Evidence map›Paper›PMID 30096469›Full record

ArticleBone2018

Factors associated with proximal femur fracture determined in a large cadaveric cohort.

Dan Dragomir-Daescu, Timothy L Rossman, Asghar Rezaei, Kent D Carlson, David F Kallmes, John A Skinner, Sundeep Khosla, Shreyasee Amin

Open access · greenAbstract read
In one paragraph

Article in Bone, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 12% 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

12 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  8. Review
  9. Article
  10. Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2020
    Article
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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

8 authors at 2 institutions in 1 country.

Dan Dragomir-DaescuDepartment of Physiology and Biomedical Engineering, Mayo Clinic, United States. Electronic address: DragomirDaescu.Dan@mayo.edu.
Timothy L RossmanDivision of Engineering, Mayo Clinic, United States.
Asghar RezaeiDepartment of Physiology and Biomedical Engineering, Mayo Clinic, United States.
Kent D CarlsonDepartment of Physiology and Biomedical Engineering, Mayo Clinic, United States.
David F KallmesDepartment of Radiology, Mayo Clinic, United States.
John A SkinnerDepartment of Radiology, Mayo Clinic, United States.
Sundeep KhoslaRobert and Arlene Kogod Center on Aging and Endocrine Research Unit, Mayo Clinic, United States.
Shreyasee AminDivision of Rheumatology, Mayo Clinic, United States; Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, United States.
WinnMed · USMayo Clinic Health System · US

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
EPIDEMIOLOGY OF AGE-RELATED BONE LOSS, &FRACTURESR01AR027065 · NIAMS · MAYO CLINIC ROCHESTER · PI AMIN, SHREYASEE, KHOSLA, SUNDEEP · 1986 to 2020
$14.9M
EXTRAMURAL RES FACIL IMPROVEMENT PROG: POLYCYSTIC KIDNEY DISEASE, RENAL STUDIES,C06RR018898 · NCRR · MAYO CLINIC · PI BURNETT, JOHN C · 2004 to 2004
$2.3M
NCATS NIH HHS UL1 TR002377NCRR NIH HHS C06 RR018898NIAMS NIH HHS R01 AR027065
6 · The paper itself

Abstract

Many researchers have used cadaveric fracture tests to determine the relationship between proximal femur (hip) fracture strength and a multitude of possible explanatory variables, typically considered one or two at a time. These variables include subject-specific proximal femur variables such as femoral neck areal bone mineral density (aBMD), sex, age, and geometry, as well as physiological hip fracture event variables such as fall speed and angle of impact. However, to our knowledge, no study has included all of these variables simultaneously in the same experimental dataset. To address this gap, the present study simultaneously included all of these subject-specific and fracture event variables in multivariate models to understand their contributions to femoral strength and fracture type. The primary aim of this study was to determine not only whether each of these variables contributed to the prediction of femoral strength, but also to determine the relative importance of each variable in strength prediction. A secondary aim was to similarly characterize the importance of these variables for the prediction of fracture type. To accomplish these aims, we characterized 197 proximal femurs (covering a wide range of subject-specific variables) with DXA and CT scans, and then tested the femurs to fracture in a sideways fall on the hip configuration. Each femur was tested using one of three fall speed conditions and one of four angles of impact (bone orientations). During each test, we acquired measurements of relevant force and displacement data. We then reduced the test data to determine femoral strength, and we used post-fracture CT scans to classify the fracture type (e.g., trochanteric, cervical). Using these results, the explanatory variables were analyzed with mixed statistical models to explain variations in hip fracture strength and fracture type, respectively. Five explanatory variables were statistically significant in explaining the variability in femoral strength: aBMD, sex, age, fall speed, and neck-shaft angle (P ≤ 0.0135). These five variables, including significant interactions, explained 80% of the variability in hip fracture strength. Additionally, when only aBMD, sex, and age (P < 0.0001) were considered in the model, again including significant interactions, these three variables alone explained 79% of the variability in hip fracture strength. So while fall speed (P = 0.0135) and neck-shaft angle (P = 0.0041) were statistically significant, the inclusion of these variables did not appreciably improve the prediction of hip fracture strength compared to the model that considered only aBMD, sex and age. For the variables we included in this study, in the ranges we considered, our findings indicate that the clinically-available information of patient age, sex and aBMD are sufficient for femoral strength assessment. These findings also suggest that there is little value in the extra effort required to characterize the effect of femoral geometry on strength, or to account for the probabilistic nature of fall-related factors such as fall speed and angle of impact. For fracture type, the only explanatory variable found to be significant was aBMD (P ≤ 0.0099). We found that the odds of having intertrochanteric fractures increased by 47% when aBMD decreased by one standard deviation (0.2 g/cm

Indexed as

AgedBiomechanical PhenomenaCadaverCohort StudiesFemaleFemoral FracturesHumansMaleMultivariate AnalysisRisk FactorsBone biomechanicsBone testing speedFemoral orientationFracture typeHip fractureProximal femur strength

Identifiers

PMID30096469
PMCPMC6342454
OpenAlexW2885965245

What OpenQuestion holds

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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.