Lateral meniscus posterior root repair in the setting of anterior cruciate ligament reconstruction restores joint mechanics to the intact state and improves clinical function: a systematic review of biomechanical and clinical outcomes.

PURPOSE: To perform a systematic review of biomechanical and clinical outcomes following lateral meniscus posterior root (LMPR) repair with concomitant anterior cruciate ligament reconstruction (ACLR). METHODS: A literature search was performed systematically using PubMed, Embase, and Medline databases in April 2022. The search included the following terms combined with Boolean operators: 'Meniscus repairs', 'Meniscal Repair', 'Posterior Horn', 'Root', 'Radial'. Inclusion criteria consisted of level I-IV human clinical and biomechanical studies reporting biomechanical data and/or outcomes following LMPR repair in the setting of ACLR. RESULTS: Three biomechanical studies, all utilizing a transtibial pullout technique, were identified, all of which reported significant improvement in joint contact pressures and mechanics and 3/4 of which reported significant improvement in anterior or rotational stability with LMPR repair. Five clinical studies, consisting of 146 patients (mean age 28.5 ± 1.1 years) undergoing LMPR repair, were identified with an average follow-up of 19.1 months (range 6.2-46 months). Across all clinical studies, Lysholm and International Knee Documentation Committee (IKDC) scores were found to improve postoperatively, with 3/4 reporting significant improvement in Lysholm (all, p ≤ 0.001) scores and 3/5 reporting significant improvement in IKDC scores when compared to preoperative values (all, p ≥ 0.004). Meniscal extrusion decreased significantly following repair in 2/4 studies (all, p ≤ 0.001). CONCLUSIONS: Biomechanically, transtibial pullout repair of the LMPR restored joint contact pressures and joint mechanics to intact levels when performed with concomitant ACLR. Clinically, LMPR repair with concurrent ACLR resulted in improved Lysholm and IKDC scores. These findings enable surgeons to determine optimal treatment plans and discuss realistic outcomes with patients when encountering LMPR injuries. LEVEL OF EVIDENCE: IV.

Single-stage autologous cartilage repair results in positive patient-reported outcomes for chondral lesions of the knee: a systematic review.

AIM: This article aims to perform a systematic review of the clinical literature regarding the efficacy of single-stage autologous cartilage repair. METHODS: A systematic review of the literature was performed using PubMed, Scopus, Web of Science, and the Cochrane Library. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. RESULTS: Twelve studies were identified; however, due to overlapping patient cohorts, nine studies were included for data extraction and analysis. Six studies applied minced cartilage, while three studies utilized enzymatically processed cartilage. Two authorship groups described single-stage techniques that exclusively utilized cartilage from the debrided lesion rim, while the remaining groups either utilized healthy cartilage or combined healthy cartilage with cartilage debrided from lesion rim. Among the included techniques, scaffold augments were used in four studies, and three studies implemented bone autograft augmentation. When summarizing patient reported outcome measures for the included studies, single-stage autologous cartilage repair demonstrated an average improvement ranging from 18.7 â€‹± â€‹5.3 to 30.0 â€‹± â€‹8.0 amongst the Knee Injury and Osteoarthritis Outcome Scores subsections, 24.3 â€‹± â€‹10.5 for the International Knee Documentation Committee subjective score, and 41.0 â€‹± â€‹10.0 for Visual Analogue Scale-Pain. CONCLUSION: Single-stage autologous cartilage repair is a promising technique with positive clinical data to date. The current study highlights the overall improvement in patient reported outcomes after repair for chondral defects to the knee with average follow-up ranging from 12 to 201 months and also the heterogeneity and variability of the single-stage surgical technique. Further discussion on the standardization of practices for a cost-effective single-stage augmented autologous cartilage technique is needed. In the future, a well-designed randomized controlled trial is needed to explore the efficacy of this therapeutic modality relative to established intervention. LEVEL OF EVIDENCE: Systematic review; Level IV.

Lower donor site morbidity with hamstring and quadriceps tendon autograft compared with bone-patellar tendon-bone autograft after anterior cruciate ligament reconstruction: a systematic review and network meta-analysis of randomized controlled trials.

