Establishment and validation of a prognostic nomogram for periprosthetic femoral fracture after total hip replacement surgery DOI Creative Commons
Jie Tang,

Ying Hu,

Ye Li

et al.

BMC Musculoskeletal Disorders, Journal Year: 2025, Volume and Issue: 26(1)

Published: April 5, 2025

In recent years, with the gaining popularity and wide application of total hip arthroplasty (THA), incidence rate periprosthetic femoral fractures (PFF) has increased. The treatment PFF is difficult many related complications. Herein, we aimed to construct a nomogram model predict occurrence after THA, in order identify high-risk populations. this retrospective analysis, selected 2,528 patients who underwent THA at Wuhan Fourth Hospital from January 2014 August 2022. Patients were randomly divided into training cohort (n = 1,770) an internal validation 758) 7:3 ratio. Least Absolute Shrinkage Selection Operator (LASSO) algorithm logistic regression analysis used perform feature convert them model. was externally validated 1,383 Renmin University. Six independent risk factors for predicting identified, namely age, female sex, revision, non-cemented prosthesis, history trauma, osteoporosis. demonstrated sufficient predictive accuracy, area under curve (AUC) values 0.798 (95% confidence interval [CI]: 0.725-0.872), 0.877 (0.798-0.957), 0.804 (0.710-0.897) training, validation, external cohorts, respectively. calibration showed good consistency between predicted actual risk. postoperative established study value helps

Language: Английский

Establishment and validation of a prognostic nomogram for periprosthetic femoral fracture after total hip replacement surgery DOI Creative Commons
Jie Tang,

Ying Hu,

Ye Li

et al.

BMC Musculoskeletal Disorders, Journal Year: 2025, Volume and Issue: 26(1)

Published: April 5, 2025

In recent years, with the gaining popularity and wide application of total hip arthroplasty (THA), incidence rate periprosthetic femoral fractures (PFF) has increased. The treatment PFF is difficult many related complications. Herein, we aimed to construct a nomogram model predict occurrence after THA, in order identify high-risk populations. this retrospective analysis, selected 2,528 patients who underwent THA at Wuhan Fourth Hospital from January 2014 August 2022. Patients were randomly divided into training cohort (n = 1,770) an internal validation 758) 7:3 ratio. Least Absolute Shrinkage Selection Operator (LASSO) algorithm logistic regression analysis used perform feature convert them model. was externally validated 1,383 Renmin University. Six independent risk factors for predicting identified, namely age, female sex, revision, non-cemented prosthesis, history trauma, osteoporosis. demonstrated sufficient predictive accuracy, area under curve (AUC) values 0.798 (95% confidence interval [CI]: 0.725-0.872), 0.877 (0.798-0.957), 0.804 (0.710-0.897) training, validation, external cohorts, respectively. calibration showed good consistency between predicted actual risk. postoperative established study value helps

Language: Английский

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