Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial.

Trainees develop surgical technical skills by learning from experts who provide context for successful task completion, identify potential risks, and guide correct instrument handling. This expert-guided training faces significant limitations in objectively assessing skills in real-time and tracking learning. It is unknown whether AI systems can effectively replicate nuanced real-time feedback, risk identification, and guidance in mastering surgical technical skills that expert instructors offer. This randomized controlled trial compared real-time AI feedback to in-person expert instruction. Ninety-seven medical trainees completed a 90-min simulation training with five practice tumor resections followed by a realistic brain tumor resection. They were randomly assigned into 1-real-time AI feedback, 2-in-person expert instruction, and 3-no real-time feedback. Performance was assessed using a composite-score and Objective Structured Assessment of Technical Skills rating, rated by blinded experts. Training with real-time AI feedback (n = 33) resulted in significantly better performance outcomes compared to no real-time feedback (n = 32) and in-person instruction (n = 32), .266, [95% CI .107 .425], p < .001; .332, [95% CI .173 .491], p = .005, respectively. Learning from AI resulted in similar OSATS ratings (4.30 vs 4.11, p = 1) compared to in-person training with expert instruction. Intelligent systems may refine the way operating skills are taught, providing tailored, quantifiable feedback and actionable instructions in real-time.

Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial.

OBJECTIVE: To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections. HYPOTHESES: (1) Providing feedback results in better learning outcomes in teaching surgical technical skill when compared to practice alone with no tailored performance feedback. (2) Providing more visual and visuospatial feedback results in better learning outcomes when compared to providing numerical feedback. DESIGN: A prospective 4-parallel-arm randomized controlled trial. SETTING: Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Canada. PARTICIPANTS: Medical students (n = 120) from 4 Quebec medical schools. RESULTS: Participants completed a virtually simulated tumor resection task 5 times while receiving 1 of 4 feedback based on their group allocation: (1) practice-alone without feedback, (2) numerical feedback, (3) visual feedback, and (4) visuospatial feedback. Outcome measures were participants' scores on 14-performance metrics and the number of expert benchmarks achieved during each task. There were no significant differences in the first task which determined baseline performance. A statistically significant interaction between feedback allocation and task repetition was found on the number of benchmarks achieved, F (10.558, 408.257)=3.220, p < 0.001. Participants in all feedback groups significantly improved their performance compared to baseline. The visual feedback group achieved significantly higher number of benchmarks than the practice-alone group by the third repetition of the task, p = 0.005, 95%CI [0.42 3.25]. Visual feedback and visuospatial feedback improved performance significantly by the second repetition of the task, p = 0.016, 95%CI [0.19 2.71] and p = 0.003, 95%CI [0.4 2.57], respectively. CONCLUSION: Simulations with autonomous visual computer assistance may be effective pedagogical tools in teaching bimanual operative skills via visual and visuospatial feedback information delivery.

AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training.