PURPOSE: To perform a meta-analysis of RCTs evaluating donor site morbidity after bone-patellar tendon-bone (BTB), hamstring tendon (HT) and quadriceps tendon (QT) autograft harvest for anterior cruciate ligament reconstruction (ACLR). METHODS: PubMed, OVID/Medline and Cochrane databases were queried in July 2022. All level one articles reporting the frequency of specific donor-site morbidity were included. Frequentist model network meta-analyses with P-scores were conducted to compare the prevalence of donor-site morbidity, complications, all-cause reoperations and revision ACLR among the three treatment groups. RESULTS: Twenty-one RCTs comprising the outcomes of 1726 patients were included. The overall pooled rate of donor-site morbidity (defined as anterior knee pain, difficulty/impossibility kneeling, or combination) was 47.3% (range, 3.8-86.7%). A 69% (95% confidence interval [95% CI]: 0.18-0.56) and 88% (95% CI: 0.04-0.33) lower odds of incurring donor-site morbidity was observed with HT and QT autografts, respectively (p < 0.0001, both), when compared to BTB autograft. QT autograft was associated with a non-statistically significant reduction in donor-site morbidity compared with HT autograft (OR: 0.37, 95% CI: 0.14-1.03, n.s.). Treatment rankings (ordered from best-to-worst autograft choice with respect to donor-site morbidity) were as follows: (1) QT (P-score = 0.99), (2) HT (P-score = 0.51) and (3) BTB (P-score = 0.00). No statistically significant associations were observed between autograft and complications (n.s.), reoperations (n.s.) or revision ACLR (n.s.). CONCLUSION: ACLR using HT and QT autograft tissue was associated with a significant reduction in donor-site morbidity compared to BTB autograft. Autograft selection was not associated with complications, all-cause reoperations, or revision ACLR. Based on the current data, there is sufficient evidence to recommend that autograft selection should be personalized through considering differential rates of donor-site morbidity in the context of patient expectations and activity level without concern for a clinically important change in the rate of adverse events. LEVEL OF EVIDENCE: Level I.

The Altmetric Attention Score Is Associated With Citation Rates and May Reflect Academic Impact in the Total Joint Arthroplasty Literature.

Background: Given the increasing interest and potential use of social media for the promotion of orthopedic literature, there is a need to better understand Altmetrics. Purposes: We sought to determine the relationship between the Altmetric Attention Score (AAS) and the number of citations for articles on total joint arthroplasty (TJA) published in orthopedics journals. We also sought to determine the predictors of greater social media attention for these articles. Methods: Articles on TJA published in Bone and Joint Journal (BJJ), Journal of Bone and Joint Surgery (JBJS), Clinical Orthopedics and Related Research (CORR), Journal of Arthroplasty, Journal of Knee Surgery, Hip International, and Acta Orthopaedica in 2016 were extracted (n = 498). One-way analysis of variance with Bonferroni corrections was used to compare AAS and citations across journals. Multivariate regressions were used to determine predictors of social media attention and number of citations. Results: The mean AAS and number of citations were 7.5 (range: 0-289) and 16.7 (range: 0-156), respectively. Significant between-group effects were observed according to journal for AAS and number of citations. Publishing an article in JBJS was the strongest predictor of higher number of citations. Publishing an article in BJJ was the only independent predictor of higher AAS, while publishing an article in JBJS or CORR trended toward statistical significance. A higher AAS was a significant predictor of a higher number of citations. Number of citations and number of study references were positive predictors of greater social media attention on Twitter and Facebook. Conclusions: In articles on TJA published in 7 journals in 2016, a higher AAS was a associated with a higher number of citations. Various bibliometric characteristics were found to be significantly associated with greater social media attention; the most common influences were number of citations and number of references. Researchers in orthopedics can use this information when considering how to assess the impact of their work.

Development and internal validation of machine learning algorithms for predicting complications after primary total hip arthroplasty.

INTRODUCTION: Complications after total hip arthroplasty (THA) may result in readmission or reoperation and impose a significant cost on the healthcare system. Understanding which patients are at-risk for complications can potentially allow for targeted interventions to decrease complication rates through pursuing preoperative health optimization. The purpose of the current was to develop and internally validate machine learning (ML) algorithms capable of performing patient-specific predictions of all-cause complications within two years of primary THA. METHODS: This was a retrospective case-control study of clinical registry data from 616 primary THA patients from one large academic and two community hospitals. The primary outcome was all-cause complications at a minimum of 2-years after primary THA. Recursive feature elimination was applied to identify preoperative variables with the greatest predictive value. Five ML algorithms were developed on the training set using tenfold cross-validation and internally validated on the independent testing set of patients. Algorithms were assessed by discrimination, calibration, Brier score, and decision curve analysis to quantify performance. RESULTS: The observed complication rate was 16.6%. The stochastic gradient boosting model achieved the best performance with an AUC = 0.88, calibration intercept = 0.1, calibration slope = 1.22, and Brier score = 0.09. The most important factors for predicting complications were age, drug allergies, prior hip surgery, smoking, and opioid use. Individual patient-level explanations were provided for the algorithm predictions and incorporated into an open access digital application: https://sorg-apps.shinyapps.io/tha_complication/ CONCLUSIONS: The stochastic boosting gradient algorithm demonstrated good discriminatory capacity for identifying patients at high-risk of experiencing a postoperative complication and proof-of-concept for creating office-based applications from ML that can perform real-time prediction. However, this clinical utility of the current algorithm is unknown and definitions of complications broad. Further investigation on larger data sets and rigorous external validation is necessary prior to the assessment of clinical utility with respect to risk-stratification of patients undergoing primary THA. LEVEL OF EVIDENCE: III, therapeutic study.

A Guide for the Application of Statistics in Biomedical Studies Concerning Machine Learning and Artificial Intelligence.

