Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach.

BACKGROUND: Pancreatic cancer is the third leading cause of cancer deaths in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for up to 90% of all cases. Patient-reported symptoms are often the triggers of cancer diagnosis and therefore, understanding the PDAC-associated symptoms and the timing of symptom onset could facilitate early detection of PDAC. OBJECTIVE: This paper aims to develop a natural language processing (NLP) algorithm to capture symptoms associated with PDAC from clinical notes within a large integrated health care system. METHODS: We used unstructured data within 2 years prior to PDAC diagnosis between 2010 and 2019 and among matched patients without PDAC to identify 17 PDAC-related symptoms. Related terms and phrases were first compiled from publicly available resources and then recursively reviewed and enriched with input from clinicians and chart review. A computerized NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review followed by adjudication. Finally, the developed algorithm was applied to the validation data set to assess performance and to the study implementation notes. RESULTS: A total of 408,147 and 709,789 notes were retrieved from 2611 patients with PDAC and 10,085 matched patients without PDAC, respectively. In descending order, the symptom distribution of the study implementation notes ranged from 4.98% for abdominal or epigastric pain to 0.05% for upper extremity deep vein thrombosis in the PDAC group, and from 1.75% for back pain to 0.01% for pale stool in the non-PDAC group. Validation of the NLP algorithm against adjudicated chart review results of 1000 notes showed that precision ranged from 98.9% (jaundice) to 84% (upper extremity deep vein thrombosis), recall ranged from 98.1% (weight loss) to 82.8% (epigastric bloating), and F1-scores ranged from 0.97 (jaundice) to 0.86 (depression). CONCLUSIONS: The developed and validated NLP algorithm could be used for the early detection of PDAC.

Risk Factors for Acute Asthma Exacerbations in Adults With Mild Asthma.

BACKGROUND: Although individuals with mild asthma account for 30% to 40% of acute asthma exacerbations (AAEs), relatively little attention has been paid to risk factors for AAEs in this population. OBJECTIVE: To identify risk factors associated with AAEs in patients with mild asthma. METHODS: This was a retrospective cohort study. We used administrative data from a large managed care organization to identify 199,010 adults aged 18 to 85 years who met study criteria for mild asthma between 2013 and 2018. An asthma-coded qualifying visit (index visit) was identified for each patient. We then used information at the index visit or from the year before the index visit to measure potential risk factors for AAEs in the subsequent year. An AAE was defined as either an asthma-coded hospitalization or emergency department visit, or an asthma-related systemic corticosteroid administration (intramuscular or intravenous) or oral corticosteroid dispensing. Poisson regression models with robust SEs were used to estimate the adjusted risk ratios for future AAEs. RESULTS: In the study cohort, mean age was 44 years and 64% were female; 6.5% had AAEs within 1 year after the index visit. In multivariate models, age, sex, race, ethnicity, smoking status, body mass index, prior acute asthma care, and a variety of comorbidities and other clinical characteristics were significant predictors for future AAE risk. CONCLUSION: Population-based disease management strategies for asthma should be expanded to include people with mild asthma in addition to those with moderate to severe disease.

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 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.

Household Food Insecurity Is Associated With Physical Activity in Youth and Young Adults With Diabetes: A Cross-Sectional Study.

BACKGROUND: Physical activity (PA) is essential for optimal diabetes management. Household food insecurity (HFI) may negatively affect diabetes management behaviors. The purpose of this study was to cross-sectionally examine the association between HFI and PA in youth and young adults (YYA) with type 1 (N = 1998) and type 2 (N = 391) diabetes from the SEARCH for Diabetes in Youth Study. METHODS: HFI was measured with the US Household Food Security Survey Module. PA was measured with the International Physical Activity Questionnaire Short Form. Walking, moderate-intensity PA (excluding walking), vigorous-intensity PA, moderate- to vigorous-intensity PA, and total PA were estimated as minutes per week, while time spent sitting was assessed in minutes per day. All were modeled with median regression. Meeting PA guidelines or not was modeled using logistic regression. RESULTS: YYA with type 1 diabetes who experienced HFI spent more time walking than those who were food secure. YYA with type 2 diabetes who experienced HFI spent more time sitting than those who were food secure. CONCLUSIONS: Future research should examine walking for leisure versus other domains of walking in relation to HFI and use objective PA measures to corroborate associations between HFI and PA in YYA with diabetes.

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.

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.

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.

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.

Clinical and Imaging Predictors of Pancreatic Cancer in Patients Hospitalized for Acute Pancreatitis.

