Evidence map›Paper›PMID 33575347›Full record

ArticleBioMed research international2021

Predicting the Failure Risk of Internal Fixation Devices in Chinese Patients Undergoing Spinal Internal Fixation Surgery: Development and Assessment of a New Predictive Nomogram.

Chong Liu, Zide Zhang, Yuan Ma, Tuo Liang, Chaojie Yu, Zhaojun Lu, Guoyong Xu, Zequn Wang, Jiarui Chen, Jie Jiang and 4 more

Abstract read
In one paragraph

Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. 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

14 authors.

Chong LiuGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0003-2479-3001
Zide ZhangGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-0654-9606
Yuan MaBeijing Haidian Hospital, No. 29 Zhongguancun Street, Haidian District, Beijing 100080, China.
Tuo LiangGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-3003-416X
Chaojie YuGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0001-8510-6717
Zhaojun LuGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-0720-0465
Guoyong XuGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0003-0636-6831
Zequn WangGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-7405-4218
Jiarui ChenGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0001-8197-4592
Jie JiangGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-4599-1544
Tianyou ChenGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.
Hao LiGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.
Zhen YeGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.
Xinli ZhanGuangxi Medical University, No. 22 Shuangyong Road, Nanning, Guangxi 530021, China.ORCID https://orcid.org/0000-0002-6936-481X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current study is aimed at developing and validating a nomogram of the risk of failure of internal fixation devices in Chinese patients undergoing spinal internal fixation. We collected data from a total of 1139 patients admitted for spinal internal fixation surgery at the First Affiliated Hospital of Guangxi Medical University from May 2012 to February 2019. Of these, 1050 patients were included in the spinal internal fixation group and 89 patients in the spinal internal fixation device failure group. Patients were divided into training and validation tests. The risk assessment of the failure of the spinal internal fixation device used 14 characteristics. In the training test, the feature selection of the failure model of the spinal internal fixation device was optimized using the least absolute shrinkage and selection operator (LASSO) regression model. Based on the characteristics selected in the LASSO regression model, multivariate logistic regression analysis was used for constructing the model. Identification, calibration, and clinical usefulness of predictive models were assessed using C-index, calibration curve, and decision curve analysis. A validation test was used to validate the constructed model. In the training test, the risk prediction nomogram included gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. The model demonstrated moderate predictive power with a C-index of 0.722 (95% confidence interval: 0.644-0.800) and the area under the curve (AUC) of 0.722. Decision curve analysis depicted that the failure risk nomogram was clinically useful when the probability threshold for internal fixation device failure was 3%. The C-index of the validation test was 0.761. This novel nomogram of failure risk for spinal instrumentation includes gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. It can be used for evaluating the risk of instrumentation failure in patients undergoing spinal instrumentation surgery.

Indexed as

Internal FixatorsNomogramsSpinal FusionAdultChinaFemaleHumansMaleMiddle AgedProsthesis FailureRetrospective StudiesRisk AssessmentRisk FactorsSpineTreatment Failure

Identifiers

PMID33575347
PMCPMC7857875

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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.