Evidence map›Paper›PMID 31663283›Full record

ArticleOrthopaedic surgery2019

Prediction of Collapse Using Patient-Specific Finite Element Analysis of Osteonecrosis of the Femoral Head.

Tai-Xian Li, Ze-Qing Huang, Yan Li, Zhi-Peng Xue, Ji-Gao Sun, Huan-Huan Gao, Hai-Jun He, Wei-Heng Chen

Open access · goldAbstract read
In one paragraph

Article in Orthopaedic surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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  12. Clinical Characteristics, Current Treatment Options, Potential Mechanisms, Biomarkers, and Therapeutic Targets in Avascular Necrosis of Femoral Head.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2024
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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 1 institution in 1 country.

Tai-Xian LiWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Ze-Qing HuangWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Yan LiWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Zhi-Peng XueWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Ji-Gao SunWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Huan-Huan GaoWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Hai-Jun HeWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Wei-Heng ChenWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-0831-1201
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

Beijing Municipal Natural Science Foundation of China 7142170National Natural Science Foundation of China 81373801Research and development project (10 drugs for 10 diseases) to support and safeguard the G20 Z151100003815028
6 · The paper itself

Abstract

objectiveTo develop a prediction method for femoral head collapse by using patient-specific finite element analysis of osteonecrosis of the femoral head (ONFH).

methodsThe retrospective study recruited 40 patients with ARCO stage-II ONFH (40 pre-collapse hips). Patients were divided into two groups according to the 1-year follow-up outcomes: patient group without femoral head collapse (noncollapse group, n = 20) and patient group with collapse (collapse group, n = 20). CT scans of the hip were performed for all patients once they joined the study. Patient-specific finite element models were generated based on these original CT images following the same procedures: segmenting the necrotic lesion and viable proximal femur, meshing the computational models, assigning different material properties according to the Hounsfield unit distribution, simulating the stress loading of the slow walking gait, and measuring the distribution of the von Mises stress. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the maximum level of the von Mises stress. The optimal cut-off value was selected based on the Youden index and the corresponding predictive accuracy was reported as well.

resultsThe mean level of the maximum von Mises stress in the collapse group was 2.955 ± 0.539 MPa, whereas the mean stress level in the noncollapse group was 1.923 ± 0.793 MPa (P < 0.01). ROC analysis of the maximum von Mises stress found that the area under the ROC curve was 0.842 (95% CI: 0.717-0.968, P < 0.01). The maximum Youden index was 0.60, which corresponded to two optimal cut-off values: 2.7801 MPa (sensitivity: 0.70; specificity: 0.90; predictive accuracy: 80.00%; LR+: 7), and 2.7027 MPa (sensitivity: 0.75; specificity: 0.85; predictive accuracy: 77.50%; LR+: 5).

conclusionFinite element analysis is a potential method for femoral head collapse prediction among pre-collapse ONFH patients. The maximum level of the von Mises stress on the weight-bearing surface of the femoral head could be a good biomechanical marker to classify the collapse risk. The collapse prediction method based on patient-specific finite element analysis is, thus, suitable to apply to clinical practice, but further testing on a larger dataset is desirable.

Indexed as

Finite Element AnalysisModels, AnatomicStress, MechanicalAdultAgedFemaleFemur Head NecrosisHumansImaging, Three-DimensionalMaleMiddle AgedPrinting, Three-DimensionalRetrospective StudiesTomography, X-Ray ComputedWeight-BearingYoung AdultDiagnostic testFemoral head collapse predictionFinite element analysisOsteonecrosis of the femoral head

Identifiers

PMID31663283
PMCPMC6819171
OpenAlexW2982683954

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.