Background The minimum size required for a successful quadrupled hamstring autograft ACL reconstruction remains controversial. The risks of ACL re-tear in younger patients who tend to participate in a higher level of sports activity, and female athletes who have numerous predisposing factors, are poorly defined. Purpose To identify risk factors for graft re-tears within 2 years of ACL surgery. The hypotheses are that female sex, a smaller size graft, and younger patients will increase the odds of failure. Study Design Cohort Study. Level of evidence, 3.

A cohort of 503 athletes undergoing primary, autograft hamstring ACL reconstruction, performed by a single surgeon using the same surgical technique and rehabilitation protocol, between September-December 2012, was followed for a total duration of 2 years. Return to play was allowed between 6 and 12 months post-surgery upon completion of functional testing. Exclusion criteria included infections, revisions, double bundle techniques, multi-ligament injuries, non-compliance, BTB/allografts/hybrid grafts. Primary outcome consisted of binary data (ACL graft re-tear or no tear) as measured on physical exam (Lachman and pivot shift) and MRI. Multivariate logistic regression statistical analysis with model fitting was used to investigate the predictive value of sex, age, and graft size on ACL re-tear. Secondary sensitivity analyses were performed on the adolescent subgroup, age and graft size as categorical variables, and testing for interactions among variables. Sample size was calculated based on the rule of 10 events per independent variable for logistic regression.

The mean age of the 503 athletes was 27.5 (SD 10.6; range = 12–61). There were 235 females (47%) and 268 males (53%) with a 6 % rate of re-tears (28 patients; 17 females). Mean graft size was 7.9 (SD 0.6; range = 6–10). Univariate analyses of graft size, sex, and age only in the model showed that younger age (odds ratio [OR] = 0.86; 95% confidence interval [CI] = 0.80–0.93; P = .001] and smaller graft size (OR = 0.36; 95% CI = 0.18–0.70; P = .003) were significantly predictive of re-tear. Female sex was correlated with re-tear but was not significant (OR = 1.8; 95% CI = 0.84–3.97; P = .13). Multivariate analysis with all 3 variables in the model showed similar significant results. Graft size < 8 mm (OR = 2.95; 95% CI = 1.33–6.53; P = .008) and age < 25 (OR = 7.01; 95% CI = 2.40–20.53; P = .001) were significantly predictive of re-tear. Entire model was statistically significant (Omnibus test P = .001; Hosmer-Lemeshow statistic P = .68; Receiver Operating Curve [ROC] = 0.8).

Surgeons should counsel their patients who are female, younger than 25 and with a graft size less than 8 mm accordingly and consider modifying their surgical or rehabilitation techniques to mitigate these re-tear risks.

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.

Femoroacetabular Impingement (FAI) syndrome, characterised by abnormal hip contact causing symptoms and osteoarthritis, is measured using the International Hip Outcome Tool (iHOT). This study uses machine learning to predict patient outcomes post-treatment for FAI, focusing on achieving a minimally clinically important difference (MCID) at 52 weeks. A retrospective analysis of 6133 patients from the NAHR who underwent hip arthroscopic treatment for FAI between November 2013 and March 2022 was conducted. MCID was defined as half a standard deviation (13.61) from the mean change in iHOT score at 12 months. SKLearn Maximum Absolute Scaler and Logistic Regression were applied to predict achieving MCID, using baseline and 6-month follow-up data. The model's performance was evaluated by accuracy, area under the curve, and recall, using pre-operative and up to 6-month postoperative variables. A total of 23.1% (1422) of patients completed both baseline and 1-year follow-up iHOT surveys. The best results were obtained using both pre and postoperative variables. The machine learning model achieved 88.1% balanced accuracy, 89.6% recall, and 92.3% AUC. Sensitivity was 83.7% and specificity 93.5%. Key variables determining outcomes included MCID achievement at 6 months, baseline iHOT score, 6-month iHOT scores for pain, and difficulty in walking or using stairs. The study confirmed the utility of machine learning in predicting long-term outcomes following arthroscopic treatment for FAI. MCID, based on the iHOT 12 tools, indicates meaningful clinical changes. Machine learning demonstrated high accuracy and recall in distinguishing between patients achieving MCID and those who did not. This approach could help early identification of patients at risk of not meeting the MCID threshold one year after treatment