Importance: To better elucidate the role of artificial intelligence (AI) in surgical skills training requires investigations in the potential existence of a hidden curriculum. Objective: To assess the pedagogical value of AI-selected technical competencies and their extended effects in surgical simulation training. Design, Setting, and Participants: This cohort study was a follow-up of a randomized clinical trial conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at the Montreal Neurological Institute, McGill University, Montreal, Canada. Surgical performance metrics of medical students exposed to an AI-enhanced training curriculum were compared with a control group of participants who received no feedback and with expert benchmarks. Cross-sectional data were collected from January to April 2021 from medical students and from March 2015 to May 2016 from experts. This follow-up secondary analysis was conducted from June to September 2022. Participants included medical students (undergraduate year 0-2) in the intervention cohorts and neurosurgeons to establish expertise benchmarks. Exposure: Performance assessment and personalized feedback by an intelligent tutor on 4 AI-selected learning objectives during simulation training. Main Outcomes and Measures: Outcomes of interest were unintended performance outcomes, measured by significant within-participant difference from baseline in 270 performance metrics in the intervention cohort that was not observed in the control cohort. Results: A total of 46 medical students (median [range] age, 22 [18-27] years; 27 [59%] women) and 14 surgeons (median [range] age, 45 [35-59] years; 14 [100%] men) were included in this study, and no participant was lost to follow-up. Feedback on 4 AI-selected technical competencies was associated with additional performance change in 32 metrics over the entire procedure and 20 metrics during tumor removal that was not observed in the control group. Participants exposed to the AI-enhanced curriculum demonstrated significant improvement in safety metrics, such as reducing the rate of healthy tissue removal (mean difference, -7.05 × 10-5 [95% CI, -1.09 × 10-4 to -3.14 × 10-5] mm3 per 20 ms; P < .001) and maintaining a focused bimanual control of the operative field (mean difference in maximum instrument divergence, -4.99 [95% CI, -8.48 to -1.49] mm, P = .006) compared with the control group. However, negative unintended effects were also observed. These included a significantly lower velocity and acceleration in the dominant hand (velocity: mean difference, -0.13 [95% CI, -0.17 to -0.09] mm per 20 ms; P < .001; acceleration: mean difference, -2.25 × 10-2 [95% CI, -3.20 × 10-2 to -1.31 × 10-2] mm per 20 ms2; P < .001) and a significant reduction in the rate of tumor removal (mean difference, -4.85 × 10-5 [95% CI, -7.22 × 10-5 to -2.48 × 10-5] mm3 per 20 ms; P < .001) compared with control. These unintended outcomes diverged students' movement and efficiency performance metrics away from the expertise benchmarks. Conclusions and Relevance: In this cohort study of medical students, an AI-enhanced curriculum for bimanual surgical skills resulted in unintended changes that improved performance in safety but negatively affected some efficiency metrics. Incorporating AI in course design requires ongoing assessment to maintain transparency and foster evidence-based learning objectives.

Performance in a Simulated Virtual Reality Anterior Cervical Discectomy and Fusion Task: Disc Residual, Rate of Removal, and Efficiency Analyses.

BACKGROUND AND OBJECTIVES: Anterior cervical discectomy and fusion (ACDF) is among the most common spine procedures. The Sim-Ortho virtual reality simulator platform contains a validated ACDF simulated task for performance assessment. This study aims to develop a methodology to extract three-dimensional data and reconstruct and quantitate specific simulated disc tissues to generate novel metrics to analyze performance metrics of skilled and less skilled participants. METHODS: We used open-source platforms to develop a methodology to extract three-dimensional information from ACDF simulation data. Metrics generated included, efficiency index, disc volumes removed from defined regions, and rate of tissue removal from superficial, central, and deep disc regions. A pilot study was performed to assess the utility of this methodology to assess expertise during the ACDF simulated procedure. RESULTS: The system outlined, extracts data allowing the development of a methodology which accurately reconstructs and quantitates 3-dimensional disc volumes. In the pilot study, data sets from 27 participants, divided into postresident, resident, and medical student groups, allowed assessment of multiple novel metrics, including efficiency index (surgical time spent in actively removing disc), where the postresident group spent 61.8% of their time compared with 53% and 30.2% for the resident and medical student groups, respectively ( P = .01). During the annulotomy component, the postresident group removed 47.4% more disc than the resident groups and 102% more than the medical student groups ( P = .03). CONCLUSION: The methodology developed in this study generates novel surgical procedural metrics from 3-dimensional data generated by virtual reality simulators and can be used to assess surgical performance.

Occipital artery-to-PICA bypass: how I do it.

BACKGROUND: Posterior inferior cerebellar artery (PICA) aneurysms are uncommon, and their management is challenging because of the complex angioarchitecture of the PICA and the frequently nonsaccular aneurysm presentation. Endovascular therapy may not be feasible. METHODS: We describe our technique of clip trapping with occipital artery (OA)-to-PICA bypass to treat a PICA aneurysm. Because the aneurysm affected the ipsilateral, dominant PICA, an OA-PICA bypass was chosen to ensure adequate flow and reduce risk to the contralateral PICA supply. CONCLUSION: The OA-PICA anastomosis is a safe and effective method to successfully achieve flow preservation with bypass reconstruction and aneurysm trapping.

