The SIFK score: a validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee

The Relationship Between Medial Meniscal Extrusion and Outcome Measures for Knee Osteoarthritis: A Systematic Review

Background

Medial meniscal extrusion (MME) has been associated with knee osteoarthritis (OA). However, there is no standardized method to measure MME.

Purpose/Hypothesis

The purpose of this study was to investigate the relationship between MME and outcome measures related to knee OA and discuss different magnetic resonance imaging (MRI) methods of measuring MME. It was hypothesized that MME would be associated with outcome measures of OA and that the distance extruded over the tibial plateau would be the most common MRI method to measure MME.

Study Design

Systematic review; Level of evidence, 3.

Methods

The MEDLINE, Embase, Cochrane Library, Scopus, Web of Science Core Collection, Global Index Medicus, and ClinicalTrials.gov databases were systematically searched. The inclusion criteria were studies that (1) measured MME on nonoperated knees using MRI; (2) evaluated knee OA with at least 1 knee OA grading scale, outcome measure, or direct characterization of cartilage or bone; (3) statistically evaluated the association between MME and knee OA outcome measure; (4) were randomized controlled trials, nonrandomized controlled trials, cohort studies, or case series; and (5) reported original results.

Results

A total of 19 studies were included, of which 14 reported MME as the distance extruded over the tibial plateau, 7 reported MME as the volume extruded over the tibial plateau, and 1 reported MME as the percentage of the tibial plateau covered by the meniscus. All studies reported that MME was significantly associated with at least 1 OA outcome measure-including increased Kellgren-Lawrence grade, osteophytes, cartilage damage, varus alignment, knee pain, bone marrow lesions, and progression to arthroplasty. Eight studies found that MME was associated with worse OA outcomes over time (range, 2-10 years).

Conclusion

All 19 reviewed studies reported that MME was associated with at least 1 knee OA outcome measure reflective of worsening arthritis, suggesting a strong association between OA and MME. Future research is needed to investigate this relationship and standardize the methods of measuring MME.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Risk of Arthroplasty in Patients with Subchondral Insufficiency Fractures of the Knee: A Matched Study of the Implantable Shock Absorber using a Validated Predictive Model

Subchondral insufficiency fractures of the knee (SIFK) can result in high rates of osteoarthritis and arthroplasty. The implantable shock absorber (ISA) implant is a titanium and polycarbonate urethane device which reduces the load on the medial compartment of the knee by acting as an extra-articular load absorber while preserving the joint itself. The purpose of this study was to evaluate whether partially unloading the knee with the ISA altered the likelihood of progression to arthroplasty utilizing a validated predictive risk model (SIFK score). A retrospective case-control (2:1) study was performed on patients with SIFK without any previous surgery and on those implanted with the ISA with the primary outcome being progression to arthroplasty compared with nonoperative treatment at 2 years. Baseline and final radiographs, as well as magnetic resonance imagings, were reviewed for the evaluation of meniscus or ligament injuries, insufficiency fractures, and subchondral edema. Patients from a prospective study were matched using the exact SIFK Score, a validated predictive score for progression to arthroplasty in patients with SIFK, to those who received the ISA implant. Kaplan-Meier analysis was conducted to assess survival. A total of 57 patients (38 controls:19 ISA) with a mean age of 60.6 years and 54% female were included. The SIFK score was matched exactly between cases and controls for all patients. The 2-year survival rate of 100% for the ISA group was significantly higher than the corresponding rate of 61% for the control group (p < 0.01). In ISA, 0% of the patients converted to arthroplasty at 2 years, and 5% (one patient) had hardware removal at 1 year. When stratified by risk, the ISA group did not have a significantly higher survival compared with low-risk (p = 0.3) or medium-risk (p = 0.2) controls, though it had a significantly higher survival for high-risk groups at 2 years (100 vs. 15%, p < 0.01). SIFK of the medial knee can lead to significant functional limitation and high rates of conversion to arthroplasty. Implants such as the ISA have the potential to alter the progression to arthroplasty in these patients, especially those at high risk.

The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty

BACKGROUND

The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates.

METHODS

Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70%) and holdout test set (30%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model.

RESULTS

The artificial intelligence model achieved an accuracy of 87.8% on the holdout test set and a quadratic Cohen's kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8% was achieved for predicting Surgery versus No Surgery and 88% for TKA versus not TKA was achieved.

CONCLUSION

The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.

Improved 2-Year Freedom from Arthroplasty in Patients with High-Risk SIFK Scores and Medial Knee Osteoarthritis Treated with an Implantable Shock Absorber versus Non-Operative Care

OBJECTIVE

Subchondral insufficiency fracture of the knee (SIFK) is associated with high rates of osteoarthritis (OA) and arthroplasty. The implantable shock absorber (ISA) is an extra-capsular implant that unloads the medial knee compartment. This study compared the 2-year freedom from arthroplasty rates in subjects with medial knee OA and SIFK when treated with an ISA versus a matched cohort of patients treated non-surgically.

