Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (< 3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (> 65 years of age), and body mass index (BMI) of < 25 kg/m2), 5-item modified frailty index (5MFI), and 6MFI. METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the American College of Surgeons National Surgical Quality Improvement Program database and randomly split 80:20 to compose the training and validation cohorts. There were 3 ML models - extreme gradient boosting, random forest, and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas under the receiver operating characteristic curve (AUCs, extreme gradient boosting = 0.94, random forest = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

INTRODUCTION: The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA. MATERIALS AND METHODS: We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA. Two comparison groups, readmissions (n = 79; 3.6%) and non-readmissions (n = 2091; 96.4%) were established. Univariate and multivariate logistic regression analyses were then performed with readmission as the outcome variable to determine whether preoperative PROMIS scores could predict 90-day readmission. RESULTS: The study cohort consisted of 2170 patients overall. Non-white patients (OR = 3.53, 95% CI [1.16, 10.71], p = 0.026) and patients with cardiovascular or cerebrovascular disease (CVD) (OR = 1.66, 95% CI [1.01, 2.71], p = 0.042) were found to have significantly higher odds of 90-day readmission after TKA. Preoperative PROMIS-PF10a (p = 0.25), PROMIS-GPH (p = 0.38), and PROMIS-GMH (p = 0.07) scores were not significantly associated with 90-day readmission. CONCLUSION: This study demonstrates that preoperative PROMIS scores may not be used to predict 90-day readmission following primary TKA. Non-white patients and patients with CVD are 3.53 and 1.66 times more likely to be readmitted, highlighting existing racial disparities and medical comorbidities contributing to readmission in patients undergoing TKA.

Effects of prior antibiotic use on clarithromycin resistance in Helicobacter pylori.

BACKGROUND: Macrolide antibiotics are widely used to treat various infections such as pneumonia and sinusitis, and previous exposure to macrolides is presumed to be a risk factor for standard triple therapy failure in Helicobacter pylori (H. pylori) eradication. We aimed to determine whether previous use of macrolide antibiotics could affect clarithromycin resistance of H. pylori. MATERIALS AND METHODS: From the Korea National Health Insurance Service (NHIS2021-1-775) database, a total of 46,160 patients who were tested for clarithromycin resistance of H. pylori from 2016 to 2019 in Korea were identified. Their history of antibiotics in the past 10 years and history of respiratory comorbidity in the past 1 year were investigated. RESULTS: Clarithromycin resistance rate of H. pylori in Korea was 16.2%. A multivariate analysis revealed that female sex (OR: 1.472, p < .001), age > 50 years (OR: 1.340, p < .001), previous use of macrolide antibiotics (clarithromycin, OR: 2.902, p < .001; azithromycin, OR: 1.930, p < .001; erythromycin, OR: 2.060, p = .001; roxithromycin, OR: 2.022, p < .001), and history of respiratory comorbidity (sinusitis, OR: 1.271, p < .001; laryngopharyngitis, OR: 1.135, p = .032; bronchitis, OR: 1.245, p = .001; pneumonia, OR: 1.335, p = .026) were independent risk factors of clarithromycin resistance in H. pylori. CONCLUSIONS: The use of macrolide antibiotics and a recent diagnosis of respiratory disease might increase clarithromycin resistance of H. pylori.

Patient-Reported Outcomes Measurement Information System Physical Function and Global Physical Health Subscale Strongly Correlate and Perform Similarly to the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form.

