Aims. To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. Methods. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results. The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Cite this article: Bone Jt Open 2023;4(3):168–181

Aims. Rotational acetabular osteotomy (RAO) has been reported to be effective in improving symptoms and preventing osteoarthritis (OA) progression in patients with mild to severe develomental dysplasia of the hip (DDH). However, some patients develop secondary OA even when the preoperative joint space is normal; determining who will progress to OA is difficult. We evaluated whether the preoperative cartilage condition may predict OA progression following surgery using T2 mapping MRI. Methods. We reviewed 61 hips with early-stage OA in 61 patients who underwent RAO for DDH. They underwent preoperative and five-year postoperative radiological analysis of the hip. Those with a joint space narrowing of more than 1 mm were considered to have 'OA progression'. Preoperative assessment of articular cartilage was also performed using 3T MRI with the T2 mapping technique. The region of interest was defined as the weightbearing portion of the acetabulum and femoral head. Results. There were 16 patients with postoperative OA progression. The T2 values of the centre to the anterolateral region of the acetabulum and femoral head in the OA progression cases were significantly higher than those in patients without OA progression. The preoperative T2 values in those regions were positively correlated with the narrowed joint space width. The receiver operating characteristic analysis revealed that the T2 value of the central portion in the acetabulum provided excellent discrimination, with OA progression patients having an area under the curve of 0.858. Furthermore, logistic regression analysis showed T2 values of the centre to the acetabulum’s anterolateral portion as independent predictors of subsequent OA progression (p < 0.001). Conclusion. This was the first study to evaluate the relationship between intra-articular degeneration using T2 mapping MRI and postoperative OA progression. Our findings suggest that preoperative T2 values of the hip can be better prognostic factors for OA progression than radiological measures following RAO. Cite this article: Bone Joint J 2021;103-B(9):1472–1478

Introduction. The Comprehensive Care for Joint Replacement (CJR) model for total hip arthroplasty (THA) involves a target reimbursement set by the Center for Medicare and Medicaid Services (CMS). Many patients exceed these targets, but predicting risk for incurring these excess costs remains challenging, and we hypothesized that select patient characteristics would adequately predict CJR cost overruns. Methods. Demographic factors and comorbidities were retrospectively reviewed in 863 primary unilateral CJR THAs performed between 2013 and 2017 at a single institution. A predictive model was built from 31 validated comorbidities and a base set of 5 patient factors (age, gender, BMI, ASA, marital status). A multivariable logistic regression model was refined to include only parameters predictive of exceeding the target reimbursement level. These were then assigned weights relative to the weakest parameter in the model. Results. The overall cost of care for 225 patients (26.1%) exceeded the target price, and a comprehensive model containing all 36 parameters demonstrated adequate discrimination (AUC: 0.748). This model was narrowed to 12 parameters retained for their statistical value in predicting excess cost, without substantial loss of predictive ability (AUC: 0.735). A single score formed from the sum of each patient's weighted parameters also showed adequate discrimination (AUC: .732), with predicted risk for exceeding CJR targets ranging from 10% for a patient score of 10 to 80% for a score of 30. Average scores for patients exceeding the target price were significantly higher than those who did not (19.5 vs 15.0, p < 0.0001). Conclusions. A model composed of weighted comorbidities and base demographics provides adequate discrimination in predicting whether THA costs will exceed CJR targets. This not only helps identify patients who may benefit from further pre-operative optimization, but also allows health systems to predict the likely minimum incurred costs for select patients scheduled for surgery

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. Results. A main effects model combining age, sex, American Society for Anesthesiologists (ASA) class, the presence of cancer, diseases of the central nervous system, kidney disease, and diagnosed obesity had good discrimination, both internally (AUC = 0.78, 95% confidence interval (CI) 0.75 to 0.81) and externally (AUC = 0.75, 95% CI 0.73 to 0.76). This model was superior to traditional models based on the Charlson (AUC = 0.66, 95% CI 0.62 to 0.70) and Elixhauser (AUC = 0.64, 95% CI 0.59 to 0.68) comorbidity indices. The model was well calibrated for predicted probabilities up to 5%. Conclusion. We developed a parsimonious model that may facilitate individualized risk assessment prior to one of the most common surgical interventions. We have published a web calculator to aid clinical decision-making. Cite this article: Bone Joint J 2021;103-B(3):469–478

