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.

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.

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.

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.

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.

Diabetes and pancreatic cancer: recent insights with implications for early diagnosis, treatment and prevention.

PURPOSE OF REVIEW: Recent insights into the complex relationship between diabetes and pancreatic cancer have the potential to help direct future approaches to early detection, treatment and prevention. RECENT FINDINGS: Insulin resistance and hyperinsulinemia have been identified as factors that relate to risk of pancreatic cancer among patients with long-standing diabetes. In contrast, weight loss in the setting of new-onset diabetes can help identify patients at an increased risk for harbouring pancreatic-cancer related disturbances in glucose metabolism. Insights into the implications of poor glycaemic control in patients undergoing resection for pancreatic cancer have the potential to improve both surgical and oncologic outcomes. Finally, among antidiabetic medications, metformin continues to be evaluated as a potential adjunctive therapeutic agent, although recent evidence supports the safety of incretins with respect to pancreatic cancer. SUMMARY: This review highlights recent developments in these areas with an emphasis on opportunities for improved early diagnosis, treatment and prevention in pancreatic cancer.

Validation of the Enriching New-Onset Diabetes for Pancreatic Cancer Model in a Diverse and Integrated Healthcare Setting.

BACKGROUND: The risk of pancreatic cancer is elevated among people with new-onset diabetes (NOD). Based on Rochester Epidemiology Project Data, the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model was developed and validated. AIMS: We validated the END-PAC model in a cohort of patients with NOD using retrospectively collected data from a large integrated health maintenance organization. METHODS: A retrospective cohort of patients between 50 and 84 years of age meeting the criteria for NOD in 2010-2014 was identified. Each patient was assigned a risk score (< 1: low risk; 1-2: intermediate risk; ≥ 3: high risk) based on the values of the predictors specified in the END-PAC model. Patients who developed pancreatic ductal adenocarcinoma (PDAC) within 3 years were identified using the Cancer Registry and California State Death files. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: Out of the 13,947 NOD patients who were assigned a risk score, 99 developed PDAC in 3 years (0.7%). Of the 3038 patients who had a high risk, 62 (2.0%) developed PDAC in 3 years. The risk increased to 3.0% in white patients with a high risk. The AUC was 0.75. At the 3+ threshold, the sensitivity, specificity, PPV, and NPV were 62.6%, 78.5%, 2.0%, and 99.7%, respectively. CONCLUSIONS: It is critical that prediction models are validated before they are implemented in various populations and clinical settings. More efforts are needed to develop screening strategies most appropriate for patients with NOD in real-world settings.

Incidence and risk factors of oral feeding intolerance in acute pancreatitis: Results from an international, multicenter, prospective cohort study.

BACKGROUND: Inability to advance to an oral diet, or oral feeding intolerance, is a common complication in patients with acute pancreatitis associated with worse clinical outcomes. The factors related to oral feeding intolerance are not well studied. OBJECTIVE: We aimed to determine the incidence and risk factors of oral feeding intolerance in acute pancreatitis. METHODS: Patients were prospectively enrolled in the Acute Pancreatitis Patient Registry to Examine Novel Therapies in Clinical Experience, an international acute pancreatitis registry, between 2015 and 2018. Oral feeding intolerance was defined as worsening abdominal pain and/or vomiting after resumption of oral diet. The timing of the initial feeding attempt was stratified based on the day of hospitalization. Multivariable logistic regression was performed to assess for independent risk factors/predictors of oral feeding intolerance. RESULTS: Of 1233 acute pancreatitis patients included in the study, 160 (13%) experienced oral feeding intolerance. The incidence of oral feeding intolerance was similar irrespective of the timing of the initial feeding attempt relative to hospital admission day (p = 0.41). Patients with oral feeding intolerance were more likely to be younger (45 vs. 50 years of age), men (61% vs. 49%), and active alcohol users (44% vs. 36%). They also had higher blood urea nitrogen (20 vs. 15 mg/dl; p < 0.001) and hematocrit levels (41.7% vs. 40.5%; p = 0.017) on admission; were more likely to have a nonbiliary acute pancreatitis etiology (69% vs. 51%), systemic inflammatory response syndrome of 2 or greater on admission (49% vs. 35%) and at 48 h (50% vs. 26%), develop pancreatic necrosis (29% vs. 13%), moderate to severe acute pancreatitis (41% vs. 24%), and have a longer hospital stay (10 vs. 6 days; all p < 0.04). The adjusted analysis showed that systemic inflammatory response syndrome of 2 or greater at 48 h (odds ratio 3.10; 95% confidence interval 1.83-5.25) and a nonbiliary acute pancreatitis etiology (odds ratio 1.65; 95% confidence interval 1.01-2.69) were independent risk factors for oral feeding intolerance. CONCLUSION: Oral feeding intolerance occurs in 13% of acute pancreatitis patients and is independently associated with systemic inflammatory response syndrome at 48 h and a nonbiliary etiology.

