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ObjectivesAccurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements.Materials and methodsThe AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated.ResultsA total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45–5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and − 0.70–1.95 mm for lengths. On average, AI was 130 s faster than clinicians.ConclusionAutomated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.

Purpose The purpose of this study was to evaluate the reliability of a newly developed AI-algorithm for the evaluation of long leg radiographs (LLR) after total knee arthroplasties (TKA). Methods In the validation cohort 200 calibrated LLRs of eight different common unconstrained and constrained knee systems were analysed. Accuracy and reproducibility of the AI-algorithm were compared to manual reads regarding the hip–knee–ankle (HKA) as well as femoral (FCA) and tibial component (TCA) angles. In the evaluation cohort all institutional LLRs with TKAs in 2018 ( n  = 1312) were evaluated to assess the algorithms’ ability of handling large data sets. Intraclass correlation (ICC) coefficient and mean absolute deviation (sMAD) were calculated to assess conformity between the AI software and manual reads. Results Validation cohort: The AI-software was reproducible on 96% and reliable on 92.1% of LLRs with an output and showed excellent reliability in all measured angles (ICC > 0.97) compared to manual measurements. Excellent results were found for primary unconstrained TKAs. In constrained TKAs landmark setting on the femoral and tibial component failed in 12.5% of LLRs ( n  = 9). Evaluation cohort: Mean measurements for all postoperative TKAs ( n  = 1240) were 0.2° varus ± 2.5° (HKA), 89.3° ± 1.9° (FCA), and 89.1° ± 1.6° (TCA). Mean measurements on preoperative revision TKAs ( n  = 74) were 1.6 varus ± 6.4° (HKA), 90.5° ± 3.1° (FCA), and 88.9° ± 4.1° (TCA). Conclusions AI-powered applications are reliable for automated analysis of lower limb alignment on LLRs with TKAs. They are capable of handling large data sets and could, therefore, lead to more standardized and efficient postoperative quality controls. Level of evidence Diagnostic Level III.

Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020. Automated landmark placement was conducted using the AI-algorithm LAMA™ and the measurements were used to determine femoral, tibial and total leg-length. Linear regression equations were subsequently derived for stature estimation. The estimated regression equations have a shallower slope and larger intercept in males and females (Femur-male: slope = 2.08, intercept = 77.49; Femur-female: slope = 1.9, intercept = 79.81) compared to the formulae previously derived by Trotter and Gleser 1952 (Femur-male: slope = 2.38, intercept = 61.41; Femur-female: slope = 2.47, intercept = 54.13) and Trotter and Gleser 1958 (Femur-male: slope = 2.32, intercept = 65.53). All long-bone measurements showed a high correlation ( r  ≥ 0.76) with stature. The linear equations we derived tended to overestimate stature in short persons and underestimate stature in tall persons. The differences in slopes and intercepts from those published by Trotter and Gleser (1952, 1958) may result from an ongoing secular increase in stature. Our study illustrates that AI-algorithms are a promising new tool enabling large-scale measurements.

IntroductionPatients who require a spacer exchange as part of a two-stage procedure for the treatment of periprosthetic hip and knee joint infections (PJI) have high failure rates. Little is known about the clinical impact of microbiological results and changes in the microbiological spectrum and resistance pattern in these patients.Material and MethodsBetween 01/2011 and 12/2019, 312 patients underwent a total of 327 two-stage revision arthroplasties at our institution. A spacer exchange was required in 52/312 (16.7%) patients (27 knee/25 hip). Microbiological results, antibiotic resistance patterns, patient’s host factors as well as re-revision and re-infection rates at a median follow-up of 47.8 months (range 12.2–116.7 months) were analyzed. A propensity score (PS)-matched analysis of patients who underwent spacer exchange and patients treated with standard two-stage procedure was performed.ResultsWe found a high number of microbiological spectrum changes in patients with multiple culture positive procedures between explantations and spacer exchanges (10/12 [83.3%]), spacer exchanges and reimplantations (3/4 [75%]) as well as between reimplantations and subsequent re-revision surgeries (5/6 [83.3%]). In 9/52 (17.3%) patients, same microorganisms were detected repeatedly in two different procedures. We observed changes in the antibiotic resistance patterns in 6/9 (66.7%) of these patients. High re-infection rates were found in patients with culture positive reimplantations (10/12 [83.3%]), and low re-infection rates were found in patients with culture negative reimplantations (2/40 [5%]; p < 0.001). Between patients with and without spacer exchange, no differences were found in the re-revision rates (13/52 [25%] with vs. 13/52 [25%] without; p = 1.00) as well as re-infection rates (12/52 [23.1%] with vs. 8/52 [15.4%] without; p = 0.32).ConclusionsChanges in microbiological spectrum and antibiotic resistance patterns between stages are common in patients who require a spacer exchange. If eradication of the microorganism at reimplantation can be accomplished, comparable re-revision rates to standard two-stage procedures can be achieved.

