Big data analysis reveals significant increases in complications, costs, and hospital stay in revision total knee arthroplasty compared to primary TKA.

INTRODUCTION: Despite significant advancements in total knee arthroplasty (TKA), some patients require revision surgery (R-TKA) due to complications such as infection, mechanical loosening, instability, periprosthetic fractures, and persistent pain. This study aimed to explore the specific causes leading to R-TKA, associated complications, including infection, mechanical failure, and wound issues, as well as costs, mortality rates, and hospital length of stay (LOS) using data from a large national database. METHODS: Data from the nationwide inpatient sample (NIS), the largest publicly available all-payer inpatient care database in the United States were analysed from 1 January 2016 to 31 December 2019. The study included 44,649 R-TKA cases, corresponding to 223,240 patients, with exclusions for nonelective admissions. Various statistical analyses were used to assess clinical outcomes, including in-hospital mortality, postoperative complications, LOS, and hospitalization costs. RESULTS: Among 2,636,880 TKA patients, 8.4% underwent R-TKA. R-TKA patients had higher rates of chronic conditions, including mental disorders (36.4%) and renal disease (9.9%). Additionally, these patients often experienced instability, necessitating revision surgery. Infection (22.3%) was the primary reason for R-TKA, followed by mechanical loosening (22.9%) and instability. Compared to primary TKA patients, R-TKA patients exhibited higher in-hospital mortality (0.085% vs. 0.025%), longer LOS (3.1 vs. 2.28 days), and higher total charges ($97,815 vs. $62,188). Postoperative complications, including blood transfusion (4.6% vs. 1.3%), acute kidney injury (4.4% vs. 1.8%), venous thromboembolism (0.55% vs. 0.29%), infection, and wound problems, were significantly higher in R-TKA patients. CONCLUSIONS: This study provides detailed insights into t LOS, costs, and complications associated with specific etiologies of revision TKA. Our findings emphasize the need for targeted preoperative optimization and patient education. This approach can help reduce the incidence and burden of R-TKA, improve patient care, optimize resource allocation, and potentially decrease the overall rates of complications in revision surgeries. LEVEL OF EVIDENCE: Level III.

Optimizing the patellofemoral compartment in total knee arthroplasty: Is it time for dynamic assessment?

Despite improvements in implant design, surgical techniques and assistive technologies for total knee arthroplasty (TKA), anterior knee pain (AKP) remains frequently reported, even by satisfied patients. This persistent problem calls for better understanding and management of the patellofemoral or anterior compartment during surgery, just as the techniques and strategies deployed to optimize the flexion and extension spaces through personalized alignment, bone cuts and ligament balancing. Assistive technologies such as navigation and robotics provide new tools to manage this 'third space' through precise pre-operative planning and dynamic intra-operative assessment. Such endeavors must start with clear definitions of the 'third space', how it should be measured, what constitutes its 'safe zone', and how it affects outcomes. There are yet no established methods to evaluate the patellofemoral compartment, and no clear thresholds to define over- or under-stuffing. Static assessment using lateral radiographs provides a limited understanding and depends considerably on flexion angle, while dynamic evaluation at multiple flexion angles or using intra-operative computer or robotic-assistance enables a broader perspective and solutions to manage patellar tracking and anterior offset. Future studies should investigate the impact of variations in anterior offset in TKA, define its safe zone, and understand the effects of of thresholds for over- or under-stuffing. Experimental methods such as in-vivo motion analysis and force sensors could elucidate the influence of anterior offset on flexion and extension biomechanics.

Multiligament knee injury (MLKI): an expert consensus statement on nomenclature, diagnosis, treatment and rehabilitation.