DESIGN

This retrospective case-control study compared 2-year conversion rates to arthroplasty in SIFK score-, age-, and body mass index (BMI)-matched control subjects without prior surgical history with ISA-implanted subjects from an ongoing prospective study. Baseline and final radiographs, and MRIs were reviewed for evaluation of meniscus or ligament injuries, insufficiency fractures, and subchondral edema. Kaplan-Meier analysis assessed survival.

RESULTS

Forty-two patients (21 Control: 21 ISA), mean age = 52.3 ± 8.7 years, BMI = 29.5 ± 3.9 kg/m2, 40% female were evaluated. Both ISA and Control arms had the same numbers of low (n = 4), medium (n = 11), and high-risk (n = 6) SIFK scores. One- and 2-year freedom-from-arthroplasty rates were both 100% for ISA subjects, and 76% and 55%, respectively, for Controls (P = 0.001 for cross-group comparison). Control knees with low, medium, and high-risk SIFK scores had respective 1- and 2-year survival rates of 100% and 100%, 90% and 68% (P = 0.07 vs. ISA), and 33% and 0% (P = 0.002 vs. ISA).

CONCLUSIONS

ISA intervention was strongly associated with avoidance of arthroplasty at a minimum 2 years, especially in patients with high-risk SIFK scores. SIFK severity scoring predicted relative risk of conversion to arthroplasty through at least 2 years in non-surgically treated subjects.

Meniscal Extrusion: Diagnosis, Etiology, and Treatment Options

PURPOSE OF REVIEW

The concept of meniscal extrusion has recently been recognized as a hallmark of meniscus dysfunction. This review examines contemporary literature regarding the pathophysiology, classification, diagnosis, treatment, and future directions for investigation regarding meniscus extrusion.

RECENT FINDINGS

Meniscus extrusion, defined as >3 mm of radial displacement of the meniscus, leads to altered knee biomechanics and accelerated knee joint degeneration. Meniscus extrusion has been associated with degenerative joint disease, posterior root and radial meniscal tears, and acute trauma. Meniscus centralization and meniscotibial ligament repair have been proposed as techniques to address meniscal extrusion with promising biomechanical, animal model, and early clinical reports. Further studies on the epidemiology of meniscus extrusion and associated long-term nonoperative outcomes will help to elucidate its role in meniscus dysfunction and resultant arthritic development. Understanding and appreciation for the anatomic attachments of the meniscus will help to inform future repair techniques. Long-term reporting on the clinical outcomes of meniscus centralization techniques will yield insights into the clinical significance of meniscus extrusion correction.

Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

PURPOSE

This study aims to describe and assess the current stage of the artificial intelligence (AI) technology integration in preventive orthopaedics of the knee and hip joints.

MATERIALS AND METHODS

The study was conducted in strict compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Literature databases were searched for articles describing the development and validation of AI models aimed at diagnosing knee or hip joint pathologies or predicting their development or course in patients. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and QUADAS-AI tools.

RESULTS

56 articles were found that meet all the inclusion criteria. We identified two problems that block the full integration of AI into the routine of an orthopaedic physician. The first of them is related to the insufficient amount, variety and quality of data for training, and validation and testing of AI models. The second problem is the rarity of rational evaluation of models, which is why their real quality cannot always be evaluated.

CONCLUSION

The vastness and relevance of the studied topic are beyond doubt. Qualitative and optimally validated models exist in all four scopes considered. Additional optimization and confirmation of the models' quality on various datasets are the last technical stumbling blocks for creating usable software and integrating them into the routine of an orthopaedic physician.

Mosaicplasty With High Tibial Osteotomy for Knee Subchondral Insufficiency Fracture Had Better Magnetic Resonance Observation of Cartilage Repair Tissue Scores With Less Bone Marrow Edema and Better Plug Union and Less Plug Necrosis Compared With Mosaicplasty Alone

PURPOSE

To determine the magnetic resonance imaging (MRI) findings after mosaicplasty (MOS) for knee subchondral insufficiency fracture (SIFK), and to analyze the relationship between MRI findings and clinical outcomes.

METHODS

We retrospectively reviewed the cases of consecutive patients who underwent MOS for SIFK with/without high tibial osteotomy (HTO) between January 1998 and December 2015. The MRI findings at 12 months after the surgery were assessed by the modified magnetic resonance observation of cartilage repair tissue (MOCART) score to determine the degree of bone marrow edema (BME), plug union, and plug necrosis. The clinical outcomes were assessed by Lysholm score to clarify the minimal clinically important difference (MCID) and patient acceptable symptom state analysis.