MCID and PASS are thresholds driven from PROMS to reflect clinical effectiveness. Statistical significance can be derived from a change in PROMS, whereas MCID and PASS reflect clinical significance. Its role has been increasingly used in the world of young adult hip surgery with several publications determining the thresholds for Femoro-acetabular impingement FAI. To our knowledge MCID and PASS for patient undergoing PAO for dysplasia has not been reported. 593 PAOs between 1/2013 and 7/2023 were extracted from the Northumbria Hip Preservation Registry. Patients with available PROMS at 1year and/or 2years were included. PAOs for retroversion, residual Perthes and those combined with FO were excluded. MCID was calculated using the distribution method 0.5SD of baseline score(1). PASS was calculated using anchor method, ROC analysis performed, and value picked maximizing Youden index. A Logistic Regression analysis was performed to determine which independent variables correlated with achieving MCID and PASS. The MCID threshold for iHOt12 was 8.6 with 83.4 and 86.3 % of patients achieved it at 1 and 2 years respectively. The PASS score at 1 and 2 year follow up was 43 and 44 respectively, with 72.6 and 75.2% achieving it at 1 and 2 year postop. At 2 years a Higher preop iHOT 12 was associated with not achieving MCID and PASS (p<0.05). Preop acetabular version was negatively correlated with achieving MCID and previous hip arthroscopy was negatively correlated with PASS. The % of patients achieving MCID and PASS mimics that of FAI surgery (2). The negative correlation with preop iHOT12 reaffirms the importance of patient selection. The negative correlation of hip arthroscopy highlights the importance of having a high index of suspicion for dysplasia prior to hip arthroscopy and poorer outcomes of patients with mixed CAM and dysplasia pathology

Introduction. Knee arthroplasty (KA), encompassing Total Knee Replacement (TKR) and Unicompartmental Knee Replacement (UKR), is one of the most common orthopedic procedures, aimed at alleviating severe knee arthritis. Postoperative KA management, especially radiographic imaging, remains a substantial financial burden and lacks standardised protocols for its clinical utility during follow-up. Method. In this retrospective multicentre cohort study, data were analysed from January 2014 to March 2020 for adult patients undergoing primary KA at Imperial NHS Trust. Patients were followed over a five-year period. Four machine learning models were developed to evaluate if post-operative X-ray frequency can predict revision surgery. The best-performing model was used to assess the risk of revision surgery associated with different number of X-rays. Result. The study assessed 289 knees with a 2.4% revision rate. The revision group had more X-rays on average than the primary group. The best performing model was Logistic Regression (LR), which indicated that each additional X-ray raised the revision risk by 52% (p<0.001). Notably, having four or more X-rays was linked to a three-fold increase in risk of revision (OR=3.02; p<0.001). Our results align with the literature that immediate post-operative X-rays have limited utility, making the 2nd post-operative X-ray of highest importance in understanding the patient's trajectory. These insights can enhance management by improving risk stratification for patients at higher revision surgery risk. Despite LR being the best-performing model, it is limited by the dataset's significant class imbalance. Conclusion. X-ray frequency can independently predict revision surgery. This study provides insights that can guide surgeons in evidence-based post-operative decision-making. To use those findings and influence post-operative management, future studies should build on this predictive model by incorporating a more robust dataset, surgical indications, and X-ray findings. This will allow early identification of high-risk patients, allowing for personalised post-operative recommendations

Abstract. Introduction. Minimising postoperative complications and mortality in COVID-19 patients who were undergoing trauma and orthopaedic surgeries is an international priority. Aim was to develop a predictive nomogram for 30-day morbidity/mortality of COVID-19 infection in patients who underwent orthopaedic and trauma surgery during the coronavirus pandemic in the UK in 2020 compared to a similar period in 2019. Secondary objective was to compare between patients with positive PCR test and those with negative test. Methods. Retrospective multi-center study including 50 hospitals. Patients with suspicion of SARS-CoV-2 infection who had underwent orthopaedic or trauma surgery for any indication during the 2020 pandemic were enrolled in the study (2525 patients). We analysed cases performed on orthopaedic and trauma operative lists in 2019 for comparison (4417). Multivariable Logistic Regression analysis was performed to assess the possible predictors of a fatal outcome. A nomogram was developed with the possible predictors and total point were calculated. Results. Of the 2525 patients admitted for suspicion of COVID-19, 658 patients had negative preoperative test, 151 with positive test and 1716 with unknown preoperative COVID-19 status. Preoperative COVID-19 status, sex, ASA grade, urgency and indication of surgery, use of torniquet, grade of operating surgeon and some comorbidities were independent risk factors associated with 30-day complications/mortality. The 2020 nomogram model exhibited moderate prediction ability. In contrast, the prediction ability of total points of 2019 nomogram model was excellent. Conclusions. Nomograms can be used by orthopaedic and trauma surgeons as a practical and effective tool in postoperative complications and mortality risk estimation

