Knee Arthroscopy Prior to Revision TKA Is Associated with Increased Re-Revision for Stiffness

A mapping review on preoperative prognostic factors and outcome measures of revision total knee arthroplasty

To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration. We performed a systematic literature search in MEDLINE, Embase, and Web of Science. The search string included multiple synonyms of the following keywords: "revision TKA", "outcome" and "prognostic factor". We searched for studies assessing the association between at least one prognostic factor and at least one outcome measure after rTKA surgery. Data on sample size, study design, prognostic factors, outcomes, and the direction of the association was extracted and included in an evidence map. After screening of 5,660 articles, we included 166 studies reporting prognostic factors for outcomes after rTKA, with a median sample size of 319 patients (30 to 303,867). Overall, 50% of the studies reported prospectively collected data, and 61% of the studies were performed in a single centre. In some studies, multiple associations were reported; 180 different prognostic factors were reported in these studies. The three most frequently studied prognostic factors were reason for revision (213 times), sex (125 times), and BMI (117 times). Studies focusing on functional scores and patient-reported outcome measures as prognostic factor for the outcome after surgery were limited (n = 42). The studies reported 154 different outcomes. The most commonly reported outcomes after rTKA were: re-revision (155 times), readmission (88 times), and reinfection (85 times). Only five studies included costs as outcome. Outcomes and prognostic factors that are routinely registered as part of clinical practice (e.g. BMI, sex, complications) or in (inter)national registries are studied frequently. Studies on prognostic factors, such as functional and sociodemographic status, and outcomes as healthcare costs, cognitive and mental function, and psychosocial impact are scarce, while they have been shown to be important for patients with osteoarthritis.

Knee Arthroscopy Prior to Total Knee Arthroplasty: Temporal Relationship to Surgical Complications

Mechanical knee symptoms secondary to knee osteoarthritis (OA) may warrant knee arthroscopy (KA). Degenerative changes may progress and require a subsequent total knee arthroplasty (TKA). Recent studies have evaluated the timing of KA prior to TKA, associated a narrow interval with increased post-TKA complications. However, an updated analysis is required. We compared surgical outcomes in recipients of KA prior to TKA as stratified by four, time-dependent cohorts (< 12, 12 to 16, 16 to 20, and 20 to 24 weeks prior to TKA). We specifically compared: 90-day (1) manipulations under anesthesia (MUAs); (2) septic revisions at 90 days, 1 year, and 2 years; as well as (3) how various independent risk factors influenced the manipulations or revisions. We queried a national database for patients undergoing TKA. Patients who underwent KA with the following intervals: < 12 (n = 1,023), 12 to 16 (n = 816), 16 to 20 (n = 1,957), and 20 to 24 weeks (1,727) prior to TKA were compared with those patients who did not have a history of KA (n = 5,000). Bivariate analyses were utilized to assess 90 days through 2 years surgical outcomes. Afterwards, multivariate regression models were utilized to assess for independent risk factors. The unadjusted analyses showed an increase in MUA through 2 years among all the KA recipients (p < 0.001). Septic revisions and surgical site infections at all time points were not associated with any of the four arthroscopy time intervals (p > 0.476). The adjusted analyses showed an increased risk for 90-day MUA among all TKA cohorts (p < 0.001). Risk for septic revisions did not reach significance. Delaying TKA by 24 weeks in KA recipients was not associated with a decreased risk in septic revisions. However, there was an apparent risk of MUA at 90 days for all KA cohorts suggesting that waiting after KA before TKA makes no difference in MUA rates. These results update existing literature, may serve as an adjunct to current practice guidelines, and can contribute to shared decision making in the preoperative setting.

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.

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.