Satisfaction in Surgically Treated Patients with Degenerative Cervical Myelopathy: An observational study from the Canadian Spine Outcomes and Research Network.

BACKGROUND CONTEXT: Healthcare reimbursement is evolving towards a value-based model, entwined and emphasizing patient satisfaction. Factors associated with satisfaction after degenerative cervical myelopathy (DCM) surgery have not been previously established. PURPOSE: Our primary objective was to ascertain satisfaction rates and satisfaction predictors at 3 and 12 months following surgical treatment for DCM. DESIGN: This is a prospective cohort study within Canadian Spine Outcomes and Research Network (CSORN). PATIENT SAMPLE: Patients in the study were surgically treated for DCM patients who completed 3-month and 12-month follow-ups within CSORN between 2015 and 2021. OUTCOME MEASURES: Data analyzed included patient demographic, surgical variables, patient-reported outcomes (NDI, NRS-NP, NRS-AP, SF-12-MCS, SF-12-PCS, ED-5Q, PHQ-8), MJOA and self-reported satisfaction on a Likert scale. METHODS: Multivariable regression analysis was conducted to identify significant factors associated with satisfaction, address multicollinearity and ensure predictive accuracy. This process was conducted separately for the 3-month and 12-month follow-ups. RESULTS: Six hundred and sixty-three patients were included, with an average age of 60, and an even distribution across MJOA scores (mild, moderate, severe). At 3-month and 12-month follow-up, satisfaction rates were 86% and 82%, respectively. At 12 months, logistic regression showed the odds of being satisfied varied by +24%, -3%, -10%, -14%, +3%, and +12% for each 1-point change between baseline and 12 months in MJOA, NDI, NRS-NP, NRS-AP, SF-12-MCS, SF-12-PCS. Satisfaction increased 11-fold for each 0.1-point increased in ED-5Q from baseline to 12 months. At baseline, for every 1-point increase in SF-12-MCS, the odds of being satisfied increased by 7%. At 3 months, all PROs (except for NRS-AP change and baseline SF-12-MCS) predicted satisfaction. All logistic regression analyses demonstrated excellent predictive accuracy, with the highest 12-month AUC of 0.86 (95%CI = 0.81 - 0.90). No patient demographic or surgical factors influenced satisfaction. CONCLUSION: Improvement in Patient Reported Outcomes and MJOA are strongly associated with patient satisfaction after surgery for DCM. The only baseline PRO associated with 12-months satisfaction was SF-12-MCS. No modifiable patient baseline characteristic or surgical variables were associated with satisfaction.

Machine learning models can predict subsequent publication of North American Spine Society (NASS) annual general meeting abstracts.

BACKGROUND CONTEXT: Academic meetings serve as an opportunity to present and discuss novel ideas. Previous studies have identified factors predictive of publication without generating predictive models. Machine learning (ML) presents a novel tool capable of generating these models. As such, the objective of this study was to use ML models to predict subsequent publication of abstracts presented at a major surgical conference. STUDY DESIGN/SETTING: Database study. METHODS: All abstracts from the North American Spine Society (NASS) annual general meetings (AGM) from 2013-2015 were reviewed. The following information was extracted: number of authors, institution, location, conference category, subject category, study type, data collection methodology, human subject research, and FDA approval. Abstracts were then searched on the PubMed, Google Scholar, and Scopus databases for publication. ML models were trained to predict whether the abstract would be published or not. Quality of models was determined by using the area under the receiver operator curve (AUC). The top ten most important factors were extracted from the most successful model during testing. RESULTS: A total of 1119 abstracts were presented, with 553 (49%) abstracts published. During training, the model with the highest AUC and accuracy metrics was the partial least squares (AUC of 0.77±0.05, accuracy of 75.5%±4.7%). During testing, the model with the highest AUC and accuracy was the random forest (AUC of 0.69, accuracy of 67%). The top ten features for the random forest model were (descending order): number of authors, year, conference category, subject category, human subjects research, continent, and data collection methodology. CONCLUSIONS: This was the first study attempting to use ML to predict the publication of complete articles after abstract presentation at a major academic conference. Future studies should incorporate deep learning frameworks, cognitive/results-based variables and aim to apply this methodology to larger conferences across other fields of medicine to improve the quality of works presented.

Comparison of major spine navigation platforms based on key performance metrics: a meta-analysis of 16,040 screws.