Background. Postoperative recovery after routine total hip arthroplasty (THA) can lead to the development of prolonged opioid use but there are few tools for predicting this adverse outcome. The purpose of this study was to develop machine learning algorithms for preoperative prediction of prolonged post-operative opioid use after THA. Methods. A retrospective review of electronic health records was conducted at two academic medical centers and three community hospitals to identify adult patients who underwent THA for osteoarthritis between January 1. st. , 2000 and August 1. st. , 2018. Prolonged postoperative opioid prescriptions were defined as continuous opioid prescriptions after surgery to at least 90 days after surgery. Five machine learning algorithms were developed to predict this outcome and were assessed by discrimination, calibration, and decision curve analysis. Results. Overall, 5507 patients underwent THA, of which 345 (6.3%) had prolonged postoperative opioid prescriptions. The factors determined for prediction of prolonged postoperative opioid prescriptions were: age, duration of pre-operative opioid exposure, preoperative hemoglobin, and certain preoperative medications (anti-depressants, benzodiazepines, non-steroidal anti-inflammatory drugs, and beta-2-agonists). The elastic-net penalized logistic regression model achieved the best performance across discrimination (c-statistic = 0.77), calibration, and decision curve analysis. This model was incorporated into a digital application able to provide both predictions and explanations; available here: . https://sorg-apps.shinyapps.io/thaopioid/. Conclusion. If externally validated in independent populations, the algorithms developed in this study could improve preoperative screening and support for THA patients at high-risk for prolonged postoperative opioid use. Early identification and intervention in high-risk cases may mitigate the long-term adverse consequence of opioid dependence. For any tables or figures, please contact the authors directly

Cobalt chrome alloy is commonly used in joint replacement surgery. However, it is recognised that some patients develop lymphocyte mediated delayed type hypersensitivity (DTH) responses to this material, which may result in extensive bone and soft tissue destruction. Phase 1. United Kingdom: From an existing database, we identified extreme phenotype patient groups following metal on metal (MoM) hip resurfacing or THR: ALVAL with low wearing prostheses; ALVAL with high wear; no ALVAL with high wear; and asymptomatic patients with implants in situ for longer than ten years. Class I and II HLA genotype frequency distributions were compared between these patients’ groups, and in silico peptide binding studies were carried out using validated methodology. Phase 2. United Kingdom: We expanded the study to include more patients, including those with intermediary phenotypes to test whether an algorithm could be developed incorporating “risk genotypes”, patient age, sex and metal exposure. This model was trained in phase 3. Phase 3. United Kingdom, Australia, United States. Patients from other centres were invited to give DNA samples. The data set was split in two. 70% was used to develop machine learning models to predict failure secondary to DTH. The predictions were tested using the remaining blinded 30% of data, using time-dependent AUROCs, and integrated calibration index performance statistics. A total of 606 DNA samples, from 397 males and 209 female patients, were typed. This included 176 from patients with failed prostheses, and 430 from asymptomatic patients at a mean of >10 years follow up. C-index and ROC(t) scores suggested a high degree of discrimination, whilst the IBS indicated good calibration and further backed up the indication of high discriminatory ability. At ten years, the weighted mean survival probability error was < 4%. At present, there are no tests in widespread clinical use which use a patient's genetic profile to guide implant selection or inform post-operative management. The algorithm described herein may address this issue and we suggest that the application may not be restricted to the field of MoM hip arthroplasty

Patients may be able to return to higher level activities following hip arthroplasty with modern techniques and prostheses, but the Oxford hip score, the standard PROM used by the NJS exhibits severe skew and kurtosis. The commonest score is 48/48. Most patients score above 40 preventing any discrimination between approaches or prostheses. We therefore sought both subjective and objective metrics which were relevant and valid without skew or high kurtosis in postoperative patients. The Metabolic Equivalent of Task (MET) reports energy usage in kcal/min burnt across a range of activities, condensed into a score of 0–25. A MET over 8 is considered ‘conditioning exercise’ tethered to life expectancy. A 2 point difference in average MET is considered a clinically relevant difference. Walking speed is a simple valid metric tethered to life expectancy, with a 0.1m/sec difference in walking speed equates to a clinically important difference. Oxford Hip Score (OHS), and the MET were prospectively recorded in 221 primary hip arthroplasty procedures pre-operatively and at 1-year using a web based application. Pre and postoperative Gait analysis was undertaken on a subgroup of 34 patients, in comparison with age and sex matched controls. Post-operatively, the OHS demonstrated significant skewed distributions with ceiling effects of 41% scoring 48/48. The MET was normally distributed around a mean of 10.3, with a standard deviation of 3.8 and no ceiling effect. Walking speed was normally distributed around a mean of 1.8m/sec, with a standard deviation was 0.15 m/sec. The MET is a simple patient reported score, which is normally distributed in patients following hip arthroplasty, around a mean of 10.3 with a standard deviation of 3.8. This valid activity metric correlates well with fast walking speed. This is also normally distributed with a standard deviation of over 0.1m/sec confirming low kurtosis. These simple measures have face validity: undertaking less active pastimes and being unable to keep up with other walkers are obviously inadvisable. The normal kurtosis of these metrics suggest that they may able to detect clinically relevant differences in outcome which are undetectable with commonly used PROMs. For surgeons developing less invasive approaches or using novel stems, these measures may detect clinically important improvements undetectable by the Oxford Hip Score

