Evidence map›Paper›PMID 40188048›Full record

ArticleBMC musculoskeletal disorders2025

Establishment and validation of a prognostic nomogram for periprosthetic femoral fracture after total hip replacement surgery.

Jie Tang, Ying Hu, Ye Li, Shenghao Zhao, Yong Hu

Abstract readValidation Study
In one paragraph

Article in BMC musculoskeletal disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Jie Tang *Department of Bone and Joint Surgery, Wuhan Fourth Hospital, Wuhan City, 430034, Hubei Province, China.
Ying Hu *Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan City, 430060, Hubei Province, China.
Ye LiDepartment of Bone and Joint Surgery, Wuhan Fourth Hospital, Wuhan City, 430034, Hubei Province, China.
Shenghao ZhaoDepartment of Bone and Joint Surgery, Wuhan Fourth Hospital, Wuhan City, 430034, Hubei Province, China. zshggjk@163.com.
Yong HuDepartment of Bone and Joint Surgery, Renmin Hospital of Wuhan University, Wuhan City, 430060, Hubei Province, China. hyrm2017@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn recent years, with the gaining popularity and wide application of total hip arthroplasty (THA), the incidence rate of periprosthetic femoral fractures (PFF) has increased. The treatment of PFF is difficult and has many related complications. Herein, we aimed to construct a nomogram model to predict occurrence of PFF after THA, in order to identify high-risk populations.

methodsIn this retrospective analysis, we selected 2,528 patients who underwent THA at Wuhan Fourth Hospital from January 2014 to August 2022. Patients were randomly divided into a training cohort (n = 1,770) and an internal validation cohort (n = 758) in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and logistic regression analysis were used to perform feature analysis and convert them into a nomogram model. The model was externally validated in 1,383 THA patients at Renmin Hospital of Wuhan University.

resultsSix independent risk factors for predicting PFF were identified, namely age, female sex, hip revision, non-cemented prosthesis, history of trauma, and osteoporosis. The nomogram demonstrated sufficient predictive accuracy, with area under the curve (AUC) values of 0.798 (95% confidence interval [CI]: 0.725-0.872), 0.877 (0.798-0.957), and 0.804 (0.710-0.897) in the training, internal validation, and external validation cohorts, respectively. The calibration curve showed good consistency between the predicted risk of the model and the actual risk.

conclusionsThe nomogram model for postoperative PFF after THA established in this study has good predictive value and helps identify high-risk populations.

Indexed as

Arthroplasty, Replacement, HipFemoral FracturesNomogramsPeriprosthetic FracturesPostoperative ComplicationsAgedAged, 80 and overFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsFemurPeriprosthetic fracturePredictive modelingTotal hip arthroplasty

Identifiers

PMID40188048
PMCPMC11971908

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