Multiligament knee injuries (MLKIs) represent a broad spectrum of pathology with potentially devastating consequences. Currently, disagreement in the terminology, diagnosis and treatment of these injuries limits clinical care and research. This study aimed to develop consensus on the nomenclature, diagnosis, treatment and rehabilitation strategies for patients with MLKI, while identifying important research priorities for further study. An international consensus process was conducted using validated Delphi methodology in line with British Journal of Sports Medicine guidelines. A multidisciplinary panel of 39 members from 14 countries, completed 3 rounds of online surveys exploring aspects of nomenclature, diagnosis, treatment, rehabilitation and future research priorities. Levels of agreement (LoA) with each statement were rated anonymously on a 5-point Likert scale, with experts encouraged to suggest modifications or additional statements. LoA for consensus in the final round were defined 'a priori' if >75% of respondents agreed and fewer than 10% disagreed, and dissenting viewpoints were recorded and discussed. After three Delphi rounds, 50 items (92.6%) reached consensus. Key statements that reached consensus within nomenclature included a clear definition for MLKI (LoA 97.4%) and the need for an updated MLKI classification system that classifies injury mechanism, extent of non-ligamentous structures injured and the presence or absence of dislocation. Within diagnosis, consensus was reached that there should be a low threshold for assessment with CT angiography for MLKI within a high-energy context and for certain injury patterns including bicruciate and PLC injuries (LoA 89.7%). The value of stress radiography or intraoperative fluoroscopy also reached consensus (LoA 89.7%). Within treatment, it was generally agreed that existing literature generally favours operative management of MLKI, particularly for young patients (LoA 100%), and that single-stage surgery should be performed whenever possible (LoA 92.3%). This consensus statement will facilitate clinical communication in MLKI, the care of these patients and future research within MLKI.

Weight-bearing pain and implant migration, progressive radiolucencies, radiolucency more than 2 mm and subsidence on radiographs and CT are generally accepted criteria for knee arthroplasty loosening: An international Delphi consensus study.

PURPOSE: Establishing the diagnosis of loosening in total or unicondylar knee arthroplasty remains a challenge with different clinical and radiological signs evaluated in study designs with high risk of bias, where few or incomplete criteria are formulated for establishing the diagnosis of implant loosening. This study aimed at evaluating the variability between different clinical and radiological criteria and establish a consensus regarding clinical and radiological criteria for the diagnosis of knee arthroplasty loosening. METHODS: Highly specialized knee surgeons focusing on revision arthroplasty were invited to take part in an international panel for a Delphi consensus study. In the first round, the participants were asked to state their most important clinical and radiological criteria for implant loosening. In a second round, the panel's agreement with the collected criteria was rated on a 5-point Likert scale (1-5). High variability was defined by receiving at least one score each indicating complete disagreement and complete agreement. Consensus was established when over 70% of participants rated a criterion as 'fully agree' (5) or 'mostly agree' (4). RESULTS: High variability was observed in 56% of clinical criteria and 38% of radiological criteria. A consensus was reached on one clinical (weight-bearing pain [82%]) and four radiological criteria, that is, implant migration, progressive radiolucencies, subsidence and radiolucencies >2 mm on X-ray or computed tomography (CT) (84%-100%). CONCLUSION: Amongst specialized knee revision surgeons, there is high variability in clinical and radiological criteria that are seen as important contributing factors to diagnosis of knee implant loosening. A consensus was reached on weight-bearing pain as clinical criterion and on implant migration, progressive radiolucencies, subsidence and radiolucencies of more than 2 mm on X-ray or CT as radiological criteria. The variability rates observed, along with the criteria that reached consensus, offer important insights for the standardization of diagnostic protocols. LEVEL OF EVIDENCE: Level V.

The artificial intelligence advantage: Supercharging exploratory data analysis.

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.

Mechanically aligned total knee arthroplasty does not yield uniform outcomes across all coronal plane alignment of the knee (CPAK) phenotypes.

PURPOSE: Patient dissatisfaction rates following total knee arthroplasty (TKA) reported in the literature reach 20%. The optimal coronal alignment is still under debate. The aim of this retrospective study was to compare clinical outcomes in different coronal plane alignment of the knee (CPAK) phenotypes undergoing mechanically aligned (MA) TKA. The hypothesis was that knees with preoperative varus arithmetic hip-knee-ankle angle (aHKA) would achieve inferior clinical outcomes after surgery compared to other aHKA categories. Additionally, another objective was to assess CPAK phenotypes distribution in the study population. METHODS: A retrospective selection was made of 180 patients who underwent MA TKA from April 2021 to December 2022, with a 1-year follow-up. Coronal knee alignment was classified according to the CPAK classification. Clinical outcome evaluations were measured using the Knee Society Score (KSS), Oxford Knee Score (OKS), Short Form Survey 12 and Forgotten Joint Score (FJS). Differences in clinical outcomes were considered statistically significant with a p value <0 .05. RESULTS: Patients with varus aHKA achieved significantly inferior outcomes at final follow-up compared to other aHKA categories in KSS pt. 1 (79.7 ± 17.2 vs. 85.6 ± 14.7; p = 0.028), OKS (39.2 ± 9.2 vs. 42.2 ± 7.2; p = 0.019) and FJS (75.4 ± 31.0 vs. 87.4 ± 22.9; p =0 .003). The most common aHKA category was the varus category (39%). The most common CPAK phenotypes were apex distal Types I (23.9%), II (22.8%) and III (13.3%). CONCLUSION: MA TKA does not yield uniform outcomes across all CPAK phenotypes. Varus aHKA category shows significantly inferior results at final follow-up. The most prevalent CPAK categories are varus aHKA and apex distal JLO, with phenotypes I, II and III being the most common. However, their gender distribution varies significantly. LEVEL OF EVIDENCE: Level IV.