Introduction The assessment of the knee alignment on long leg radiographs (LLR) postoperative to corrective knee osteotomies (CKOs) is highly dependent on the reader’s expertise. Artificial Intelligence (AI) algorithms may help automate and standardise this process. The study aimed to analyse the reliability of an AI-algorithm for the evaluation of LLRs following CKOs. Materials and methods In this study, we analysed a validation cohort of 110 postoperative LLRs from 102 patients. All patients underwent CKO, including distal femoral (DFO), high tibial (HTO) and bilevel osteotomies. The agreement between manual measurements and the AI-algorithm was assessed for the mechanical axis deviation (MAD), hip knee ankle angle (HKA), anatomical-mechanical-axis-angle (AMA), joint line convergence angle (JLCA), mechanical lateral proximal femur angle (mLPFA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibia angle (mMPTA) and mechanical lateral distal tibia angle (mLDTA), using the intra-class-correlation (ICC) coefficient between the readers, each reader and the AI and the mean of the manual reads and the AI-algorithm and Bland–Altman Plots between the manual reads and the AI software for the MAD, HKA, mLDFA and mMPTA. Results In the validation cohort, the AI software showed excellent agreement with the manual reads (ICC: 0.81–0.99). The agreement between the readers (Inter-rater) showed excellent correlations (ICC: 0.95–0. The mean difference in the DFO group for the MAD, HKA, mLDFA and mMPTA were 0.50 mm, − 0.12°, 0.55° and 0.15°. In the HTO group the mean difference for the MAD, HKA, mLDFA and mMPTA were 0.36 mm, − 0.17°, 0.57° and 0.08°, respectively. Reliable outputs were generated in 95.4% of the validation cohort. Conclusion  he application of AI-algorithms for the assessment of lower limb alignment on LLRs following CKOs shows reliable and accurate results. Level of evidence Diagnostic Level III.

Purpose Unexpected-positive-intraoperative-cultures (UPIC) are common in presumed aseptic revision-total-knee-arthroplasties (rTKA). However, the clinical significance is not entirely clear. In contrast, in some presumably septic rTKA, identification of an underlying pathogen was not possible, so-called unexpected-negative-intraoperative-cultures (UNIC). The purpose of this study was to evaluate the potential use of synovial alpha-defensin (AD) levels in these patients. Methods Synovial AD levels from 143 rTKAs were evaluated retrospectively from our prospectively maintained institutional periprostetic joint infection (PJI) biobank and database. The 2018-International Consensus Meeting (ICM) criteria was used to define the study groups. Samples from UPICs with a minimum of one positive intraoperative culture (ICM 2- ≥ 6) ( n  = 20) and UNIC’s (ICM ≥ 6) ( n  = 14) were compared to 34 septic culture-positive samples (ICM ≥ 6) and 75 aseptic culture-negative (ICM 0-1). Moreover, AD-lateral-flow-assay (ADLF) and an enzyme-linked-immunosorbent-assay (ELISA) in detecting the presence of AD in native and centrifuged synovial fluid specimens was performed. Concentration of AD determined by ELISA and ADLF methods, as well as microbiological, and histopathological results, serum and synovial parameters along with demographic factors were analysed. Results AD was positive in 31/34 (91.2%) samples from the septic culture-positive group and in 14/14 (100%) samples in the UNIC group. All UPIC samples showed a negative AD result. Positive AD samples were highly associated with culture positive and histopathological results ( p  < 0.001). No high-virulent microorganisms (0/20) were present in the UPIC group, compared to infected-group (19/34; 55.9%). High virulent microorganisms showed a positive AD result in 89.5% (17/19) of the cases. Methicillin resistant Staphylococcus epidermis (MRSE) infections had significantly higher AD levels than with methicillin susceptible S. epidermdis (MSSE) ( p  = 0.003). ELISA and ADLF tests were positive with centrifuged (8/8) and native (8/8) synovial fluid. Conclusion AD showed a solid diagnostic performance in infected and non-infected revisions, and it provided an additional value in the diagnosis of UPIC and UNIC associated to rTKAs. Pathogen virulence as well as antibiotic resistance pattern may have an effect on AD levels. Centrifugation of synovial fluid had no influence on ADLF results.