With the plethora of machine learning (ML) analyses published in the orthopaedic literature within the last 5 years, several attempts have been made to enhance our understanding of what exactly ML means and how it is used. At its most fundamental level, ML comprises a branch of artificial intelligence that uses algorithms to analyze and learn from patterns in data without explicit programming or human intervention. On the other hand, traditional statistics require a user to specifically choose variables of interest to create a model capable of predicting an outcome, the output of which (1) may be falsely influenced by the variables chosen to be included by the user and (2) does not allow for optimization of performance. Early publications have served as succinct editorials or reviews intended to ease audiences unfamiliar with ML into the complexities that accompany the subject. Most commonly, the focus of these studies concerns the terminology and concepts surrounding ML because it is important to understand the rationale behind performing such studies. Unfortunately, these publications only touch on the most basic aspects of ML and are too frequently repetitive. Indeed, the conclusion of these articles reiterate that the potential clinical utility of these algorithms remains tangential at best in their current form and caution against premature adoption without external validation. By doing so, our perspective and ability to draw our own conclusions from these studies have not advanced, and we are left concluding with each subsequent study that a new algorithm is published for an outcome of interest that cannot be used until further validation. What readers now need is to regress to embrace the principles of the scientific method that they have used to critically assess vast numbers of publications before this wave of newly applied statistical methodology-a guide to interpret results such that their own conclusions can be drawn. LEVEL OF EVIDENCE: Level V, expert opinion.

Clinical and Magnetic Resonance Imaging Outcomes After Microfracture Treatment With and Without Augmentation for Focal Chondral Lesions in the Knee: A Systematic Review and Meta-analysis.

BACKGROUND: Focal cartilage lesions represent a common source of knee pain and disability, with the potential for the development and progression of osteoarthritis. Currently, microfracture (MFx) represents the most utilized first-line surgical treatment for small, focal chondral lesions. Recent investigations have examined methods of overcoming the limitations of MFx utilizing various augmentation techniques. PURPOSE: To perform a systematic review and meta-analysis evaluating clinical and radiographic outcomes in patients undergoing isolated MFx versus MFx augmented with orthobiologics or scaffolds for focal chondral defects of the knee. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: A systematic review was performed to identify studies evaluating outcomes and adverse events in patients undergoing isolated MFx versus augmented MFx for focal chondral defects in the knee from 1945 to June 1, 2021. Data were extracted from each article that met the inclusion/exclusion criteria. Meta-analyses were performed for all outcomes reported in a minimum of 3 studies. RESULTS: A total of 14 studies were identified, utilizing 7 different types of injectable augmentation regimens and 5 different scaffolding regimens. Across the 14 studies, a total of 744 patients were included. The mean patient age was 46.8 years (range, 34-58 years), and 58.3% (n = 434/744) of patients were women. The mean final follow-up time was 26.7 months (range, 12-60 months). The mean chondral defect size ranged from 1.3 to 4.8 cm2. A post hoc analysis comparing mean improvement in postoperative outcomes scores compared with preoperative values found no significant differences in the improvement in the visual analog scale (VAS), International Knee Documentation Committee (IKDC), or Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores between patients undergoing isolated MFx and those undergoing MFx + augmentation. Patients undergoing MFx + augmentation reported significantly greater improvements in the Lysholm score and postoperative MOCART (magnetic resonance observation of cartilage repair tissue) scores compared with the isolated MFx group. CONCLUSION: Patients undergoing combined MFx + augmentation reported significant improvements in mean Lysholm and MOCART scores, without significant improvements in VAS, IKDC, or WOMAC scores when compared with patients undergoing isolated MFx.

External Validation of a Machine Learning Algorithm for Predicting Clinically Meaningful Functional Improvement After Arthroscopic Hip Preservation Surgery.

BACKGROUND: Individualized risk prediction has become possible with machine learning (ML), which may have important implications in enhancing clinical decision making. We previously developed an ML algorithm to predict propensity for clinically meaningful outcome improvement after hip arthroscopy for femoroacetabular impingement syndrome. External validity of prognostic models is critical to determine generalizability, although it is rarely performed. PURPOSE: To assess the external validity of an ML algorithm for predicting clinically meaningful improvement after hip arthroscopy. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: An independent hip preservation registry at a tertiary academic medical center was queried for consecutive patients/athletes who underwent hip arthroscopy for femoroacetabular impingement syndrome between 2015 and 2017. By assuming a minimal clinically important difference (MCID) outcome/event proportion of 75% based on the original study, a minimum sample of 132 patients was required. In total, 154 patients were included. Age, body mass index, alpha angle on anteroposterior pelvic radiographs, Tönnis grade and angle, and preoperative Hip Outcome Score-Sports Subscale were used as model inputs to predict the MCID for the Hip Outcome Score-Sports Subscale 2 years postoperatively. Performance was assessed using identical metrics to the internal validation study and included discrimination, calibration, Brier score, and decision curve analysis. RESULTS: The concordance statistic in the validation cohort was 0.80 (95% CI, 0.71 to 0.87), suggesting good to excellent discrimination. The calibration slope was 1.16 (95% CI, 0.74 to 1.61) and the calibration intercept 0.13 (95% CI, -0.26 to 0.53). The Brier score was 0.15 (95% CI, 0.12 to 0.18). The null model Brier score was 0.20. Decision curve analysis revealed favorable net treatment benefit for patients with use of the algorithm as compared with interventional changes made for all and no patients. CONCLUSION: The performance of this algorithm in an independent patient population in the northeast region of the United States demonstrated superior discrimination and comparable calibration to that of the derivation cohort. The external validation of this algorithm suggests that it is a reliable method to predict propensity for clinically meaningful improvement after hip arthroscopy and is an essential step forward toward introducing initial use in clinical practice. Potential uses include integration into electronic medical records for automated prediction, enhanced shared decision making, and more informed allocation of resources to optimize patient outcomes.