Characterization of patients with advanced chronic pancreatitis using natural language processing of radiology reports.

STUDY AIM: To develop and apply a natural language processing algorithm for characterization of patients diagnosed with chronic pancreatitis in a diverse integrated U.S. healthcare system. METHODS: Retrospective cohort study including patients initially diagnosed with chronic pancreatitis (CP) within a regional integrated healthcare system between January 1, 2006 and December 31, 2015. Imaging reports from these patients were extracted from the electronic medical record system and split into training, validation and implementation datasets. A natural language processing (NLP) algorithm was first developed through the training dataset to identify specific features (atrophy, calcification, pseudocyst, cyst and main duct dilatation) from free-text radiology reports. The validation dataset was applied to validate the performance by comparing against the manual chart review. The developed algorithm was then applied to the implementation dataset. We classified patients with calcification(s) or ≥2 radiographic features as advanced CP. We compared etiology, comorbid conditions, treatment parameters as well as survival between advanced CP and others diagnosed during the study period. RESULTS: 6,346 patients were diagnosed with CP during the study period with 58,085 radiology studies performed. For individual features, NLP yielded sensitivity from 88.7% to 95.3%, specificity from 98.2% to 100.0%. A total of 3,672 patients met cohort inclusion criteria: 1,330 (36.2%) had evidence of advanced CP. Patients with advanced CP had increased frequency of smoking (57.8% vs. 43.0%), diabetes (47.6% vs. 35.9%) and underweight body mass index (6.6% vs. 3.6%), all p<0.001. Mortality from pancreatic cancer was higher in advanced CP (15.3/1,000 person-year vs. 2.8/1,000, p<0.001). Underweight BMI (HR 1.6, 95% CL 1.2, 2.1), smoking (HR 1.4, 95% CL 1.1, 1.7) and diabetes (HR 1.4, 95% CL 1.2, 1.6) were independent risk factors for mortality. CONCLUSION: Patients with advanced CP experienced increased disease-related complications and pancreatic cancer-related mortality. Excess all-cause mortality was driven primarily by potentially modifiable risk factors including malnutrition, smoking and diabetes.

Endoscopic History and Provider Characteristics Influence Gastric Cancer Survival in Asian Americans.

Gastric carcinoma (GC) disproportionately affects Asian Americans. We examined whether history of upper gastrointestinal (GI) endoscopy was associated with lower stage at GC diagnosis among Asian Americans and whether origin of providers influenced referral for endoscopy. We employed Surveillance Epidemiology and End Results-Medicare data on Asian Americans diagnosed with GC in 2004-2013 (n = 1,554). Stage distribution, GI conditions at diagnosis, and history of endoscopy were compared between Asian ethnic groups. Multivariate logistic regression adjusting for age, sex, poverty level, tumor location, and histology was used to examine the association of ethnicity and endoscopic history with stage I disease at diagnosis of GC. Koreans were more likely to be diagnosed with stage I, T1a GC and have prior history of endoscopy, compared with other Asian ethnicities (24% vs. 8% for stage I, T1a; 40% vs. 15% for endoscopy). Patients with primary care providers of concordant ethnic origin were more likely to have history of endoscopy. Asian American patients with GC with history of endoscopy were more likely to be diagnosed with GC at stage I disease (adjusted OR, 3.07; 95% confidence interval, 2.34-4.02). Compared with other Asian Americans, Koreans were diagnosed with GC at earlier stages owing to common history of endoscopy, which was more often undergone by patients with primary care providers of concordant ethnic origin. Overall, upper GI endoscopy was associated with early detection of GC in Asian Americans. Novelty and Impact. It is well-established that Asian Americans in the United States are disproportionately affected by gastric cancer. In our study we found that Asian American patients treated by physicians of similar ethnic background are more likely to undergo upper GI endoscopy in the United States, leading to early detection of gastric cancer and longer survival. Given this, targeted endoscopic screening in Asian Americans should be considered for early detection of GC.

Association of Glycated Hemoglobin Levels With Risk of Pancreatic Cancer.

Importance: New-onset diabetes after the age of 50 years is a potential indicator of pancreatic cancer. Understanding the associations between hyperglycemia, diabetes, and pancreatic cancer, including pancreatic ductal adenocarcinoma, is key to developing an approach to early detection. Objective: To assess the association of elevation in glycated hemoglobin (HbA1c) with the risk of pancreatic cancer. Design, Setting, and Participants: This cohort study was conducted using data collected from an integrated health care system in California. A total of 851 402 patients aged 50 to 84 years who had HbA1c measurements taken between 2010 and 2014 were identified as the base cohort, with 12 contemporaneous cohorts created based on varying HbA1c thresholds (ie, 6.1%, 6.3%, 6.5%, and 6.7%) and prior diabetes status. Data analysis was conducted from August 2018 to September 2019. Main Outcomes and Measures: New cases of pancreatic cancer identified through cancer registry and California death files during a 3-year period. Three-year risk, incidence rate, sensitivity, number of patients needed to screen to detect 1 case, timing, and stage at diagnosis were determined. Results: Among 851 402 patients in the base cohort, 447 502 (52.5%) were women, 255 441 (30.0%) were Hispanic participants, 383 685 (45.1%) were non-Hispanic white participants, 100 477 (11.8%) were Asian participants, and 88 969 (10.4%) were non-Hispanic black participants, with a median (interquartile range) age of 62 (56-69) years and a median (interquartile range) HbA1c level of 6.0% (5.7%-6.6%). The incidence rate of pancreatic cancer was 0.45 (95% CI, 0.43-0.49) per 1000 person-years. After excluding prior diabetes as well as confirmation of new-onset hyperglycemia based on an HbA1c level of 6.5%, a total of 20 012 patients remained, with 74 of 1041 pancreatic ductal adenocarcinoma cases (7.1%) from the base cohort included. The rate of pancreatic cancer was 0.72 (95% CI, 0.32-1.42) per 1000 person-years among Asian patients, 0.83 (95% CI, 0.35-1.71) per 1000 person-years among non-Hispanic black patients, 0.84 (95% CI, 0.48-1.37) per 1000 person-years among Hispanic patients, and 2.37 (95% CI, 1.75-3.14) per 1000 person-years among non-Hispanic white patients. Overall, 42 of 74 cancers (56.8%) were diagnosed within 1 year of the index laboratory test. Among 1041 total cases, 708 (68.0%) had staging information available, of whom 465 (65.7%) had stage III or IV disease at diagnosis. In the base cohort, the number needed to undergo evaluation to identify a single case of pancreatic ductal adenocarcinoma was 818 (95% CI, 770-869), with estimates ranging from 206 (95% CI, 160-264) to 600 (95% CI, 540-666) in the subcohorts. Conclusions and Relevance: The findings of this study suggest that screening patients for pancreatic cancer based solely on elevation in HbA1c level is unlikely to represent an effective strategy. Future efforts to identify a high-risk population based on changes in glycemic parameters should account for racial/ethnic differences.

Identification of Individuals at Increased Risk for Pancreatic Cancer in a Community-Based Cohort of Patients With Suspected Chronic Pancreatitis.

OBJECTIVES: We lack reliable methods for identifying patients with chronic pancreatitis (CP) at increased risk for pancreatic cancer. We aimed to identify radiographic parameters associated with pancreatic cancer in this population. METHODS: We conducted a retrospective cohort study of patients with suspected CP within an integrated healthcare system in Southern California in 2006-2015. Patients were identified by a diagnostic code and confirmed by imaging findings (parenchymal calcification, ductal stones, glandular atrophy, pseudocyst, main duct dilatation, duct irregularity, abnormal side branch, or stricture) defined by the natural language processing of radiographic reports. We used Cox regression to determine the relationship of smoking, alcohol use, acute pancreatitis, diabetes, body mass index, and imaging features with the risk of incident pancreatic cancer at least 1 year after abnormal pancreas imaging. RESULTS: We identified 1,766 patients with a diagnostic code and an imaging feature for CP with a median follow-up of 4.5 years. There were 46 incident pancreatic cancer cases. Factors that predicted incident pancreatic cancer after 1-year of follow-up included obesity (hazard ratio 2.7, 95% confidence interval: 1.2-6.1) and duct dilatation (hazard ratio 10.5, 95% confidence limit: 4.0-27). Five-year incidence of pancreatic cancer in this population with duct dilatation was 6.3%. DISCUSSION: High incidence of pancreatic cancer in suspected patients with CP with pancreatic duct dilatation warrants regular surveillance for pancreatic cancer.