PURPOSE: The objective of this meta-analysis is to compare available computer-assisted navigation platforms by key performance metrics including pedicle screw placement accuracy, operative time, neurological complications, and blood loss. METHODS: A systematic review was conducted using major databases for articles comparing pedicle screw accuracy of computer-assisted navigation to conventional (freehand or fluoroscopy) controls via post-operative computed tomography. Outcome data were extracted and pooled by random-effects model for analysis. RESULTS: All navigation platforms demonstrated significant reduction in risk of breach, with Stryker demonstrating the highest accuracy compared to controls (OR 0.16 95% CI 0.06 to 0.41, P < 0.00001, I2 = 0%) followed by Medtronic. There were no significant differences in accuracy or most surgical outcome measures between platforms; however, BrainLab demonstrated significantly faster operative time compared to Medtronic by 30 min (95% CI - 63.27 to - 2.47, P = 0.03, I2 = 74%). Together, there was significantly lower risk of major breach in the navigation group compared to controls (OR 0.42, 95% CI 0.27-0.63, P < 0.0001, I2 = 56%). CONCLUSIONS: When comparing between platforms, Stryker demonstrated the highest accuracy, and Brainlab the shortest operative time, both followed by Medtronic. No significant difference was found between platforms regarding neurologic complications or blood loss. Overall, our results demonstrated a 60% reduction in risk of major breach utilizing computer-assisted navigation, coinciding with previous studies, and supporting its validity. This study is the first to directly compare available navigation platforms offering insight for further investigation and aiding in the institutional procurement of platforms. LEVEL 3 EVIDENCE: Meta-analysis of Level 3 studies.

In-office needle arthroscopy is a cost-effective alternative for operating room diversion in medial meniscectomy: a financial analysis.

BACKGROUND: In-office needle arthroscopy (IONA) has been described as a diagnostic alternative to magnetic resonance imaging (MRI) for intra-articular pathology. However, few studies have analyzed its impact on cost and wait times when used as a therapeutic intervention. The purpose of this study was to investigate the impact on cost and wait times associated with offering IONA for partial medial meniscectomy as an alternative to traditional operating room (OR) arthroscopy for patients with irreparable medial meniscus tears on MRI. METHODS: Two models were created comparing the existing care pathway (current state) to a proposed future state utilizing IONA. Data sources were accounting data from an academically affiliated hospital in Canada and supplemented with literature values. A Monte Carlo simulation combined with DuPont analysis running 10,000 simulations was conducted to calculate the revenue, expenses, profits, and effect on surgical waitlists (i.e., throughput) between the states. Sensitivity analyses examined the influence of patient preference and revision rates on profit and throughput. Two-sample Student's t test was performed (p < .05). RESULTS: An average of 198 (standard deviation (SD) 31) patients underwent arthroscopic meniscectomy or repair each year from 2016 to 2020. The IONA revision rate was calculated as 20.3%. Compared to the current state, annual expenses in the IONA pathway were significantly reduced ($266,912.68 versus $281,415.23, p < .0001), while improving throughput by 21.2% (3.54%). Sensitivity analysis revealed 10% of patients need to select IONA over traditional OR arthroscopy with the revision rate remaining below 40% for the proposed state profit to be higher than the current state. CONCLUSIONS: IONA is a cost-effective alternative to traditional OR arthroscopy in patients undergoing partial medial meniscectomy. The next steps are to assess patients' perceptions of IONA as an alternative to traditional OR arthroscopy, and to carry out clinical trials to determine the efficacy, patient-reported outcome metrics, and complications of IONA.

Key Stakeholder Perceptions of Standard vs. Total Cost of Ownership: A Procurement Analysis for Orthopaedic-Powered Instruments.

This study compares standard procurement methodology (SPM) with total cost of ownership (TCO) methodology for the procurement of orthopaedic-powered instruments. The authors conducted semi-structured standardized interviews with key hospital procurement stakeholders following consolidated criteria for reporting qualitative research. Of the 33 hospital procurement stakeholders interviewed, all (100%) reported that SPM would be easier to use than TCO. However, only six (18%) preferred SPM over TCO. Barriers to the adoption of TCO emerged as a theme. Creating TCO frameworks can help to simplify the process for procurement agents and facilitate its adoption in the healthcare sector.

Optimizing spine surgery instrument trays to immediately increase efficiency and reduce costs in the operating room.

