Citations

Background: Following the adoption of new nomenclature for steatotic liver disease, we aimed to build consensus on the use of International Classification of Diseases codes and recommendations for future research and advocacy. Methods: Through a two-stage Delphi process, a core group (n = 20) reviewed draft statements and recommendations (n = 6), indicating levels of agreement. Following revisions, this process was repeated with a large expert panel (n = 243) from 73 countries. Results: Consensus ranged from 88.8% to 96.9% (mean = 92.3%). Conclusions: This global consensus statement provides guidance on harmonizing the International Classification of Diseases coding for steatotic liver disease and future directions to advance the field.

Study Design Cadaveric study. Objectives The purpose of this study was to compare a novel, integrated 3D navigational system (NAV) and conventional fluoroscopy in the accuracy, efficiency, and radiation exposure of thoracolumbar percutaneous pedicle screw (PPS) placement. Methods Twelve skeletally mature cadaveric specimens were obtained for twelve individual surgeons. Each participant placed bilateral PS at 11 segments, from T8 to S1. Prior to insertion, surgeons were randomized to the sequence of techniques and the side (left or right). Following placement, a CT scan of the spine was obtained for each cadaver, and an independent reviewer assessed the accuracy of screw placement using the Gertzbein grading system. Outcome metrics of interest included a comparison of breach incidence/severity, screw placement time, total procedure time, and radiation exposure between the techniques. Bivariate statistics were employed to compare outcomes at each level. Results A total of 262 screws (131 using each technique) were placed. The incidence of cortical breaches was significantly lower with NAV compared to FG (9% vs 18%; P = .048). Of breaches with NAV, 25% were graded as moderate or severe compared to 39% in the FG subgroup ( P = .034). Median time for screw placement was significantly lower with NAV (2.7 vs 4.1 min/screw; P = .012), exclusive of registration time. Cumulative radiation exposure to the surgeon was significantly lower for NAV-guided placement (9.4 vs 134 μGy, P = .02). Conclusions The use of NAV significantly decreased the incidence of cortical breaches, the severity of screw breeches, screw placement time, and radiation exposure to the surgeon when compared to traditional FG.

Enteropathy-associated T cell lymphoma (EATL) is an aggressive subtype of non-Hodgkin’s lymphoma often associated with coeliac disease (CD). We describe a previously healthy man in his 50 s who presented with a history of abdominal pain, failure to thrive and significant weight loss over a 3-month period. Investigations revealed a positive coeliac serology, diffuse duodenal atrophy with multiple duodenal and jejunal ulcers on endoscopy and mesenteric lymphadenopathy on CT imaging. Duodenal tissue biopsy confirmed a diagnosis of EATL Stage IVB. Chemotherapy with cyclophosphamide, doxorubicin, vincristine and prednisone regimen was initiated. This case highlights the need for greater awareness and consideration of EATL in individuals with worsening malabsorption and abdominal pain, irrespective of coeliac history.

Background: Diagnosis of heart failure with preserved ejection fraction (HFpEF) is challenging due to non-diagnostic imaging outputs and discordant clinical features. Using only a single videoclip from a standard transthoracic echocardiogram (TTE), we developed an artificial intelligence (AI) model to differentiate patients with HFpEF from those without HFpEF. In a separate test population, we assessed the model’s ability to predict cardiac mortality. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber videoclips to classify patients with HFpEF (cases) versus without HFpEF (controls). Overall and cardiac mortality were obtained from the National Death Index. Results: The model was independently tested in 646 cases and 638 controls from 8 centers in 4 states. Sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) were high and sustained in various subgroups, including increased left atrial volume index, average E/e’ ≥15, and pulmonary artery systolic pressure >35 mm Hg. During follow-up (median [IQR] 3.4 [1.7-6.5] years), 540 patients died. There were 135 cardiac deaths attributable to heart failure (47%), coronary artery disease (41%), valve disease (4%), arrhythmia (4%), and others (5%). Compared to a negative output, all-cause mortality was higher in patients with a positive (HR 2.9 [2.4-3.5]) or uncertain output (HR 1.8 [1.2-2.5]), and cardiac mortality (Figure) was also higher in patients with a positive (HR 7.7 [4.5-13.3]) or uncertain output (HR 3.4 [1.4-8.1]). Cardiac mortality differences according to AI model prediction remained after age adjustment (positive: HR 5.9 [3.3-10.4], p<0.001; uncertain: HR 2.8 [1.2-6.8], p=0.02). Conclusion: A novel AI model to detect HFpEF based on a single routinely acquired TTE videoclip also identifies those at increased risk of cardiac mortality.

