Citations

INTRODUCTION: To update the global burden of inflammatory bowel disease (IBD) using data from the Global Burden of Disease 2021. METHODS: Data from Global Burden of Disease 2021 were analyzed to assess the IBD burden. RESULTS: In 2021, there were 375,140 new cases and 3.83 million total cases of IBD. Elderly onset IBD accounted for 11% of incidences. 167 countries increased IBD incidence rate, with rates rising in female patients (APC: +0.06%) and the elderly (APC: +0.14%) but stable in male patients and the overall population. DISCUSSION: Although the global burden of IBD has decreased overall, it has increased in female patients and the elderly.

Background: Although BP is associated with kidney outcomes, its clinical significance in living kidney donors (LKD) is unknown. We aim to examine the association between pre-donation BP and the risk of kidney function decline in LKD. Methods: A retrospective cohort study using OPTN/SRTR including adult LKD, who underwent donation between 6/1972 and 9/2022, was utilized to examine the association of quartile (Q) of pre-donation SBP and DBP with time-to-event of ≥35% decline in post-donation eGFR from pre-donation eGFR by multiple Cox regression. Results: Of 174,311 adult LKD, mean±SD age was 41±12 years and 60% were female. Mean pre-donation SBP and DBP were 122±13 and 74±9 mmHg, respectively. Mean pre-donation SBP in Q 1, 2, 3, and 4 were 106±6, 117±2, 126±3, and 140±9 mmHg, respectively and the corresponding DBP were 62±5, 71±2, 78±2, and 86±5 mmHg, respectively. The median (IQR) of pre-donation eGFR was 91 (75, 111) mL/min/1.73 m2. Median(IQR) eGFR at immediate post-donation, 6-, 12-, and 24-months post-donation were 50 (40, 62), 54 (44, 67), 55 (45, 68), and 57 (47, 70) mL/min/1.73 m2, respectively. Out of 38,780 LKD with post-donation eGFR data at 6, 12, or 24 months, 31,054 had the event of ≥35% decline in post-donation eGFR from pre-donation eGFR with a median time to follow-up of 6.77 months(IQR 5.93, 12.67). The incidence rate of the event was 0.08 person-months. Compared to patients with SBP in Q 1, those with SBP in Q 2, 3, and 4 had a significantly increased risk for the event(HRSBP Q2,3,4 1.05, 1.08, and 1.15; P <0.001 – 0.003) but only patients with DBP in Q 3 and 4 had a significantly increased risk for the event (HRSBP Q3,4 1.04 and 1.09). After adjusting for age, gender, race/ethnicity, U.S. citizenship, education level, history of pre-donation HTN, diabetes, pre-donation BMI, DBP, and urine protein:creatinine ratio, there was a J-shaped association between pre-donation SBP and post-donation eGFR decline and only patients in the Q 2 became significantly associated with lower the risk for the event (HR 0.87, 95%CI 0.76, 0.99; Fig1). The pre-donation DBP – post-donation eGFR association was similar to the SBP – eGFR association but there was no statistically significant in all Q (Fig2). There was no effect-measured modifier for all covariates. Conclusions: Pre-donation SBP in a normal range(113 - 120 mmHg) is associated with the lowest risk for a post-donation eGFR decline. BP control in the normal range should be achieved for potential LKD.