BackgroundPeriprosthetic joint infections (PJI) are a major concern in shoulder arthroplasty, which in some cases require two-stage exchange. While it was shown that low-virulence bacteria are the most isolated pathogens in shoulder PJI, little is known about changes in microbiological spectrum and resistance patterns during two-stage revision.MethodsThis retrospective study included all patients (n = 25) who received a two-stage revision from January 2011 to December 2020 for shoulder PJI in one institution. Microbiological spectrum, antimicrobial resistance patterns, and re-revision rates of culture positive first- and second-stage procedures were analyzed. The mean follow-up time was 29.7 months (range 8; 115 months). At final follow-up, subjective shoulder value (SSV) and visual analog scale (VAS) score for pain and satisfaction with the surgery were assessed.ResultsIn 25 patients, a total of 54 2-stage exchange procedures were performed and positive cultures were obtained in 36 of these surgeries (66.7%). A total of 7 out of 25 patients (28.0%) showed a positive microbiological culture at first and second stages. In those patients, the mean time between first and second stages was 30.9 weeks (range 6; 70). Three out of those seven patients (42.9%) had a polymicrobial spectrum with one microorganism persistent at stage two, including Cutibacterium acnes (n = 1) and Staphylococcus epidermidis (MRSE) (n = 2). In all these cases, antimicrobial resistance patterns changed. All cultures with monomicrobial spectrum (n = 4) at first stage showed a changed spectrum. Patients with positive first- and second-stage revisions showed a mean SSV of 49.3% ± 23.5 versus 52.9% ± 29.5 in single positive patients (p = 0.76). Re-revision was performed in five cases, two of those in patients with positive first- and second-stage cultures.ConclusionThere is a high rate of changes in microbiological spectrum and resistance patterns between culture positive first- and second-stage procedures as well as subsequent re-revisions. Intraoperative samples during reimplantation should be taken and resistance reconsidered in case of re-revision.

Background In recent years, total hip arthroplasty via the direct anterior approach (DAA) has become more common. Little is known on the influence of the surgical approach on the microbiological spectrum and resistance pattern in periprosthetic hip joint infections. The aim of the present study was to evaluate the microbiological spectrum and resistance pattern in periprosthetic hip joint infections comparing the direct anterior versus lateral approach in a matched-cohort analysis at a single institution. Methods Patients who underwent revision hip arthroplasty due to PJI following primary total hip arthroplasty with culture positive microbiology were analyzed. In all study patients, both the primary surgery and the revisions surgery were performed at the same institution. Only patients in whom primary surgery was performed via a direct anterior or lateral approach were included ( n  = 87). A matched cohort analysis was performed to compare the microbiological spectrum and resistance pattern in PJI following direct anterior ( n  = 36) versus lateral ( n  = 36) primary THA. Results We identified both a significantly different microbiological spectrum and resistance pattern in PJI comparing direct anterior versus lateral approach THA. Cutibacterium avidum was obtained more frequently in the anterior subgroup (22.2% vs. 2.8%, p  = 0.028). In the subgroup of infections with Staphylococcus aureus ( n  = 12), methicillin resistance was detected in 3/5 cases in the direct anterior group versus 0/7 cases in the lateral group ( p  = 0.045). Overall, Staphylococcus epidermidis was the most common causative microorganism in both groups (direct anterior: 36.1%; lateral: 27.8%, p  = 0.448). Conclusion The present study indicates a potential influence of the localization of the skin incision in THA on the microbiological spectrum and resistance pattern in PJI. Cutibacterium avidum seemed to be a more common causative microorganism in PJI in patients who underwent direct anterior compared to lateral approach THA.