Spheno-Orbital Meningiomas.

Meningiomas are the most common intracranial brain tumor. Spheno-orbital meningiomas are a rare subtype that originate at the sphenoid wing and characteristically extend to the orbit and surrounding neurovascular structures via bony hyperostosis and soft tissue invasion. This review summarizes early characterizations of spheno-orbital meningiomas, presently understood tumor characteristics, and current management strategies.

Hypophysitis: Defining Histopathologic Variants and a Review of Emerging Clinical Causative Entities.

Inflammatory disease of the pituitary gland is known as hypophysitis. There are multiple histological subtypes, the most common being lymphocytic, and the pathogenesis is variable and diverse. Hypophysitis can be primary and idiopathic or autoimmune related, or secondary to local lesions, systemic disease, medications, and more. Although hypophysitis was previously accepted as an exceedingly rare diagnosis, a greater understanding of the disease process and new insights into possible etiologic sources have contributed to an increased frequency of recognition. This review provides an overview of hypophysitis, its causes, and detection strategies and management.

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8-10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13-30 Hz, p = 0.049), beta 1 (15-18 Hz, p = 0.014), and beta 2 (19-22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME CORE COMPETENCIES: Practice-Based Learning and Improvement.

Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study.

BACKGROUND: Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training. OBJECTIVE: To (1) provide validity for the simulated bimanual subpial tumor resection task and (2) to use this simulation in qualitative and quantitative evaluation of nondominant hand skills for bipolar forceps utilization. METHODS: In this case series study, 45 right-handed participants performed a simulated subpial tumor resection using simulated bipolar forceps in the nondominant hand for assisting the surgery and hemostasis. A 10-item questionnaire was used to assess task validity. The nondominant hand skills across 4 expertise levels (neurosurgeons, senior trainees, junior trainees, and medical students) were analyzed by 2 visual models and performance metrics. RESULTS: Neurosurgeon median (range) overall satisfaction with the simulated scenario was 4.0/5.0 (2.0-5.0). The visual models demonstrated a decrease in high force application areas on pial surface with increased expertise level. Bipolar-pia mater interactions were more focused around the tumoral region for neurosurgeons and senior trainees. These groups spent more time using the bipolar while interacting with pia. All groups spent significantly higher time in the left upper pial quadrant than other quadrants. CONCLUSION: This work introduces new approaches for the evaluation of nondominant hand skills which may help surgical trainees by providing both qualitative and quantitative feedback.

Artificial Neural Network Approach to Competency-Based Training Using a Virtual Reality Neurosurgical Simulation.

BACKGROUND: The methodology of assessment and training of surgical skills is evolving to deal with the emergence of competency-based training. Artificial neural networks (ANNs), a branch of artificial intelligence, can use newly generated metrics not only for assessment performance but also to quantitate individual metric importance and provide new insights into surgical expertise. OBJECTIVE: To outline the educational utility of using an ANN in the assessment and quantitation of surgical expertise. A virtual reality vertebral osteophyte removal during a simulated surgical spine procedure is used as a model to outline this methodology. METHODS: Twenty-one participants performed a simulated anterior cervical diskectomy and fusion on the Sim-Ortho virtual reality simulator. Participants were divided into 3 groups, including 9 postresidents, 5 senior residents, and 7 junior residents. Data were retrieved from the osteophyte removal component of the scenario, which involved using a simulated burr. The data were manipulated to initially generate 83 performance metrics spanning 3 categories (safety, efficiency, and motion) of which only the most relevant metrics were used to train and test the ANN. RESULTS: The ANN model was trained on 6 safety metrics to a testing accuracy of 83.3%. The contributions of these performance metrics to expertise were revealed through connection weight products and outlined 2 identifiable learning patterns of technical skills. CONCLUSION: This study outlines the potential utility of ANNs which allows a deeper understanding of the composites of surgical expertise and may contribute to the paradigm shift toward competency-based surgical training.

Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation.

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors.

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence.

OBJECTIVE: Understanding the variation of learning curves of experts and trainees for a given surgical procedure is important in implementing formative learning paradigms to accelerate mastery. The study objectives were to use artificial intelligence (AI)-derived metrics to determine the learning curves of participants in 4 groups with different expertise levels who performed a series of identical virtual reality (VR) subpial resection tasks and to identify learning curve differences among the 4 groups. METHODS: A total of 50 individuals participated, 14 neurosurgeons, 4 neurosurgical fellows and 10 senior residents (seniors), 10 junior residents (juniors), and 12 medical students. All participants performed 5 repetitions of a subpial tumor resection on the NeuroVR (CAE Healthcare) platform, and 6 a priori-derived metrics selected using the K-nearest neighbors machine learning algorithm were used to assess participant learning curves. Group learning curves were plotted over the 5 trials for each metric. A mixed, repeated-measures ANOVA was performed between the first and fifth trial. For significant interactions (p < 0.05), post hoc Tukey's HSD analysis was conducted to determine the location of the significance. RESULTS: Overall, 5 of the 6 metrics assessed had a significant interaction (p < 0.05). The 4 groups, neurosurgeons, seniors, juniors, and medical students, showed an improvement between the first and fifth trial on at least one of the 6 metrics evaluated. CONCLUSIONS: Learning curves generated using AI-derived metrics provided novel insights into technical skill acquisition, based on expertise level, during repeated VR-simulated subpial tumor resections, which will allow educators to develop more focused formative educational paradigms for neurosurgical trainees.

Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial.

Importance: To better understand the emerging role of artificial intelligence (AI) in surgical training, efficacy of AI tutoring systems, such as the Virtual Operative Assistant (VOA), must be tested and compared with conventional approaches. Objective: To determine how VOA and remote expert instruction compare in learners' skill acquisition, affective, and cognitive outcomes during surgical simulation training. Design, Setting, and Participants: This instructor-blinded randomized clinical trial included medical students (undergraduate years 0-2) from 4 institutions in Canada during a single simulation training at McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal, Canada. Cross-sectional data were collected from January to April 2021. Analysis was conducted based on intention-to-treat. Data were analyzed from April to June 2021. Interventions: The interventions included 5 feedback sessions, 5 minutes each, during a single 75-minute training, including 5 practice sessions followed by 1 realistic virtual reality brain tumor resection. The 3 intervention arms included 2 treatment groups, AI audiovisual metric-based feedback (VOA group) and synchronous verbal scripted debriefing and instruction from a remote expert (instructor group), and a control group that received no feedback. Main Outcomes and Measures: The coprimary outcomes were change in procedural performance, quantified as Expertise Score by a validated assessment algorithm (Intelligent Continuous Expertise Monitoring System [ICEMS]; range, -1.00 to 1.00) for each practice resection, and learning and retention, measured from performance in realistic resections by ICEMS and blinded Objective Structured Assessment of Technical Skills (OSATS; range 1-7). Secondary outcomes included strength of emotions before, during, and after the intervention and cognitive load after intervention, measured in self-reports. Results: A total of 70 medical students (41 [59%] women and 29 [41%] men; mean [SD] age, 21.8 [2.3] years) from 4 institutions were randomized, including 23 students in the VOA group, 24 students in the instructor group, and 23 students in the control group. All participants were included in the final analysis. ICEMS assessed 350 practice resections, and ICEMS and OSATS evaluated 70 realistic resections. VOA significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group (P < .001). Realistic Expertise Scores were significantly higher for the VOA group compared with instructor (mean difference, 0.53 [95% CI, 0.40 to 0.67] points; P < .001) and control (mean difference. 0.49 [95% CI, 0.34 to 0.61] points; P < .001) groups. Mean global OSATS ratings were not statistically significant among the VOA (4.63 [95% CI, 4.06 to 5.20] points), instructor (4.40 [95% CI, 3.88-4.91] points), and control (3.86 [95% CI, 3.44 to 4.27] points) groups. However, on the OSATS subscores, VOA significantly enhanced the mean OSATS overall subscore compared with the control group (mean difference, 1.04 [95% CI, 0.13 to 1.96] points; P = .02), whereas expert instruction significantly improved OSATS subscores for instrument handling vs control (mean difference, 1.18 [95% CI, 0.22 to 2.14]; P = .01). No significant differences in cognitive load, positive activating, and negative emotions were found. Conclusions and Relevance: In this randomized clinical trial, VOA feedback demonstrated superior performance outcome and skill transfer, with equivalent OSATS ratings and cognitive and emotional responses compared with remote expert instruction, indicating advantages for its use in simulation training. Trial Registration: ClinicalTrials.gov Identifier: NCT04700384.