RESULTS

In total, 58 patients (17 men and 41 women) were enrolled in this study. Among them, 30 knees were treated by MOS alone and 28 knees were treated by MOS with HTO. The MOCART scores of patients who received MOS alone were significantly lower in BME score (P = .0060), plug union score (P = .0216), and in plug necrosis score (P = .0326) than patients who received MOS with HTO. BME lesion was less likely to persist among elderly (odds ratio 1.20, P = .0248) and female (OR 41.8, P = .0118) patients. The MCID of Lysholm score was 6.6 in MOS alone and 8.4 in MOS with HTO cases, but there were no significant association between MRI findings and the postoperative Lysholm score.

CONCLUSIONS

The MOS with HTO cases had better MOCART scores with less BME, better plug union, and less plug necrosis compared with MOS alone cases. Female and older patients had better resolution of BME, but there was no significant correlation between MRI findings and the postoperative Lysholm score. All cases in both groups showed improvement of Lysholm score exceeding MCID; thus, MOS may be effective as a joint preserving surgery for SIFK.

LEVEL OF EVIDENCE

Level IV, clinical case series.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction

Purpose

To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction.

Methods

A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression.

Results

Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD.

Conclusions

An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients.

Clinical Relevance

Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care.

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.

Current Reviews in Musculoskeletal Medicine: Current Controversies for Treatment of Meniscus Root Tears

PURPOSE OF REVIEW

The role of the meniscus in preserving the biomechanical function of the knee joint has been clearly defined. The hypothesis that meniscus root integrity is a prerequisite for meniscus function is supported by the development of progressive knee osteoarthritis (OA) following meniscus root tears (MRTs) treated either non-operatively or with meniscectomy. Consequently, there has been a resurgence of interest in the diagnosis and treatment of MRTs. This review examines the contemporary literature surrounding the natural history, clinical presentation, evaluation, preferred surgical repair technique and outcomes.

RECENT FINDINGS

Surgeons must have a high index of suspicion in order to diagnose a MRT because of the nonspecific clinical presentation and difficult visualization on imaging. Compared with medial MRTs that commonly occur in middle age/older patients, lateral meniscus root injuries tend to occur in younger males with lower BMIs, less cartilage degeneration, and with concomitant ligament injury. Subchondral insufficiency fractures of the knee have been found to be associated with both MRTs and following arthroscopic procedures. Meniscus root repair has demonstrated good outcomes, and acute injuries with intact cartilage should be repaired. Cartilage degeneration, BMI, and malalignment are important considerations when choosing surgical candidates. Meniscus centralization has emerged as a viable adjunct strategy aimed at correcting meniscus extrusion. Meniscus root repair results in a decreased rate of OA and arthroplasty and is economically advantageous when compared with nonoperative treatment and partial meniscectomy. The transtibial pull-through technique with the addition of centralization for the medial meniscus is associated with encouraging early results.

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.Level of evidence VI.

Extrusion of the medial meniscus is a potential predisposing factor for post-arthroscopy osteonecrosis of the knee

Background

Post-arthroscopic osteonecrosis of the knee (PAONK) is a rare condition. No studies have analyzed the relationship between the meniscus extrusion and PAONK. The purpose of this retrospective study is to test a hypothesis that the degree of the medial meniscus (MM) extrusion might be significantly greater in the knees with PAONK than in the matched control knees both before and after the meniscectomy.

Methods

Ten knees with PAONK were detected out of a total of 876 knees which had undergone arthroscopic partial meniscectomy of the MM. Ten matched control knees were randomly selected out of the remaining 866 knees without PAONK. The clinical data of these 20 patients were retrospectively collected from the medical records. To evaluate the location of the menisci on the joint line, Extrusion width and Inner width were defined on a coronal section of magnetic resonance imaging (MRI). The intra- and inter-rater reliability was evaluated by calculating the intra- and inter-class coefficients. Statistical comparisons between the 2 groups were made using the 3 non-parametric tests.

Results

Before the meniscectomy, the Extrusion width of the MM (mean 4.7 ± 1.4 mm) was significantly greater than that (3.0 ± 1.3 mm) in the Control group (P = 0.0195). In the MRI taken in a range from 3 to 50 weeks after the meniscectomy, the Extrusion width of the MM (5.9 ± 1.1 mm) in the PAONK group was significantly greater than that (3.4 ± 1.4 mm) in the Control group (P = 0.0009), and the Inner width of the MM (0.6 ± 1.7 mm) in the PAONK group was significantly less than that (3.9 ± 1.0 mm) in the Control group (P = 0.0001).

Conclusion

A significant relationship was found between the degree of the MM extrusion and the onset of PAONK. This study suggested that the extrusion of the MM is a potential predisposing factor for PAONK.