Background and Aims: Identifying factors associated with increased short-term risk of pancreatic cancer in the setting of acute pancreatitis (AP) can inform clinical care decisions and expedite cancer diagnosis. Methods: A retrospective cohort study of patients hospitalized for AP between 2007 and 2017 in an integrated health-care system in Southern California. AP cases were identified by diagnosis code with laboratory confirmation. Multivariable Cox proportional hazards regression model was used to assess risk of pancreatic cancer within 3 years of AP, adjusting for patient demographics, clinical parameters (body mass index, AP etiology, chronic pancreatitis, diabetes) and radiographic imaging features. Results: Among 9,490 patients hospitalized with AP, the mean (standard deviation) age was 55.8 (17.8) years, 55% were women, and 42% were Hispanic. Majority of AP cases were biliary (64%), 12% were alcohol-related, 5% were hypertriglyceridemia-induced, and 19% were other/unknown etiology. Ninety-five (1%) patients were diagnosed with pancreatic cancer within 3 years of AP (4.2 cases/1000 person-years). Risk factors for pancreatic cancer were age ≥65 years (hazard risk [HR]: 2.5, 95% confidence interval [CI]: 1.2-5.3), male sex (HR: 1.9, 95% CI: 1.2-2.8), Asian/Pacific Islander race (HR: 2.0, 95% CI: 1.1-3.6), and underweight body mass index (HR: 2.6, 95% CI: 1.1-6.5). In addition, other/unknown AP etiology (HR: 2.0, 95% CI: 1.3-3.1) and dilatation of the main pancreatic duct (HR: 6.6, 95% CI: 4.2-10.5) were independently associated with increased risk of pancreatic cancer. Conclusion: In addition to older age, the lack of well-established etiology, underweight body habitus, and main pancreatic duct dilatation were independently associated with increased short-term risk of pancreatic cancer among patients hospitalized for AP.

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.

Combination Treatment with Sodium Nitrite and Isoquercetin on Endothelial Dysfunction among Patients with CKD: A Randomized Phase 2 Pilot Trial.

BACKGROUND AND OBJECTIVES: Endothelial dysfunction is common among patients with CKD. We tested the efficacy and safety of combination treatment with sodium nitrite and isoquercetin on biomarkers of endothelial dysfunction in patients with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This randomized, double-blind, placebo-controlled phase 2 pilot trial enrolled 70 patients with predialysis CKD. Thirty-five were randomly assigned to combination treatment with sodium nitrite (40 mg twice daily) and isoquercetin (225 mg once daily) for 12 weeks, and 35 were randomly assigned to placebo. The primary outcome was mean change in flow-mediated vasodilation over the 12-week intervention. Secondary and safety outcomes included biomarkers of endothelial dysfunction, inflammation, and oxidative stress as well as kidney function, methemoglobin, and adverse events. Intention-to-treat analysis was conducted. RESULTS: Baseline characteristics, including age, sex, race, cigarette smoking, history of hypertension and diabetes, use of renin-angiotensin system blockers, BP, fasting glucose, lipid profile, kidney function, urine albumin-creatinine ratio, and endothelial biomarkers, were comparable between groups. Over the 12-week intervention, flow-mediated vasodilation increased 1.1% (95% confidence interval, -0.1 to 2.3) in the treatment group and 0.3% (95% confidence interval, -0.9 to 1.5) in the placebo group, and net change was 0.8% (95% confidence interval, -0.9 to 2.5). In addition, changes in biomarkers of endothelial dysfunction (vascular adhesion molecule-1, intercellular adhesion molecule-1, E-selectin, vWf, endostatin, and asymmetric dimethylarginine), inflammation (TNF-α, IL-6, C-reactive protein, IL-1 receptor antagonist, and monocyte chemoattractant protein-1), and oxidative stress (oxidized LDL and nitrotyrosines) were not significantly different between the two groups. Furthermore, changes in eGFR, urine albumin-creatinine ratio, methemoglobin, and adverse events were not significantly different between groups. CONCLUSIONS: This randomized phase 2 pilot trial suggests that combination treatment with sodium nitrite and isoquercetin did not significantly improve flow-mediated vasodilation or other endothelial function biomarkers but also did not increase adverse events compared with placebo among patients with CKD. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: Nitrite, Isoquercetin, and Endothelial Dysfunction (NICE), NCT02552888.

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.

Coronary Artery Calcification and Risk of Cardiovascular Disease and Death Among Patients With Chronic Kidney Disease.

