Evidence map›Paper›PMID 31240508›Full record

ArticleAnnals of biomedical engineering2019

Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM).

Julio Carballido-Gamio, Aihong Yu, Ling Wang, Yongbin Su, Andrew J Burghardt, Thomas F Lang, Xiaoguang Cheng

Open access · greenAbstract read
In one paragraph

Article in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Augmenting Osteoporosis Imaging with Machine Learning.Current osteoporosis reports · 2021
    Pooled it
  2. Simulated low-dose multi-detector computed tomography: spatial effects on surrogate parameters of bone strength at the proximal femur.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Article
  3. Male-female spatio-temporal differences of age-related bone changes show faster bone deterioration in older women at femoral regions associated with incident hip fracture.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024
    Article
  4. Article
  5. Article
  6. Applications of Machine Learning in Bone and Mineral Research.Endocrinology and metabolism (Seoul, Korea) · 2021
    Review
  7. Article
  8. Economic evaluations of big data analytics for clinical decision-making: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  9. Machine Learning Applications in Orthopaedic Imaging.The Journal of the American Academy of Orthopaedic Surgeons · 2020
    Article
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

7 authors at 3 institutions in 2 countries.

Julio Carballido-GamioDepartment of Radiology, University of Colorado Anschutz Medical Campus, 12700 E 19th Ave, Room 1208, Mail Stop C278, Aurora, CO, 80045, USA. Julio.Carballido-Gamio@ucdenver.edu.ORCID http://orcid.org/0000-0002-6593-7182
Aihong YuDepartment of Radiology, Beijing Jishuitan Hospital, Beijing, China.
Ling WangDepartment of Radiology, Beijing Jishuitan Hospital, Beijing, China.
Yongbin SuDepartment of Radiology, Beijing Jishuitan Hospital, Beijing, China.
Andrew J BurghardtDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Thomas F LangDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Xiaoguang ChengDepartment of Radiology, Beijing Jishuitan Hospital, Beijing, China.
Peking University · CNUniversity of California, San Francisco · USUniversity of Colorado Anschutz Medical Campus · US

Funding

Multi-Parametric Spatial Assessment of Bone with HR-pQCTR01AR068456 · NIAMS · UNIVERSITY OF COLORADO DENVER · PI BURGHARDT, ANDREW JOHN, CARBALLIDO-GAMIO, JULIO · 2016 to 2020
$1.4M
CT-based modeling to analyze variation in skeletal response to osteoporosis drugsR01AR064140 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KEYAK, JOYCE H., LANG, THOMAS F · 2013 to 2016
$1.2M
Beijing Bureau of Health 215 Program 2009-2-03Beijing Bureau of Health 215 Program 2013-3-033Beijing Technology Foundation for Selected Overseas Chinese Scholar and Beijing Talents Fund 2015000021467Capital Characteristic Clinic Project Z141107002514072National Natural Science Foundation of China 81071131NIAMS NIH HHS R01 AR064140NIAMS NIH HHS R01AR064140NIAMS NIH HHS R01 AR068456NIAMS NIH HHS R01AR068456
6 · The paper itself

Abstract

Studies using quantitative computed tomography (QCT) and data-driven image analysis techniques have shown that trabecular and cortical volumetric bone mineral density (vBMD) can improve the hip fracture prediction of dual-energy X-ray absorptiometry areal BMD (aBMD). Here, we hypothesize that (1) QCT imaging features of shape, density and structure derived from data-driven image analysis techniques can improve the hip fracture discrimination of classification models based on mean femoral neck aBMD (Neck.aBMD), and (2) that data-driven cortical bone thickness (Ct.Th) features can improve the hip fracture discrimination of vBMD models. We tested our hypotheses using statistical multi-parametric modeling (SMPM) in a QCT study of acute hip fracture of 50 controls and 93 fragility fracture cases. SMPM was used to extract features of shape, vBMD, Ct.Th, cortical vBMD, and vBMD in a layer adjacent to the endosteal surface to develop hip fracture classification models with machine learning logistic LASSO. The performance of these classification models was evaluated in two aspects: (1) their hip fracture classification capability without Neck.aBMD, and (2) their capability to improve the hip fracture classification of the Neck.aBMD model. Assessments were done with 10-fold cross-validation, areas under the receiver operating characteristic curve (AUCs), differences of AUCs, and the integrated discrimination improvement (IDI) index. All LASSO models including SMPM-vBMD features, and the majority of models including SMPM-Ct.Th features performed significantly better than the Neck.aBMD model; and all SMPM features significantly improved the hip fracture discrimination of the Neck.aBMD model (Hypothesis 1). An interesting finding was that SMPM-features of vBMD also captured Ct.Th patterns, potentially explaining the superior classification performance of models based on SMPM-vBMD features (Hypothesis 2). Age, height and weight had a small impact on model performances, and the model of shape, vBMD and Ct.Th consistently yielded better performances than the Neck.aBMD models. Results of this study clearly support the relevance of bone density and quality on the assessment of hip fracture, and demonstrate their potential on patient and healthcare cost benefits.

Indexed as

Models, StatisticalAbsorptiometry, PhotonAgedAged, 80 and overArea Under CurveBone DensityCase-Control StudiesCortical BoneFemaleFemur NeckHip FracturesHumansMiddle AgedROC CurveTomography, X-Ray ComputedBone mineral density (BMD)Cortical bone thickness (Ct.Th)FractureHipQuantitative computed tomography (QCT)Statistical multi-parametric modeling (SMPM)

Identifiers

PMID31240508
PMCPMC7012369
OpenAlexW2954365284

What OpenQuestion holds

Textmetadata
LicenceTDM
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.