Background Gastrointestinal cancers comprise nearly one-third of global mortality from cancer, yet the comprehensive global burden of these cancers remains uninvestigated. Objective We aimed to assess the global, regional and national burden of gastrointestinal cancers. Designs Data on oesophagus, gastric, colorectal, liver, pancreas and biliary tract cancers were extracted from the Global Burden of Disease 2021 database. Age-standardised incidence rate (ASIR) and age-standardised death rate (ASDR) were calculated by sex, region and Sociodemographic Index (SDI). Results In 2021, there were 5.26 million incidences and 3.70 million deaths from gastrointestinal cancer. The greatest burden is from colorectal, followed by gastric, oesophageal, pancreatic, liver and biliary tract cancer. We noted geographical and socioeconomic differences in ASIR and ASDR across all types of cancers. From 2000 to 2021, ASIR increased for colorectal cancer (annual percent change (APC): 0.10%, 95% CI 0.05% to 0.14%), pancreatic cancer (APC: 0.27%, 95% CI 0.14% to 0.41%), and liver cancer from metabolic dysfunction-associated steatotic liver disease (APC: 0.62%, 95% CI 0.58% to 0.67%) and alcohol-related liver disease (APC: 0.26%, 95% CI 0.22% to 0.30%). ASDR increased for pancreatic cancer (APC: 0.18%, 95% CI 0.02% to 0.34%). Higher SDI countries had higher incidence rates for most types of gastrointestinal cancer. Conclusions Although the ASIR of oesophageal, gastric and biliary tract cancer has decreased, the ASIR still increased in colorectal, pancreatic and liver cancer from steatotic liver disease. Public policies are important for controlling gastrointestinal cancers—most importantly, reducing alcohol consumption, hepatitis B immunisation and tackling the burden of metabolic diseases.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Objective: With drastic variations in bariatric practices, consensus is lacking on an optimal approach for revisional bariatric surgeries. Materials and Methods: The authors reviewed and consolidated bariatric surgery literature to provide specific revision suggestions based on each index surgery, including adjustable gastric band (AGB), sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), biliopancreatic diversion with duodenal switch (BPD-DS), single anastomosis duodenal-ileal bypass with sleeve (SADI-S), one anastomosis gastric bypass (OAGB), and vertical banded gastroplasty (VBG). Results: AGB has the highest weight recurrence rate and can be converted to RYGB, SG, and BPD-DS. After index SG, common surgical options include a resleeve or RYGB. The RYGB roux limb can be distalized and pouch resized in context of reflux, and the entire anatomy can be revised into BPD-DS. Data analyzing revisional surgery after a single anastomosis duodenal-ileal bypass with sleeve was limited. In patients with one anastomosis gastric bypass and vertical banded gastroplasty anatomy, most revisions were the conversion to RYGB. Conclusions: As revisional bariatric surgery becomes more common, the best approach depends on the patient’s indication for surgery and preexisting anatomy.

8106 Background: Due to lack of prospective data, the optimal first line treatment approach for patients (pts) with pure LCNEC histology remains uncertain. Methods: Across 17 centers, we conducted a retrospective analysis of metastatic pure LCNEC (diagnosed at local institutions) who received 1st-line systemic therapy between 2015-2023. Pts were either treated with chemotherapy (chemo), immunotherapy (IO), or a combination (chemoIO). Clinical outcomes were progression-free survival (PFS), overall survival (OS), objective response rates (ORR), and treatment-related adverse events (trAE) as defined using CTCAE 5.0. Survival analysis by genomic alteration status ( TP53, RB1, STK11, KEAP1, KRAS) and PD-L1 status were performed. Results: We identified 161 pts with median age of 67 years (IQR: 58-83) at 1st line systemic therapy; 54% (n = 87) were males. Median follow-up was 55 months (mo), 62 mo, and 29 mo for the chemo, IO, and chemoIO groups, respectively. 79% (n = 127) were former or current smokers. 1st-line treatments were chemo (n = 94), IO (n = 11), or chemoIO (n = 56). Of 85 pts with PD-L1 status, 58 (68%) were 0% and 27 (32%) were ≥1%. The most common chemo regimen was platinum-etoposide (n = 72, 78%). ChemoIO regimens included carboplatin/etoposide/atezolizumab (n = 23, 43%) and carboplatin/pemetrexed/pembrolizumab (n = 17, 31%). In the IO group, 1 patient received dual IO. Lung (n = 74, 46%) and liver (n = 71, 41%) were the most common sites of metastasisThere was no significant difference in PFS across the groups (median PFS [mPFS] chemoIO: 5.7 mo, 95% CI: 5.0-6.3, chemo: 5.1 mo, 95% CI: 3.1-5.9; IO: 3.6 mo, 95% CI: 1.7-6.5) on multivariable analysis adjusting for ECOG and “M” stage (p = 0.2 and 0.24 for chemoIO-chemo and chemoIO-IO comparisons, respectively). There was no difference in OS (mOS chemo: 11 mo, 95% CI: 7.6-17.4; IO: 13.6 mo, 95% CI: 5.9-not reached; chemoIO: 12.2 mo, 95% CI: 7.4-20.6, p = 0.5 and 0.8 for chemoIO-chemo and chemoIO-IO comparisons, respectively). ORR was 35.2% (31/88) in chemo, 25% (2/8) in IO, and 35.7% (20/56) in chemoIO ( p= 0.46). There were no significant differences in PFS and OS outcomes by treatment group when divided by genetic profile (n = 79, methods), or by PD-L1 status (0% vs ≥1%). Any grade trAE occurred in 48 (51%), 5 (45%), and 28 (50%) pts treated with chemo, IO, and chemoIO, respectively. Grade ≥3 toxicity profiles are shown (table). Conclusions: For 1st-line treatment of LCNEC, similar PFS, OS, and ORR were seen for chemo, IO, and chemoIO. This questions the added benefit of chemoIO compared to chemo alone, and warrants future clinical trials to discern the optimal 1st line treatment. [Table: see text]

