Search

Background: Early revision for total hip arthroplasty is a serious adverse outcome. There are multiple contributing risk factors for early revision. Risk factors can exist at the level of the surgeon and the level of the institution. The primary research question of this study was to determine the relative contribution of surgeon-level and hospital-level variance to rates of early revision (overall and for infection) after primary total hip arthroplasty., Methods: This is a registry-based study from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR). Data for the most commonly used stem (Exeter V40) were used to reduce prosthesis variation from the analysis. A mixed effects Cox Model (also known as a frailty model) with crossed random effects for surgeon and hospital was used. Outcomes were early revision (within 2 years) for all causes and for infection. This model allowed for the risk of early revision to be explained by the variability at the surgeon level or hospital level., Results: There were 32 031 procedures performed by 735 surgeons across 250 hospitals between 1 January 2015 and 31 December 2019. Surgeon variability significantly contributed to overall variation in revision for any cause and revision for infection (P < 0.0001). There was no significant contribution of hospital-level variation to overall revision or for infection., Conclusions: Surgeon-level factors play a more important role than institution-level factors in early revision after primary total hip arthroplasty. If surgeons are identified as having a higher risk of revision, there is potential for surgeon-level practice change to reduce the risk of early revision., (© 2024 Royal Australasian College of Surgeons.)

Purpose: To evaluate the Oxford Knee Score (OKS), EQ-5D-5L utility index and EQ-5D visual analogue scale (EQ-VAS) for health-related quality of life outcome measurement in patients undergoing elective total knee arthroplasty (TKA) surgery., Methods: In this prospective multi-centre study, the OKS and EQ-5D-5L index scores were collected preoperatively, six weeks (6w) and six months (6 m) following TKA. The OKS, EQ-VAS and EQ-5D-5L index were evaluated for minimally important difference (MID), concurrent validity, predictive validity (Spearman's Rho of predicted and observed values from a generalised linear regression model (GLM)), responsiveness (effect size (ES) and standard response mean (SRM)). The MID for the individual patient was determined utilising two approaches; distribution-based and anchor-based., Results: 533 patients were analysed. The EQ-5D-5L utility index showed good concurrent validity with the OKS (r = 0.72 preoperatively, 0.65 at 6w and 0.69 at 6 m). Predictive validity for the EQ-5D-5L index was lower than OKS when regressed. Responsiveness was large for all fields at 6w for the EQ-5D-5L and OKS (EQ-5D-5L ES 0.87, SRM 0.84; OKS ES 1.35, SRM 1.05) and 6 m (EQ-5D-5L index ES 1.31, SRM 0.95; OKS ES 1.69, SRM 1.59). The EQ-VAS returned poorer results, at 6w an ES of 0.37 (small) and SRM of 0.36 (small). At 6 m, the EQ-VAS had an ES of 0.59 (moderate) and SRM of 0.47 (small). It, however, had similar predictive validity to the OKS, and better than the EQ-5D-5L index. MID determined using anchor approach, was shown that for OKS at 6 weeks it was 8.84 ± 9.28 and at 6 months 13.37 ± 9.89. For the EQ-5D-5L index at 6 weeks MID was 0.23 ± 0.39, and at 6 months 0.26 ± 0.36., Conclusions: The EQ-5D-5L index score and the OKS demonstrate good concurrent validity. The EQ-5D-5L index demonstrated lower predictive validity at 6w, and 6 m than the OKS, and both PROMs had adequate responsiveness. The EQ-VAS had poorer responsiveness but better predictive validity than the EQ-5D-5L index. This article includes MID estimates for the Australian knee arthroplasty population., (© 2023. The Author(s).)

Background: Traumatic tarsometatarsal joint (TMTJ) injuries are uncommon, but can cause significant morbidity due to delayed or missed diagnosis. Recent evidence highlights the importance of achieving anatomical reduction via operative management. This study aims to analyse trends in rates of open reduction internal fixation (ORIF) for Lisfranc injuries in Australia according to Nationwide claims data., Methods: Claims according to the Medicare Benefits Schedule (MBS) on ORIF of traumatic TMTJ injuries were collated for the period from January 2000 to December 2020. Paediatric patients were excluded. Two negative binomial models were utilized to analyse the trends in TMTJ injuries over time after controlling for sex, age group and population changes. Results were absolute and per 100 000 population., Results: 7840 patients underwent TMTJ ORIF over the period studied. There was a mean yearly increase of 12% (P < 0.001). Age group (P < 0.001) and year (P < 0.001) were significant predictors for TMTJ fixation, whilst sex was not (P = 0.48). Patients older than 65 years were noted to have a 53% lower rate of TMTJ ORIF per person, when compared to the reference group of 25-34 year-olds (P < 0.001). Five-year block analysis revealed increases in rate of fixation for all age groups., Conclusion: Rates of operative fixation for TMTJ injuries are increasing in Australia. This is likely due to improved diagnostics, understanding of optimal treatment goals, and increased orthopaedic subspecialisation. Further studies with clinical and patient reported outcomes, as well as comparing rates of operative intervention to incidence, will be useful., (© 2023 Royal Australasian College of Surgeons.)