Current State of Research Gap-Years in Orthopedic Surgery Residency Applicants: Program Directors' Perspectives.

Background: The purpose of this study was to determine how orthopedic residency program directors (PDs) evaluate residency applicants who participated in a research gap-year (RGY). Methods: A 23 question electronically administered survey was created and emailed to all Accreditation Council for Graduate Medical Education (ACGME) orthopedic residency PDs for the 2020-21 application cycle. PDs were emailed directly if active contact information was identifiable. If not, program coordinators were emailed. The survey contained questions regarding the background information of programs and aimed at identifying how PDs view and evaluate residency applicants who participated in a RGY. Descriptive statistics for each question were performed. Results: Eighty-four (41.8%) of 201 PDs responded. Most respondent programs (N=62, 73.8%) identified as an academic center. The most common geographic region was the Midwest, N=33 (39.3%). Few programs (N=3, 3.8%) utilize a publication "cut-off" when screening residency applicants. When asked how many peer-reviewed publications were necessary to deem a RGY as "productive," responses ranged from 0-15 publications (median interquartile range 4.5 [3-5]). Forty-one (53.3%) PDs stated they would council medical students to take a RGY with USMLE Step 1 scores being the #1 factor guiding that advice. More PDs disagree than agree (N=35, 43.6%; vs N=22, 28.2%) that applicants who complete a RGY are more competitive applicants, and 35 PDs (45.5%) agree research experiences will become more important in resident selection as USMLE Step 1 transitions to Pass/Fail. Conclusion: Program directors have varying views on residency applicants who did a RGY. While few programs use a publication cutoff, the median number of publications deemed as being a "productive" RGY was approximately 5. Many PDs agree that research experiences will become more important as USMLE Step becomes Pass/Fail. This information can be useful for students interested in pursuing a RGY and for residency programs when evaluating residency applicants. Level of Evidence: IV.

Patient compliance and satisfaction with topical benzoyl peroxide gel prior to shoulder surgery.

Background: Cutibacterium acnes is a common pathogen leading to postoperative shoulder infections. Many centers are utilizing 5% topical benzoyl peroxide (BPO) gel to decrease cutibacterium acnes bioburden prior to surgery. The purpose of this study was to evaluate patient compliance, tolerance, and side effects of applying BPO to the shoulder prior to surgery. Our hypothesis was that BPO would be well tolerated with few, minor side effects, and patients would comply with a regimen of 5 treatments spanning 48 hours prior to surgery. Methods: All patients undergoing operative intervention, either open or arthroscopic, about the shoulder from August 7, 2020, through July 15, 2021, by a single surgeon were instructed to apply BPO to the shoulder after showering, on dry skin, in the morning and evening, starting 48 hours before their surgical date. Patients were instructed to apply a half-dollar-sized quantity for each treatment. There were a total of 5 topical applications. On the day of surgery, patients were given a 6-question survey regarding side effects experienced, BPO treatments missed, ease of treatment, and a scenario question. Demographic information was collected, and a satisfaction survey was administered upon study completion. Results: A total of 183 patients out of a possible 284 eligible patients (64.4%) completed the survey. The median (interquartile range) age at surgery was 59.9 years. Sixty-four participants (35%) in the study cohort were female. One-hundred thirteen (61.7%) had an arthroscopic surgical approach, whereas 70 (38.3%) underwent open shoulder surgery. Most patients (N = 152, 83.5%) experienced no side effects from the topical 5% BPO gel treatments. Twenty-two (12.0%) patients missed 1 treatment, 10 (5.5%) missed 2 treatments, 1 (0.5%) missed 3 treatments, 3 (1.6%) missed 4 treatments, and 4 (2.2%) missed all 5 treatments. The majority of patients, 143 (78.1%), completed all 5 treatments. When prompted to choose between serial skin preparation treatments at home leading up to surgery or a single light-based decolonization procedure in the preoperative holding area on the day of surgery, 111 (60.7%) preferred to undergo antimicrobial treatment at home and arrive for surgery as otherwise scheduled. Conclusion: Serial preoperative applications of topical 5% BPO gel are well tolerated by the majority of patients. Over 20% of patients missed at least one application of BPO. Most patients prefer home-based antimicrobial treatments compared with arriving earlier on the day of surgery for a single light-based antimicrobial treatment.

Posterolateral Corner Reconstruction: Surgical Technique and Postoperative Rehabilitation.

