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.

Interventions for Pancreatitis-New Approaches, Knowledge Gaps, and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

ABSTRACT: There exists no cure for acute, recurrent acute or chronic pancreatitis and treatments to date have been focused on managing symptoms. A recent workshop held by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) focused on interventions that might disrupt or perhaps even reverse the natural course of this heterogenous disease, aiming to identify knowledge gaps and research opportunities that might inform future funding initiatives for NIDDK. The breadth and variety of identified active or planned clinical trials traverses the spectrum of the disease and was conceptually grouped for the workshop into behavioral, nutritional, pharmacologic and biologic, and mechanical interventions. Cognitive and other behavioral therapies are proven interventions for pain and addiction, but barriers exist to their use. Whilst a disease specific instrument quantifying pain is now validated, an equivalent is lacking for nutrition - and both face challenges in ease and frequency of administration. Multiple pharmacologic agents hold promise. Ongoing development of Patient Reported Outcome (PRO) measurements can satisfy Investigative New Drug (IND) regulatory assessments. Despite multiple randomized clinical trials demonstrating benefit, great uncertainty remains regarding patient selection, timing of intervention, and type of mechanical intervention (endoscopic versus surgery). Challenges and opportunities to establish beneficial interventions for patients were identified.

Female patients delay seeking medical care with alcohol-associated acute pancreatitis.

BACKGROUND/OBJECTIVE: Alcohol consumption is increasing in women, who more frequently report abdominal symptoms compared to men. We aimed to examine differences in presentation of acute pancreatitis [AP] in male and female patients hospitalized with alcohol-associated AP. METHODS: We analyzed 138 patients enrolled in an ongoing case-crossover study of alcohol-associated AP conducted across 5 medical centers in the U.S. Patients meeting the Revised Atlanta Classification of AP and who scored 3 or higher on the AUDIT-C instrument were invited to participate in the study and were interviewed while hospitalized with AP. Sex differences in the timing and type of pancreas-associated pain, alcohol consumption, clinical presentation, and quality of life were examined by Chi-squared tests, Wilcoxon rank sum tests and t-tests. RESULTS: Female patients reported significantly longer interval from onset of pain to deciding to seek medical attention (median 40 h, interquartile range [IQR] 14, 74) as compared to males (14 h, IQR 4, 50; p = 0.005). While male patients were more likely to have been admitted to the intensive care unit [ICU] (21%) as compared to female patients (7%; p = 0.04), the incidence of SIRS or severe AP did not differ by sex. Quality of life measures as reported through the PROMIS-29 instrument were equally suboptimal in both sexes. Anxiety disorders were diagnosed more frequently among females (61%) than in males (41%, p = 0.009). CONCLUSION: In a large case series of alcohol-associated AP, we found that female patients delayed seeking medical care compared to males. However, there were no differences in the type, location and intensity of abdominal pain.

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.

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.

Development of a Clinical Prediction Model for Diabetes in Chronic Pancreatitis: The PREDICT3c Study.

OBJECTIVE: Diabetes that arises from chronic pancreatitis (CP) is associated with increased morbidity and mortality. Methods to predict which patients with CP are at greatest risk for diabetes are urgently needed. We aimed to examine independent risk factors for diabetes in a large cohort of patients with CP. RESEARCH DESIGN AND METHODS: This cross-sectional study comprised 645 individuals with CP enrolled in the PROCEED study, of whom 276 had diabetes. We conducted univariable and multivariable regression analyses of potential risk factors for diabetes. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) analysis, and accuracy was evaluated by cross validation. Exploratory analyses were stratified according to the timing of development of diabetes relative to the diagnosis of pancreatitis. RESULTS: Independent correlates of diabetes in CP included risk factors for type 2 diabetes (older age, overweight/obese status, male sex, non-White race, tobacco use) as well as pancreatic disease-related factors (history of acute pancreatitis complications, nonalcoholic etiology of CP, exocrine pancreatic dysfunction, pancreatic calcification, pancreatic atrophy) (AUROC 0.745). Type 2 diabetes risk factors were predominant for diabetes occurring before pancreatitis, and pancreatic disease-related factors were predominant for diabetes occurring after pancreatitis. CONCLUSIONS: Multiple factors are associated with diabetes in CP, including canonical risk factors for type 2 diabetes and features associated with pancreatitis severity. This study lays the groundwork for the future development of models integrating clinical and nonclinical data to identify patients with CP at risk for diabetes and identifies modifiable risk factors (obesity, smoking) on which to focus for diabetes prevention.

Urine biomarkers enable pancreatic cancer detection up to 2 years before diagnosis.

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Recruitment and Retention Strategies for the Diabetes RElated to Acute Pancreatitis and Its Mechanisms Study: From the Type 1 Diabetes in Acute Pancreatitis Consortium.

