Loomis, Erica A

BACKGROUND AND OBJECTIVES: Ventriculoperitoneal shunt (VPS) placement is a common treatment for idiopathic normal pressure hydrocephalus (iNPH), but patients may require VPS revision. This study aims to evaluate the effectiveness of a standardized VPS placement protocol by comparing indications and rates of shunt revision compared with a historical institutional cohort. METHODS: All patients treated with VPS for “suspected” iNPH at a tertiary care center between January 2000 and January 2023 were retrospectively reviewed and divided into temporally distinct historical (before 2017) and standardized (after 2017) cohorts. Cumulative incidence and timing of VPS revision were compared using competing risks analyses. RESULTS: A total of 561 patients (median age 75.0 years) were identified with 324 (57.8%) in the historical and 237 (42.2%) in the standardized cohorts. Significant variations in surgical technique and shunt hardware were noted between the cohorts. With a median follow-up of 11.4 months for historical and 12.4 months for standardized cohort, shunt revisions occurred in 19.4% and 8.4% of patients in the historical and standardized cohorts, respectively ( P < .001). Revision indications also varied significantly with lower rates of overdrainage ( P < .001), infection ( P < .001), and proximal malfunction ( P = .042) but higher rate of distal malfunction ( P = .03) in the standardized cohort. Notably, no infections were noted in the standardized cohort. CONCLUSION: Risk of shunt revision among iNPH patients may be associated with modifiable factors related to surgical technique and shunt hardware selection. Importantly, no short-term complications such as hemorrhage, infection, or catheter malposition were observed with the standardized protocol. Development and refinement of standardized shunt protocols for iNPH patients should be commonplace to facilitate tracking, identification, and modification of modifiable technical risk factors.

Background and Objectives: There are many well-described approaches to symptomatic ventral hernia management; however, there remains a significant patient population with limited options for a durable ventral hernia repair with a reasonable risk of infection and recurrence. Drawing from the orthopedic literature, we changed our approach to this clinical problem and developed a palliative ventral hernioplasty pathway. Materials and Methods: A retrospective review (2017–2019) of patients’ palliative ventral hernioplasty was performed. Results: In total, 43 patients included, with a female preponderance of 24 (58.6%) and a mean age 61.5 ± 11.5 years. The mean BMI was 38.1 kg/m2 (IQR: 25.4–62), and 28 patients (65.1%) had a history of prior wound/mesh infection. Urgent repair was performed in 14 patients. Overall polypropylene prosthetic was implanted in 26 patients, and bioprosthetic/absorbable mesh was used in the remaining. The mean surface area of the implanted mesh was 561 cm2. The most common wound complications identified were skin separation (30.2%) and seroma formation (48.8%). Hernia recurrence occurred in four (9.3%) patients, with a mean follow-up of 24.1 months (9–37). Three patients had central lightweight mesh rupture and one had a recurrence (bioprosthetic mesh); all were subsequently repaired. Conclusions: Despite the small number of patients, our palliative ventral hernia repair pathway offers durable repair with an acceptable risk of recurrence and mesh infection in patients who would otherwise be considered nonoperative.

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value.