Vaccinations in early life are not associated with development of islet autoimmunity in type 1 diabetes high-risk children: Results from prospective cohort data.

AIMS/HYPOTHESIS: Vaccinations in early childhood potentially stimulate the immune system and may thus be relevant for the pathogenesis of autoimmune diseases such as type 1 diabetes (T1D). We determined the association of vaccination burden with T1D-associated islet autoimmunity in children with high familial risk followed prospectively from birth. METHODS: A total of 20,570 certified vaccination records from 1918 children were correlated with time to onset of T1D-associated islet autoimmunity using Cox regression, considering multiple time periods up until age two years and vaccination types, and adjusting for HLA genotype, sex, delivery mode, season of birth, preterm delivery and maternal T1D status. Additionally, prospective claims data of 295,420 subjects were used to validate associations for the tick-borne encephalitis (TBE) vaccination. RESULTS: Most vaccinations were not associated with a significantly increased hazard ratio (HR) for islet autoimmunity (e.g. HR [95% confidence interval]: 1.08 [0.96-1.21] per additional vaccination against measles, mumps and rubella at age 0-24months). TBE vaccinations within the first two years of life were nominally associated with a significantly increased autoimmunity risk (HR: 1.44 [1.06-1.96] per additional vaccination at age 0-24months), but this could not be confirmed with respect to outcome T1D in the validation cohort (HR: 1.02 [0.90-1.16]). CONCLUSIONS: We found no evidence that early vaccinations increase the risk of T1D-associated islet autoimmunity development. The potential association with early TBE vaccinations could not be confirmed in an independent cohort and appears to be a false positive finding.

Perioperative transfusion of leukocyte depleted blood products in gastric cancer patients negatively influences oncologic outcome: A retrospective propensity score weighted analysis on 610 curatively resected gastric cancer patients.

The influence of perioperative transfusion (PT) on outcome following surgery for gastric cancer (GC) remains controversial, with randomized trials lacking and observational series confounded by patient risk factors. This analysis determines the association between reception of leukocyte-depleted blood products and post-operative survival for GC.Data from 610 patients who underwent curative surgery for GC in a German tertiary care clinic from 2001 to 2013 were included. Kaplan-Meier survival curves and Cox proportional hazards regression were applied to determine the association of PT and clinical and patient risk factors for overall and relapse-free survival. Propensity score analysis was performed to adjust for observational biases in reception of PT.Higher Union International Contre le Cancer/American Joint Committee on Cancer (UICC/AJCC)-stages (P <0.001), postoperative complications and severity according to the Clavien-Dindo (CD) classification (P <0.001), PT (P = 0.02), higher age (P <0.001), and neoadjuvant chemotherapy (P <0.001) were related to increased mortality rates. Higher UICC-stages (P <0.001), neoadjuvant chemotherapy (P <0.001), and type of surgery (P = 0.02) were independently associated with increased relapse rates. Patients were more likely to receive PT with higher age (P = 0.05), surgical extension to adjacent organs/structures (P = 0.002), tumor location (P = 0.003), and female gender (P = 0.03). In the adjusted propensity score weighted analysis, PT remained associated with an increased risk of death (hazard ratio (HR): 1.31, 95% CI: 1.01-1.69, P = 0.04).Because of the association of PT with negative influence on patient survival following resection for GC, risks from application of blood products should be weighed against the potential benefits.

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators.

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.

The Next Generation of Clinical Decision Making Tools: Development of a Real-Time Prediction Tool for Outcome of Prostate Biopsy in Response to a Continuously Evolving Prostate Cancer Landscape.

PURPOSE: We evaluate whether annual updating of the PCPT Risk Calculator would improve institutional validation compared to static use of the PCPT Risk Calculator alone. MATERIALS AND METHODS: Data from 5 international cohorts including SABOR, Cleveland Clinic, ProtecT, Tyrol and Durham VA, comprising 18,400 biopsies, were used to evaluate an institution specific annual recalibration of the PCPT Risk Calculator. Using all prior years as a training set and the current year as the test set, annual recalibrations of the PCPT Risk Calculator were compared to static use of the PCPT Risk Calculator in terms of AUC and the Hosmer-Lemeshow goodness of fit statistic. RESULTS: For predicting high grade disease the median AUC (higher is better) of the recalibrated PCPT Risk Calculator (static PCPT Risk Calculator) across all test years for the 5 cohorts was 67.3 (67.5), 65.0 (60.4), 73.4 (73.4), 73.9 (74.1) and 69.6 (67.2), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated better fit for recalibration compared to the static PCPT Risk Calculator for Cleveland Clinic, ProtecT and the Durham VA but not for SABOR and Tyrol. For predicting overall cancer median AUC was 63.5 (62.7), 61.0 (57.3), 62.1 (62.5), 66.9 (67.3) and 68.5 (65.5), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated a better fit for recalibration in all cohorts except for Tyrol. CONCLUSIONS: A simple method has been provided to tailor the PCPT Risk Calculator to individual hospitals to optimize its accuracy for the patient population at hand.