The cone repair allows right ventricle rehabilitation with excellent tricuspid valve function following the Starnes procedure.

BACKGROUND: We present a case series of right ventricle (RV) rehabilitation after the Starnes procedure in patients with Ebstein anomaly (EA), applying the Cone repair of the tricuspid valve (TV) to achieve 2-ventricle or 1.5-ventricle physiology. METHODS: This is a retrospective database analysis from 2 institutions in North America. We included all consecutive cases of Cone repair after the Starnes procedure. The data are expressed as median and interquartile range (IQR). RESULTS: Eleven patients underwent RV rehabilitation between 2019 and 2023 after initial Starnes palliation at a median age of 27 months (IQR, 20.5 months). All patients were critically ill before their Starnes procedure, and 4 were on extracorporeal membrane oxygenation. Before the Cone repair, the median preoperative regurgitant velocity at the Starnes patch was 1.65 m/s (IQR, 1.3 m/s). During the Cone procedure, 9 patients required a concomitant pulmonary valve repair, of whom 3 needed a transannular monocusp patch. Four patients were successfully rerouted to a 2-ventricle repair, and 7 patients with a previous Glenn achieved 1.5-ventricle circulation. There were no cases of heart block and no deaths. Seven patients had trivial, 3 patients had mild, and 1 patient had moderate tricuspid regurgitation (TR) at a median follow-up of 11 months (IQR, 21.5 months). There was no significant TV stenosis; all patients had good functional status at the last follow-up despite severe RV dysfunction in 1 patient. CONCLUSIONS: After the Starnes procedure, the Cone repair allowed RV rehabilitation, resulting in trivial or mild TR at a midterm follow-up. The Starnes procedure is a reproducible technique that no longer commits patients to lifetime single-ventricle physiology.

Impact of the 2018 change in US allocation policy on adults with congenital heart disease.

BACKGROUND: The US adult heart allocation policy was changed on October 18, 2018. This study aims to evaluate its impact on orthotopic heart transplantation (OHT) for adults with congenital heart disease (ACHD). METHODS: The United Network for Organ Sharing database was used to perform 2 comparisons: waitlist outcomes among listed ACHD candidates, and post-transplant outcomes in those transplanted. Waitlisted candidates were stratified by date of waitlisting: Period 1: 2010 to 2013; Period 2: 2014 to October 17, 2018 and Period 3: October 18, 2018 to March 20, 2020. Transplanted ACHD patients were similarly stratified but by date of transplantation. Competing risk regression for waitlist outcomes was performed. Post-transplant survival was analyzed using the Kaplan-Meier method and multivariable Cox regression. RESULTS: Nine hundred and seventy-six patients with ACHD were waitlisted for OHT in our study: 343(35.1%), 466(47.8%), and 167(17.1%) in periods 1, 2, and 3. Post-policy change, 1-year cumulative incidence of waitlist mortality or deterioration decreased (p = 0.02). Six hundred and forty-eight patients were transplanted: 221(34.1%), 329(50.8%) and 98(15.1%) respectively. In those transplanted, post-policy median waitlist time (174, 161 and 38 days, p < 0.001) decreased and the use of intra-aortic balloon pumps increased (1.4%, 4.9% and 19.4%, p < 0.001). Compared to periods 1 and 2, risk-adjusted post-transplant 1-year mortality was similar to period 3 (HR 1.10, 95% CI 0.52-2.32; p = 0.81) (HR 1.19, 95% CI 0.58-2.46, p = 0.63). CONCLUSIONS: The recent US allocation policy change may have resulted in reduced waitlist times and 1-year waitlist mortality for OHTs in ACHD patients. Early post-transplant outcomes appear comparable post-policy change.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone. METHODS: A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset. RESULTS: The support vector machine/convolutional neural network-based models classified nodules into 6 categories resulting in an area under the curve of 0.59/0.65 when differentiating atypical adenomatous hyperplasia versus adenocarcinoma in situ, 0.87/0.86 with minimally invasive adenocarcinoma versus invasive adenocarcinoma, 0.76/0.72 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma, 0.89/0.87 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma + invasive adenocarcinoma, and 0.93/0.92 atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma. Classifying benign versus atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma resulted in a micro-average area under the curve of 0.93/0.94 for the support vector machine/convolutional neural network models, respectively. The convolutional neural network-based methods had higher sensitivities than the support vector machine-based methods but lower specificities and accuracies. CONCLUSIONS: The machine learning algorithms demonstrated reasonable performance in differentiating benign versus preinvasive versus invasive adenocarcinoma from computed tomography images alone. However, the prediction accuracy varies across its subtypes. This holds the potential for improved diagnostic capabilities with less-invasive means.

Navigation bronchoscopy: A new tool for pulmonary infections.

Infections remain a common cause of lung nodules, masses, and cavities. Safe tissue sampling is required to establish a diagnosis, differentiate between malignant and infectious causes, and provide microbiological material for characterization and sensitivity analysis. Tissue samples could be obtained bronchoscopically, percutaneously, or through surgical biopsy. Among these, bronchoscopy is the safest by avoiding the complications of pleural and chest wall puncture including pneumothorax, pain, pleural contamination and empyema, and hemothorax. However, the diagnostic yield with conventional bronchoscopy for small, peripheral lesions is poor. Electromagnetic navigation bronchoscopy (ENB) is a technique where the bronchoscope and working channel are guided through the bronchial tree to accurately reach a peripheral lesion. It dramatically improves on the diagnostic yield of peripheral lesions especially of small lesions, and its role has developed beyond diagnosis to treatment enablement and to direct therapy. Its role in infection is less defined, but it has value especially in the diagnosis of fungal and mycobacterial infections and in cavitating lesions. This review will explore what electromagnetic navigation bronchoscopy is, its use in diagnosis and therapy, and its role in the management of pulmonary infections. The potential for local therapy delivery for infection is also discussed.