ABSTRACT: Recruitment and retention of patients with acute pancreatitis (AP) in clinical studies can be challenging. While some obstacles are similar to other clinical conditions, some are unique to AP. Identifying potential barriers early and developing targeted solutions can help optimize recruitment and retention in AP studies. Such pre-emptive and detailed planning can help prospective, longitudinal studies focus on exocrine and endocrine complications of AP in accurately measuring outcomes. This article highlights the challenges in recruitment and retention strategies in AP studies and reviews available resources to create opportunities to address them. We describe the multifaceted approach used by the Recruitment and Retention Committee of the Type 1 Diabetes in Acute Pancreatitis Consortium, which builds upon earlier experiences to develop a recruitment and retention plan for the DREAM (Diabetes RElated to Acute pancreatitis and its Mechanisms) study.

Uniting epidemiology and experimental models: pancreatic steatosis and pancreatic cancer.

Research from epidemiologic studies and experimental animal models provide insights into the role of pancreatic steatosis in the development of pancreatic cancer. Epidemiologic data demonstrate that pancreatic steatosis is widely prevalent and significantly associated with both development and progression of pancreatic cancer. By focusing on current experimental models, this review elucidates potential cellular mechanisms underlying not only the pathophysiology of pancreatic steatosis itself, but also the pathogenesis behind pancreatic steatosis's role in changing the tumour microenvironment and accelerating the development of pancreatic cancer. This review further explores the impact of bariatric surgery on pancreatic steatosis and pancreatic cancer. Synthesizing knowledge from both epidemiologic studies and experimental animal models, this review identifies gaps in current knowledge regarding pancreatic steatosis and its role in carcinogenesis and proposes future research directions to elucidate the possible mechanisms underlying other obesity-associated cancers.

Prediction of Pancreatic Cancer in Diabetes Patients with Worsening Glycemic Control.

BACKGROUND: Worsening glycemic control indicates elevated risk of pancreatic ductal adenocarcinoma (PDAC). We developed prediction models for PDAC among those with worsening glycemic control after diabetes diagnosis. METHODS: In 2000-2016 records within the Veterans Affairs Health System (VA), we identified three cohorts with progression of diabetes: (i) insulin initiation (n = 449,685), (ii) initiation of combination oral hypoglycemic medication (n = 414,460), and (iii) hemoglobin A1c (HbA1c) ≥8% with ≥Δ1% within 15 months (n = 593,401). We computed 12-, 36-, and 60-month incidence of PDAC and developed prediction models separately for males and females, with consideration of >30 demographic, behavioral, clinical, and laboratory variables. Models were selected to optimize Akaike's Information Criterion, and performance for predicting 12-, 36-, and 60-month incident PDAC was evaluated by bootstrap. RESULTS: Incidence of PDAC was highest for insulin initiators and greater in males than in females. Optimism-corrected c-indices of the models for predicting 36-month incidence of PDAC in the male population were: (i) 0.72, (ii) 0.70, and (iii) 0.71, respectively. Models performed better for predicting 12-month incident PDAC [c-index (i) 0.78, (ii) 0.73, (iii) 0.76 for males], and worse for predicting 60-month incident PDAC [c-index (i) 0.69, (ii) 0.67, (iii) 0.68 for males]. Model performance was lower among females. For subjects whose model-predicted 36-month PDAC risks were ≥1%, the observed incidences were (i) 1.9%, (ii) 2.2%, and (iii) 1.8%. CONCLUSIONS: Sex-specific models for PDAC can estimate risk of PDAC at the time of progression of diabetes. IMPACT: Our models can identify diabetes patients who would benefit from PDAC screening.

Patient Adherence to Lung CT Screening Reporting & Data System-Recommended Screening Intervals in the United States: A Systematic Review and Meta-Analysis.

Lung cancer screening (LCS) is effective in reducing mortality, particularly when patients adhere to follow-up recommendations standardized by the Lung CT Screening Reporting & Data System (Lung-RADS). Nevertheless, patient adherence to recommended intervals varies, potentially diminishing benefit from screening. We conducted a systematic review and meta-analysis of patient adherence to Lung-RADS-recommended screening intervals. We systematically searched MEDLINE, EMBASE, Web of Science, the Cochrane Central Register of Controlled Trials, and major radiology and oncology conference archives between April 28, 2014, and December 17, 2020. Eligible studies mentioned patient adherence to the recommendations of Lung-RADS. The review protocol was registered with PROSPERO (CRD42020189326). We identified 24 eligible studies for qualitative summary, of which 21 were suitable for meta-analysis. The pooled adherence rate was 57% (95% confidence interval: 46%-69%) for defined adherence (e.g., an annual incidence screen was performed within 15 mo) among 6689 patients and 65% (95% confidence interval: 55%-75%) for anytime adherence among 5085 patients. Large heterogeneity in adherence rates between studies was observed (I2 = 99% for defined adherence, I2 = 98% for anytime adherence). Heterogeneous adherence rates were associated with Lung-RADS scores, with significantly higher adherence rates among Lung-RADS 3 to 4 than Lung-RADS 1 to 2 (p < 0.05). Patient adherence to Lung-RADS-recommended screening intervals is suboptimal across clinical LCS programs in the United States, especially among patients with results of Lung-RADS categories 1 to 2. To improve adherence rates, future research may focus on implementing tailored interventions after identifying barriers to LCS. We also propose a minimum standardized set of data elements for future pooled analyses of LCS adherence on the basis of our findings.

