Pancreatic cancer is the fourth leading cause of cancer death with a 5-year survival rate of approximately 10%. 1 Noncomparative data from 2013 showed increasing incidence of pancreatic cancer in older White women and men and in younger non-Hispanic White women. 2 However, there are limited data on recent trends in pancreatic cancer incidence.The aim of this study was to perform an age-and sexspecific time trend analysis of pancreatic cancer incidence rates.Methods | Pancreatic cancer incidence rates per 100 000 population were obtained (age-adjusted to the 2000 US population and adjusted for reporting delay) for 2000 to 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. The SEER program collects information from cancer registries covering 37% of US population. The Cedars-Sinai institutional review board exempted the study because the data were deidentified and publicly available.Time trends were quantified using Monte Carlo permutation analysis to fit the simplest joinpoint model using the incidence rate data. 3 Annual percentage change and average annual percentage change (AAPC) were calculated. A 2-sided t test was performed to evaluate if annual percentage change was significant. Pairwise comparison between trends was performed to assess identicalness and equality. 4 Age-and sex-specific analyses also were conducted. Younger adults were defined as those younger than 55 years and older adults were defined as those aged 55 years or older. 5 A post hoc analysis also was performed for individuals aged 15 to 34 years and for those aged 35 to 54 years.Version 8.3.9 of the SEER*Stat program (National Cancer Institute) and version 4.9 of the Joinpoint Regression program (National Cancer Institute) were used. A 2-sided P < .05 was considered statistically significant for the overall group. Multiple testing correction was used and P < .025 was considered statistically significant for the analyses of the younger and older age groups and P < .0125 was considered statistically significant for the age groups of 15 to 34 years and 35 to 54 years.
Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.
BackgroundClinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.MethodsProviders who had received at least one electronic AKI alert in the previous 6 months, as part of a separate randomized controlled trial (clinicaltrials.gov #01862419), were asked to complete a survey concerning their opinions about this specific AKI alert system. Individual approval of the alert system was defined by a provider's desire to continue receiving the alert after termination of the trial.ResultsA total of 98 individuals completed the survey, including 62 physicians, 27 pharmacists and 7 non-physician providers. Sixty-nine percent of responders approved the alert, with no significant difference among the various professions (P = 0.28). Alert approval was strongly correlated with the belief that the alerts improved patient care (P < 0.0001), and negatively correlated with the belief that alerts did not provide novel information (P = 0.0001). With each additional 30 days of trial duration, odds of approval decreased by 20% (3–35%) (P = 0.02).ConclusionsThe alert system was generally well received, although approval waned with time. Approval was correlated with the belief that this type of alert improved patient care. These findings suggest that perceived efficacy is critical to the success of future alert trials.
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