Citations

AbstractBackgroundNutritional status in pediatric patients undergoing heart transplantation (HT) is frequently a focus of clinical management and requires high resource utilization. Pre‐operative nutrition status has been shown to affect post‐operative mortality but no studies have been performed to assess how nutritional status may change and the risk of developing nutritional comorbidities long‐term in the post‐transplant period.MethodsA single‐center retrospective chart review of patients ≥2 years of age who underwent heart transplantation between 1/1/2005 and 4/30/2020 was performed. Patient data were collected at listing, time of transplant, 1‐year, and 3‐year follow‐up post‐transplant. Nutrition status was classified based on body mass index (BMI) percentile in the primary analysis. Alternative nutritional indices, namely the nutrition risk index (NRI), prognostic nutrition index (PNI), and BMI z‐score, were utilized in secondary analyses.ResultsOf the 63 patients included, the proportion of patients with overweight/obese status increased from 21% at listing to 41% at 3‐year follow‐up. No underweight patients at listing became overweight/obese at follow‐up. Of patients who were overweight/obese at listing, 88% maintained that status at 3‐year follow‐up. Overweight/obese status at listing, 1‐year, and 3‐year post‐transplantation were significantly associated with developing metabolic syndrome. In comparison to the alternative nutritional indices, BMI percentile best predicted post‐transplant metabolic syndrome.ConclusionsThe results suggest that pediatric patients who undergo heart transplantation are at risk of developing overweight/obesity and related nutritional sequelae (ie, metabolic syndrome). Improved surveillance and interventions targeted toward overweight/obese HT patients should be investigated to reduce the burden of associated comorbidities.

Magnetic resonance imaging (MRI) can facilitate accurate organ delineation and optimal dose distributions in high-dose-rate (HDR) MRI-Assisted Radiosurgery (MARS). Its use for this purpose has been limited by the lack of positive-contrast MRI markers that can clearly delineate the lumen of the HDR applicator and precisely show the path of the HDR source on T1- and T2-weighted MRI sequences. We investigated a novel MRI positive-contrast HDR brachytherapy or interventional radiotherapy line marker, C4:S, consisting of C4 (visible on T1-weighted images) complexed with saline. Longitudinal relaxation time (T1) and transverse relaxation time (T2) for C4:S were measured on a 1.5 T MRI scanner. High-density polyethylene (HDPE) tubing filled with C4:S as an HDR brachytherapy line marker was tested for visibility on T1- and T2-weighted MRI sequences in a tissue-equivalent female ultrasound training pelvis phantom. Relaxivity measurements indicated that C4:S solution had good T1-weighted contrast (relative to oil [fat] signal intensity) and good T2-weighted contrast (relative to water signal intensity) at both room temperature (relaxivity ratio > 1; r2/r1 = 1.43) and body temperature (relaxivity ratio > 1; r2/r1 = 1.38). These measurements were verified by the positive visualization of the C4:S (C4/saline 50:50) HDPE tube HDR brachytherapy line marker on both T1- and T2-weighted MRI sequences. Orientation did not affect the relaxivity of the C4:S contrast solution. C4:S encapsulated in HDPE tubing can be visualized as a positive line marker on both T1- and T2-weighted MRI sequences. MRI-guided HDR planning may be possible with these novel line markers for HDR MARS for several types of cancer.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Introduction and importance: Chest pain is a frequent reason patients seek medical attention. The broad spectrum of potential etiologies makes determining the underlying cause of chest pain complex. Among cardiovascular etiologies, aortitis is a rare but life-threatening possibility that should be considered in the differential diagnosis. Case presentation: A 53-year-old female with a history of smoking presented with progressively worsening chest and epigastric pain over several weeks. She had seen multiple physicians previously for the same symptoms with unremarkable work-ups. Physical examination was notable for severe tenderness upon palpation of her lower abdomen. The electrocardiogram and troponins were unremarkable. Computed tomography of the abdomen revealed aneurysmal dilatation of the abdominal aorta, soft tissue thickening, and surrounding inflammatory stranding, consistent with aortitis. Infectious and autoimmune work-ups were unremarkable. Intravenous steroids were initiated, and her symptoms improved significantly. Her aortitis was attributed to inflammation secondary to chronic smoking. Clinical discussion: Aortitis is a rare condition with varied clinical presentations. Etiologies of aortitis include infection and non-infectious inflammation. Diagnosis of aortitis requires a thorough clinical assessment and prompt imaging of the aorta, with computed tomography being the preferred imaging modality. Conclusion: Evaluation for cardiovascular chest pain must extend beyond an electrocardiogram and troponin level. Imaging should be considered in patients with atypical symptoms. Aortitis is a rare but important diagnosis requiring immediate treatment.

Context.— Title 45, section 164.524 of the Code of Federal Regulations states that health care systems must provide patient health records upon that patient’s request. For complex testing, such as next-generation sequencing (NGS), this raises questions related to what data should be released and the laboratory considerations regarding the release of this data. Objective.— To describe the laboratory implications of releasing different NGS data files and the limitations for the clinical use of different NGS data files. Design.— The College of American Pathologists workgroup, composed of laboratorians with expertise regarding NGS testing, reviewed pertinent literature, including title 45, section 164.524, and the Health and Human Services “Guidance on Individuals’ Right to Access Health Information.” Results.— From an accreditation standpoint, validation of NGS includes both the wet bench and data processing (bioinformatics) portions, and appropriately validated laboratory testing is required to ensure quality patient results. NGS testing generates intermediate data files that have not completed the fully validated process but are often kept by the laboratory. These files may be requested by patients, but most patients will not be aware of the test validation process and the limitations of data that have not gone through a fully validated process. Conclusions.— Laboratories should encourage patients to receive their health data and to help individuals understand the content, uses, and limitations of laboratory data they have requested or received. NGS data used in a nonvalidated manner should not be used for clinical purposes without confirmation by a clinically validated method.