Background: Over-crowded surgical trays result in perioperative inefficiency and unnecessary costs. While methodologies to reduce the size of surgical trays have been described in the literature, they each have their own drawbacks. In this study, we compared three methods: (1) clinician review (CR), (2) mathematical programming (MP), and (3) a novel hybrid model (HM) based on surveys and cost analysis. While CR and MP are well documented, CR can yield suboptimal reductions and MP can be laborious and technically challenging. We hypothesized our easy-to-implement HM would result in a reduction of surgical instruments in both the laminectomy tray (LT) and basic neurosurgery tray (BNT) that is comparable to CR and MP. Methods: Three approaches were tested: CR, MP, and HM. We interviewed 5 neurosurgeons and 3 orthopedic surgeons, at our institution, who performed a total of 5437 spine cases, requiring the use of the LT and BNT over a 4-year (2017-2021) period. In CR, surgeons suggested which surgical instruments should be removed. MP was performed via the mathematical analysis of 25 observations of the use of a LT and BNT tray. The HM was performed via a structured survey of the surgeons' estimated instrument usage, followed by a cost-based inflection point analysis. Results: The CR, MP, and HM approaches resulted in a total instrument reduction of 41%, 35%, and 38%, respectively, corresponding to total cost savings per annum of $50,211.20, $46,348.80, and $44,417.60, respectively. Conclusions: While hospitals continue to examine perioperative services for potential inefficiencies, surgical inventory will be increasingly scrutinized. Despite MP being the most accurate methodology to do so, our results suggest that savings were similar across all three methods. CR and HM are significantly less laborious and thus are practical alternatives.

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.

Background: There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care. Methods: A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted. Results: Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level. Conclusions: High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Clinical applications of augmented reality in orthopaedic surgery: a comprehensive narrative review.

PURPOSE: The development of augmented reality (AR) technology allows orthopaedic surgeons to incorporate and visualize surgical data, assisting the execution of both routine and complex surgical operations. Uniquely, AR technology allows a surgeon to view the surgical field and superimpose peri-operative imaging, anatomical landmarks, navigation guidance, and more, all in one view without the need for conjugate gaze between multiple screens. The aim of this literature review was to introduce the fundamental requirements for an augmented reality system and to assess the current applications, outcomes, and potential limitations to this technology. METHODS: A literature search was performed using MEDLINE and Embase databases, by two independent reviewers, who then collaboratively synthesized and collated the results of the literature search into a narrative review focused on the applications of augmented reality in major orthopaedic sub-specialties. RESULTS: Current technology requires that pre-operative patient data be acquired, and AR-compatible models constructed. Intra-operatively, to produce manipulatable virtual images into the user's view in real time, four major components are required including a camera, computer image processing technology, tracking tools, and an output screen. The user is provided with a heads-up display, which is a transparent display, enabling the user to look at both their natural view and the computer-generated images. Currently, high-quality evidence for clinical implementation of AR technology in the orthopaedic surgery operating room is lacking; however, growing in vitro literature highlights a multitude of potential applications, including increasing operative accuracy, improved biomechanical angular and alignment parameters, and potentially reduced operative time. CONCLUSION: While the application of AR systems in surgery is currently in its infancy, we anticipate rapid and widespread implementation of this technology in various orthopaedic sub-specialties.

A Dedicated Orthopaedic Trauma Room Improves Efficiency While Remaining Financially Net Positive.

OBJECTIVES: To determine the impact of dedicated orthopaedic trauma room (DOTR) implementation on operating room efficiency and finances. DESIGN: Retrospective cost-analysis. SETTING: Single midsized academic-affiliated community hospital in Toronto, Canada. PARTICIPANTS: All patients that underwent the most frequently performed orthopaedic trauma procedures (hip hemiarthroplasty, open reduction internal fixation of the ankle, femur, elbow and distal radius), over a 4-year period from 2016 to 2019 were included. INTERVENTION: Patient data acquired for 2 years before the implementation of a DOTR was compared with data acquired for a 2-year period after its implementation, adjusting for the number of cases performed. MAIN OUTCOME MEASUREMENTS: The primary outcome was surgical duration. The secondary outcome was financial impact, including after-hours costs incurred and opportunity cost of displaced elective surgeries. RESULTS: One thousand nine hundred sixty orthopaedic cases were examined pre- and post-DOTR. All procedures had reduced total operative time post-DOTR (mean improvement of 33.4%). The number of daytime operating hours increased 21%, whereas after-hours decreased by 37.8%. Overtime staffing costs were reduced by $24,976 alongside increase in opportunity costs of $22,500. This resulted in a net profit of $2476. CONCLUSIONS: Our results support the premise that DOTRs improve operating room efficiency and can be cost efficient. Our study also specifically addresses the hesitation regarding potential loss of profit from elective surgeries. Widespread implementation can improve patient care while still remaining financially favorable. LEVEL OF EVIDENCE: Economic Level IV. See Instructions for Authors for a complete description of levels of evidence.