BACKGROUND: Arthroplasty surgeons use a variety of patient-reported outcome measures (PROMs) to assess functional well-being, including the Knee Injury and Osteoarthritis Outcome Score (KOOS) Physical Function short form (KOOS-PS), Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PROMIS PF SF 10a), and PROMIS Global-10 Physical Health subscale. However, there is a paucity of literature assessing their concurrent validity and performance. METHODS: Between June 2016 and December 2020, patient visits at an arthroplasty clinic for knee concerns were identified. Patients who completed KOOS-PS, PROMIS PF SF 10a, and PROMIS Global-10, including its physical and mental health subscales, at the same visit were identified. Spearman rho (ρ) correlations were calculated and ceiling and floor effects identified. Overall, 5,303 patient encounters were included. RESULTS: Among physical function domains, strong correlation existed between the KOOS-PS and PROMIS PF SF 10a (ρ = 0.76, P < .001), KOOS-PS and PROMIS Global Physical Health (ρ = 0.71, P < .001), and PROMIS PF SF 10a and PROMIS Global Physical Health (ρ = 0.78, P < .001). No physical function-focused PROM had an appreciable floor effect (ie, at or more than 1%). The KOOS-PS had a small but measurable ceiling effect (n = 105 [2.0%]). CONCLUSIONS: All of the examined PROMs are acceptable to measure the functional status of patients with knee pathology, with the PROMIS Global-10 also being able to capture elements of mental health too. The PROMIS Global-10 may be of most value of the PROMs assessed, as the United States Centers for Medicare and Medicaid Services already incorporate the mental health component into new alternative payment models.

Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty.

BACKGROUND: Nonhome discharge disposition following primary total knee arthroplasty (TKA) is associated with a higher rate of complications and constitutes a socioeconomic burden on the health care system. While existing algorithms predicting nonhome discharge disposition varied in degrees of mathematical complexity and prediction power, their capacity to generalize predictions beyond the development dataset remains limited. Therefore, this study aimed to establish the machine learning model generalizability by performing internal and external validations using nation-scale and institutional cohorts, respectively. METHODS: Four machine learning models were trained using the national cohort. Recursive feature elimination and hyper-parameter tuning were applied. Internal validation was achieved through five-fold cross-validation during model training. The trained models' performance was externally validated using the institutional cohort and assessed by discrimination, calibration, and clinical utility. RESULTS: The national (424,354 patients) and institutional (10,196 patients) cohorts had non-home discharge rates of 19.4 and 36.4%, respectively. The areas under the receiver operating curve of the model predictions were 0.83 to 0.84 during internal validation and increased to 0.88 to 0.89 during external validation. Artificial neural network and histogram-based gradient boosting elicited the best performance with a mean area under the receiver operating curve of 0.89, calibration slope of 1.39, and Brier score of 0.14, which indicated that the two models were robust in distinguishing non-home discharge and well-calibrated with accurate predictions of the probabilities. The low inter-dataset similarity indicated reliable external validation. Length of stay, age, body mass index, and sex were the strongest predictors of discharge destination after primary TKA. CONCLUSION: The machine learning models demonstrated excellent predictive performance during both internal and external validations, supporting their generalizability across different patient cohorts and potential applicability in the clinical workflow.

Validation and Generalizability of Machine Learning Models for the Prediction of Discharge Disposition Following Revision Total Knee Arthroplasty.

BACKGROUND: Postoperative discharge to facilities account for over 33% of the $ 2.7 billion revision total knee arthroplasty (TKA)-associated annual expenditures and are associated with increased complications when compared to home discharges. Prior studies predicting discharge disposition using advanced machine learning (ML) have been limited due to a lack of generalizability and validation. This study aimed to establish ML model generalizability by externally validating its prediction for nonhome discharge following revision TKA using national and institutional databases. METHODS: The national and institutional cohorts comprised 52,533 and 1,628 patients, respectively, with 20.6 and 19.4% nonhome discharge rates. Five ML models were trained and internally validated (five-fold cross-validation) on a large national dataset. Subsequently, external validation was performed on our institutional dataset. Model performance was assessed using discrimination, calibration, and clinical utility. Global predictor importance plots and local surrogate models were used for interpretation. RESULTS: The strongest predictors of nonhome discharge were patient age, body mass index, and surgical indication. The area under the receiver operating characteristic curve increased from internal to external validation and ranged between 0.77 and 0.79. Artificial neural network was the best predictive model for identifying patients at risk for nonhome discharge (area under the receiver operating characteristic curve = 0.78), and also the most accurate (calibration slope = 0.93, intercept = 0.02, and Brier score = 0.12). CONCLUSION: All five ML models demonstrated good-to-excellent discrimination, calibration, and clinical utility on external validation, with artificial neural network being the best model for predicting discharge disposition following revision TKA. Our findings establish the generalizability of ML models developed using data from a national database. The integration of these predictive models into clinical workflow may assist in optimizing discharge planning, bed management, and cost containment associated with revision TKA.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty.

BACKGROUND: The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data. METHODS: Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis. RESULTS: The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts. CONCLUSIONS: This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty.

BACKGROUND: Existing machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA. METHODS: A total of 246,265 THAs were analyzed from a large database. Prolonged LOS was defined as exceeding the 75th percentile of all LOSs in the cohort. Candidate predictors of prolonged LOS were selected by recursive feature elimination and used to construct four machine learning models-artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor. The model performance was assessed by discrimination, calibration, and utility. RESULTS: All models exhibited excellent performance in discrimination (area under the receiver operating characteristic curve [AUC] = 0.72 to 0.74) and calibration (slope: 0.83 to 1.18, intercept: -0.01 to 0.11, Brier score: 0.185 to 0.192) during both training and testing sessions. The artificial neural network was the best performer with an AUC of 0.73, calibration slope of 0.99, calibration intercept of -0.01, and Brier score of 0.185. All models showed great utility by producing higher net benefits than the default treatment strategies in the decision curve analyses. Age, laboratory tests, and surgical variables were the strongest predictors of prolonged LOS. CONCLUSION: The excellent prediction performance of machine learning models demonstrated their capacity to identify patients prone to prolonged LOS. Many factors contributing to prolonged LOS can be optimized to minimize hospital stay for high-risk patients.

Hypofractionated stereotactic radiosurgery for aggressive vertebral haemangioma and useful follow-up imaging modality: case report and review of the literature.

We report the use of an advanced magnetic resonance image (MRI) sequence to detect the treatment response after SRS for aggressive vertebral haemangioma (VH). A 63-year-old female patient presented with back pain, bilateral lower extremity weakness (grade IV), and sensory change in the saddle area. MRI revealed a vertebral body mass compressing the spinal cord at T10, which had high T2 and low T1 signal intensity. Three-dimensional volumetric sagittal time-resolved imaging of contrast kinetics (TRICKS) abdominal magnetic resonance angiography (MRA) showed it to be hypervascular. SRS with the Novalis beam shaping system (BrainLAB; Heimstetten®, Germany) was performed on the gross tumor volume of 14.954 mL. 30 Gy was given to the 90% isodose line in 5 fractions. Seven days later, the patient underwent decompressive laminectomy for weakness. Seven months later, the patient's motor weakness was improved to allow for unassisted gait, and back pain and sensory changes resolved. Follow-up MRI revealed no significant change on T1 and T2 signal intensity images. However, TRICKS abdominal MRA demonstrated disapprearance of the hypervascularity. Seven years after SRS, the same signal intensity images showed shrinkage of the mass and resolution of compression of the spinal cord, and the signal intensity of the T1 image was changed to iso- and high signal intensity.

Complete blood platelet and lymphocyte ratios increase diagnostic accuracy of periprosthetic joint infection following total hip arthroplasty.

INTRODUCTION: Systemically, changes in serum platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte (MLR) represent primary responses to early inflammation and infection. This study aimed to determine whether PLR, PVR, NLR, and MLR can be useful in diagnosing periprosthetic joint infection (PJI) in total hip arthroplasty (THA) patients. METHODS: A total of 464 patients that underwent revision THA with calculable PLR, PVR, NLR, and MLR in 2 groups was evaluated: 1) 191 patients with a pre-operative diagnosis of PJI, and 2) 273 matched patients treated for revision THA for aseptic complications. RESULTS: The sensitivity and specificity of PLR combined with erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), synovial white blood cell count (WBC) and synovial polymorphonuclear leukocytes (PMN) (97.9%; 98.5%) is significantly higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01). The sensitivity and specificity of PVR combined with ESR, CRP and synovial WBC, and synovial PMN (98.4%; 98.2%) is higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01). CONCLUSION: The study results demonstrate that both PLR and PVR calculated from complete blood counts when combined with serum and synovial fluid markers have increased diagnostic sensitivity and specificity in diagnosing periprosthetic joint infection in THA patients. LEVEL OF EVIDENCE: III, case-control retrospective analysis.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