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

BACKGROUND

Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI- and ML-based application for cognitive support and decision-making in TJA.

METHODS

A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models.

RESULTS

After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories.

CONCLUSIONS

AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patient-reported outcomes and were less accurate in predicting hospital readmissions and reoperations.

Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction

PURPOSE

Overnight admission following anterior cruciate ligament reconstruction has implications on clinical outcomes as well as cost benefit, yet there are few validated risk calculators for reliable identification of appropriate candidates. The purpose of this study is to develop and validate a machine learning algorithm that can effectively identify patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction.

METHODS

A retrospective review of a national surgical outcomes database was performed to identify patients who underwent elective ACL reconstruction from 2006 to 2018. Patients admitted overnight postoperatively were identified as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), linear discriminant classifier (LDA), and adaptive boosting algorithms (AdaBoost), and an additional model was produced as a weighted ensemble of the four final algorithms.

RESULTS

Overall, of the 4,709 patients included, 531 patients (11.3%) required at least one overnight stay following ACL reconstruction. The factors determined most important for identification of candidates for inpatient admission were operative time, anesthesia type, age, gender, and BMI. Smoking history, history of COPD, and history of coagulopathy were identified as less important variables. The following factors supported overnight admission: operative time > 200 min, age < 35.8 or > 53.5 years, male gender, BMI < 25 or > 31.2 kg/m², positive smoking history, history of COPD and the presence of preoperative coagulopathy. The ensemble model achieved the best performance based on discrimination assessed via internal validation (AUC = 0.76), calibration, and decision curve analysis. The model was integrated into a web-based open-access application able to provide both predictions and explanations.

CONCLUSION

Modifiable risk factors identified by the model such as increased BMI, operative time, anesthesia type, and comorbidities can help clinicians optimize preoperative status to prevent costs associated with unnecessary admissions. If externally validated in independent populations, this algorithm could use these inputs to guide preoperative screening and risk stratification to identify patients requiring overnight admission for observation following ACL reconstruction.

LEVEL OF EVIDENCE

IV.

Post-arthroscopic Subchondral Insufficiency Fractures of the Knee Yield High Rate of Conversion to Arthroplasty

PURPOSE

To evaluate both the potential causes and resultant outcomes in patients in whom subchondral insufficiency fracture of the knee (SIFK) develops after arthroscopy.

METHODS

We performed a retrospective review of all patients with a magnetic resonance imaging diagnosis of SIFK after arthroscopic meniscectomy and chondroplasty over a 12-year period.

RESULTS

A total of 28 patients were included, with a mean age of 61 years and mean follow-up period of 5.7 years. SIFK showed a predilection for the medial compartment (n = 25, 89%), specifically the medial femoral condyle (n = 21, 75%). In 7 patients (25%), SIFK developed in both the femoral condyle and tibial plateau in the ipsilateral compartment. Fifteen patients (54%) went on to conversion to arthroplasty at a mean of 0.72 years. The rate of survival free of conversion to arthroplasty was 57%, 45%, and 40% at 1 year, 2 years, and 5 years, respectively. Furthermore, 63% of patients with a meniscal tear and SIFK in the same compartment went on to arthroplasty (P = .04). There was an increased risk of conversion to arthroplasty if SIFK was present in both the femur and tibia in the same compartment (P = .04). A higher Kellgren-Lawrence grade at the time of the SIFK diagnosis increased the likelihood of eventual arthroplasty (P = .03). The presence of SIFK in both the femur and tibia in the ipsilateral compartment, an increased Kellgren-Lawrence grade, and a meniscal tear or prior meniscectomy in the same compartment as SIFK were associated with an increased risk of eventual arthroplasty.

CONCLUSIONS

Post-arthroscopic SIFK most commonly occurs in the medial compartment, particularly in patients who underwent a prior meniscectomy. The presence of meniscal root and radial tears in these patients is notable (75%). Ultimately, there is a high rate of progression of arthrosis (33%) and eventual conversion to arthroplasty.

LEVEL OF EVIDENCE

Level IV, case series.

Early tibial component loosening of medial UKA after severe medial plateau SONK-report of three cases

Spontaneous osteonecrosis or subchondral bone insufficiency fracture of the knee is a frequent injury in elderly female patients. The medial femoral condyle followed by the medial plateau is the most prevalent sites. When its evolution after conservative treatment is not favorable, medial unicompartimental arthroplasty is a surgical option with good results. We report three cases of early tibial component loosening of medial unicompartimental arthroplasty that could be related to a severe subchondral bone insufficiency fracture of the tibial plateau. In these cases of severe involvement of the tibial plateau, a more careful evaluation would be recommended to choose between unicompartimental and total knee replacement to avoid this early loosening.