Introduction. The development of an algorithm that provides accurate individualised estimates of revision risk could help patients make informed surgical treatment choices. This requires building a survival model based on fixed and modifiable risk factors that predict outcome at the individual level. Here we compare different survival models for predicting prosthesis survivorship after hip replacement for osteoarthritis using data from the National Joint Registry for England, Wales, Northern Ireland and the Isle of Man (NJR). Methods. In this comparative study we implemented parametric and flexible parametric (FP) methods and random survival forests (RSF). The overall performance of the parametric models was compared using Akaike information criterion (AIC). The preferred parametric model and the RSF algorithm were further compared in terms of the Brier score, concordance index and calibration via repeated five-fold cross-validation. Results. The dataset contains 327 238 hip replacements for osteoarthritis carried out in England and Wales between 2003 and 2015. The AIC value for the FP model was the lowest. The averages of survival probability estimates are shown in Figure 1. The integrated Brier score of the FP model and the RSF approach over 10 years were similar: 0.011 (95% confidence interval: 0.011–0.011). The concordance index of the FP model at 10 years was 59.4% (95% confidence interval: 59.4%–59.4%). The concordance index was 56.2% (56.1%–56.3%) for the RSF method. Calibration plots for the FP model and the RSF algorithm are presented in Figure 2. Conclusion. The flexible parametric model outperformed other commonly used survival models across chosen validation criteria. However, it does not provide high discriminatory power at the individual level. This might be due to variable outcomes generated by different fixation or bearing types that cannot be captured using a single model. More complex multi-state models may provide better discrimination. For any figures and tables, please contact the authors directly

Most patients presenting with loss of hip motion secondary to FAI have a combination of cam and pincer morphology. In this study, we present a composite index for predicting joint ROM based on anatomic parameters derived from both the femur and acetabulaum using a single reformatted CT slice. Computer models of the hip joint were reconstructed from CT scans of 31 patients with mixed-type FAI (Average alpha angle: 73.6±11.1°, average LCE: 38.9±7.2°). The internal rotation of the hip at impingement was measured at 90° flexion using custom software. With the joint in neutral, a single slice perpendicular to the acetabular rim was taken at the 2 o'clock position. A set of 11 femoral and acetabular parameters measured from this slice were correlated with hip ROM using stepwise logistic regression. Three anatomic parameters provided significant discrimination of cases impinging at <15 and >15 degrees IR: femoral anteversion (28%, p=0.026), the arc of anterior femoral head sphericity (10%, p=0.040), and the LCE in the 2 o'clock plane (10%, p=0.048). This led to the following definition of the Impingement Index: 0.16*(fem version) +0.11*(ant arc)−0.17*(LCE) which correctly classified 82% of cases investigated. None of the traditional parameters (e.g. alpha angle) were significantly correlated with ROM. Our study has identified alternative morphologic parameters that could act as strong predictors of FAI in preoperative assessments. Using this information, each patient's individual risk of impingement may be estimated, regardless of the relative contributions of deformities of the femur and the acetabulum