Importance: Coronary artery calcification (CAC) is highly prevalent in dialysis-naive patients with chronic kidney disease (CKD). However, there are sparse data on the association of CAC with subsequent risk of cardiovascular disease and all-cause mortality in this population. Objective: To study the prospective association of CAC with risk of cardiovascular disease and all-cause mortality among dialysis-naive patients with CKD. Design, Setting, and Participants: The prospective Chronic Renal Insufficiency Cohort study recruited adults with an estimated glomerular filtration rate of 20 to 70 mL/min/1.73 m2 from 7 clinical centers in the United States. There were 1541 participants without cardiovascular disease at baseline who had CAC scores. Exposures: Coronary artery calcification was assessed using electron-beam or multidetector computed tomography. Main Outcomes and Measures: Incidence of cardiovascular disease (including myocardial infarction, heart failure, and stroke) and all-cause mortality were reported every 6 months and confirmed by medical record adjudication. Results: During an average follow-up of 5.9 years in 1541 participants aged 21 to 74 years, there were 188 cardiovascular disease events (60 cases of myocardial infarction, 120 heart failures, and 27 strokes; patients may have had >1 event) and 137 all-cause deaths. In Cox proportional hazards models adjusted for age, sex, race, clinical site, education level, physical activity, total cholesterol level, high-density lipoprotein cholesterol level, systolic blood pressure, use of antihypertensive treatment, current cigarette smoking, diabetes status, body mass index, C-reactive protein level, hemoglobin A1c level, phosphorus level, troponin T level, log N-terminal pro-B-type natriuretic peptide level, fibroblast growth factor 23 level, estimated glomerular filtration rate, and proteinuria, the hazard ratios associated with per 1 SD log of CAC were 1.40 (95% CI, 1.16-1.69; P < .001) for cardiovascular disease, 1.44 (95% CI, 1.02-2.02; P = .04) for myocardial infarction, 1.39 (95% CI, 1.10-1.76; P = .006) for heart failure, and 1.19 (95% CI, 0.94-1.51; P = .15) for all-cause mortality. In addition, inclusion of CAC score led to an increase in the C statistic of 0.02 (95% CI, 0-0.09; P < .001) for predicting cardiovascular disease over use of all the above-mentioned established and novel cardiovascular disease risk factors. Conclusions and Relevance: Coronary artery calcification is independently and significantly related to the risks of cardiovascular disease, myocardial infarction, and heart failure in patients with CKD. In addition, CAC improves risk prediction for cardiovascular disease, myocardial infarction, and heart failure over use of established and novel cardiovascular disease risk factors among patients with CKD; however, the changes in the C statistic are small.

Loss of executive function after dialysis initiation in adults with chronic kidney disease.

The association of dialysis initiation with changes in cognitive function among patients with advanced chronic kidney disease is poorly described. To better define this, we enrolled participants with advanced chronic kidney disease from the Chronic Renal Insufficiency Cohort in a prospective study of cognitive function. Eligible participants had a glomerular filtration rate of 20 ml/min/1.73m2 or less, or dialysis initiation within the past two years. We evaluated cognitive function by a validated telephone battery at regular intervals over two years and analyzed test scores as z scores. Of 212 participants, 123 did not transition to dialysis during follow-up, 37 transitioned to dialysis after baseline, and 52 transitioned to dialysis prior to baseline. In adjusted analyses, the transition to dialysis was associated with a significant loss of executive function, but no significant changes in global cognition or memory. The estimated net difference in cognitive z scores at two years for participants who transitioned to dialysis during follow-up compared to participants who did not transition to dialysis was -0.01 (95% confidence interval -0.13, 0.11) for global cognition, -0.24 (-0.51, 0.03) for memory, and -0.33 (-0.60, -0.07) for executive function. Thus, among adults with advanced chronic kidney disease, dialysis initiation was associated with loss of executive function with no change in other aspects of cognition. Larger studies are needed to evaluate cognition during dialysis initiation.

The Associations between Peripheral Artery Disease and Physical Outcome Measures in Men and Women with Chronic Kidney Disease.

BACKGROUND: Peripheral artery disease (PAD) is highly prevalent and associated with significant morbidity and mortality, but sex-based differences are incompletely understood. We sought to define the associations between PAD and physical outcome measures and to determine if these associations differed by sex in the Chronic Renal Insufficiency Cohort. METHODS: Among 3,543 participants, we assessed the cross-sectional relationship between PAD severity defined by ankle-brachial index; and (1) physical activity (metabolic equivalent [MET]-hr/wk), (2) walking pace (slow versus medium and/or fast), and (3) physical function (12-item Short Form Health Survey [SF-12]) at baseline. RESULTS: In a multivariable linear regression model, PAD severity was not associated with physical activity defined by total MET-hr per wk in men or women (P = 0.432). However, PAD severity was significantly associated with walking activity (P = 0.037), although this relationship did not differ by sex (P = 0.130). Similarly, PAD severity was significantly associated with walking pace (P < 0.001), although this relationship did not differ by sex (P = 0.086). In contrast, there was an independent association between PAD severity and SF-12 (P = 0.018), with a significant interaction by sex (P < 0.001). CONCLUSIONS: These data suggest that tools used to evaluate the functional consequences of PAD should focus on walking activity and walking pace, as well as physical function, where sex-specific associations should be accounted for.