Hostetter, Logan

Introduction Bronchoscopic lung volume reduction with endobronchial valves (EBV) is an effective procedure for patient with severe emphysema to improve lung function, exercise tolerance, dyspnoea and quality of life. Optimisation of patient and treatment lobe selection is essential for successful EBV outcomes. While clinical selection criteria are rigorous, many centres use a multidisciplinary team and rely on previous clinical experience for the selection process. To aid objective clinical decision making, we present a mathematical model to facilitate patient and target lobe selection. Methods A multidisciplinary team reviewed quantitative high-resolution computed tomography (HRCT) analysis from 119 patients to select candidates for EBV and to select a treatment lobe. Two logistic regression models, (1) candidacy for EBV placement and (2) target-lobe selection, were developed based on the normalised distributions of the four quantitative HRCT variables (fissure completeness score, per cent of voxel density with HU < −910 and HU < −950, and lobar volumes) across all five lung lobes. An external cohort of 50 patients (25 candidates and 25 non-candidates) was used to validate the prediction model. Results Performance measures of the training cohort demonstrated an area under the curve (AUC) of 0.91, accuracy of 81%, sensitivity of 93% and specificity of 78% compared with the multidisciplinary teams’ target lobe selection. The validation cohort demonstrated an AUC of 0.89, accuracy of 84%, sensitivity of 88% and specificity of 83% compared with the multidisciplinary team decision making. Conclusions Endobronchial valve lung volume reduction remains a potent palliative measure to improve quality of life in patients with hyperinflated emphysema. Our model for target lobe selection harnesses the multidisciplinary experience at a tertiary care centre to objectively select candidates and target lobes to assist clinicians’ decision making. Future studies investigating prediction of lobar collapse and functional improvement after target lobe selection using our model are needed.

Abstract Rationale Stereotactic body radiation therapy (SBRT) represents an effective therapeutic strategy for early-stage non–small cell lung cancer (NSCLC); however, local and systemic recurrences represent ongoing challenges. Computed tomography (CT) radiomics-based risk models can potentially be used to predict the risk of local recurrence on pretreatment CT scans. Objective Development of a radiomics model to predict local recurrence after SBRT in patients with NSCLC. Methods This single-institution study includes a retrospective case-control training set (20 patients with local recurrence and 40 control subjects) and an independent validation set (198 consecutive cases) of patients with early-stage NSCLC treated with SBRT. Tumors were semiautomatically segmented, and 102 quantitative radiomic features, including texture, landscape, spatial, nodule shape, and nodule surface features, were extracted. These features were included in three separate multivariable models to predict the risk of recurrence on the basis of pre-SBRT, post-SBRT, and the difference between the pre-SBRT and post-SBRT scans (Delta model). The pre-SBRT model was subsequently validated in an independent validation set. Results Thirteen independent variables were selected for the models using the Boruta algorithm. The sensitivity, specificity, and area under the curve of the pre-SBRT, post-SBRT, and Delta models were 85%, 90%, and 0.91; 85%, 92.5%, and 0.92; and 85%, 92.5%, and 0.94, respectively. The pre-SBRT model was validated in the independent validation set (area under the curve, 0.89; confidence interval, 0.83–0.92), because this model was believed to be the most useful to assist in individualized treatment planning. Conclusions Radiomic analysis facilitated the development of three high-performing models predicting local recurrence using either pre-SBRT CT, post-SBRT CT, or the change between these two. We successfully validated the most clinically relevant model, the pre-SBRT model. Although this model needs further validation, it may facilitate individualized surveillance, treatment planning, and selection of adjuvant therapy.

Objective Extracorporeal cardiopulmonary resuscitation (ECPR) utilizes veno-arterial extracorporeal membrane oxygenation (VA-ECMO) in cardiac arrest patients to reduce the risk of mortality and multiorgan dysfunction from systemic hypoperfusion. We aimed to compare clinical outcomes of patients receiving ECPR versus conventional cardiopulmonary resuscitation (CCPR) for refractory cardiac arrest. Data Sources This was a systematic review and meta-analysis. A librarian searched the main databases, Ovid MEDLINE (including epub ahead of print, in-process & other non-indexed citations), Ovid EMBASE and Ovid Cochrane Central Register of Controlled Trials from inception through July 2024. Study Selection We included randomized controlled trials and observational studies that compared the outcomes of ECPR to CCPR in cardiac arrest patients. Primary outcomes were neurological sequelae and survival. Data Extraction We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two reviewers independently screened articles, extracted data on selected articles and performed risk of bias assessments using ROBINS-I for non-randomized controlled trials and the revised Cochrane risk of bias tool for randomized controlled trials with disagreements settled by a third independent reviewer. Data Synthesis Out of 3458 studies identified and screened, 28 studies including 304,360 cardiac arrest patients met eligibility criteria and were included. Survival at hospital discharge was 20% for ECPR versus 3.3% for CCPR (OR 0.48 [CI 0.27, 0.84]). Favorable neurological outcome at hospital discharge was 11.8% for ECPR versus 1.9% for CCPR (OR 0.41 [CI 0.17, 1.01]). Complications from bleeding were ten times higher in the ECPR group (35.3% vs 3.7%; OR 0.08 [0.03, 0.24]). Conclusions ECPR appeared to be superior to CCPR for improved neurological outcome and survival in cardiac arrest patients, although bleeding was increased. There was large heterogeneity in the included studies and outcomes reported. Future prospective studies may improve the identification of subgroups of patients that will benefit most from ECPR. Systematic review and meta-analysis registration: PROSPERO - CRD42023394128.

Abstract Large language models are becoming ubiquitous in the editing and generation of written content and are actively being explored for their use in medical education. The use of artificial intelligence (AI) engines to generate content in academic spaces is controversial and has been meet with swift responses and guidance from academic journals and publishers regarding the appropriate use or disclosure of use of AI engines in professional writing. To date, there is no guidance to applicants of graduate medical education programs in using AI engines to generate application content—primarily personal statements and letters of recommendation. In this Perspective, we review perceptions of using AI to generate application content, considerations for the impact of AI in holistic application review, ethical challenges regarding plagiarism, and AI text classifiers. Finally, included are recommendations to the graduate medical education community to provide guidance on use of AI engines in applications to maintain the integrity of the application process in graduate medical education.