Objectives. The National Hip Fracture Database (NHFD) publishes hospital-level risk-adjusted mortality rates following hip fracture surgery in England, Wales and Northern Ireland. The performance of the risk model used by the NHFD was compared with the widely-used Nottingham Hip Fracture Score. Methods. Data from 94 hospitals on patients aged 60 to 110 who had hip fracture surgery between May 2013 and July 2013 were analysed. Data were linked to the Office for National Statistics (ONS) death register to calculate the 30-day mortality rate. Risk of death was predicted for each patient using the NHFD and Nottingham models in a development dataset using logistic regression to define the models’ coefficients. This was followed by testing the performance of these refined models in a second validation dataset. Results. The 30-day mortality rate was 5.36% in the validation dataset (n = 3861), slightly lower than the 6.40% in the development dataset (n = 4044). The NHFD and Nottingham models showed a slightly lower discrimination in the validation dataset compared with the development dataset, but both still displayed moderate discriminative power (c-statistic for NHFD = 0.71, 95% confidence interval (CI) 0.67 to 0.74; Nottingham model = 0.70, 95% CI 0.68 to 0.75). Both models defined similar ranges of predicted mortality risk (1% to 18%) in assessment of calibration. Conclusions. Both models have limitations in predicting mortality for individual patients after hip fracture surgery, but the NHFD risk adjustment model performed as well as the widely-used Nottingham prognostic tool and is therefore a reasonable alternative for risk adjustment in the United Kingdom hip fracture population. Cite this article: Bone Joint Res 2017;6:550–556

Aims

The Forgotten Joint Score-12 (FJS-12) is a validated patient-reported outcome measure (PROM) tool designed to assess artificial prosthesis awareness during daily activities following total hip arthroplasty (THA). The patient-acceptable symptom state (PASS) is the minimum cut-off value that corresponds to a patient’s satisfactory state-of-health. Despite the validity and reliability of the FJS-12 having been previously demonstrated, the PASS has yet to be clearly defined. This study aims to define the PASS of the FJS-12 following primary THA.

Aims

This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model.

Aims

The aims of this study were to validate the Forgotten Joint Score-12 (FJS-12) in the postoperative evaluation of periacetabular osteotomy (PAO), identify factors associated with joint awareness after PAO, and determine the FJS-12 threshold for patient-acceptable symptom state (PASS).

Aims

Type 2 diabetes mellitus (T2DM) impairs bone strength and is a significant risk factor for hip fracture, yet currently there is no reliable tool to assess this risk. Most risk stratification methods rely on bone mineral density, which is not impaired by diabetes, rendering current tests ineffective. CT-based finite element analysis (CTFEA) calculates the mechanical response of bone to load and uses the yield strain, which is reduced in T2DM patients, to measure bone strength. The purpose of this feasibility study was to examine whether CTFEA could be used to assess the hip fracture risk for T2DM patients.

Aims

A proximal femur fracture (PFF) is a common orthopaedic presentation, with an incidence of over 25,000 cases reported in the Australian and New Zealand Hip Fracture Registry (ANZHFR) in 2018. Hip fractures are known to have high mortality. The purpose of this study was to determine the utility of the Clinical Frailty Scale (CFS) in predicting 30-day and one-year mortality after a PFF in older patients.

Objectives

Different criteria for assessing the reduction quality of trochanteric fractures have been reported. The Baumgaertner reduction quality criteria (BRQC) are relatively common and the Chang reduction quality criteria (CRQC) are relatively new. The objectives of the current study were to compare the reliability of the BRQC and CRQC in predicting mechanical complications and to investigate the clinical implications of the CRQC.

Aims

Leucocyte esterase (LE) has been shown to be an accurate marker of prosthetic joint infection (PJI), and has been proposed as an alternative to frozen section (FS) histology for intraoperative diagnosis. In this study, the intraoperative assessment of LE was compared with FS histology for the diagnosis of prosthetic hip infection.

Objectives

In order to address acetabular defects, porous metal revision acetabular components and augments have been developed, which require fixation to each other. The fixation technique that results in the smallest relative movement between the components, as well as its influence on the primary stability with the host bone, have not previously been determined.

Aims

This study presents the long-term survivorship, risk factors for prosthesis survival, and an assessment of the long-term effects of changes in surgical technique in a large series of patients treated by metal-on-metal (MoM) hip resurfacing arthroplasty (HRA).

Objectives

Alarm over the reported high failure rates for metal-on-metal (MoM) hip implants as well as their potential for locally aggressive Adverse Reactions to Metal Debris (ARMDs) has prompted government agencies, internationally, to recommend the monitoring of patients with MoM hip implants. Some have advised that a blood ion level >7 µg/L indicates potential for ARMDs. We report a systematic review and meta-analysis of the performance of metal ion testing for ARMDs.