Anatomic posterolateral corner (PLC) reconstruction is utilized for ligamentous knee instability associated with PLC injury in patients who desire a return to active lifestyles1,2. The fibular collateral ligament (FCL) and popliteal tendon (PLT) are reconstructed in anatomic fashion according to techniques described by LaPrade et al.3-7. Description: Various PLC reconstruction techniques have been described; however, the preferred reconstruction technique of the senior author is the method developed by LaPrade et al. that restores the anatomy of the 3 primary stabilizers of the PLC, including the FCL, PLT, and popliteofibular ligament3,5,6. Alternatives: Alternative nonoperative treatments include knee immobilization for 4 weeks and physical therapy. Surgical alternatives include PLC repair, which involves repair of the lateral collateral ligament, PLT, and/or popliteofibular ligament if structures can be anatomically reduced to their attachment site. However, repair of acute grade-III PLC injuries with staged treatment of concurrent cruciate injuries is associated with a substantially higher postoperative PLC failure rate8-10. Rationale: Clinical outcomes have demonstrated that primary repairs have significantly higher rates of reoperation compared with reconstruction; therefore, reconstruction is recommended. Treatment of grade-III PLC injuries with reconstruction of midsubstance tears and any associated cruciate ligament tears results in significantly improved objective stability11. In addition, anatomic PLC reconstruction has demonstrated improved subjective and objective patient outcomes compared with nonsurgical treatment or repair5,11,12. Expected Outcomes: Reconstruction of the PLC offers excellent outcomes after surgery. Studies have shown that the fibular-based technique for treatment of a chronic isolated PLC injury showed good results in terms of clinical outcome, restoring knee varus and rotational stability13. Important Tips: Patients with associated proximal tibiofibular joint instability will benefit from this reconstruction because this technique will add stability to the joint.This surgical approach is technically demanding, requiring proficiency with surgical dissection.Damage to the common peroneal nerve can potentially occur. Careful dissection and placement of retractors should be observed.Risks include surgical failure due to unrecognized malalignment; especially in chronic cases, the patient should have a complete evaluation of the standing alignment and tibial slope12. Acronyms and Abbreviations: FCL = fibular collateral ligamentPFL = popliteofibular ligamentPLC = posterolateral cornerIT = iliotibialIKDC = International Knee Documentation CommitteeACL = anterior cruciate ligamentPCL = posterior cruciate ligamentPEEK = polyetheretherketonePROM = passive range of motion.

A Novel Fluoroscopic View for Improved Assessment of the Safety of the Posterosuperior Screw in Femoral Neck Fracture Fixation.

BACKGROUND: The purpose of the present study was to determine specific fluoroscopic views of the femoral neck to accurately identify partially extraosseous ("in-out-in"; IOI) placement of the posterosuperior screw for fixation of femoral neck fractures. METHODS: A 3.2-mm guide pin was placed in the posterosuperior aspect of 2 synthetic femur models: 1 entirely intraosseous and 1 IOI. Sequential fluoroscopic images were made at 5° intervals in order to identify which fluoroscopic projections identified IOI guide pin placement. These images were utilized to inform screw placement and assessment in the second phase of the study, which involved the use of cadaveric specimens. In Phase II, the posterosuperior screw of the inverted triangle was placed in 10 cadaveric specimens with use of a standard posteroanterior fluoroscopic view and 1 of 2 lateral views, either (1) neck in line with the shaft, i.e., 0° lateral; or (2) a -15° rollunder view. The final fluoroscopic views (i.e., the posteroanterior and multiple lateral and oblique views) were randomized and blinded for review by 10 orthopaedic residents and 5 attending orthopaedic traumatologists. Specimens were stripped of soft tissue and inspected for screw perforation. RESULTS: Overall accuracy of respondents was 68.8%, with no difference between the attending traumatologists (71.8%) and resident surgeons (67.4%; p = 0.173). Interobserver reliability was moderate (κ = 0.496). Dissection identified that 4 (40%) of 10 screws were extraosseous. All of the extraosseous screws were placed with use of the 0° lateral view. The -15° rollunder lateral view was the most sensitive (81.7%) and specific (92.2%) view for identifying IOI screw placement. CONCLUSIONS: Surgeons often utilize the standard posteroanterior and 0° lateral fluoroscopic views to safely place screws; however, many of these screws are IOI. The addition of a -15° rollunder lateral view significantly improved identification of IOI screws in the posterosuperior femoral neck. Unidentified IOI screw placement may result in damage to the blood supply of the femoral head.

Comparison of Surgical Time, Short-term Adverse Events, and Implant Placement Accuracy Between Manual, Robotic-assisted, and Computer-navigated Total Hip Arthroplasty: A Network Meta-analysis of Randomized Controlled Trials.

