The Effect of Subsidence on Segmental and Global Lordosis at Long-term Follow-up After Anterior Cervical Discectomy and Fusion

Objective Subsidence following anterior cervical discectomy and fusion (ACDF) may lead to disruptions of cervical alignment and lordosis. The purpose of this study was to evaluate the effect of subsidence on segmental, regional, and global lordosis. Methods This was a retrospective cohort study performed between 2016–2021 at a single institution. All measurements were performed using lateral cervical radiographs at the immediate postoperative period and at final follow-up greater than 6 months after surgery. Associations between subsidence and segmental lordosis, total fused lordosis, C2–7 lordosis, and cervical sagittal vertical alignment change were determined using Pearson correlation and multivariate logistic regression analyses. Results One hundred thirty-one patients and 244 levels were included in the study. There were 41 one-level fusions, 67 two-level fusions, and 23 three-level fusions. The median follow-up time was 366 days (interquartile range, 239–566 days). Segmental subsidence was significantly negatively associated with segmental lordosis change in the Pearson (r = -0.154, p = 0.016) and multivariate analyses (beta = -3.78; 95% confidence interval, -7.15 to -0.42; p = 0.028) but no associations between segmental or total fused subsidence and any other measures of cervical alignment were observed. Conclusion We found that subsidence is associated with segmental lordosis loss 6 months following ACDF. Surgeons should minimize subsidence to prevent long-term clinical symptoms associated with poor cervical alignment

Evaluating the effects of age on the long-term functional outcomes following anatomic total shoulder arthroplasty

Background In the past decade, the number of anatomic total shoulder arthroplasty (aTSA) procedures has steadily increased. Patients over 65 years of age comprise the vast majority of recipients, and outcomes have been well documented; however, patients are opting for definitive surgical treatment at younger ages.We aim to report on the effects of age on the long-term clinical outcomes following aTSA. Methods Among the patients who underwent TSA, 119 shoulders were retrospectively analyzed. Preoperative and postoperative clinical outcome data were collected. Linear regression analysis (univariate and multivariate) was conducted to evaluate the associations of clinical outcomes with age. Kaplan-Meier curves and Cox regression analyses were performed to evaluate implant survival. Results At final follow-up, patients of all ages undergoing aTSA experienced significant and sustained improvements in all primary outcome measures compared with preoperative values. Based on multivariate analysis, age at the time of surgery was a significant predictor of postoperative outcomes. Excellent implant survival was observed over the course of this study, and Cox regression survival analysis indicated age and sex to not be associated with an increased risk of implant failure. Conclusions When controlling for sex and follow-up duration, older age was associated with significantly better patient-reported outcome measures. Despite this difference, we noted no significant effects on range of motion or implant survival. Level of evidenceIV

A feasibility study on AI-controlled closed-loop electrical stimulation implants

Abstract Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we study the feasibility of more computationally efficient machine learning methods to control ES implants. For this, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n = 11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 0.03 in an intra-subject setting and 0.2 in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting