Early Hypophosphatemia as a Prognostic Marker in Acute Pancreatitis.

OBJECTIVES: Acute pancreatitis (AP) is a complex disease representing a significant portion of gastrointestinal-related hospitalizations in the U.S. Understanding risk factors of AP might provide attractive therapeutic targets. We evaluated hypophosphatemia as a risk factor for worse outcomes in AP. METHODS: We performed a retrospective review of electronic health records of patients with AP admissions from 01/01/2012-12/31/2021 at Cedars-Sinai Medical Center, evaluating patients with serum phosphate measured within 48 hours of admission. Multivariable logistic regression modeling was employed to evaluate associations with ICU admission, AP severity, and a multivariable log-linear model was employed to examine associations with length of stay (LOS). RESULTS: Of 1,526 patients admitted for AP, 33% (499) had a serum phosphate level measured within 48 hours. Patients with hypophosphatemia were more likely to have been admitted to the ICU (AOR = 4.57; 95% CI: 2.75-7.62; P < 0.001), have a longer hospital stay (log-LOS = 0.34; SE; 0.09; 95% CI: 0.17-0.52; P < 0.001), and were more likely to have moderate or severe AP (AOR = 1.80; 95% CI: 1.16-2.80; P < 0.001) compared to those without hypophosphatemia. CONCLUSION: Serum phosphate is infrequently measured in patients with AP and shows promise as a rapid, inexpensive, and early prognostic marker for worse outcomes of AP.

Stomach Cancer Prediction Model (SCoPM): An approach to risk stratification in a diverse U.S. population.

BACKGROUND AND AIMS: Population-based screening for gastric cancer (GC) in low prevalence nations is not recommended. The objective of this study was to develop a risk-prediction model to identify high-risk patients who could potentially benefit from targeted screening in a racial/ethnically diverse regional US population. METHODS: We performed a retrospective cohort study from Kaiser Permanente Southern California from January 2008-June 2018 among individuals age ≥50 years. Patients with prior GC or follow-up <30 days were excluded. Censoring occurred at GC, death, age 85 years, disenrollment, end of 5-year follow-up, or study conclusion. Cross-validated LASSO regression models were developed to identify the strongest of 20 candidate predictors (clinical, demographic, and laboratory parameters). Records from 12 of the medical service areas were used for training/initial validation while records from a separate medical service area were used for testing. RESULTS: 1,844,643 individuals formed the study cohort (1,555,392 training and validation, 289,251 testing). Mean age was 61.9 years with 53.3% female. GC incidence was 2.1 (95% CI 2.0-2.2) cases per 10,000 person-years (pyr). Higher incidence was seen with family history: 4.8/10,000 pyr, history of gastric ulcer: 5.3/10,000 pyr, H. pylori: 3.6/10,000 pyr and anemia: 5.3/10,000 pyr. The final model included age, gender, race/ethnicity, smoking, proton-pump inhibitor, family history of gastric cancer, history of gastric ulcer, H. pylori infection, and baseline hemoglobin. The means and standard deviations (SD) of c-index in validation and testing datasets were 0.75 (SD 0.03) and 0.76 (SD 0.02), respectively. CONCLUSIONS: This prediction model may serve as an aid for pre-endoscopic assessment of GC risk for identification of a high-risk population that could benefit from targeted screening.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study.

PURPOSE: Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging. METHODS: We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis). RESULTS: We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001). CONCLUSION: In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Association of endoscopic biopsy sampling methods with detection of precursor lesions of gastric cancer.

BACKGROUND AND AIMS: The yield of various endoscopic biopsy sampling methods for detection of precursor lesions of noncardia gastric cancer in a real-world setting remains unclear. Our objective was to evaluate the association of endoscopic biopsy sampling methods with detection of gastric intestinal metaplasia (GIM) and gastric dysplasia (GD). METHODS: We conducted a case-control study of adult patients who underwent EGD with biopsy sampling between 2010 and 2021 in a racially and ethnically diverse U.S. healthcare system. Cases were patients with histopathologic findings of GIM and/or GD. Control subjects were matched 1:1 by age, procedure date, and medical center. We compared the detection of GIM and GD using 4 different biopsy sampling methods: unspecified, specified stomach location, 2+2, and the Sydney protocol. Additionally, we assessed trends in use of sampling methods (Cochrane-Armitage) and identified patient and endoscopist factors associated with their use (logistic regression). RESULTS: We identified 20,938 GIM and 455 GD matched pairs. A greater proportion of GIM cases were detected using 2+2 (31.3% vs 25.3%, P < .0001) and the Sydney protocol (9.1% vs 1.0%, P < .0001) compared with control subjects. Similarly, a greater proportion of GD cases were detected using the Sydney protocol (15.6% vs .4%, P < .0001). We observed an increasing trend in the use of the Sydney protocol during the study period (3.8%-16.1% in cases, P < .0001; 1%-1.1% in control subjects, P = .005). Male and Asian American patients were more likely to undergo 2+2 or the Sydney protocol, whereas female and Hispanic endoscopists were more likely to perform sampling using these protocols. CONCLUSIONS: The application of the Sydney protocol is associated with an increased detection of precursor lesions of gastric cancer in routine clinical practice.

Association of Glycated Hemoglobin With a Risk of Pancreatic Cancer in High-Risk Individuals Based on Genetic and Family History.

INTRODUCTION: Screening for pancreatic cancer (PC) is suggested for high-risk individuals. Additional risk factors may enhance early detection in this population. METHODS: Retrospective cohort study among patients with germline variants and/or familial pancreatic cancer in an integrated healthcare system between 2003 and 2019. We calculated the incidence rate (IR) by risk category and performed a nested case-control study to evaluate the relationship between HbA1C and PC within 3 years before diagnosis (cases) or match date (controls). Cases were matched 1:4 by age, sex, and timing of HbA1c. Logistic regression was performed to assess an independent association with PC. RESULTS: We identified 5,931 high-risk individuals: 1,175(19.8%) familial PC, 45(0.8%) high-risk germline variants ( STK11, CDKN2A ), 4,097(69.1%) had other germline variants ( ATM, BRCA 1, BRCA 2, CASR, CDKN2A, CFTR, EPCAM, MLH1, MSH2, MSH6, PALB2, PRSS1, STK11, and TP53 ), and 614(10.4%) had both germline variants and family history. Sixty-eight patients (1.1%) developed PC; 50% were metastatic at diagnosis. High-risk variant was associated with greatest risk of PC, IR = 85.1(95% confidence interval: 36.7-197.6)/10,000 person-years; other germline variants and first-degree relative had IR = 33 (18.4, 59.3), whereas IR among ≥2 first-degree relative alone was 10.7 (6.1, 18.8). HbA1c was significantly higher among cases vs controls (median = 7.0% vs 6.4%, P = 0.02). In multivariable analysis, every 1% increase in HbA1c was associated with 36% increase in odds of PC (odds ratio 1.36, 95% confidence interval: 1.08-1.72). Pancreatitis was independently associated with a risk of PC (odds ratio 3.93, 95% confidence limit 1.19, 12.91). DISCUSSION: Risk of PC varies among high-risk individuals. HbA1c and history of pancreatitis may be useful additional markers for early detection in this patient population.

Lactated Ringers Use in the First 24 Hours of Hospitalization Is Associated With Improved Outcomes in 999 Patients With Acute Pancreatitis.

INTRODUCTION: Recent pilot trials in acute pancreatitis (AP) found that lactated ringers (LR) usage may result in decreased risk of moderately severe/severe AP compared with normal saline, but their small sample sizes limit statistical power. We investigated whether LR usage is associated with improved outcomes in AP in an international multicenter prospective study. METHODS: Patients directly admitted with the diagnosis of AP were prospectively enrolled at 22 international sites between 2015 and 2018. Demographics, fluid administration, and AP severity data were collected in a standardized prospective manner to examine the association between LR and AP severity outcomes. Mixed-effects logistic regression analysis was performed to determine the direction and magnitude of the relationship between the type of fluid administered during the first 24 hours and the development of moderately severe/severe AP. RESULTS: Data from 999 patients were analyzed (mean age 51 years, female 52%, moderately severe/severe AP 24%). Usage of LR during the first 24 hours was associated with reduced odds of moderately severe/severe AP (adjusted odds ratio 0.52; P = 0.014) compared with normal saline after adjusting for region of enrollment, etiology, body mass index, and fluid volume and accounting for the variation across centers. Similar results were observed in sensitivity analyses eliminating the effects of admission organ failure, etiology, and excessive total fluid volume. DISCUSSION: LR administration in the first 24 hours of hospitalization was associated with improved AP severity. A large-scale randomized clinical trial is needed to confirm these findings.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

Obesity and alcoholic etiology as risk factors for multisystem organ failure in acute pancreatitis: Multinational study.

BACKGROUND: Multisystem organ failure (MSOF) is the most important determinant of mortality in acute pancreatitis (AP). Obesity and alcoholic etiology have been examined as potential risk factors for MSOF, but prior studies have not adequately elucidated their independent effects on the risk of MSOF. OBJECTIVE: We aimed to determine the adjusted effects of body mass index (BMI) and alcoholic etiology on the risk of MSOF in subjects with AP. METHODS: A prospective observational study of 22 centers from 10 countries was conducted. Patients admitted to an APPRENTICE consortium center with AP between August 2015 and January 2018 were enrolled. Multivariable logistic regression was used to estimate the adjusted effects of BMI, etiology, and other relevant covariates on the risk of MSOF. Models were stratified by sex. RESULTS: Among 1544 AP subjects, there was a sex-dependent association between BMI and the risk of MSOF. Increasing BMI was associated with increased odds of MSOF in males (OR 1.10, 95% confidence interval [CI] 1.04-1.15) but not in females (OR 0.98, 95% CI 0.90-1.1). Male subjects with AP, whose BMIs were 30-34 and >35 kg/m2 , had odds ratios of 3.78 (95% CI 1.62-8.83) and 3.44 (95% CI 1.08-9.99), respectively. In females, neither higher grades of obesity nor increasing age increased the risk of MSOF. Alcoholic etiology was independently associated with increased odds of MSOF compared with non-alcohol etiologies (OR 4.17, 95% CI 2.16-8.05). CONCLUSION: Patients with alcoholic etiology and obese men (but not women) are at substantially increased risk of MSOF in AP.

Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach.

BACKGROUND: New-onset diabetes (NOD) has been suggested as an early indicator of pancreatic cancer. However, the definition of NOD by the American Diabetes Association requires 2 simultaneous or consecutive elevated glycemic measures. We aimed to apply a machine-learning approach using electronic health records to predict the risk in patients with recent-onset hyperglycemia. MATERIALS AND METHODS: In this retrospective cohort study, health plan enrollees 50 to 84 years of age who had an elevated (6.5%+) glycated hemoglobin (HbA1c) tested in January 2010 to September 2018 with recent-onset hyperglycemia were identified. A total of 102 potential predictors were extracted. Ten imputation datasets were generated to handle missing data. The random survival forests approach was used to develop and validate risk models. Performance was evaluated by c -index, calibration plot, sensitivity, specificity, and positive predictive value. RESULTS: The cohort consisted of 109,266 patients (mean age: 63.6 y). The 3-year incidence rate was 1.4 (95% confidence interval: 1.3-1.6)/1000 person-years of follow-up. The 3 models containing age, weight change in 1 year, HbA1c, and 1 of the 3 variables (HbA1c change in 1 y, HbA1c in the prior 6 mo, or HbA1c in the prior 18 mo) appeared most often out of the 50 training samples. The c -indexes were in the range of 0.81 to 0.82. The sensitivity, specificity, and positive predictive value in patients who had the top 20% of the predicted risks were 56% to 60%, 80%, and 2.5% to 2.6%, respectively. CONCLUSION: Targeting evaluation at the point of recent hyperglycemia based on elevated HbA1c could offer an opportunity to identify pancreatic cancer early and possibly impact survival in cancer patients.

Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis.

INTRODUCTION: Pancreatic cancer is the third leading cause of cancer deaths among men and women in the United States. We aimed to detect early changes on computed tomography (CT) images associated with pancreatic ductal adenocarcinoma (PDAC) based on quantitative imaging features (QIFs) for patients with and without chronic pancreatitis (CP). METHODS: Adults 18 years and older diagnosed with PDAC in 2008-2018 were identified. Their CT scans 3 months-3 years before the diagnosis date were matched to up to 2 scans of controls. The pancreas was automatically segmented using a previously developed algorithm. One hundred eleven QIFs were extracted. The data set was randomly split for training/validation. Neighborhood and principal component analyses were applied to select the most important features. A conditional support vector machine was used to develop prediction algorithms separately for patients with and without CP. The computer labels were compared with manually reviewed CT images 2-3 years before the index date in 19 cases and 19 controls. RESULTS: Two hundred twenty-seven of 554 scans of non-CP cancer cases/controls and 70 of 140 scans of CP cancer cases/controls were included (average age 71 and 68 years, 51% and 44% females for non-CP patients and patients with CP, respectively). The QIF-based algorithms varied based on CP status. For non-CP patients, accuracy measures were 94%-95% and area under the curve (AUC) measures were 0.98-0.99. Sensitivity, specificity, positive predictive value, and negative predictive value were in the ranges of 88%-91%, 96%-98%, 91%-95%, and 94%-96%, respectively. QIFs on CT examinations within 2-3 years before the index date also had very high predictive accuracy (accuracy 95%-98%; AUC 0.99-1.00). The QIF-based algorithm outperformed manual rereview of images for determination of PDAC risk. For patients with CP, the algorithms predicted PDAC perfectly (accuracy 100% and AUC 1.00). DISCUSSION: QIFs can accurately predict PDAC for both non-CP patients and patients with CP on CT imaging and represent promising biomarkers for early detection of pancreatic cancer.

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS: This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS: The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION: Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

Risk Prediction of Pancreatic Cancer in Patients With Abnormal Morphologic Findings Related to Chronic Pancreatitis: A Machine Learning Approach.

BACKGROUND AND AIMS: A significant factor contributing to poor survival in pancreatic cancer is the often late stage at diagnosis. We sought to develop and validate a risk prediction model to facilitate the distinction between chronic pancreatitis-related vs potential early pancreatic ductal adenocarcinoma (PDAC)-associated changes on pancreatic imaging. METHODS: In this retrospective cohort study, patients aged 18-84 years whose abdominal computed tomography/magnetic resonance imaging reports indicated duct dilatation, atrophy, calcification, cyst, or pseudocyst between January 2008 and November 2019 were identified. The outcome of interest is PDAC in 3 years. More than 100 potential predictors were extracted. Random survival forests approach was used to develop and validate risk models. Multivariable Cox proportional hazard model was applied to estimate the effect of the covariates on the risk of PDAC. RESULTS: The cohort consisted of 46,041 (mean age 66.4 years). The 3-year incidence rate was 4.0 (95% confidence interval CI 3.6-4.4)/1000 person-years of follow-up. The final models containing age, weight change, duct dilatation, and either alkaline phosphatase or total bilirubin had good discrimination and calibration (c-indices 0.81). Patients with pancreas duct dilatation and at least another morphological feature in the absence of calcification had the highest risk (adjusted hazard ratio [aHR] = 14.15, 95% CI 8.7-22.6), followed by patients with calcification and duct dilatation (aHR = 7.28, 95% CI 4.09-12.96), and patients with duct dilation only (aHR = 6.22, 95% CI 3.86-10.03), compared with patients with calcifications alone as the reference group. CONCLUSION: The study characterized the risk of pancreatic cancer among patients with 5 abnormal morphologic findings based on radiology reports and demonstrated the ability of prediction algorithms to provide improved risk stratification of pancreatic cancer in these patients.

Gastric Cancer Screening in First-Degree Relatives: A Pilot Study in a Diverse Integrated Healthcare System.

INTRODUCTION: Family history of gastric cancer has been shown as an independent risk factor of gastric cancer development and is associated with increased risk of progression to gastric cancer among patients with gastric intestinal metaplasia (GIM). METHODS: Between 2017 and 2020, we conducted a prospective pilot screening program of patients with a confirmed first-degree relative with gastric cancer to evaluate the feasibility of screening and prevalence of precursor lesions (e.g., GIM or dysplasia) on biopsy. RESULTS: A total of 61 patients completed screening by upper endoscopy with a mapping biopsy protocol: 27 (44%) were found to have GIM and 4 (7%) were found with low-grade dysplasia. DISCUSSION: Our pilot screening program identified a high prevalence of precursor lesions for gastric cancer among asymptomatic patients with a first-degree relative with gastric cancer. Careful endoscopic inspection and standardized biopsy protocols may aid in prompt identification of these precursor lesions in those at risk of gastric cancer.

Satisfaction With Telehealth Among Patients With Irritable Bowel Syndrome During COVID-19: Cross-Sectional Survey Findings.

INTRODUCTION: Coronavirus disease 2019 rapidly shifted health care toward telehealth. We assessed satisfaction with and preferences for telehealth among patients with irritable bowel syndrome (IBS). METHODS: We conducted a cross-sectional survey in an integrated healthcare system in Southern California with members aged 18-90 years with an International Classification of Diseases 9 and 10 codes for IBS from office-based encounters between June 1, 2018, and June 1, 2020. Eligible patients were emailed a survey assessing telehealth satisfaction overall and by patient-related factors, IBS characteristics, health and technologic literacy, utilization, and coronavirus disease 2019 perceptions. We identified perceived telehealth benefits and challenges. Multivariable logistic regression identified predictors of telehealth dissatisfaction. RESULTS: Of 44,789 surveys sent, 5,832 (13.0%) patients responded and 1,632 (3.6%) had Rome IV IBS. Among 1,314 (22.5%) patients with IBS and prior telehealth use (mean age 52.6 years [17.4]; 84.9% female; and 59.4% non-Hispanic White, 29.0% Hispanic, and 5.6% non-Hispanic Black), 898 (68.3%) were satisfied, 130 (9.9%) were dissatisfied, and 286 (21.8%) felt neutral. In addition, 78.6% would use telehealth again. Independent predictors of telehealth dissatisfaction include social media use of once a week or less (adjusted odds ratio [OR] = 2.1; 1.3-3.5), duration of IBS for <1 year (adjusted OR = 8.2; 1.9-35.8), and willingness to travel 60 plus minutes for face-to-face visits (adjusted OR = 2.6; 1.4-3.7). Patients' main concern with telehealth was a lack of physical examination. DISCUSSION: Most of the patients with IBS are satisfied with telehealth. Shorter duration of IBS diagnosis, comfort with technology, and increased willingness to travel were associated with telehealth dissatisfaction. These predictors may help identify a target population for a focused IBS-telehealth program.

