement of the FRAX tool. We recommend that future research should be focused on validating the impact of these sociodemographic and fall characteristics on a broader scale, along with exploring the implications of clinical surrogates related to falls.

Scientific ReportsVol. 15(1)
University Medical Center Groningen (NL), Harvard University (US), University of Groningen (NL), Massachusetts General Hospital (US), Mass General Brigham (US)
Openalex Percentile: Top 16%
Hip and Femur Fractures
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.