Treating Chronic Non-Cancer Pain (CNCP) with long-term, high dose and more potent opioids puts patients at increased risk of harm, whilst providing limited pain relief. Socially deprived areas mapped from Index of Multiple Deprivation (IMD) scores show higher rates of high dose, strong opioid prescribing compared to more affluent areas. To explore if opioid prescribing is higher in more deprived areas of Liverpool (UK) and assess the incidence of high dose prescribing to improve clinical pathways for opioid weaning. This retrospective observational study used primary care practice and patient level opioid prescribing data for N = 30,474 CNCP patients across Liverpool Clinical Commissioning Group (LCCG) between August 2016 and August 2018. A Defined Daily Dose (DDD) was calculated for each patient prescribed opioids. DDD was converted into a Morphine Equivalent Dose (MED) and patients stratified according to high (≥120mg) MED cut off. The association between prescribing and deprivation was analysed by linking GP practice codes and IMD scores across LCCG. 3.5% of patients were prescribed an average dose above 120mg MED/day. Patients prescribed long-term, high dose, strong opioids were more likely to be female, aged 60+, prescribed three opioids and reside in the North of Liverpool where there is a higher density of areas in the IMD most deprived deciles. A small but significant proportion of CNCP patients across Liverpool are currently prescribed opioids above the recommended dose threshold of 120mg MED. Identification of fentanyl as a contributor to high dose prescribing resulted in changes to prescribing practice, and reports from NHS pain clinics that fewer patients require tapering from fentanyl. In conclusion, higher rates of high dose opioid prescribing continue to be evident in more socially deprived areas further increasing health inequalities.

Background Patients with mental health conditions represent a significant concern in emergency departments, consistently ranking as the third or fourth most prevalent diagnoses during consultations. Globally, over the past two decades, there was a marked increase in such incidences, largely driven by a rise in nonurgent visits related to somatic complaints. However, the implications of these nonurgent visits for mental health patients remain unclear, and warrant further investigation. The aim of this study is to determine the significance of nonurgent mental health patients in emergency departments through pioneering a nationwide dataset collected over a 10-year observation period. Methods The study utilized German outpatient billing data at emergency departments of general hospitals (only emergency cases) from 16 Associations of Statutory Health Insurance Physicians (Kassenärztliche Vereinigungen (KVs)), overall covering the years 2012 to 2022, and analyzed in further detail for diagnoses of cannabis (THC-) related disorders (F12.x), depressive Episode (F32.x) and anxiety disorder (F41.x) in the year 2019. Results The analyses revealed a notable increase in the number of cases per 100,000 inhabitants over the study period, with an accelerated growth trend observed from 2016 onwards. Pandemic-related effects were evident during 2020–2021, followed by a renewed rise in such incidences during the post-pandemic period. Conclusions The increase in nonurgent mental health emergency cases could be influenced by prevalence and demographic changes, shifts in healthcare-seeking behavior, outpatient undersupply and enhanced documentation of psychiatric diagnoses. Consequently, further detailed analysis of the underlying causes and their implications for such evidence is required. In addition to expanding low-threshold services and public education, improved coordination within the regional care system—particularly through a strengthened gatekeeper role of general practitioners—could play a crucial role.