Introduction. Next generation sequencing (NGS) has been shown to facilitate detection of microbes in a clinical sample, particularly in the setting of culture-negative periprosthetic joint infection (PJI). However, it is unknown whether every microbial DNA signal detected by NGS is clinically relevant. This multi-institutional study was conceived to 1) identify species detected by NGS that may predict PJI, then 2) build a predictive model for PJI in a developmental cohort; and 3) validate the predictive utility of the model in a separate multi-institutional cohort. Methods. This multicenter investigation involving 15 academic institutions prospectively collected samples from 194 revision total knee arthroplasties (TKA) and 184 revision hip arthroplasties (THA) between 2017–2019. Patients undergoing reimplantation or spacer exchange procedures were excluded. Synovial fluid, deep tissue and swabs were obtained at the time of surgery and shipped to MicrogenDx (Lubbock, TX) for NGS analysis. Deep tissue specimens were also sent to the institutional labs for culture. All patients were classified per the 2018 Consensus definition of PJI. Microbial DNA analysis of community similarities (ANCOM) was used to identify 17 candidate bacterial species out of 294 (W-value >50) for differentiating infected vs. noninfected cases. Logistic Regression with LASSO model selection and random forest algorithms were then used to build a model for predicting PJI. For this analysis, ICM classification was the response variable (gold standard) and the species identified through ANCOM were the predictor variables. Recruited cases were randomly split in half, with one half designated as the training set, and the other half as the validation set. Using the training set, a model for PJI diagnosis was generated. The optimal resulting model was then tested for prediction ability with the validation set. The entire model-building procedure and validation was iterated 1000 times. From the model set, distributions of overall assignment rate, specificity, sensitivity, positive predictive value (PPV) and negative predicative value (NPV) were assessed. Results. The overall predictive accuracy achieved in the model was 75.9% (Figure 1). There was a high accuracy in true-negative and false-negative classification of patients using this predictive model (Figure 2), which has previously been a criticism of NGS interpretation and reporting. Specificity was 97.1%, PPV was 75.0%, and NPV was 76.2%. On comparison of the distribution of abundances between ICM-positive and ICM-negative patients, Staphylococcus aureus was the strongest contributor (F=0.99) to the predictive power of the model (Figure 3). In contrast, Cutibacterium acnes was less predictive (F=0.309) and noted to be abundant across both infected and noninfected revision TJA samples. Discussion. This study is the first to utilize predictive modeling algorithms on a large prospective multicenter database in order to transform analytic NGS data into a clinically relevant diagnostic signal. Our collaborative findings suggest the microbial DNA signal identified on NGS may be an independent useful adjunct for the diagnosis of PJI, as well as help identify causative organisms. Further work applying artificial intelligence tools will improve accuracy, predictive power and clinical utility of high-throughput sequencing technology. For figures, tables, or references, please contact authors directly

Aims: We undertook an analysis to determine the prognostic indicators of successful outcome following decompression for radiculopathy from lumbar spinal stenosis. Methods: 203 patients underwent spinal decompression in a þve year period till June 2001 and were subsequently followed up. Age, sex, number of levels, the speciþc levels involved, type of stenosis, symptoms, duration, bilaterality were preoperative factors looked at. The type of decompression, number of levels decompressed, the speciþc levels and intra-operative complications were noted. Postoperative resolution of pain, duration to alleviation of pain were assessed. Patient satisfaction and discharge from clinic indicated successful outcome. Referral to the pain clinic reßected a failure of treatment. Results: 65% of patients who had primary decompression experienced satisfactory improvement in symptoms. Logistic Regression analysis showed that the presence of radicular pain at þrst review signiþcantly increased the likelihood of failure of surgery and referral to the pain clinic for (p=0.02) for leg symptoms. 57% of patients who had decompression following previous surgery at the same level were relieved of leg pain. The duration of leg pain alone adversely affected þnal outcome (p=0.01) amongst all the factors assessed including complications from surgery. The complication rate from revision surgery (7/30) was signiþcantly greater (p=0.01) than primary decompression (16/173). Conclusions: Persistance of radicular pain early after decompression increases the likelihood of eventual failure to improve symptoms Patients with long periods of pain prior to decompression following previous back surgery should be cautioned about the decreased likelihood of success and an increased risk of complications