Background: Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.4 billion annually in the United States alone. Forty percent of delirium episodes are preventable, and accurate risk stratification can decrease the incidence and improve clinical outcomes in patients. A previously developed clinical prediction model (the SORG Orthopaedic Research Group hip fracture delirium machine-learning algorithm) is highly accurate on internal validation (in 28,207 patients with hip fractures aged 60 years or older in a US cohort) in identifying at-risk patients, and it can facilitate the best use of preventive interventions; however, it has not been tested in an independent population. For an algorithm to be useful in real life, it must be valid externally, meaning that it must perform well in a patient cohort different from the cohort used to "train" it. With many promising machine-learning prediction models and many promising delirium models, only few have also been externally validated, and even fewer are international validation studies., Question/purpose: Does the SORG hip fracture delirium algorithm, initially trained on a database from the United States, perform well on external validation in patients aged 60 years or older in Australia and New Zealand?, Methods: We previously developed a model in 2021 for assessing risk of delirium in hip fracture patients using records of 28,207 patients obtained from the American College of Surgeons National Surgical Quality Improvement Program. Variables included in the original model included age, American Society of Anesthesiologists (ASA) class, functional status (independent or partially or totally dependent for any activities of daily living), preoperative dementia, preoperative delirium, and preoperative need for a mobility aid. To assess whether this model could be applied elsewhere, we used records from an international hip fracture registry. Between June 2017 and December 2018, 6672 patients older than 60 years of age in Australia and New Zealand were treated surgically for a femoral neck, intertrochanteric hip, or subtrochanteric hip fracture and entered into the Australian & New Zealand Hip Fracture Registry. Patients were excluded if they had a pathological hip fracture or septic shock. Of all patients, 6% (402 of 6672) did not meet the inclusion criteria, leaving 94% (6270 of 6672) of patients available for inclusion in this retrospective analysis. Seventy-one percent (4249 of 5986) of patients were aged 80 years or older, after accounting for 5% (284 of 6270) of missing values; 68% (4292 of 6266) were female, after accounting for 0.06% (4 of 6270) of missing values, and 83% (4690 of 5661) of patients were classified as ASA III/IV, after accounting for 10% (609 of 6270) of missing values. Missing data were imputed using the missForest methodology. In total, 39% (2467 of 6270) of patients developed postoperative delirium. The performance of the SORG hip fracture delirium algorithm on the validation cohort was assessed by discrimination, calibration, Brier score, and a decision curve analysis. Discrimination, known as the area under the receiver operating characteristic curves (c-statistic), measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities, a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest., Results: The SORG hip fracture algorithm, when applied to an external patient cohort, distinguished between patients at low risk and patients at moderate to high risk of developing postoperative delirium. The SORG hip fracture algorithm performed with a c-statistic of 0.74 (95% confidence interval 0.73 to 0.76). The calibration plot showed high accuracy in the lower predicted probabilities (intercept -0.28, slope 0.52) and a Brier score of 0.22 (the null model Brier score was 0.24). The decision curve analysis showed that the model can be beneficial compared with no model or compared with characterizing all patients as at risk for developing delirium., Conclusion: Algorithms developed with machine learning are a potential tool for refining treatment of at-risk patients. If high-risk patients can be reliably identified, resources can be appropriately directed toward their care. Although the current iteration of SORG should not be relied on for patient care, it suggests potential utility in assessing risk. Further assessment in different populations, made easier by international collaborations and standardization of registries, would be useful in the development of universally valid prediction models. The model can be freely accessed at: https://sorg-apps.shinyapps.io/hipfxdelirium/ ., Level of Evidence: Level III, therapeutic study., Competing Interests: All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2022 by the Association of Bone and Joint Surgeons.)

