Evidence map›Paper›PMID 37614297›Full record

ArticleJBMR plus2023

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

Lisa Langsetmo, John T Schousboe, Brent C Taylor, Jane A Cauley, Howard A Fink, Peggy M Cawthon, Deborah M Kado, Kristine E Ensrud, Osteoporotic Fractures in Men (MrOS) Research Group

Open access · goldAbstract read
In one paragraph

Article in JBMR plus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
13.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 41 citations in OpenAlex.

  1. Review
  2. The Osteoporotic Fractures in Men study (MrOS): a 25-yr landmark study of skeletal health in older men.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
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  17. Assessment of fracture risk in diabetic patients.Journal of diabetes and metabolic disorders · 2024
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Lisa LangsetmoCenter for Care Delivery and Outcomes Research, VA Health Care System Minneapolis MN USA.ORCID https://orcid.org/0000-0003-2245-1657
John T SchousboeRheumatology Research HealthPartners Institute Bloomington MN USA.ORCID https://orcid.org/0000-0002-9329-0750
Brent C TaylorCenter for Care Delivery and Outcomes Research, VA Health Care System Minneapolis MN USA.
Jane A CauleyDepartment of Epidemiology, School of Public Health University of Pittsburgh Pittsburgh PA USA.ORCID https://orcid.org/0000-0003-0752-4408
Howard A FinkCenter for Care Delivery and Outcomes Research, VA Health Care System Minneapolis MN USA.
Peggy M CawthonCalifornia Pacific Medical Center Research Institute San Francisco CA USA.
Deborah M KadoDepartment of Medicine Stanford University Stanford CA USA.
Kristine E EnsrudCenter for Care Delivery and Outcomes Research, VA Health Care System Minneapolis MN USA.ORCID https://orcid.org/0000-0002-9069-3036
Osteoporotic Fractures in Men (MrOS) Research Group
University of Minnesota · USCalifornia Pacific Medical Center · USUniversity of Pittsburgh · USVA Palo Alto Health Care System · US

Funding

Oregon Clinical and Translational Research Institute - The National COVID Cohort Collaborative (N3C)UL1TR002369 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Cynthia D Morris, Christopher G. Slatore · 2017 to 2026
$78.4M
Long term fracture risk and change in peripheral bone in the oldest old men: The MrOS studyR01AG066671 · NIA · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI BOUXSEIN, MARY L, CAWTHON, PEGGY MANNEN · 2020 to 2024
$13.3M
OSTEOPOROTIC FRACTURES IN MENU01AG018197 · NIA · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI CUMMINGS, STEVEN RON · 1999 to 2011
$12.7M
Osteoporotic Fractures in Men (MrOS) - MinneapolisU01AG042145 · NIA · UNIVERSITY OF MINNESOTA · PI ENSRUD, KRISTINE · 2013 to 2019
$5.3M
Osteporotic Fractures in Men (MrOS)- Admin CenterU01AG027810 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2017
$5.1M
Osteoporotic Fractures in Men (MrOS) - PortlandU01AG042124 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2013 to 2019
$3.0M
Osteoporatic Fractures in Men (MrOS)U01AG042168 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KADO, DEBORAH · 2013 to 2017
$3.0M
Osteoporotic Fractures in Men (Mr. Os) Palo AltoU01AG042143 · NIA · STANFORD UNIVERSITY · PI STEFANICK, MARCIA L. · 2013 to 2017
$3.0M
Osteoporotic Fractures in Men (Mr.OS)U01AG042139 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CAULEY, JANE ANN · 2013 to 2017
$2.9M
Osteoporotic Fractures in Men_MrOS Renewal_BirminghamU01AG042140 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SHIKANY, JAMES M · 2013 to 2019
$2.7M
Fracture Risk Prediction After Age 80 YearsR21AG067094 · NIA · UNIVERSITY OF MINNESOTA · PI ENSRUD, KRISTINE · 2020 to 2021
$336k
NCATS NIH HHS UL1 TR002369NIA NIH HHS R01 AG066671NIA NIH HHS R21 AG067094NIA NIH HHS U01 AG018197NIA NIH HHS U01 AG027810NIA NIH HHS U01 AG042124NIA NIH HHS U01 AG042139NIA NIH HHS U01 AG042140NIA NIH HHS U01 AG042143NIA NIH HHS U01 AG042145NIA NIH HHS U01 AG042168
6 · The paper itself

Abstract

Targeted 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.

Indexed as

HIP FRACTUREMACHINE LEARNINGRANDOM FOREST

Identifiers

PMID37614297
PMCPMC10443071
OpenAlexW4383069768

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.