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Purpose To evaluate the differences in clinical outcomes and complication rates between open and arthroscopic iliopsoas (IP) tenotomy between 1998 and 2023 at our institution. Methods Patients who underwent arthroscopic iliopsoas fractional lengthening (IPFL) or open tenotomy for IP tendinitis following total hip arthroplasty (THA) between 1988 and 2023 were identified. Patients were excluded if they underwent revision IP tendon release, had additional concomitant procedures, or had less than a 24‐month postoperative follow‐up. Patient records were reviewed, and patient‐reported outcomes were collected via electronic survey. Results Fifty‐two patients (36 arthroscopic, 16 open) were followed for an average of 68.7 (24.0‐203.7) months. Overall surgery satisfaction was 7.9 (scale 0‐10) following arthroscopic IPFL and 6.0 following open tenotomy ( P = .178). Eighty‐six percent of patients reported improvement in their anterior groin pain following arthroscopic IPFL, compared to 45% following open tenotomy ( P = .065). In the arthroscopic cohort, 64% reported improved hip flexion strength, compared to 36% in the open cohort ( P = .158). Visual analog scale for pain at rest was lower following arthroscopic IPFL (1.1 ± 2.2 vs 3.3 ± 3.2, P = .0092), while pain with use did not differ between cohorts (3.1 ± 1.3 vs 2.6 ± 1.1, P = .786). There were no differences in postoperative Tegner (3.1 ± 1.3 vs 2.6 ± 1.1, P = .302), modified Harris Hip Score (77.0 ± 19.0 vs 69.2 ± 26.9, P = .073), or Single Assessment Numeric Evaluation (70.9 ± 28.7 vs 66.7 ± 31.8, P = .571) scores between arthroscopic and open cohorts. Revision THA rates did not differ following arthroscopic or open procedures (8.3% vs 25%, P = .104). Conclusions Patients have improved pain at rest following arthroscopic IPFL when compared to open tenotomy for IP tendinitis following THA. Overall complication and reoperation rates were low for both techniques. Level of Evidence Level III, retrospective cohort study.

AbstractObjectiveThe effects of brain stimulation for diseases like epilepsy are delayed, making stimulation optimization difficult. The parameters for anterior thalamic nuclei (ANT) deep brain stimulation (DBS) for focal drug‐resistant epilepsy management are often restricted to those used in the SANTE landmark trial. There is little evidence regarding effective alternatives, and low‐frequency stimulation is typically neglected. We prospectively compare a widely differing stimulation parameter set to typical settings.MethodsThis randomized, modified crossover, open trial compares the efficacy and safety of an alternative set of parameters using continuous low‐frequency stimulation (cLFS) with a longer pulse width (7 Hz, 200 μs, continuous) compared to SANTE's intermittent high‐frequency stimulation (iHFS) with a short pulse width (145 Hz, 90 μs, cycling 1 min on/5 min off). After 3 months on a randomly assigned first set, patients are switched to the other settings, unless seizure‐free. Patients are re‐evaluated after 3 more months, which marks the completion of the trial. After that, they can either remain on the same settings or switch back.ResultsSixteen patients with a median baseline seizure frequency of 13.8 seizures/month (interquartile range [IQR] = 2.7–22.8) were included in the analysis. At trial completion (median follow‐up = 30 weeks, IQR = 26–35), ANT‐DBS significantly reduced median seizure frequency (62%, IQR = 18–81, Wilcoxon test statistic [W] = 99, p = .008). Both iHFS (33%, IQR = 0–65, W = 81, p = .02) and cLFS (73%, IQR = 30–79, W = 105, p = .001) significantly reduced median seizure frequency. cLFS showed improved median seizure frequency reduction compared to iHFS (W = 63, p = .03) and was not associated with any moderate or severe adverse effects.SignificanceResults support cLFS for ANT‐DBS as a safe and effective alternative to typical iHFS parameters. Stimulation with widely differing parameter sets may be as effective as or, in some situations, more effective than typical stimulation parameters.

AbstractObjectiveEpilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures despite maximal medical management. If patients' seizures localize to a discrete brain region, termed a seizure onset zone, resection may be curative. Localization is often confirmed with stereotactic electroencephalography; however, this may require patients to stay in the hospital for weeks to capture spontaneous seizures. Automated localization of seizure onset zones could therefore improve presurgical evaluation and decrease morbidity.MethodsUsing more than 1 000 000 interictal stereotactic electroencephalography segments collected from 78 patients, we performed five‐fold cross‐validation and testing on a multichannel, multiscale, one‐dimensional convolutional neural network to classify seizure onset zones.ResultsAcross held‐out test sets, our models achieved a seizure onset zone classification sensitivity of .702 (95% confidence interval [CI] = .549–.805), specificity of .741 (95% CI = .652–.835), and accuracy of .738 (95% CI = .687–.795), which was significantly better than models trained on random labels. The models performed well across the entire brain, with top five region performance demonstrating accuracies between 70.0% and 88.4%. When split by outcomes, the models performed significantly better on patients with favorable Engel outcomes after resection or who were responsive neurostimulation responders. Finally, SHAP (Shapley Additive Explanation) value analysis on median‐normalized input data assigned consistently high feature importance to interictal spikes and large deflections, whereas similar analyses on histogram‐equalized data revealed differences in feature importance assignments to low‐amplitude segments.SignificanceThis work serves as evidence that deep learning on brief interictal intracranial data can classify seizure onset zones across the brain. Furthermore, our findings corroborate current understandings of interictal epileptiform discharges and may help uncover novel interictal morphologies. Clinical application of our models may reduce dependence on recorded seizures for localization and shorten presurgical evaluation time for drug‐resistant epilepsy patients, reducing patient morbidity and hospital costs.