Background: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of stay, and higher healthcare costs. Natural language processing (NLP) is an artificial intelligence method that assists in understanding free-text notes that may be useful in the automated surveillance of adverse events in orthopaedic surgery. A previously developed NLP algorithm is highly accurate in the detection of incidental durotomy on internal validation and external validation in an independent cohort from the same country. External validation in a cohort with linguistic differences is required to assess the transportability of the developed algorithm, referred to geographical validation. Ideally, the performance of a prediction model, the NLP algorithm, is constant across geographic regions to ensure reproducibility and model validity., Question/purpose: Can we geographically validate an NLP algorithm for the automated detection of incidental durotomy across three independent cohorts from two continents?, Methods: Patients 18 years or older undergoing a primary procedure of (thoraco)lumbar spine surgery were included. In Massachusetts, between January 2000 and June 2018, 1000 patients were included from two academic and three community medical centers. In Maryland, between July 2016 and November 2018, 1279 patients were included from one academic center, and in Australia, between January 2010 and December 2019, 944 patients were included from one academic center. The authors retrospectively studied the free-text operative notes of included patients for the primary outcome that was defined as intraoperative durotomy. Incidental durotomy occurred in 9% (93 of 1000), 8% (108 of 1279), and 6% (58 of 944) of the patients, respectively, in the Massachusetts, Maryland, and Australia cohorts. No missing reports were observed. Three datasets (Massachusetts, Australian, and combined Massachusetts and Australian) were divided into training and holdout test sets in an 80:20 ratio. An extreme gradient boosting (an efficient and flexible tree-based algorithm) NLP algorithm was individually trained on each training set, and the performance of the three NLP algorithms (respectively American, Australian, and combined) was assessed by discrimination via area under the receiver operating characteristic curves (AUC-ROC; this measures the model's ability to distinguish patients who obtained the outcomes from those who did not), calibration metrics (which plot the predicted and the observed probabilities) and Brier score (a composite of discrimination and calibration). In addition, the sensitivity (true positives, recall), specificity (true negatives), positive predictive value (also known as precision), negative predictive value, F1-score (composite of precision and recall), positive likelihood ratio, and negative likelihood ratio were calculated., Results: The combined NLP algorithm (the combined Massachusetts and Australian data) achieved excellent performance on independent testing data from Australia (AUC-ROC 0.97 [95% confidence interval 0.87 to 0.99]), Massachusetts (AUC-ROC 0.99 [95% CI 0.80 to 0.99]) and Maryland (AUC-ROC 0.95 [95% CI 0.93 to 0.97]). The NLP developed based on the Massachusetts cohort had excellent performance in the Maryland cohort (AUC-ROC 0.97 [95% CI 0.95 to 0.99]) but worse performance in the Australian cohort (AUC-ROC 0.74 [95% CI 0.70 to 0.77])., Conclusion: We demonstrated the clinical utility and reproducibility of an NLP algorithm with combined datasets retaining excellent performance in individual countries relative to algorithms developed in the same country alone for detection of incidental durotomy. Further multi-institutional, international collaborations can facilitate the creation of universal NLP algorithms that improve the quality and safety of orthopaedic surgery globally. The combined NLP algorithm has been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/nlp&#95;incidental&#95;durotomy/ . Clinicians and researchers can use the tool to help incorporate the model in evaluating spine registries or quality and safety departments to automate detection of incidental durotomy and optimize prevention efforts., Level of Evidence: Level III, diagnostic study., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2022 by the Association of Bone and Joint Surgeons.)

The South Australian Bone Bank had expanded to meet an increased demand for allograft bone. During a 5 year period from 1988 to 1992, 2361 allografts were harvested from 2146 living donors and 30 cadaveric donors. The allografts were screened by contemporary banking techniques which include a social history, donor serum tests for HIV-1, HIV-2, hepatitis B and C, syphilis serology, graft microbiology and histology. Grafts were irradiated with 25 kGy. The majority of grafts were used for arthroplasty or spinal surgery and 99 were used for tumour reconstruction. Of the donated grafts 336 were rejected by the bank. One donor was HIV-positive and two had false positive screens. There were seven donors with positive serology for hepatitis B, eight for hepatitis C and nine for syphilis. Twenty-seven grafts had positive cultures. Bone transplantation is the most frequent non-haematogenous allograft in South Australia and probably nationally. The low incidence of infectious viral disease in the donor population combined with an aggressive discard policy has ensured relative safety of the grafts. The frequency of graft rejection was similar to other bone banks but the incidence of HIV was lower.