Multi-ancestral origin of intestinal tumors: Impact on growth, progression, and drug efficacy.

BACKGROUND: Data are steadily accruing that demonstrate that intestinal tumors are frequently derived from multiple founding cells, resulting in tumors comprised of distinct ancestral clones that might cooperate or alternatively compete, thereby potentially impacting different phases of the disease process. AIM: We sought to determine whether tumors with a multi-ancestral architecture involving at least two distinct clones show increased tumor number, growth, progression, or resistance to drug intervention. METHODS: Mice carrying the Min allele of Apc were generated that were mosaic with only a subset of cells in the intestinal epithelium expressing an activated form of PI3K, a key regulatory kinase affecting several important cellular processes. These cells were identifiable as they fluoresced green, whereas all other cells fluoresced red. RESULTS: Cell lineage tracing revealed that many intestinal tumors from our mouse model were derived from at least two founding cells, those expressing the activated PI3K (green) and those which did not (red). Heterotypic tumors with a multi-ancestral architecture as evidenced by a mixture of green and red cells exhibited increased tumor growth and invasiveness. Clonal architecture also had an impact on tumor response to low-dose aspirin. Aspirin treatment resulted in a greater reduction of heterotypic tumors derived from multiple founding cells as compared to tumors derived from a single founding cell. CONCLUSION: These data indicate that genetically distinct tumor-founding cells can contribute to early intratumoral heterogeneity. The coevolution of the founding cells and their progeny enhances colon tumor progression and impacts the response to aspirin. These findings are important to a more complete understanding of tumorigenesis with consequences for several distinct models of tumor evolution. They also have practical implications to the clinic. Mouse models with heterogenous tumors are likely better for predicting drug efficacy as compared to models in which the tumors are highly homogeneous. Moreover, understanding how interactions among different populations in a single heterotypic tumor with a multi-ancestral architecture impact response to a single agent and combination therapies are necessary to fully develop personalized medicine.

Best Practices Using Ex Vivo Animal Brain Models in Neurosurgical Education to Assess Surgical Expertise.

BACKGROUND: Ex vivo animal brain simulation models are being increasingly used for neurosurgical training because these models can replicate human brain conditions. The goal of the present report is to provide the neurosurgical community interested in using ex vivo animal brain simulation models with guidelines for comprehensively and rigorously conducting, documenting, and assessing this type of research. METHODS: In consultation with an interdisciplinary group of physicians and researchers involved in ex vivo models and a review of the literature on the best practices guidelines for simulation research, we developed the "ex vivo brain model to assess surgical expertise" (EVBMASE) checklist. The EVBMASE checklist provides a comprehensive quantitative framework for analyzing and reporting studies involving these models. We applied The EVBMASE checklist to the studies reported of ex vivo animal brain models to document how current ex vivo brain simulation models are used to train surgical expertise. RESULTS: The EVBMASE checklist includes defined subsections and a total score of 20, which can help investigators improve studies and provide readers with techniques to better assess the quality and any deficiencies of the research. We classified 18 published ex vivo brain models into modified (group 1) and nonmodified (group 2) models. The mean total EVBMASE score was 11 (55%) for group 1 and 4.8 (24.2%) for group 2, a statistically significant difference (P = 0.006) mainly attributed to differences in the simulation study design section (P = 0.003). CONCLUSIONS: The present findings should help contribute to more rigorous application, documentation, and assessment of ex vivo brain simulation research.