Aim: Evaluate the incidence of complications related to timing (time between admission ad operation) and oral antiplatelet/anticoagulant therapy in patients treated for a hip fracture. Materials and Methods: We prospectively evaluated 5 groups of 30 patients each, selected out of 875 consecutive patients admitted at the First Aid Unit of our Hospital with a proximal femoral fracture: group A – patients on Warfarin therapy, treated more than 5 days after admission (in order to allow the wash-out of Warfarin, as advised by many Anaesthesiologist Associations); B – patients treated more than 5 days after admission, not on Warfarin therapy; C – patients treated less than 48 hours after admission, not on Warfarin therapy; D – patients on Aspirin/NSAIDS therapy, treated more than 5 days after admission; E – patients on Ticlopidine/Clopidogrel therapy, treated more than 5 days after admission. The groups were comparable regarding age, gender, pre-trauma walking ability, mental state, fracture type and treatment. Blood loss, number of RBC transfusions, complications during hospitalization and up to 6 months after discharge, duration of hospitalization, degree of functional recovery and 2 years mortality were recorded. Statistical analysis included Kruskall-Wallis, U-Mann-Whitney and Logistic Regression Tests (SPSS 13.0 software). Results: Group A showed higher preoperative blood loss (p=0.002), and longer hospitalization (p< 0.001), compared to all other groups. Groups D and E showed no higher complication and mortality rate in comparison to group B and C, while group A showed higher complication and mortality rate. Standing alone, timing and Warfarin appear not to be significant risk factors, while taken together they represent a high risk factor for complications ad mortality (p=0.009). Conclusion: Patients on Warfarin therapy, affected by hip fracture, are at high risk of complications and mortality, if the recommendation of postponing treatment until drug wash-out is accepted. Reversal of anticoagulation using vitamin K and straight-forward treatment should be considered. Antiplatelet therapy appears not to have the same adverse effect as anticoagulant therapy

We identified patients with a poor outcome by examining cases where nerve conduction studies had been repeated after surgery. 168 patients were identified in whom two sets of tests had been performed. 28 were excluded as either they had no pre-operative studies or had insufficient clinical information. Our study group was 140 patients (174 hands) in whom NCS had been performed before and after surgery, with adequate clinical information. Information on the clinical outcome was obtained from postal questionnaires and from hospital records. A proportion of the hands in which two tests had been performed turned out to have been retested because of presentation with symptoms in the other hand, after a satisfactory outcome from surgery on the first side. This accounted for 44 of the 174 hands, and these were used as control group. 130 hands in 92 patients were identified as having a poor outcome from surgery. Of these, 39 underwent a further operation; two went on to a third procedure. Logistic Regression Analysis was used to analyze the data (Stastica). There was a trend for the poor results to be more common in the elderly, but age was not a statistically significant factor, (p< 0.36). The good results were found mainly in grades 2 to 5 and this was statistically significant, (p< 0.01). A poorer outcome was seen grouped in grades 0, 1, 2 and 6 and this was statistically significant. (p< 0.01). The NCS have been validated, are reproducible and cost about £15 per study. In the group with good outcome, the grade of severity of NCS improved or remained unchanged in the majority. Of the 39 re-explorations, 17 were found to have incomplete division of the ligament. Of these, 10 showed clinical improvement after re-operation. Of the 22 with no evidence of incomplete division, 7 were improved, 10 had persistent symptoms and 5 were worse after revision surgery. We believe that pre-operative NCS are helpful for two reasons: Firstly, they provide as a baseline for comparison if the patient has unsatisfactory result following decompression. Secondly, we have shown that they are of prognostic value

Older patients with multiple medical co-morbidities are increasingly being offered and undergoing total joint arthroplasty (TJA). These patients are more likely to require intensive care support, following surgery. We prospectively evaluated the need for intensive care admission and intervention in a consecutive series of 738 patients undergoing elective hip and knee arthroplasty procedures. The mean age was 60.6 years (18 to 91; 440 women, 298 men. Risk factors, correlating with the need for critical care intervention, according to published guidelines, were analysed to identify high-risk patients who would benefit from post-operative critical care monitoring. A total of 50 patients (6.7%) in our series required critical care level interventions during their hospital stay. Six independent multivariate clinical predictors were identified (p < 0.001) including a history of congestive heart failure (odds ratio (OR) 24.26, 95% confidence interval (CI) 9.51 to 61.91), estimated blood loss >  1000 mL (OR 17.36, 95% CI 5.36 to 56.19), chronic obstructive pulmonary disease (13.90, 95% CI 4.78 to 40.36), intra-operative use of vasopressors (OR 8.10, 95% CI 3.23 to 20.27), revision hip arthroplasty (OR 2.71, 95% CI 1.04 to 7.04) and body mass index > 35 kg/m² (OR 2.70, 95% CI 123 to 5.94). The model was then validated against an independent, previously published data set of 1594 consecutive patients. The use of this risk stratification model can be helpful in predicting which high-risk patients would benefit from a higher level of monitoring and care after elective TJA and aid hospitals in allocating precious critical care resources.

Cite this article: Bone Joint J 2015;97-B:1512–18.