INTRODUCTION: Recent years have observed the increasing utilization of robotic-assisted and computer navigation techniques in total hip arthroplasty (THA), given the proposed benefits of enhanced consistency and precision in implant placement. The purpose of this study was to conduct a systematic review of randomized controlled trials (RCTs) to determine whether differences in surgical times, adverse events, and implant positioning existed between manual, robotic-assisted, and computer navigation THA. METHODS: PubMed, OVID/MEDLINE, and Cochrane databases were queried for RCTs comparing robotic-assisted versus manual THA and computer navigation versus manual THA at a minimum 1-year follow-up. Frequentist model network meta-analyses with P-scores were conducted to compare revisions, complications, and surgical times among the three treatment groups. A random-effects meta-analysis between computer navigation and manual THAs was conducted to analyze cup positioning because no robotic-assisted THA studies reported this outcome. RESULTS: Five RCTs compared robotic-assisted and manual THAs, while seven compared computer navigation and manual THAs. manual THA was associated with significantly reduced surgical time in comparison with computer navigation (mean difference: 23.3 minutes) and robotic-assisted THAs (mean difference: 8.6 minutes; P < 0.001). No difference was observed in the incidence of all-cause complications (computer navigation: 1.7%, manual: 6.6%, and robotic-assisted: 16.2%) or revisions (computer navigation: 1.0%, manual: 1.7%, and robotic-assisted 4.8%) among the three treatment groups based on the network meta-analysis. In three studies that reported acetabular implant positioning, computer navigation had a significantly higher percentage of acetabular cups placed in the Lewinnek "safe zone" compared with manual THA (79% versus 52%; P = 0.02). CONCLUSIONS: manual THA results in markedly shorter surgical times and a similar incidence of complications and revisions compared with robotic-assisted and computer navigation THAs, given the sample sizes available for study. However, computer navigation THA led to increased precision in the placement of acetabular implants.

Establishing Clinically Significant Outcomes After Anterior Cruciate Ligament Reconstruction in Pediatric Patients.

PURPOSE: The purpose of this study was to establish clinically significant outcome values for the minimal clinically important difference (MCID) and patient acceptable symptomatic state (PASS) after anterior cruciate ligament reconstruction (ACLR) in the pediatric and adolescent populations and to assess factors that were associated with achieving these outcomes. METHODS: Patients between the age of 10 to 21 who underwent ACLR between 2016 and 2018 were identified and patient-reported outcomes (PROs) were collected preoperatively and postoperatively. Intraoperative variables collected included graft choice, graft size (diameter), graft fixation method, and concomitant procedures. PROs collected for analysis were the International Knee Documentation Committee Score (IKDC) and Knee Injury and Osteoarthritis Outcome Score (KOOS). MCID and PASS were calculated using receiver operating characteristic with area under the curve analyses for delta (ie, baseline-to-postoperative change) and absolute postoperative PRO scores, respectively. RESULTS: A total of 59 patients were included in the analysis. Of the entire study population, 53 (89.8%) reported satisfaction with their surgical outcome. The established MCID threshold values based on the study population were 33.3 for IKDC, 28.6 for (KOOS) Symptoms, 19.4 for Pain, 2.9 for activities of daily living (ADL), 45.0 for Sport, and 25.0 for Quality of Life (QoL). Postoperative scores greater than the following values corresponded to the PASS: 80.5 for IKDC, 75.0 (KOOS) Symptoms, 88.9 for Pain, 98.5 for ADL, 75.0 for Sport, and 68.8 for QoL. CONCLUSION: Clinically meaningful outcomes including MCID and PASS were established for pediatric ACLR surgery using selected PRO measures, IKDC, and KOOS. Patient age, sex, graft type, and graft size were not associated with greater achievement of these outcomes. In contrast, collision sports, fixed-object high-impact rotational landing sports, and concomitant meniscectomy surgery were associated with a decreased likelihood of achieving clinically significant improvement. However, findings must be interpreted with caution due to limitations in follow-up and sample size. LEVEL OF EVIDENCE: Level IV: case series.

Association Between Preoperative Patient Factors and Clinically Meaningful Outcomes After Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Machine Learning Analysis.