Background/Objectives: Recent advances in artificial intelligence (AI) and machine learning (ML) enable targeted optimization of emergency department (ED) operations. We examine how reworking an ED’s vertical processing pathway (VPP) using AI- and ML-driven recommendations affected patient throughput. Methods: We trained a non-linear ML model using triage data from 49,350 ED encounters to generate a personalized risk score that predicted whether an incoming patient is suitable for vertical processing. This model was integrated into a stochastic patient flow framework using queueing theory to derive an optimized VPP design. The resulting protocol prioritized a vertical assessment for patients with Emergency Severity Index (ESI) scores of 4 and 5, as well as 3 when the chief complaints involved skin, urinary, or eye issues. In periods of ED saturation, our data-driven protocol suggested that any waiting room patient should become VPP eligible. We implemented this protocol during a 13-week prospective trial and evaluated its effect on ED performance using before-and-after data. Results: Implementation of the optimized VPP protocol reduced the average ED length of stay (LOS) by 10.75 min (4.15%). Adjusted analyses controlling for potential confounders during the study period estimated a LOS reduction between 7.5 and 11.9 min (2.89% and 4.60%, respectively). No adverse effects were observed in the quality metrics, including 72 h ED revisit or hospitalization rates. Conclusions: A personalized, data-driven VPP protocol, enabled by ML predictions, significantly improved the ED throughput while preserving care quality. Unlike standard fast-track systems, this approach adapts to ED saturation and patient acuity. The methodology is customizable to patient populations and ED operational characteristics, supporting personalized patient flow optimization across diverse emergency care settings.

Abstract Vaccination is increasingly acknowledged as an effective preventive measure not only against specific infections, but also for the prevention of cardiovascular disease in high-risk patients. Specifically, a growing body of evidence suggests that vaccines against influenza, SARS-CoV-2, respiratory syncytial virus, herpes zoster, and other viruses significantly reduce infection and for influenza the incidence of major adverse cardiovascular events in vaccinated individuals. This clinical consensus statement examines the existing literature and accumulated evidence and offers practical clinical advice on vaccination timing and target demographics, specifically addressing complex clinical scenarios with a focus on cardiovascular conditions. It includes guidelines for vaccinating vulnerable populations such as immunosuppressed individuals, patients with congenital heart disease, and pregnant women as well as safety and potential complications of the procedure.

Opioid use disorders (OUDs) pose a substantial global health burden, with methadone maintenance treatment (MMT) widely adopted as an intervention; however, MMT is associated with immunosuppression, metabolic disturbances, and dysbiosis of the gut microbiota. Despite the potential of acupuncture in reducing methadone dosages and opioid addiction, the underlying biological mechanisms remain unclear. Therefore, we aimed to integrate clinical trial data with multi-omics analysis, including single-cell sequencing, transcriptomics, metabolomics, and metagenomics, to evaluate the effects of acupuncture in patients undergoing MMT. We collected peripheral blood mononuclear cells, plasma, and fecal samples from 48 MMT participants in a randomized, placebo-controlled trial. Participants were divided into acupuncture ( n = 25) and sham-acupuncture ( n = 23) groups. After 8 weeks of intervention, 84% of patients in the acupuncture group achieved ≥20% reduction in methadone dosage, compared to 39% in the sham-acupuncture group ( P < 0.01). Our findings revealed that acupuncture may activate the defense response to viruses, with altered immune cell functions in classical monocytes correlating with clinical responses to reduced methadone doses. Acupuncture might ameliorate intestinal microbial disruptions caused by OUD by up-regulating Bilophila and modulating bile acid metabolism. Furthermore, acupuncture up-regulated galectin-9 (LGALS9)-mediated intercellular communication between classical monocytes and other immune subsets. To further validate the mechanistic link between bile acid metabolism and immune regulation, we conducted in vitro experiments using THP-1 monocytes treated with cholic acid. The results showed that bile acid exposure suppressed galectin-9 and IFN-γ expression, while low-dose bile acid (simulating acupuncture effects) partially reversed this effect. These findings support a bile acid–galectin–interferon axis that may be modulated by acupuncture in OUD. Collectively, our results provide clinical and mechanistic evidence supporting acupuncture as a potential adjunct therapy to mitigate the adverse effects of long-term opioid use.