BACKGROUND: A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty. METHODS: A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis. RESULTS: Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis. CONCLUSION: This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Can machine learning models predict failure of revision total hip arthroplasty?

INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.

BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty. METHODS: A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis. RESULTS: The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty.

BACKGROUND: Despite advancements in total hip arthroplasty (THA) and the increased utilization of tranexamic acid, acute blood loss anemia necessitating allogeneic blood transfusion persists as a post-operative complication. The prevalence of allogeneic blood transfusion in primary THA has been reported to be as high as 9%. Therefore, this study aimed to develop and validate novel machine learning models for the prediction of transfusion rates following primary total hip arthroplasty. METHODS: A total of 7265 consecutive patients who underwent primary total hip arthroplasty were evaluated using a single tertiary referral institution database. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with transfusion rates. Four state-of-the-art machine learning algorithms were developed to predict transfusion rates following primary THA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The factors most significantly associated with transfusion rates include tranexamic acid usage, bleeding disorders, and pre-operative hematocrit (< 33%). The four machine learning models all achieved excellent performance across discrimination (AUC > 0.78), calibration, and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-specific transfusion rates following primary total hip arthroplasty. The results represent a novel application of machine learning, and has the potential to improve outcomes and pre-operative planning. LEVEL OF EVIDENCE: III, case-control retrospective analysis.

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

INTRODUCTION: The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort. METHODS: The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility. RESULTS: ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS. CONCLUSION: ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models.

BACKGROUND: Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty. METHODS: A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN). RESULTS: We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time. CONCLUSIONS: This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

Sex, menopause, and age differences in the associations of persistent organic pollutants with thyroid hormones, thyroxine-binding globulin, and peripheral deiodinase activity: A cross-sectional study of the general Korean adult population.

Persistent organic pollutants (POPs) can disrupt the thyroid hormone system in humans. We assessed the associations of several POPs with serum thyroid hormones (T3 and T4) and thyroid-stimulating hormone, and investigated the modulating effects of sex, menopausal status, and age on these associations, in a subgroup of the adult population (n = 1250) from the Korean National Environmental Health Survey. PCB105 and PCB118 were negatively associated with total T4 in premenopausal females and males aged <50, whereas the associations were insignificant in other groups. PCB180, p,p'-DDE, and p,p'-DDT showed positive associations with total T3 in postmenopausal females; however, among males aged ≥50, PCB118, PCB138, and p,p'-DDE showed negative associations with total T3. The effects of exposure to multiple POPs were examined in multi-factor analyses. Factor 2 comprised PCB52, hexachlorobenzene, and BDE-47 was associated with an increase in free T4 in premenopausal females (ß = 0.015, p = 0.024), while Factor 1, which contained most POPs, was associated with a change in total T3 in postmenopausal females (ß = 0.032, p = 0.040) and males aged ≥50 (ß = -0.039, p = 0.023). Changes in total T4 or total T3 could be explained by differences in thyroxine-binding globulin (TBG) and peripheral deiodinase activity (GD). Negative associations of TBG with PCB105 in premenopausal females and PCB153 in males aged <50 may mediate the effect of decreasing total T4. PCB180, p,p'-DDE, p,p'-DDT, and Factor 1 were positively associated with GD, which is consistent with an increased total T3 in postmenopausal females. PCB118 was negatively associated with GD and total T3 in males aged ≥50. BDE-47 and ß-hexachlorocyclohexane were associated with thyroid autoantibodies in premenopausal females and males aged <50. Our observations suggest that the thyroid-disrupting effects of POPs may differ by sex, sex hormonal status, and age, and may be mediated by TBG and GD.

Epidemiology of Ankle Fractures in Korea: A Nationwide Population-Based Study.

BACKGROUND: Although ankle fractures are among the most common fractures, nationwide population-based data on the epidemiology of patients with ankle fractures are scarce. This study aimed to perform an epidemiological analysis of all ankle fractures in Korea from 2010 through 2018. METHODS: We used national registries from the Korean Health Insurance Review and Assessment Service from 2009 to 2018. The annual incidence of the ankle fracture was calculated. The incidence was also calculated according to gender and age. Trends of fracture subtypes were also analyzed. Then, the incidence of ankle fractures by seasonal variation was investigated. RESULTS: A total of 735,073 ankle fractures were identified in 461,497,758 people for 10 years. The annual incidence of ankle fracture was 171.37/100,000 persons in 2018, with a male to female ratio of 0.78. Interesting differences in the ankle fracture trends were observed between gender. Male shows the highest incidence in adolescence, and the even distribution has lasted for the rest of their lives. In females, the incidence of ankle fracture showed an increasing tendency as their age increased. There was a clear difference in the incidence rate of each season according to age. Ankle fractures occurred more in spring and autumn in children and adolescents and most in winter in the elderly. CONCLUSION: Ankle fracture risk was different between sex and exhibited seasonal variations. Our findings can be used for epidemiological awareness and prevention campaigns for ankle fractures.

Asymmetrical tibial polyethylene geometry-cruciate retaining total knee arthroplasty does not fully restore in-vivo articular contact kinematics during strenuous activities.

PURPOSE: A new CR TKA design with concave medial and convex lateral tibial polyethylene bearing components was introduced recently to improve functional outcomes. This study aimed to investigate in-vivo articular contact kinematics in unilateral asymmetrical tibial polyethylene geometry CR TKA patients during strenuous knee flexion activities. METHODS: Fifteen unilateral CR TKA patients (68.4 ± 5.8 years; 6 male/9 female) were evaluated for both knees during sit-to-stand, single-leg deep lunges and step-ups using validated combined computer tomography and dual fluoroscopic imaging system. Medial and lateral condylar contact positions were quantified during weight-bearing flexion activities. The Wilcoxon signed-rank test was performed to determine if there is a significant difference in articular contact kinematics during strenuous flexion activities between CR TKA and the non-operated knees. RESULTS: Contact excursions of the lateral condyle in CR TKAs were significantly more anteriorly located than the contralateral non-operated knee during sit-to-stand (3.7 ± 4.8 mm vs - 7.8 ± 4.3 mm) and step-ups (- 1.5 ± 3.2 mm vs - 6.3 ± 5.8 mm). Contact excursions of the lateral condyle in CR TKAs were significantly less laterally located than the contralateral non-operated knee during sit-to-stand (21.4 ± 2.8 mm vs 24.5 ± 4.7 mm) and single-leg deep lunges (22.6 ± 4.4 mm vs 26.2 ± 5.7 mm, p < 0.05). Lateral condyle posterior rollback was not fully restored in CR TKA patients during sit-to-stand (9.8 ± 6.7 mm vs 12.9 ± 8.3 mm) and step-ups (8.1 ± 4.8 mm vs 12.2 ± 6.4 mm). Lateral pivoting patterns were observed in 80%, 73% and 69% of patients during sit-to-stand, step-ups and single-leg deep lunges respectively. CONCLUSION: Although lateral femoral rollback and lateral pivoting patterns were observed during strenuous functional daily activities, asymmetric contact kinematics still persisted in unilateral CR TKA patients. This suggests the specific investigated contemporary asymmetrical tibial polyethylene geometry CR TKA design evaluated in this study does not fully replicate healthy knee contact kinematics during strenuous functional daily activities. LEVEL OF EVIDENCE: III.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty.

PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.