Introduction The discovery of MAPK/ERK pathway mutations has revolutionized the care for patients with histiocytic neoplasms. However, mutational analysis on biopsy specimens is often limited by low tumor cellularity leading to testing failure or disease involvement of difficult-to-reach sites (e.g., central nervous system). Recently, molecular analysis of cell-free DNA (cfDNA) has shown promise as a viable tool for discovering driver mutations in multiple cancers. A recent study of mainly BRAF-mutated non-Langerhans cell histiocytosis (LCH) showed 50% concordance between tumor and plasma cfDNA analysis for BRAF-V600E. However, it is unknown whether cfDNA has a role in the evaluation of cases that harbor mutations beyond BRAF-V600E. We conducted this study to address this unanswered question using a large cohort of individuals with diverse histiocytic neoplasms from a tertiary-care institution. Methods We included 40 cases with histiocytic neoplasms with stored plasma and germline DNA in this study. Tissue molecular analysis was conducted using a targeted next generation sequencing (NGS) using commercial assays or whole exome sequencing (WES) using an in-house assay. A custom panel of 226 genes, including exonic and intronic regions, was used for the cfDNA assay in this study. To avoid cfDNA false positives, germline mutations identified from normal DNA sequencing were filtered using a BostonGene ®-developed error reduction algorithm.The total length of the analyzed genomic regions was 1.56 MB. For samples with an input greater than 30 ng, a 0.2% variant allele frequency (VAF) limit of detection (LOD) was used; a 0.4% VAF LOD was used for samples less than 30 ng. Results The final analysis cohort included 38 patients with histiocytic neoplasms after exclusion of 2 cases (failure of germline DNA library preparation and mismatch of germline and somatic DNA, respectively). Disease types included Erdheim-Chester disease (ECD; n=13, 34%), Langerhans cell histiocytosis (LCH; n=13, 34%), and Rosai-Dorfman disease (RDD; n=12, 32%). 24 cases were multisystem (63%), and the remaining were single-system. Commonly involved organs included bone (n=25, 66%), kidney (n=12, 32%), skin (n=8, 21%), central nervous system (n=7, 18%), and pituitary (n=7, 18%). Most cases (n=31, 82%) underwent plasma collection pre-treatment, 5 cases underwent collection after histiocytosis-directed therapy, and 2 while on treatment. Tissue NGS/WES data were available for 28 (74%) cases; 5 cases (13%) had insufficient tissue cellularity, and 5 (13%) did not undergo any tissue sequencing. Among those who underwent successful tissue sequencing assays, the most common common driver alterations was BRAF V600E (n=5, 26%). Other alterations included those in MAP2K1 (n=4, 21%), BRAF-fusions (n=3, 10%; partners UBDT2, RNF11, and LMTK2, respectively), and single cases of KRAS, NRAS and BRAF indel, respectively. cfDNA analysis showed at least one variant in 23 cases (60%). Correlation between tissue and cfDNA for known driver mutations was poor and concordant findings were only seen in one case with ECD harboring BRAF V600E in tissue as well as plasma (VAF 0.5%). Among the 5 cases where tissue mutational analysis had failed, cfDNA showed mutations in MUTYH, PTCH2, NOTCH3 SDHD, and TMEM127, respectively (Figure 1). Among the 5 cases where tissue molecular assessment could not be undertaken due to various reasons, cfDNA showed alterations in 4 cases, encompassing NOTCH1, TET2, BRCA1, MSH6, PIK3CD, and FUS genes. Conclusions In a large cohort of adults with diverse histiocytic neoplasms, we found poor correlation between tumor and plasma cfDNA molecular findings. The reason for the lack of correlation may be related to low tumor shedding especially among non- BRAF V600E cases. cfDNA analysis may have a role to identify potential drivers of pathogenesis among cases that are unable to successfully undergo tissue molecular analysis. Further studies are underway to characterize other novel alterations that were discovered in the cfDNA analysis.