8602 Background: LCNECs are a rare subtype of lung cancer, with limited clinical trial data and an unclear consensus on first-line treatment. Even less is known about the optimal treatment approach for pts with mixed LCNECs, a pathologic subtype where tumors contain both LCNEC histology as well as features of adenocarcinoma, squamous cell, or small cell lung cancer. Methods: We conducted an international multi-institutional retrospective analysis (n=13 institutions) of pts with advanced or metastatic mixed LCNECs who received 1st-line systemic therapy with either chemotherapy alone (chemo) or chemo-immunotherapy (chemoIO) between 2010-2023. Clinical outcomes included progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and treatment-related adverse events (trAEs). Survival comparisons were made via the log-rank test. Results: Seventy-three pts with mixed LCNECs received either chemo (n=42, 57.5%) or chemoIO (n=31, 42.5%). The most common chemo regimens were carboplatin/etoposide (n=27) and cisplatin/etoposide (n=8), while the most common chemoIO was carboplatin/etoposide/atezolizumab (n=19). Median age at 1st-line therapy initiation was 65 (IQR: 60-72). 64% of pts were male (n=47). The majority were former smokers (n=41, 56.2%), with 22 current smokers (30.1%), and 10 non-smokers (13.7%). Mixed histologies included LCNEC/NSCLC (n=45, 56.2%), LCNEC/SCLC (n=17), LCNEC/carcinoid (n=1), and unspecified mixed LCNEC (n=10). Median PFS for chemo was 5 months (95% CI 2.7-9.2) vs. 6 months (95% CI 4.4-8.3) for chemoIO ( p=0.86). Median OS for chemo was 14.2 months (95% CI 10.4-22.5) vs. 17.2 months (95% CI 9.4-27.2) for chemoIO ( p=0.66). The ORR did not differ between chemo (n=18, 45%) and chemoIO (n=15, 48.4%) ( p=0.81). In the chemo group, 60% (n=25) experienced trAEs of any grade, with 23.8% (n=10) experiencing grade 3+ events and four requiring hospitalizations. For chemoIO pts, 61% (n=19) experienced trAEs, with 25.8% (n=8) experiencing grade 3+ events and six requiring hospitalizations. Neutropenia was the most common grade 3+ trAE for both chemo (n=5) and chemoIO (n=4). Overall, there were no significant differences between chemo or chemoIO groups for ORR ( p=0.81), all-grade trAEs ( p=0.88), grade 3+ trAEs ( p=0.84), PFS ( p=0.86), or median OS ( p=0.66). There were no significant differences in median PFS ( p=0.46) or OS ( p=0.99) for pts with mixed LCNEC/NSCLC vs. LCNEC/SCLC. Conclusions: For mixed LCNECs, there was no significant difference in PFS or OS between first-line treatment with chemo vs. chemoIO, suggesting limited benefit from immunotherapy for these pts. Additionally, survival outcomes did not differ between mixed LCNEC/NSCLC and LCNEC/SCLC, suggesting a biology primarily driven by their neuroendocrine component.

Background/Objective: Clostridioides difficile infection (CDI) is a common healthcare-associated ailment, presenting major health and economic challenges, especially for the elderly. Despite its prevalence, comprehensive data about CDI’s impact on the elderly are limited. Methods: This study used the Global Burden of Disease Study 2019 data to analyze CDI trends from 2000 to 2019, considering factors like sex, region, and sociodemographic index (SDI). Results: This study revealed that CDI caused approximately 18,181 deaths and 252,709 disability-adjusted life years (DALYs) among the elderly worldwide. The Americas showed the highest CDI burden, while the Eastern Mediterranean saw the steepest rate increase from 2000 to 2019. Regions with a high SDI also displayed substantial CDI impact. Conclusions: The escalating burden of CDI in the elderly, especially in high-SDI areas and the Americas, emphasizes an urgent need for targeted public health strategies.