Evaluation of Chronic Pancreatitis Prognosis Score in an American Cohort.

INTRODUCTION: Chronic Pancreatitis Prognosis Score (COPPS) was developed to discriminate disease severity and predict risk for future hospitalizations. In this cohort study, we evaluated if COPPS predicts the likelihood of hospitalization(s) in an American cohort. METHODS: The CPDPC consortium provided data and serum from subjects with chronic pancreatitis (N=279). COPPS was calculated with baseline data and stratified by severity (low, moderate, high). Primary endpoints included number and duration of hospitalizations during 12-month follow-up. RESULTS: The mean±SD COPPS was 8.4±1.6. COPPS correlated with all primary outcomes: hospitalizations for any reason (number: r=0.15, p=0.01; duration: r=0.16, p=0.01) and pancreas-related hospitalizations (number: r=0.15, p=0.02; duration: r=0.13, p=0.04). The severity distribution was 13.3% low, 66.0% moderate, and 20.8% high. 37.6% of subjects had ≥1 hospitalization(s) for any reason; 32.2% had ≥1 pancreas-related hospitalization(s). All primary outcomes were significantly different between severity groups: hospitalizations for any reason (number, p=0.004; duration, p=0.007) and pancreas-related hospitalizations (number, p=0.02; duration, p=0.04). The prevalence of continued drinking at follow-up (p=0.04) was higher in the low and moderate groups. The prevalence of anxiety at enrollment (p=0.02) and follow-up (p<0.05) was higher in the moderate and high groups. DISCUSSION: Statistically, COPPS significantly correlated with hospitalization outcomes, but the correlations were weaker than in previous studies, which may be related to the outpatient nature of the PROCEED cohort and lower prevalence of high severity disease. Studies in other prospective cohorts are needed to understand the full utility of COPPS as a potential tool for clinical risk assessment and intervention.

Deep learning model using stool pictures for predicting endoscopic mucosal inflammation in patients with ulcerative colitis.

OBJECTIVES: Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photos of patients with UC (DLSUC) to predict endoscopic mucosal inflammation. METHODS: This was a prospective multicenter study conducted in six tertiary referral hospitals. Patients scheduled to undergo endoscopy for mucosal inflammation monitoring were asked to take photos of their stool using smartphones within 1 week before the day of endoscopy. DLSUC was developed using 2161 stool pictures from 306 patients and tested on 1047 stool images from 126 patients. The ulcerative colitis endoscopic index of severity (UCEIS) was used to define endoscopic activity. The performance of DLSUC in endoscopic activity prediction was compared with that of fecal calprotectin (Fcal). RESULTS: The area under the receiver operating characteristic curve (AUC) of DLSUC for predicting endoscopic activity was 0.801 (95% confidence interval [CI] 0.717-0.873), which was not statistically different from the AUC of Fcal (0.837 [95% CI, 0.767-0.899, DeLong's P=0.458]). When rectal sparing cases (23/126, 18.2%) were excluded, the AUC of DLSUC increased to 0.849 (95% CI, 0.760-0.919). The accuracy, sensitivity, and specificity of DLSUC in predicting endoscopic activity were 0.746, 0.662, and 0.877 in all patients and 0.845, 0.745, and 0.958 in patients without rectal sparing, respectively. Active patients classified by DLSUC were more likely to experience disease relapse during a median 8-month follow-up (log-rank test, P=0.002). CONCLUSIONS: DLSUC demonstrated a good discriminating power similar to that of Fcal in predicting endoscopic activity with improved accuracy in patients without rectal sparing. This study implies that stool photos are a useful monitoring tool for typical UC.

Fetal abdominal obesity in women with one value abnormality on diagnostic test for gestational diabetes mellitus.

Previous studies have shown that fetal abdominal obesity (FAO) was already observed at the time of gestational diabetes mellitus (GDM) diagnosis and persisted until delivery despite management in older and/or obese women. In this study, we investigated whether fetuses of women with milder hyperglycemia than GDM have accelerated abdominal growth, leading to adverse pregnancy outcomes. We retrospectively reviewed the medical records of 7,569 singleton pregnant women who were universally screened using a 50-g glucose challenge test (GCT) and underwent a 3-h 100-g oral glucose tolerance test (OGTT) if GCT result was ≥140mg/dL. GDM, one value abnormality (OVA), and normal glucose tolerance (NGT, NGT1: GCT negative, NGT2: GCT positive & OGTT negative) were diagnosed using Carpenter-Coustan criteria. With fetal biometry data measured simultaneously with 50-g GCT, relative fetal abdominal overgrowth was investigated by assessing the fetal abdominal overgrowth ratios (FAORs) of the ultrasonographically estimated gestational age (GA) of abdominal circumference(AC) per actual GA by the last menstruation period(LMP), biparietal diameter(BPD) or femur length(FL), respectively. FAO was defined as FAOR ≥90th percentile The FAORs of GA-AC/GA-LMP and GA-AC/GA-BPD were significantly higher in OVA subjects compared to NGT subjects but not in NGT2 subjects. Although the frequency of FAO in OVA (12.1%) was between that of NGT (9.6%) and GDM (18.3%) without statistically significant difference, the prevalence of large for gestational age at birth and primary cesarean delivery rates were significantly higher in OVA (9.8% and 29.7%) than in NGT (5.1% and 21.5%, p<0.05). Particularly, among OVA subjects with FAO, the prevalence (33.3% and 66.7%) was significantly higher than in those without FAO (9.7% and 24.2%, p<0.05). The degree of fetal abdominal growth acceleration in OVA subjects was intermediate between that of NGT and GDM subjects. OVA subjects with FAO at the time of GDM diagnosis were strongly associated with adverse pregnancy outcomes.

A Machine Learning-Based Diagnostic Model for Crohn's Disease and Ulcerative Colitis Utilizing Fecal Microbiome Analysis.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn's disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.