Citations

ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

PURPOSE Germline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services. METHODS A multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (> 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider). RESULTS Large germline panels and somatic testing were recommended for metastatic PCA. Reflex testing—initial testing of priority genes followed by expanded testing—was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches. CONCLUSION This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance.

Abstract Purpose: Growing mechanistic and epidemiologic evidence suggests that both metformin and statins have anti-cancer effects and are associated with reduced pancreatic cancer mortality. However, the joint effect of combination statin/metformin on pancreatic cancer mortality is unknown. By applying a robust analytic approach, we aim to examine the association between metformin and/or statin use and overall survival in a large cohort of patients with primary pancreatic adenocarcinoma (PDAC). Patients and Methods: 12,572 elderly patients with primary PDAC diagnosed between 2008 and 2012 were identified by using the Surveillance, Epidemiology, and End Results (SEER), and the detailed information on metformin and statin use was extracted from Medicare Part D. A Cox proportional hazard model with time-varying covariate adjusted for the propensity score was applied to assess the association of metformin and /or statin use with overall survival while controlling for potential confounders. Results: Of total 12,572 PDAC patients (median age = 76 years), 11,526 (91.77%) patients died before the study ended with a median follow-up of 3.8 months (interquartile range: 1.4-10.1). By the time-varying covariate analysis, statin use was significantly associated with an improved overall survival [Hazard ratio (HR), 0.938; 95% confidence interval (CI), 0.898 to 0.979], and more pronounced in post-diagnosis statin users (HR, 0.693; 95% CI, 0.555 to 0.864). However, metformin use was not significantly associated with overall survival (HR, 1.011; 95% CI, 0.939 to 1.091), neither in pre-diagnosis users (HR, 1.015; 95% CI, 0.970 to 1.061) nor in post-diagnosis users (HR, 0.990; 95% CI, 0.866 to 1.131). Additionally, no superior beneficial effect was observed for dual users (HR, 0.998; 95% CI, 0.947 to 1.053). Conclusions: Exposures to statin, not metformin, was significantly associated with reduced overall mortality of PDAC, indicating potential benefits of statin use on improving survival among PDAC patients. Further prospective validation is required to confirm these findings. Citation Format: Jian-Yu E, Shou-En Lu, Yong Lin, Judith M. Graber, David Rotter, Lanjing Zhang, Gloria M. Petersen, Kitaw Demissie, Grace Lu-Yao, Xiang-Lin Tan. Effects of concomitant metformin and statins on overall survival of pancreatic cancer patients: results from SEER-Medicare data analyses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3288. doi:10.1158/1538-7445.AM2017-3288

Abstract Background: Published evidence indicates that individual use of metformin and statin is associated with reduced cancer mortality. However, their differential and joint effects on pancreatic cancer survival are inconclusive. Methods: We identified a large population-based cohort of 12,572 patients ages 65 years or older with primary pancreatic ductal adenocarcinoma (PDAC) diagnosed between 2008 and 2011 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database. Exposure to metformin and statins was ascertained from Medicare Prescription Drug Event files. Cox proportional hazards models with time-varying covariates adjusted for propensity scores were used to assess the association while controlling for potential confounders. Results: Of 12,572 PDAC patients, 950 (7.56%) had used metformin alone, 4,506 (35.84%) had used statin alone, and 2,445 (19.45%) were dual users. Statin use was significantly associated with improved overall survival [HR, 0.94; 95% confidence interval (CI), 0.90–0.98], and survival was more pronounced in postdiagnosis statin users (HR, 0.69; 95% CI, 0.56–0.86). Metformin use was not significantly associated with overall survival (HR, 1.01; 95% CI, 0.94–1.09). No beneficial effect was observed for dual users (HR, 1.00; 95% CI, 0.95–1.05). Conclusions: Our findings suggest potential benefits of statins on improving survival among elderly PDAC patients; further prospective studies are warranted to corroborate the putative benefit of statin therapy in pancreatic cancer. Impact: Although more studies are needed to confirm our findings, our data add to the body of evidence on potential anticancer effects of statins. Cancer Epidemiol Biomarkers Prev; 26(8); 1225–32. ©2017 AACR.