BACKGROUND: The International Hip Outcome Tool 12-Item Questionnaire (IHOT-12) has been proposed as a more appropriate outcome assessment for hip arthroscopy populations. The extent to which preoperative patient factors predict achieving clinically meaningful outcomes among patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (FAIS) remains poorly understood. PURPOSE: To determine the predictive relationship of preoperative imaging, patient-reported outcome measures, and patient demographics with achievement of the minimal clinically important difference (MCID), Patient Acceptable Symptom State (PASS), and substantial clinical benefit (SCB) for the IHOT-12 at a minimum of 2 years postoperatively. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Data were analyzed for consecutive patients who underwent hip arthroscopy for FAIS between 2012 and 2018 and completed the IHOT-12 preoperatively and at a minimum of 2 years postoperatively. Fifteen novel machine learning algorithms were developed using 47 potential demographic, clinical, and radiographic predictors. Model performance was evaluated with discrimination, calibration, decision-curve analysis and the brier score. RESULTS: A total of 859 patients were identified, with 685 (79.7%) achieving the MCID, 535 (62.3%) achieving the PASS, and 498 (58.0%) achieving the SCB. For predicting the MCID, discrimination for the best-performing models ranged from fair to excellent (area under the curve [AUC], 0.69-0.89), although calibration was excellent (calibration intercept and slopes: -0.06 to 0.02 and 0.24 to 0.85, respectively). For predicting the PASS, discrimination for the best-performing models ranged from fair to excellent (AUC, 0.63-0.81), with excellent calibration (calibration intercept and slopes: 0.03-0.18 and 0.52-0.90, respectively). For predicting the SCB, discrimination for the best-performing models ranged from fair to good (AUC, 0.61-0.77), with excellent calibration (calibration intercept and slopes: -0.08 to 0.00 and 0.56 to 1.02, respectively). Thematic predictors for failing to achieve the MCID, PASS, and SCB were presence of back pain, anxiety/depression, chronic symptom duration, preoperative hip injections, and increasing body mass index (BMI). Specifically, thresholds associated with lower likelihood to achieve a clinically meaningful outcome were preoperative Hip Outcome Score-Activities of Daily Living <55, preoperative Hip Outcome Score-Sports Subscale >55.6, preoperative IHOT-12 score ≥48.5, preoperative modified Harris Hip Score ≤51.7, age >41 years, BMI ≥27, and preoperative α angle >76.6°. CONCLUSION: We developed novel machine learning algorithms that leveraged preoperative demographic, clinical, and imaging-based features to reliably predict clinically meaningful improvement after hip arthroscopy for FAIS. Despite consistent improvements after hip arthroscopy, meaningful improvements are negatively influenced by greater BMI, back pain, chronic symptom duration, preoperative mental health, and use of hip corticosteroid injections.

Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy.

PURPOSE: To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database. METHODS: A consecutive series of primary TKA patients from two independent large academic and three community medical centers between 2012 and 2020 was identified. The primary outcomes were final tibial and femoral implant sizes obtained from an automated inventory system. Five machine learning algorithms were trained using six routinely collected preoperative features (age, sex, height, weight, and body mass index). Algorithms were validated on an independent set of patients and evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE). RESULTS: A total of 11,777 patients were included. The support vector machine (SVM) algorithm had the best performance for femoral component size(MAE = 0.73, RMSE = 1.06) with accuracies of 42.2%, 88.3%, and 97.6% for predicting exact size, ± one size, and ± two sizes, respectively. The elastic-net penalized linear regression (ENPLR) algorithm had the best performance for tibial component size (MAE 0.70, RMSE = 1.03) with accuracies of 43.8%, 90.0%, and 97.7% for predicting exact size, ± one size, and ± two sizes, respectively. CONCLUSION: Machine learning algorithms demonstrated good-to-excellent accuracy for predicting within one size of the final tibial and femoral components used for TKA. Patient height and sex were the most important factors for predicting femoral and tibial component size, respectively. External validation of these algorithms is imperative prior to use in clinical settings. LEVEL OF EVIDENCE: Case-control, III.

Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting.

BACKGROUND: There has been a considerable increase in total joint arthroplasty (TJA) research using machine learning (ML). Therefore, the purposes of this study were to synthesize the applications and efficacies of ML reported in the TJA literature, and to assess the methodological quality of these studies. METHODS: PubMed, OVID/MEDLINE, and Cochrane libraries were queried in January 2021 for articles regarding the use of ML in TJA. Study demographics, topic, primary and secondary outcomes, ML model development and testing, and model presentation and validation were recorded. The TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were used to assess the methodological quality. RESULTS: Fifty-five studies were identified: 31 investigated clinical outcomes and resource utilization; 11, activity and motion surveillance; 10, imaging detection; and 3, natural language processing. For studies reporting the area under the receiver operating characteristic curve (AUC), the median AUC (and range) was 0.80 (0.60 to 0.97) among 26 clinical outcome studies, 0.99 (0.83 to 1.00) among 6 imaging-based studies, and 0.88 (0.76 to 0.98) among 3 activity and motion surveillance studies. Twelve studies compared ML to logistic regression, with 9 (75%) reporting that ML was superior. The average number of TRIPOD guidelines met was 11.5 (range: 5 to 18), with 38 (69%) meeting greater than half of the criteria. Presentation and explanation of the full model for individual predictions and assessments of model calibration were poorly reported (<30%). CONCLUSIONS: The performance of ML models was good to excellent when applied to a wide variety of clinically relevant outcomes in TJA. However, reporting of certain key methodological and model presentation criteria was inadequate. Despite the recent surge in TJA literature utilizing ML, the lack of consistent adherence to reporting guidelines needs to be addressed to bridge the gap between model development and clinical implementation.

High Rate of Return to Work by 3 Months Following Latarjet for Anterior Shoulder Instability.