Visible fluid motion on manipulation as the new threshold for intraoperatively determined knee arthroplasty component loosening: A Delphi study.

PURPOSE: There is a lack of a clear, uniform definition for intraoperatively assessed component loosening of a knee arthroplasty component, complicating the interpretation and interchangeability of results of diagnostic studies using an intraoperative observation as the reference test. The purpose of this study was to establish a consensus among specialised knee revision surgeons regarding the definition of intraoperatively determined loosening of total or unicondylar knee arthroplasty components. METHODS: Utilising the Delphi consensus method, an international panel of highly specialised knee revision surgeons was invited to participate in a three-round process. The initiation of the first round involved the exploration of possible criteria for intraoperatively determined loosening with open questions. The second round focused on rating these criteria importance on a five-point Likert scale. For the third round, criteria that reached consensus were summarised in consecutive definitions for intraoperatively determined loosening and proposed to the panel. Consensus was established when over 70% of participants agreed with a definition for intraoperatively determined loosening. RESULTS: The 34 responding panel members described in total 60 different criteria in the first round of which 34 criteria received consensus in the second round. Summarising these criteria resulted in four different definitions as minimal requirements for intraoperatively determined loosening. Eighty-eight percent of the panel members agreed on defining a component as loose if there is visible fluid motion at the interface observed during specific movements or when gently applying direct force. CONCLUSION: This study successfully established a consensus using a Delphi method among knee revision surgeons on the definition of intraoperatively determined component loosening. By agreeing on the visibility of fluid motion as new definition, this study provides a standardised reference for future diagnostic research. This definition will enhance the interpretability and interchangeability of future diagnostic studies evaluating knee arthroplasty component loosening. LEVEL OF EVIDENCE: Level V.

No difference in postoperative patient satisfaction rates between mechanical and kinematic alignment total knee arthroplasty: A systematic review.

Purpose: The purpose of this systematic review was to compare patient satisfaction patient-reported outcomes (PROMs) levels after mechanically aligned (MA) and kinematically aligned (KA) total knee arthroplasty (TKA). Methods: A systematic literature search following PRISMA guidelines was conducted on PubMed, Embase, Medline and Scopus to identify potentially relevant articles for this review, published from the beginning of March 2013 until the end of October 2023. Only articles reporting satisfaction after KA TKA, MA TKA or both were included, which use valid and reliable tools for the evaluation and reporting of satisfaction after TKA. Title, authors, year of publication, study design, level of evidence, follow-up period, patients' demographic data, sample size, type of satisfaction score, postoperative satisfaction score, postoperative alignment, statistical significance, as well as other variables, were extracted for analysis. An Agency for Healthcare Research and Quality's (AHRQ) design-specific scale was used for assessing randomized control trials (RCTs). The nonrandomized control trials were evaluated by using the Joanna Briggs Institute's (JBI) Critical Appraisal Tool. The Newcastle-Ottawa Scale (NOS) was also used to assess cohort studies, while case series were evaluated using the NIH Quality Assessment Tool for Case Series Studies. Results: The initial search identified 316 studies, of which 178 were considered for screening. Eleven studies completely fulfilled the inclusion criteria, including one RCT, five nonrandomized control trials/quasi-experiments, three case series, and two cohort studies. The total number of patients recruited for MA TKA was 1740. Conversely, 497 patients were enrolled for KA TKA. Five studies used the visual analogue scale (VAS) for assessing postoperative patient satisfaction, four used the Knee Society Score (KSS) 2011 version and two Likert-based types of scores. Overall, the highest mean satisfaction score of KSS 2011 was 31.5 ± 6.6 in the MA group, and 29.8 ± 80 in the KA group in four studies. All of them showed high postoperative patient satisfaction rates for both MA and KA TKA, but with no statistically significant difference between them (p > 0.05). Conclusion: Both mechanically aligned total knee arthroplasty, as well as kinematically aligned total knee arthroplasty led to high rates of postoperative patient satisfaction, with no statistically significant differences between them. Level of Evidence: Level III, systematic review.

