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The Bone & Joint Journal

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The Bone & Joint Journal
Vol. 103-B, Issue 3 | Pages 469 - 478
1 Mar 2021
Garland A Bülow E Lenguerrand E Blom A Wilkinson M Sayers A Rolfson O Hailer NP

Aims

To develop and externally validate a parsimonious statistical prediction model of 90-day mortality after elective total hip arthroplasty (THA), and to provide a web calculator for clinical usage.

Methods

We included 53,099 patients with cemented THA due to osteoarthritis from the Swedish Hip Arthroplasty Registry for model derivation and internal validation, as well as 125,428 patients from England and Wales recorded in the National Joint Register for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey (NJR) for external model validation. A model was developed using a bootstrap ranking procedure with a least absolute shrinkage and selection operator (LASSO) logistic regression model combined with piecewise linear regression. Discriminative ability was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration belt plots were used to assess model calibration.


The Bone & Joint Journal
Vol. 101-B, Issue 1 | Pages 104 - 112
1 Jan 2019
Bülow E Cnudde P Rogmark C Rolfson O Nemes S

Aims

Our aim was to examine the Elixhauser and Charlson comorbidity indices, based on administrative data available before surgery, and to establish their predictive value for mortality for patients who underwent hip arthroplasty in the management of a femoral neck fracture.

Patients and Methods

We analyzed data from 42 354 patients from the Swedish Hip Arthroplasty Register between 2005 and 2012. Only the first operated hip was included for patients with bilateral arthroplasty. We obtained comorbidity data by linkage from the Swedish National Patient Register, as well as death dates from the national population register. We used univariable Cox regression models to predict mortality based on the comorbidity indices, as well as multivariable regression with age and gender. Predictive power was evaluated by a concordance index, ranging from 0.5 to 1 (with the higher value being the better predictive power). A concordance index less than 0.7 was considered poor. We used bootstrapping for internal validation of the results.