BACKGROUND:Standardized admission order entry systems (OES) and clinical decision support (CDS) tools can facilitate implementation of evidence-based practice. We conducted a mixed-methods analysis incorporating quantitative analysis of OES/CDS usage as well as qualitative analysis to identify barriers and facilitators related to usage of acute pancreatitis (AP)-specific OES-CDS. MATERIALS AND METHODS:Quantitative analysis: A retrospective cohort study of hospitalized encounters for AP (defined by diagnosis codes and lipase or amylase >3x normal) was conducted across 12 hospitals within an integrated health system from September 2014 through October 2022 to evaluate adoption of the AP OES/CDS, relationship to length-of-stay (LOS≥7 days), and in-hospital mortality adjusted for patient clinical and demographic characteristics. Qualitative analysis: Semistructured interviews of focus groups identified themes related to perceived benefits, barriers, and improvements to the OES/CDS. RESULTS:Among 16,874 AP hospitalizations during the study, annual use of the AP OES/CDS peaked in 2021 at 7.4%. Use of AP OES/CDS was associated with reduction in prolonged LOS (OR: 0.81, 95% CI: 0.69-0.94). This finding was consistent after adjusting age, sex, race/ethnicity, and comorbidity indices. No significant difference in mortality was associated with OES usage (OR: 0.89, 95% CI: 0.61-1.32). Barriers to OES adoption included lack of awareness, competition with other tools, and complexity of patient case-mix. CONCLUSION:Although adoption was limited, usage of an AP OES/CDS tool was associated with a decrease in prolonged length-of-stay. Future efforts to expand the reach of such instruments through provider awareness and refinement of the instrument may facilitate broader impact for management of patients with acute pancreatitis.

Background: Screening and eradication of Helicobacter pylori reduce the risk of gastric cancer in patients with a family history. We assessed patient perspectives on H. pylori screening and treatment within a diverse regional US population.Methods: Between July 2022 and August 2022, we conducted a cross-sectional study among patients with >= 1 first-degree relative(s) with gastric cancer. Eligible patients were between 18 and 75 years of age without history of H. pylori infection or gastric cancer. A survey assessed interest in testing and willingness to complete treatment for H. pylori. Interested patients were offered H. pylori testing and treatment. We examined interest and effectiveness of treatment by race and ethnicity.Results: We identified 15,255 eligible patients, and 1,500 patients were randomly selected for the survey; 280 (19%) patients, including two relatives not initially invited but asked to participate, responded following outreach. Respondents were 65% male and averaged 57 years (SD = 13) with 36% Hispanic, 36% non-Hispanic White, 15% Asian/Pacific Islander, and 9% non-Hispanic Black. Overall, 223 (80%) were interested in H. pylori screening; of these, 89% would take antibiotics as prescribed. Willingness to screen was consistent across racial and ethnic groups. Among 223 respondents interested in screening, 128 (57%) completed testing with 15 screen-detected cases; all 15 completed treatment, and 11 had confirmed eradication.Conclusions: Patients with family history of gastric cancer had a high level of interest in H. pylori screening and successful eradication when detected.Impact: A screen-and-treat strategy for H. pylori may be considered for patients with family history of gastric cancer.

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.

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.

The widespread use of social media significantly impacts users' emotions. Negative emotions, in particular, are frequently produced, which can drastically affect mental health. Recognizing these emotional states is essential for implementing effective warning systems for social networks. However, detecting emotions during passive social media use---the predominant mode of engagement---is challenging. We introduce the first predictive model that estimates user emotions during passive social media consumption alone. We conducted a study with 29 participants who interacted with a controlled social media feed. Our apparatus captured participants' behavior and their physiological signals while they browsed the feed and filled out self-reports from two validated emotion models. Using this data for supervised training, our emotion classifier robustly detected up to 8 emotional states and achieved 83% peak accuracy to classify affect. Our analysis shows that behavioral features were sufficient to robustly recognize participants' emotions. It further highlights that within 8 seconds following a change in media content, objective features reveal a participant's new emotional state. We show that grounding labels in a componential emotion model outperforms dimensional models in higher-resolutional state detection. Our findings also demonstrate that using emotional properties of images, predicted by a deep learning model, further improves emotion recognition.