A Case-CrossovEr study deSign to inform tailored interventions to prevent disease progression in Acute Pancreatitis (ACCESS-AP) - study design and population.

BACKGROUND/OBJECTIVES: Alcohol is the most common etiology of recurrent acute pancreatitis and chronic pancreatitis. The extent and timing of drinking that increases the transient risk of acute pancreatitis is yet unknown. METHODS: We designed a case-crossover study to determine the effective hazard period of drinking in relation to episodes of pancreatitis. We aim to evaluate the dose-response relationship between excess drinking and pancreatitis comparing the extent of drinking during case and control periods from the same individual. We aim to recruit 160 patients hospitalized with acute pancreatitis, whose AUDIT-C score reaches 3 or higher. Interviews of each enrolled patient to define their 15-day history of alcohol consumption employing the timeline follow-back method. Long-term drinking and smoking will be investigated as modifiers of the impact of short-term excess drinking. Patients are followed-up for evaluation of usual alcohol consumption during asymptomatic periods following the index hospitalization. Blood and urine specimens are collected while the patients are hospitalized and during a standard-of-care follow-up visit. RESULTS: We have recruited 31 patients to date, with a median age of 33 years. Females and non-White participants make up 26% and 35% of the enrolled population, respectively. Forty-eight % of patients have had a prior history of acute pancreatitis. CONCLUSIONS: Our study will shed light on the impact of short-term changes in drinking on triggering acute pancreatitis. It will provide data on other covarying factors of drinking and behaviors changes after acute pancreatitis.

Lifetime smoking history and cohort-based smoking prevalence in chronic pancreatitis.

BACKGROUND/OBJECTIVE: Smoking prevalence in patients with chronic pancreatitis [CP] is high. We aimed to understand lifetime history of smoking and cohort trends in CP patients to inform effective strategies for smoking cessation. METHOD: Data on 317 CP patients from the North American Pancreatitis Study 2 [NAPS2] Continuation and Validation Study and the NAPS2 Ancillary Study were analyzed. Smoking history was assessed for each phase of life from the onset of smoking to study enrollment. Data on second-hand smoke and drinking history were also collected. We compared demographic factors, drinking history, pain level and pancreas morphology by smoking status at age 25 (non-smoking, <1 pack per day [PPD], ≥1 PPD). We compared smoking prevalence by birth cohorts: 1930-1949, 1950-1969, 1970-1989. RESULT: Fifty-one percent of CP patients reported smoking at the time of enrollment. Those who smoked ≥1 PPD at age 25 smoked a cumulative total of 30.3 pack-years of cigarettes over a lifetime. Smoking at age 25 was associated with greater lifetime drinking and greater exposure to second-hand smoke at home and at workplace. Pancreatic atrophy and pseudocysts were more common among smokers. Pancreatic pain was more severe among smokers, and 12-13% of smokers reported smoking to alleviate pain. Male CP patients born in 1950-1969 reported the highest peak prevalence of smoking, and female CP patients born in 1970-1989 reported highest peak prevalence of smoking. CONCLUSION: CP patients exhibit intense and sustained smoking behavior once established in the 20s. Regardless, cohort analyses demonstrate that the behaviors could potentially be altered by policy changes.

Critical thresholds: key to unlocking the door to the prevention and specific treatments for acute pancreatitis.

Acute pancreatitis (AP), an acute inflammatory disorder of the exocrine pancreas, is one of the most common gastrointestinal diseases encountered in emergency departments with no specific treatments. Laboratory-based research has formed the cornerstone of endeavours to decipher the pathophysiology of AP, because of the limitations of such study in human beings. While this has provided us with substantial understanding, we cannot answer several pressing questions. These are: (a) Why is it that only a minority of individuals with gallstones, or who drink alcohol excessively, or are exposed to other causative factors develop AP? (b) Why do only some develop more severe manifestations of AP with necrosis and/or organ failure? (c) Why have we been unable to find an effective therapeutic for AP? This manuscript provides a state-of-the-art review of our current understanding of the pathophysiology of AP providing insights into the unanswered clinical questions. We describe multiple protective factors operating in most people, and multiple stressors that in a minority induce AP, independently or together, via amplification loops. We present testable hypotheses aimed at halting progression of severity for the development of effective treatments for this common unpredictable disease.