Nicotine and energy balance: A review examining the effect of nicotine on hormonal appetite regulation and energy expenditure.

Nicotine has been shown to decrease appetite, food intake (FI) and body weight, but the mechanisms are unclear. The purpose of this review was to examine research on the effects of nicotine on energy balance by exploring physiological mechanisms and hormone regulation related to FI, subjective appetite and energy expenditure (EE). We searched PubMed and MEDLINE, and included articles investigating the effects of nicotine on central appetite regulation, FI, leptin, peptide-YY (PYY), ghrelin, glucagon-like peptide-1 (GLP-1), adiponectin, cholecystokinin (CCK), orexin, and EE. A total of 65 studies were included in the qualitative synthesis and review. Our findings suggest that the decrease in appetite and FI may be attributed to nicotinic alterations of neuropeptide Y (NPY) and pro-opiomelanocortin (POMC) but the effect of nicotine on FI remains unclear. Furthermore, nicotine increases resting EE (REE) and physical activity EE (PAEE) in both smokers and non-smokers; and these increases may be a result of the catecholaminergic effect of nicotine. Decreases in body weight and appetite experienced by nicotine users results from increased EE and changes in the central hypothalamic regulation of appetite. There is not enough evidence to implicate a relationship between peripheral hormones and changes in appetite or FI after nicotine use. Although nicotine increases REE and PAEE, the effect of nicotine on other components of EE warrants further research. We conclude that further research evaluating the effect of nicotine on appetite hormones, FI and EE in humans is warranted.

Quantitation of Tissue Resection Using a Brain Tumor Model and 7-T Magnetic Resonance Imaging Technology.

BACKGROUND: Animal brain tumor models can be useful educational tools for the training of neurosurgical residents in risk-free environments. Magnetic resonance imaging (MRI) technologies have not used these models to quantitate tumor, normal gray and white matter, and total tissue removal during complex neurosurgical procedures. This pilot study was carried out as a proof of concept to show the feasibility of using brain tumor models combined with 7-T MRI technology to quantitatively assess tissue removal during subpial tumor resection. METHODS: Seven ex vivo calf brain hemispheres were used to develop the 7-T MRI segmentation methodology. Three brains were used to quantitate brain tissue removal using 7-T MRI segmentation methodology. Alginate artificial brain tumor was created in 4 calf brains to assess the ability of 7-T MRI segmentation methodology to quantitate tumor and gray and white matter along with total tissue volumes removal during a subpial tumor resection procedure. RESULTS: Quantitative studies showed a correlation between removed brain tissue weights and volumes determined from segmented 7-T MRIs. Analysis of baseline and postresection alginate brain tumor segmented 7-T MRIs allowed quantification of tumor and gray and white matter along with total tissue volumes removed and detection of alterations in surrounding gray and white matter. CONCLUSIONS: This pilot study showed that the use of animal tumor models in combination with 7-T MRI technology provides an opportunity to increase the granularity of data obtained from operative procedures and to improve the assessment and training of learners.

"Failure-to-Cross" in Patients Undergoing Percutaneous Peripheral Intervention: The Nonreimbursed Procedure.

