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Abstract Background Chronic kidney disease (CKD) is associated with higher incidence of major surgery. No studies have evaluated the association between preoperative kidney function and postoperative outcomes across a wide spectrum of procedures. We aimed to evaluate the association between CKD and 30-day postoperative outcomes across surgical specialties. Methods We selected adult patients undergoing surgery across eight specialties. The primary study endpoint was major complications, defined as death, unplanned reoperation, cardiac complication, or stroke within 30 days following surgery. Secondary outcomes included Clavien-Dindo high-grade complications, as well as cardiac, pulmonary, infectious, and thromboembolic complications. Multivariable regression was performed to evaluate the association between CKD and 30-day postoperative complications, adjusted for baseline characteristics, surgical specialty, and operative time. Results In total, 1,912,682 patients were included. The odds of major complications (adjusted odds ratio [aOR] 2.14 [95% confidence interval (CI): 2.07, 2.21]), death (aOR 3.03 [95% CI: 2.88, 3.19]), unplanned reoperation (aOR 1.57 [95% CI: 1.51, 1.64]), cardiac complication (aOR 3.51 [95% CI: 3.25, 3.80]), and stroke (aOR 1.89 [95% CI: 1.64, 2.17]) were greater for patients with CKD stage 5 vs. stage 1. A similar pattern was observed for the secondary endpoints. Conclusion This population-based study demonstrates the negative impact of CKD on operative outcomes across a diverse range of procedures and patients.

Abstract Background Although mammography can significantly reduce breast cancer mortality, many women do not receive their annual breast cancer screening. Differences in screening adherence exist by race/ethnicity, socioeconomic status (SES), and insurance status. However, more detailed investigations into the impact of neighborhood disadvantage and access to resources on screening adherence are lacking. Methods We comprehensively examined the effect of individual social, economic, and demographic factors (n = 34 variables), as well as neighborhood level SES (nSES) indicators (n = 10 variables) on breast cancer screening adherence across a multi-ethnic population (n = 472). In this cross-sectional study, participants were surveyed from 2017 to 2018. The data was analyzed using univariate regression and LASSO for variable reduction. Significant predictors were carried forward into final multivariable mixed-effect logistic regression models where odds ratios (OR), 95% confidence intervals and p-values were reported. Results Nineteen percent of participants were non-adherent to breast screening guidelines. Race/ethnicity was not associated with adherence; however, increasing age (OR = 0.97, 95%CI = 0.95–0.99, p = 0.01), renting a home (OR = 0.53, 95%CI = 0.30–0.94, p = 0.04), food insecurity (OR 0.46, 95%CI = 0.22–0.94, p = 0.01), and overcrowding (OR = 0.58, 95% CI = 0.32–0.94, p = 0.01) were significantly associated with lower breast cancer screening adherence. Conclusion Socioeconomic indicators at the individual and neighborhood levels impact low breast cancer screening adherence and may help to inform future screening interventions.

Background: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.