Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old

Author:

Langsetmo Lisa12ORCID,Schousboe John T.34ORCID,Taylor Brent C.125,Cauley Jane A.6ORCID,Fink Howard A.127,Cawthon Peggy M.8,Kado Deborah M.910,Ensrud Kristine E.125ORCID,

Affiliation:

1. Center for Care Delivery and Outcomes Research, VA Health Care System Minneapolis MN USA

2. Department of Medicine University of Minnesota Minneapolis MN USA

3. Rheumatology Research HealthPartners Institute Bloomington MN USA

4. Division of Health Policy & Management, School of Public Health University of Minnesota Minneapolis MN USA

5. Division of Epidemiology & Community Health, School of Public Health University of Minnesota Minneapolis MN USA

6. Department of Epidemiology, School of Public Health University of Pittsburgh Pittsburgh PA USA

7. Geriatric Research Education and Clinical Center, VA Health Care System Minneapolis MN USA

8. California Pacific Medical Center Research Institute San Francisco CA USA

9. Department of Medicine Stanford University Stanford CA USA

10. Geriatric Research Education and Clinical Center, VA Health Care System Palo Alto CA USA

Abstract

AbstractTargeted fracture prevention strategies among late‐life adults should balance fracture risk versus competing mortality risk. Models have previously been constructed using Fine‐Gray subdistribution methods. We used a machine learning method adapted for competing risk survival time to evaluate candidate risk factors and create models for hip fractures and competing mortality among men and women aged 80 years and older using data from three prospective cohorts (Study of Osteoporotic Fractures [SOF], Osteoporotic Fracture in Men study [MrOS], Health Aging and Body Composition study [HABC]). Random forest competing risk models were used to estimate absolute 5‐year risk of hip fracture and absolute 5‐year risk of competing mortality (excluding post–hip fracture deaths). Models were constructed for both outcomes simultaneously; minimal depth was used to rank and select variables for smaller models. Outcome specific models were constructed; variable importance was used to rank and select variables for inclusion in smaller random forest models. Random forest models were compared to simple Fine‐Gray models with six variables selected a priori. Top variables for competing risk random forests were frailty and related components in men while top variables were age, bone mineral density (BMD) (total hip, femoral neck), and frailty components in women. In both men and women, outcome specific rankings strongly favored BMD variables for hip fracture prediction while frailty and components were strongly associated with competing mortality. Model discrimination for random forest models varied from 0.65 for mortality in women to 0.81 for hip fracture in men and depended on model choice and variables included. Random models performed slightly better than simple Fine‐Gray model for prediction of competing mortality, but similarly for prediction of hip fractures. Random forests can be used to estimate risk of hip fracture and competing mortality among the oldest old. Modest gains in performance for mortality without hip fracture compared to Fine‐Gray models must be weighed against increased complexity. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Funder

National Institutes of Health

NIH

Publisher

Oxford University Press (OUP)

Subject

Orthopedics and Sports Medicine,Endocrinology, Diabetes and Metabolism

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