Purpose: The understanding of cardiac-involved ECD (ECD-CV) and how this affects outcomes and pre-existing diseases remains limited. We aimed to determine the association, burden, and severity of pre-existing and de novo cardiovascular (CV) comorbidities with ECD-CV compared to patients (pts) without cardiac-involved ECD (ECD-noCV). Methods: Records of pts with ECD diagnosed from Jan-1990 to Dec-2021, consecutively seen at Mayo Clinic, AZ, FL, and MN were reviewed. Cardiac involvement was diagnosed by imaging. CV comorbidities were captured before and after ECD diagnosis. CTCAE v3.0 based grading system was used to assess CV comorbidities which graded the conditions as mild (grade 1), moderate (grade 2), severe (grade 3), and life-threatening or disabling (grade 4). Chi-square test was used to determine if having ECD-CV worsens pre-existing CV comorbidities in pts with ECD. As per the established methods, the cumulative CV disease burden was defined after considering 8 CV morbidities that are known to have direct structural and functional implications on the heart, including hypertension (HTN), coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), atrial fibrillation (afib), pericardial effusion, valvular disease, and conduction disorders. A composite CV morbidity grade was assigned based on the highest grade in one of the 8 CV comorbidities (Oeffinger, NEJM, 2006). Lastly, a multivariant analysis was conducted using the presence of BRAFV600E mutation, smoking, age ≥ 60 years (yr), conventional vs targeted therapy, and ECD-CV in pts with a composite grade of 4. Results: Among 106 ECD pts, 38% (n=40) had cardiac involvement. The median age at diagnosis of ECD was 57 yr (range: 49-67); 61% were males. The median follow-up was 5 yr (95% CI: 3.5-6.1), and 4.1 yr (95% CI: 3.2-6.5) for ECD-CV and ECD-noCV pts, respectively p=0.92. The median time of CV diagnoses for the entire cohort before and after ECD diagnosis was 4.8 yr (range: 0.06-42.5) and 3.4 yr (range: 0.05-18.7), respectively. BRAFV600E mutation was present in 65% (n=26) of ECD-CV vs 42% (n=28) of ECD-noCV, p=0.02. Eight pts with ECD-CV died; one from sudden cardiac death, while 9 ECD-noCV pts died, p=0.39. Smoking was present in 43% (n=18) of ECD-CV pts compared to 24% with ECD-noCV (n=16), p=0.05. In comparison to ECD-noCV pts, increased prevalence of pre-existing HTN [59% (n=23) vs 35% (n=23), p=0.016] and MI [10% (n=4) vs 2% (n=1), p=0.046] was seen in ECD-CV. There was no difference in hyperlipidemia, diabetes mellitus, CAD, CHF, valvular disease, afib, and conduction disorders among the two cohorts. After ECD diagnosis, de novo diagnosis of hyperlipidemia [67% (n=26) vs 38% (n=25); p=0.004] and CHF [35% (n=14) vs 7% (n=5); p=&lt;0.001] was seen more frequently in ECD-CV pts compared to ECD-noCV. Following the diagnosis of ECD, the severity of pre-existing conditions did not change in pts with or without ECD-CV. ECD-CV pts had a greater cumulative CV disease burden as the total number of CV morbidities was greater in ECD-CV pts both before and after ECD diagnosis (Table 1). Additionally, pts with ECD-CV had greater severity of CV morbidity (composite grade ≥4) than ECD-noCV before (35% v 3%; p=&lt;0.001) and after (42.5% vs 7.6%; p=&lt;0.001) ECD diagnosis. The composite grade of 4 before ECD diagnosis was most attributable to pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (50% in pts with ECD-CV vs 100% in pts with ECD-noCV); after ECD diagnosis pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (41% in pts with ECD-CV vs 100% in pts with ECD-noCV). After considering BRAFV600E mutation, age ≥60, smoking, ECD-CV, and first-line therapy (conventional vs targeted therapy), ECD-CV was the only factor independently associated with a composite grade of ≥4, before (OR 25.6, 95% CI: 4.8-137) and after (OR 9.7, 95% CI: 3.0-30.8) ECD diagnosis. Nonetheless, smoking, age &gt;60, and ECD-CV were independent factors associated when considered a composite grade of ≥3, before and after ECD diagnosis. Conclusion: ECD-CV did not prognosticate pts and did not worsen the pre-existing CV conditions compared to pts with ECD-noCV. However, ECD-CV pts had a higher cumulative CV disease burden and a higher grade of pre-existing and de novo CV morbidities compared to ECD-noCV. The presence of cardiac involvement in ECD was independently associated with a severe cumulative CV disease burden grade.

Background: Multimorbidity is common in atrial fibrillation (AF), particularly among older patients who generally have multiple cardiovascular and non-cardiovascular chronic conditions. However, less is known about the accumulation of chronic conditions in younger patients with AF. Methods: Patients aged <65 with incident AF from 2013-2019 from a 27-county region in the Midwest (N=4907) were matched 1:1 on age (±5 years) and sex to referents from the same community. The index date for the matched pair was the date of first diagnosis of AF. Diagnosis of 20 other chronic conditions was ascertained from 3 years prior to index through 12/31/2022. Mean cumulative function curves were used to visualize the accumulation of conditions; Andersen-Gill models estimated associations between AF/referent status and accumulation of conditions. Results: The mean age was 55 years for both patients with AF and matched referents; 71% were male. In the 3 years leading up to AF diagnosis, only 4% of patients with AF had no additional chronic conditions and 69% had ≥3 chronic conditions. In contrast, 35% of referents had 0 and 32% had ≥3 chronic conditions. The accumulation of chronic conditions was accelerated in patients with AF compared to referents prior to AF diagnosis/index (Figure). After index, patients with AF had increased accumulation of chronic conditions (HR (95% CI): 1.93 (1.71-2.20), 1.82 (1.62-2.05), 1.68 (1.55-1.83), and 1.53 (1.44-1.64) for ages <50, 50-54, 55-59, and 60-64 years, respectively). After 1 year, the differences were attenuated in all age groups with no difference observed between patients with AF and referents in the oldest age group (1.26 (1.08-1.46), 1.28 (1.11-1.47), 1.13 (1.02-1.24), and 1.05 (0.97-1.13) for ages <50, 50-54, 55-59, and 60-64 years). Conclusion: Patients <65 years with AF have an increased accumulation of chronic conditions compared to those without AF, in particular prior to their AF diagnosis and within the first year after AF diagnosis.