Imaging of the Pancreas in New-Onset Diabetes: A Prospective Pilot Study.

INTRODUCTION: The aim of this study was to assess the feasibility of cross-sectional imaging for detection of pancreatic cancer (PDAC) in patients with new-onset hyperglycemia and diabetes (NOD). METHODS: We conducted a prospective pilot study from November 2018 to March 2020 within an integrated health system. Patients aged 50-85 years with newly elevated glycemic parameters without a history of diabetes were invited to complete a 3-phase contrast-enhanced computed tomography pancreas protocol scan while participating in the Prospective Study to Establish a NOD Cohort. Abnormal pancreatic findings, incidental extrapancreatic findings, and subsequent clinical evaluation were identified. Variability in clinical reporting between medical centers based on descriptors of pancreatic duct and parenchyma was assessed. RESULTS: A total of 130 of 147 participants (88.4%) consented to imaging; 93 scans were completed (before COVID-19 stay-at-home order). The median age was 62.4 years (interquartile range 56.3-68.8), 37.6% women; Hispanic (39.8%), White (29.0%), Black (14.0%), and Asian (13.3%). One (1.1%) case of PDAC (stage IV) was diagnosed, 12 of 93 participants (12.9%) had additional pancreatic findings: 5 fatty infiltration, 3 cysts, 2 atrophy, 1 divisum, and 1 calcification. There were 57 extrapancreatic findings among 52 of 93 (56%) unique patients; 12 of 57 (21.1%) prompted clinical evaluation with 2 additional malignancies diagnosed (nonsmall cell lung and renal oncocytoma). Reports from 1 participating medical center more frequently provided description of pancreatic parenchyma and ducts (92.9% vs 18.4%), P < 0.0001. DISCUSSION: High proportion of incidental findings and variability in clinical reports are challenges to be addressed for a successful NOD-based early detection strategy for PDAC.

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

The Modified Pancreatitis Activity Scoring System Shows Distinct Trajectories in Acute Pancreatitis: An International Study.

BACKGROUND & AIMS: The aims of this study were to: (1) assess the performance of the Pancreatitis Activity Scoring System (PASS) in a large intercontinental cohort of patients with acute pancreatitis (AP); and (2) investigate whether a modified PASS (mPASS) yields a similar predictive accuracy and produces distinct early trajectories between severity subgroups. METHODS: Data was prospectively collected through the Acute Pancreatitis Patient Registry to Examine Novel Therapies In Clinical Experience (APPRENTICE) consortium (2015-2018) involving 22 centers from 4 continents. AP severity was categorized per the revised Atlanta classification. PASS trajectories were compared between the three severity groups using the generalized estimating equations model. Four mPASS models were generated by modifying the morphine equivalent dose (MED), and their trajectories were compared. RESULTS: A total of 1393 subjects were enrolled (median age, 49 years; 51% males). The study cohort included 950 mild (68.2%), 315 (22.6%) moderately severe, and 128 (9.2%) severe AP. Mild cases had the lowest PASS at each study time point (all P < .001). A subset of patients with outlier admission PASS values was identified. In the outlier group, 70% of the PASS variation was attributed to the MED, and 66% of these patients were from the United States centers. Among the 4 modified models, the mPASS-1 (excluding MED from PASS) demonstrated high performance in predicting severe AP with an area under the receiver operating characteristic curve of 0.88 (vs area under the receiver operating characteristic of 0.83 in conventional PASS) and produced distinct trajectories with distinct slopes between severity subgroups (all P < .001). CONCLUSION: We propose a modified model by removing the MED component, which is easier to calculate, predicts accurately severe AP, and maintains significantly distinct early trajectories.

Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.