Limitations of the knee society score in kinematically aligned total knee arthroplasty.

Purpose: The Knee Society Scoring System (KSS) is a frequently used outcome score which quantifies functional patients' outcomes before and after total knee arthroplasty (TKA). Several problems arise when trying to implement KSS for obtaining postoperative outcomes after more personalised aligned TKAs. Scoring for valgus femorotibial angle (FTA) intervals outside moderate ranges is often poorly explained, the specific version of KSS used for outcome collection is frequently unclear and the exact measuring methods are typically not described in the literature. The aims of this systematic review were to investigate the latest user practice, the application of KSS and its limitations after kinematically aligned (KA) TKA. Methods: A systematic literature search following PRISMA guidelines was conducted on PubMed, Embase, Medline and Scopus to identify potentially relevant articles for this review, published from the beginning of January 2013 until the end of January 2023. Broad Mesh terms such as 'kinematic alignment', 'total knee arthroplasty' and 'knee society score' were used for building search strategy in each database accordingly. Articles reporting postoperative values of the objective surgeon-assessed KSS after KA TKA or KA and mechanically aligned TKA were included. For assessing included randomised control trials (RCTs), an Agency for Healthcare Research and Quality's design-specific scale for assessing RCTs was used. The non-RCTs were assessed by using the Joanna Briggs Institute Critical Appraisal Tool. The Ottawa-Newcastle Score system was also used. Studies were additionally evaluated for their radiological methodology by using a five-question checklist (Radiological Assessment Qualit criteria). Results: The initial search identified 167 studies, of which 129 were considered for screening. Ten studies reporting outcomes after KA TKA did not use the objective surgeon-assessed part of KSS for clinical outcome measurement, and 30 studies reporting outcomes after KA TKA did not use KSS at all for clinical and/or functional outcomes. From the 10 included studies, only six have used the latest KSS score (2011), the rest using its 1989 variant; and out of these six studies, only two presented values of the FTA, which is needed for calculating the KSS's 'alignment' subcomponent, the rest presenting hip-knee-ankle angle (HKA) values. Additionally, when converting these HKA values to FTA intervals, the authors of this systematic review found that KA TKA FTA intervals display limits, which tend to be outside the 'well-scored' KSS anatomical alignment interval. Conclusion: The inconsistent and nonstandardised use of the surgeon-assessed KSS across studies reviewed compromises assessment reliability and patient outcome scores. To enhance precision and comparability, it is crucial to standardise the KSS application, incorporating personalised alignment strategies for more accurate patient evaluations. Level of Evidence: Level III.

Conventionally instrumented inverse kinematic alignment for total knee arthroplasty: How is it done?

Purpose: For primary total knee arthroplasty (TKA), there is an increasing trend towards patient-specific alignment strategies such as kinematic alignment (KA) and inverse kinematic alignment (iKA), which by restoring native joint mechanics may yield higher patient satisfaction rates. Second, the most recent Australian joint registry report describes favourable revision rates for conventionally instrumented TKA compared to technology-assisted techniques such as those using navigation, robotics or custom-cutting blocks. The aim of this technique article is to describe in detail a surgical technique for TKA that: (1) utilises the principles of iKA and (2) uses conventionally instrumented guided resections thereby avoiding the use of navigation, robotics or custom blocks. Methods: A TKA technique is described, whereby inverse kinematic principles are utilised and patient-specific alignment is achieved. Additionally, the patellofemoral compartment of the knee is restored to the native patellofemoral joint line. The sequenced technical note provided may be utilised for cemented or cementless components; cruciate retaining or sacrificing designs and for fixed or rotating platforms. Results: An uncomplicated, robust and reproducible technique for TKA is described. Discussion: Knee arthroplasty surgeons may wish to harness the emerging benefits of both a conventionally instrumented technique and a patient-specific alignment strategy. Level of Evidence: Level V.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.

A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.

Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI-based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self-supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI-driven orthopaedic research. Level of Evidence: Level IV.