Rib Fracture Frailty Index: A risk stratification tool for geriatric patients with multiple rib fractures

Jeff Choi; Ben J Marafino; Edward B Vendrow; Lakshika Tennakoon; Michael Baiocchi; David A Spain; Joseph D Forrester

doi:10.1097/TA.0000000000003390

Rib Fracture Frailty Index: A risk stratification tool for geriatric patients with multiple rib fractures

J Trauma Acute Care Surg. 2021 Dec 1;91(6):932-939. doi: 10.1097/TA.0000000000003390.

Authors

Jeff Choi¹, Ben J Marafino, Edward B Vendrow, Lakshika Tennakoon, Michael Baiocchi, David A Spain, Joseph D Forrester

Affiliation

¹ From the Division of General Surgery, Department of Surgery (J.C., L.T., D.A.S., J.D.F.), Surgeons Writing About Trauma (J.C., E.B.V., L.T., D.A.S., J.D.F.), Department of Biomedical Data Science (J.C., B.J.M.), Department of Epidemiology and Population Health (B.J.M., M.B.), and Department of Computer Science (E.B.V.), Stanford University, Stanford, California.

PMID: 34446653
DOI: 10.1097/TA.0000000000003390

Abstract

Background: Rib fractures are consequential injuries for geriatric patients (age, ≥65 years). Although age and injury patterns drive many rib fracture management decisions, the impact of frailty-which baseline conditions affect rib fracture-specific outcomes-remains unclear for geriatric patients. We aimed to develop and validate the Rib Fracture Frailty (RFF) Index, a practical risk stratification tool specific for geriatric patients with rib fractures. We hypothesized that a compact list of frailty markers can accurately risk stratify clinical outcomes after rib fractures.

Methods: We queried nationwide US admission encounters of geriatric patients admitted with multiple rib fractures from 2016 to 2017. Partitioning around medoids clustering identified a development subcohort with previously validated frailty characteristics. Ridge regression with penalty for multicollinearity aggregated baseline conditions most prevalent in this frail subcohort into RFF scores. Regression models with adjustment for injury severity, sex, and age assessed associations between frailty risk categories (low, medium, and high) and inpatient outcomes among validation cohorts (odds ratio [95% confidence interval]). We report results according to Transparent Reporting of Multivariable Prediction Model for Individual Prognosis guidelines.

Results: Development cohort (n = 55,540) cluster analysis delineated 13 baseline conditions constituting the RFF Index. Among external validation cohort (n = 77,710), increasing frailty risk (low [reference group], moderate, high) was associated with stepwise worsening adjusted odds of mortality (1.5 [1.2-1.7], 3.5 [3.0-4.0]), intubation (2.4 [1.5-3.9], 4.7 [3.1-7.5]), hospitalization ≥5 days (1.4 [1.3-1.5], 1.8 [1.7-2.0]), and disposition to home (0.6 [0.5-0.6], 0.4 [0.3-0.4]). Locally weighted scatterplot smoothing showed correlations between increasing RFF scores and worse outcomes.

Conclusion: The RFF Index is a practical frailty risk stratification tool for geriatric patients with multiple rib fractures. The mobile app we developed may facilitate rapid implementation and further validation of RFF Index at the bedside.

Level of evidence: Prognostic study, level III.

MeSH terms

Aged
Cluster Analysis
Cohort Studies
Female
Fractures, Multiple* / diagnosis
Fractures, Multiple* / epidemiology
Frailty* / complications
Frailty* / diagnosis
Frailty* / physiopathology
Geriatric Assessment / methods*
Hospitalization
Humans
Injury Severity Score
Male
Outcome Assessment, Health Care
Rib Fractures* / diagnosis
Rib Fractures* / epidemiology
Risk Assessment / methods*
Spinal Fractures* / diagnosis
Spinal Fractures* / epidemiology
Trauma Centers / statistics & numerical data
United States / epidemiology