PURPOSE: To evaluate the rate and duration of return to work in patients undergoing Latarjet for failed soft-tissue stabilization or glenoid bone loss. METHODS: Consecutive patients undergoing Latarjet from 2005 to 2015 at our institution were retrospectively reviewed at a minimum of 2 years postoperatively. Patients completed a standardized and validated work questionnaire, Western Ontario Shoulder Instability Index Survey, and a satisfaction survey. RESULTS: Of 89 eligible patients who had Latarjet, 67 patients (75.3%) responded to the questionnaire, of whom 51 patients (76.1%) were employed within 3 years before surgery (mean age: 29.9 ± 11.8 years; mean follow-up: 54.6 ± 11.9 months) and had an average glenoid bone loss of 14.5 ± 6.1%. Fifty patients (98.0%) returned to work by 2.7 ± 3.0 months postoperatively; 45 patients (88.2%) patients returned to the same level of occupational intensity. Those who held sedentary, light, moderate, or heavy intensity occupations returned to their previous occupation at a rate of 100.0%, 93.3%, 90.0%, and 66.7% (P = .2) at a duration of 1.2 ± 1.6 months, 1.8 ± 1.9 months, 3.1 ± 3.5 months, and 6.5 ± 4.1 months (P = .001), respectively. The average postoperative Western Ontario Shoulder Instability Index score was 70.9 ± 34.2. Fifty patients (98.0%) noted at least "a little improvement" in their quality of life following surgery, with 35 patients (68.6%) noting great improvement. Furthermore, 49 patients (96.1%) reported being satisfied with their procedure, with 25 patients (49.0%) reporting being very satisfied. Four patients (7.8%) returned to the operating room, with 1 patient (2.0%) requiring arthroscopic shoulder stabilization. CONCLUSIONS: Approximately 98% of patients who underwent Latarjet returned to work by 2.7 ± 3.0 months postoperatively. Patients with greater-intensity occupations had a longer duration of absence before returning to their preoperative level of occupational intensity. Information regarding return to work is imperative in preoperative patient consultation to manage expectations. LEVEL OF EVIDENCE: IV, case series.

Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction.

BACKGROUND: Understanding specific risk profiles for each patient and their propensity to experience clinically meaningful improvement after anterior cruciate ligament reconstruction (ACLR) is important for preoperative patient counseling and management of expectations. PURPOSE: To develop machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) on the International Knee Documentation Committee (IKDC) score at a minimum 2-year follow-up after ACLR. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: An ACLR registry of patients from 27 fellowship-trained sports medicine surgeons at a large academic institution was retrospectively analyzed. Thirty-six variables were tested for predictive value. The study population was randomly partitioned into training and independent testing sets using a 70:30 split. Six machine learning algorithms (stochastic gradient boosting, random forest, neural network, support vector machine, adaptive gradient boosting, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and internally validated on the independent set of patients. Algorithm performance was assessed using discrimination, calibration, Brier score, and decision-curve analysis. RESULTS: A total of 442 patients, of whom 39 (8.8%) did not achieve the MCID, were included. The 5 most predictive features of achieving the MCID were body mass index ≤27.4, grade 0 medial collateral ligament examination (compared with other grades), intratunnel femoral tunnel fixation (compared with suspensory), no history of previous contralateral knee surgery, and achieving full knee extension preoperatively. The ENPLR algorithm had the best relative performance (C-statistic, 0.82; calibration intercept, 0.10; calibration slope, 1.15; Brier score, 0.068), demonstrating excellent predictive ability in the study's data set. CONCLUSION: Machine learning, specifically the ENPLR algorithm, demonstrated good performance for predicting a patient's propensity to achieve the MCID for the IKDC score after ACLR based on preoperative and intraoperative factors. The femoral tunnel fixation method was the only significant intraoperative variable. Range of motion and medial collateral ligament integrity were found to be important physical examination parameters. Increased body mass index and prior contralateral surgery were also significantly predictive of outcome.

Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing.

INTRODUCTION: Anticipation of patient-specific component sizes prior to total knee arthroplasty (TKA) is essential to avoid excessive cost associated with additional surgical trays and morbidity associated with imperfect sizing. Current methods of size prediction, including templating, are inconsistent and time-consuming. Machine learning (ML) algorithms may allow for accurate TKA component size prediction with the ability to make predictions in real-time. METHODS: Consecutive patients receiving primary TKA between 2012 and 2020 from two large tertiary academic and six community hospitals were identified. The primary outcomes were the final femoral and tibial component sizes extracted from automated inventory systems. Five ML algorithms were trained with routinely corrected demographic variables (age, height, weight, body mass index, and sex) using 80% of the study population and internally validated on an independent set of the remaining 20% of patients. Algorithm performance was evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE). RESULTS: A total of 17,283 patients that received one of 9 TKA implants from independent manufacturers were included. The SGB model accuracy for predicting ± 4-mm of the true femoral anteroposterior diameter was 83.6% and for ± 1 size of the true femoral component size was 95.0%. The SGB model accuracy for predicting ± 4-mm of the true tibial medial/lateral diameter was 83.0% and for ± 1 size of the true tibial component size was 97.8%. Patient sex was the most influential feature in terms of informing the SGB model predictions for both femoral and tibial component sizing. A TKA implant sizing application was subsequently created. CONCLUSION: Novel machine learning algorithms demonstrated good to excellent performance for predicting TKA component size. Patient sex appears to contribute an important role in predicting TKA size. A web-based real-time prediction application was created capable of integrating patient specific data to predict TKA size, which will require external validation prior to clinical use.