Background: Cardiometabolic disease (CMD) disproportionately affects African American/Black (AA) and Latino communities. CMD disparities are exacerbated by their underrepresentation in clinical trials for CMD treatments including nutritional interventions. The study aimed to (1) form a precision nutrition community consultant panel (PNCCP) representative of Latino and AA communities in Los Angeles to identify barriers and facilitators to recruitment and retention of diverse communities into nutrition clinical trials and (2) develop culturally informed strategies to improve trial diversity. Methods: A deliberative community engagement approach was used to form a PNCCP for the Nutrition for Precision Health (NPH) trial, part of the of the All of Us research initiative. The PNCCP included individuals that provide services for Latino and AA communities who met during 11 virtual sessions over 1 year. Discussion topics included enhancing recruitment and cultural acceptance of the NPH trial. We summarized CCP recommendations by theme using an inductive qualitative approach. Results: The PNCCP included 17 adults (35% AA, 47% Latino). Four thematic recommendations emerged: reducing structural barriers to recruitment, the need for recruitment materials to be culturally tailored and participant-centered, community-engaged trial recruitment, and making nutrition trial procedures inclusive and acceptable. We outlined the study response to feedback, including the constraints that limited implementation of suggestions. Conclusion: This study centers community voices regarding the recruitment and retention of AA and Latino communities into a nutrition clinical trial. It highlights the importance of community engagement early on in protocol development and maintaining flexibility to enhance inclusion of diverse communities in nutrition clinical trials.

Occupational medicine is a vital field for workplace safety and health but often encounters challenges in engaging students and effectively communicating subtle yet critical workplace hazards. To tackle these issues, we developed HistoLab VR, a Virtual Reality (VR) game that immerses participants in a histology lab environment based on real-world practice. Our comprehensive user study with 17 students and experts assessed the game’s impact on hazard awareness, interest in occupational medicine, and user experience through quantitative and qualitative measures. Our findings show that HistoLab VR not just immersed participants in a relatable histology lab worker experience but that it effectively raised awareness about subtle hazards and conveyed the inherent stress of the job. We discuss our results and highlight the potential of VR as a valuable educational tool for occupational medicine training.

Background/Objectives: The Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) model relies primarily on fasting glucose values. Health systems have increasingly shifted practice towards use of glycated hemoglobin (HbA1c) measurement. We modified the ENDPAC model using patients with new onset hyperglycemia. Methods: Four cohorts of patients 50-84 years of age with HbA1c results >= 6.2-6.5 % in 2011-2018 were identified. A combine cohort was formed. A widened eligibility criterion was applied to form additional four individual cohorts and one combined cohort. The primary outcome was the diagnosis of pancreatic cancer within 3 years after the first elevated HbA1c testing. The performance of the modified ENDPAC model was evaluated by AUC, sensitivity, positive predictive value, cases detected, and total number of patients screened. Results: The individual and combined cohorts consisted of 39,001-79,060 and 69,334-92,818 patients, respectively (mean age 63.5-65.0 years). The three-year PC incidence rates were 0.47%-0.54 %. The AUC measures were in the range of 0.75-0.77 for the individual cohorts and 0.75 for the combined cohorts. When the four individual cohorts were combined, more PC cases can be identified (149 by the combined vs. 113-116 by individual cohorts when risk score was 5+). Performance measures were compromised in nonwhites. Asian and Pacific islanders had lower sensitivity compared to other racial and ethnic groups (29 % vs. 50-60 %) when risk score was 5+. Conclusions: The modified ENDPAC model targets a broader population and thus identifies more highrisk patients for cancer screening. The differential performance needs to be considered when the model is applied to non-white population. (c) 2024 IAP and EPC. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Virtual Reality (VR) systems have traditionally required users to operate the user interface with controllers in mid-air. More recent VR systems, however, integrate cameras to track the headset's position inside the environment as well as the user's hands when possible. This allows users to directly interact with virtual content in mid-air just by reaching out, thus discarding the need for hand-held physical controllers. However, it is unclear which of these two modalities—controller-based or free-hand interaction—is more suitable for efficient input, accurate interaction, and long-term use under reliable tracking conditions. While interacting with hand-held controllers introduces weight, it also requires less finger movement to invoke actions (e.g., pressing a button) and allows users to hold on to a physical object during virtual interaction.In this paper, we investigate the effect of VR input modality (controller vs. free-hand interaction) on physical exertion, agency, task performance, and motor behavior across two mid-air interaction techniques (touch, raycast) and tasks (selection, trajectory-tracing). Participants reported less physical exertion, felt more in control, and were faster and more accurate when using VR controllers compared to free-hand interaction in the raycast setting. Regarding personal preference, participants chose VR controllers for raycast but free-hand interaction for mid-air touch. Our correlation analysis revealed that participants' physical exertion increased with selection speed, quantity of arm motion, variation in motion speed, and bad postures, following ergonomics metrics such as consumed endurance and rapid upper limb assessment. We also found a negative correlation between physical exertion and the participant's sense of agency, and between physical exertion and task accuracy.

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 1/4 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. & COPY; 2023 IAP and EPC. Published by Elsevier B.V. All rights reserved.

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.