BACKGROUND: Percutaneous peripheral intervention (PPI) is often the first mode of therapy for patients with symptomatic arterial occlusive disease. Technical success generally remains high although "failure-to-cross" still complicates 5-20% of cases. Extended efforts to cross long, occlusive lesions can utilize significant hospital and practitioner resources. The hospital is typically reimbursed for this effort as facility fees are charged by the hour and materials are charged per use. However, given the lack of a CPT® code for "failure-to-cross," practitioners are rarely appropriately compensated. The purpose of this study is to analyze the predictors, technical details, outcomes, and costs of "failure-to-cross" during PPI. METHODS: All PPI procedures over a 2-year period at a single institution were retrospectively reviewed. Clinical characteristics, results, costs, and reimbursements obtained from hospital cost accounting were compared among successful therapeutic interventions, crossing failures, and diagnostic angiograms without attempted intervention. RESULTS: A total of 146 consecutive PPIs were identified; the rate of "failure-to-cross" was 11.6% (17 patients). The majority of patients with "failure-to-cross" were male (82%) with single-vessel runoff (53%). Compared to successful interventions, the incidences of chronic limb-threatening ischemia (82% vs. 70%, P = 0.34) and infrapopliteal occlusive disease were similar (47% vs. 31%, P = 0.20). "Failure-to-cross" procedures were just as long as successful procedures; there were no significant differences in fluoroscopy time (27 ± 10 vs. 24 ± 14 min, P = 0.52), in-room time (106 ± 98 vs. 103 ± 44 min, P = 0.84), or contrast dye volume utilization (73 ± 37 vs. 96 ± 54 mL, P = 0.12). As expected, "failure-to-cross" procedures incurred far higher hospital charges and costs compared to noninterventional diagnostic angiograms (charges $13,311 ± 6,067 vs. $7,690 ± 1,942, P < 0.01; costs $5,289 ± 2,099 vs. $2,826 ± 1,198, P < 0.01). Despite the additional time and effort spent attempting to cross difficult lesions, the operators were reimbursed at the same low rate as a purely diagnostic procedure (average fee charge $7,360; average reimbursement $992). After 1 year, the 17 patients in whom lesions could not be crossed were treated with advanced interventional procedures with success (n = 2), surgical bypass grafting (n = 5), extremity amputation (n = 4), or no additional intervention in their salvaged limb (n = 6). CONCLUSIONS: Patients whose lesions cannot be crossed during PPI fare worse than patients undergoing successful interventions. Hospital costs and charges appropriately reflect the high technical difficulty and resource utilization of extended attempts at endovascular therapy. For practitioners, crossing lesions during PPI is truly a "pay-for-performance" procedure in that only successful procedures are reasonably reimbursed.

Effect of Increasing the Dietary Protein Content of Breakfast on Subjective Appetite, Short-Term Food Intake and Diet-Induced Thermogenesis in Children.

Dietary protein affects energy balance by decreasing food intake (FI) and increasing energy expenditure through diet-induced thermogenesis (DIT) in adults. Our objective was to investigate the effects of increasing the dietary protein in an isocaloric breakfast on subjective appetite, FI, blood glucose, and DIT in 9-14 y children. Two randomized repeated measures designs were used. In experiment 1, 17 children (9 boys, 8 girls) consumed isocaloric meals (450 kcal) on four separate mornings containing: 7 g (control), 15 g (low protein, LP), 30 g (medium protein, MP) or 45 g (high protein, HP) of protein. Blood glucose and subjective appetite were measured at baseline and regular intervals for 4 h, and FI was measured at 4 h. In experiment 2, 9 children (6 boys, 3 girls) consumed the control or HP breakfast on two separate mornings, and both DIT and subjective appetite were determined over 5 h. In experiment 1, all dietary protein treatments suppressed subjective appetite compared to control (p < 0.001), and the HP breakfast suppressed FI compared with the LP breakfast and control (p < 0.05). In experiment 2, DIT was higher after HP than control (p < 0.05). In conclusion, increasing the dietary protein content of breakfast had favorable effects on satiety, FI, and DIT in children.