C-Reactive Protein Gene Variants Are Associated with Postoperative C-reactive Protein Levels After Coronary Artery Bypass Surgery

Background: Elevated baseline C-reactive protein (CRP) levels are associated with increased risk for developing cardiovascular disease. Several CRP gene variants have been associated with altered baseline CRP levels in ambulatory populations. However, the influence of CRP gene variants on CRP levels during inflammatory states, such as surgery, is largely unexplored. We describe the association between candidate CRP gene variants and postoperative plasma CRP levels in patients undergoing primary, elective coronary artery bypass graft (CABG) surgery with cardiopulmonary bypass (CPB). Methods: Using a multicenter candidate gene association study design, we examined the association between seventeen candidate CRP single nucleotide polymorphisms (SNPs) and inferred haplotypes, and altered postoperative CRP levels in 604 patients undergoing CABG surgery with CPB. Perioperative CRP levels were measured immediately prior to surgery, post-CPB and on postoperative days (POD) 1–4. Results: CRP levels were significantly elevated at all postoperative time points when compared with preoperative levels (P < 0.0001). After adjusting for clinical covariates, the minor allele of the synonymous coding SNP, rs1800947 was associated with lower peak postoperative CRP levels ($P = 2.4 × 10^{-4}$) and lower CRP levels across all postoperative time points ($P = 4.8 × 10^{-5}$). rs1800947 remained highly significant after Bonferroni adjustment for multiple comparisons. Conclusion: We identified a CRP gene SNP associated with lower postoperative CRP levels in patients undergoing CABG surgery with CPB. Further investigation is needed to clarify the significance of this association between CRP gene variants and the acute-phase rise in postoperative CRP levels with regard to the risk of adverse postoperative outcomes

Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention

BACKGROUND: Precision prevention is increasingly important in HIV prevention research to move beyond universal interventions to those tailored for high-risk individuals. The current study was designed to develop machine learning algorithms for predicting adolescent HIV risk behaviours. METHODS: Comprehensive longitudinal data on adolescent risk behaviours, perceptions, peer and family influence, and neighbourhood risk factors were collected from 2564 grade-10 students at baseline followed for 24 months over 2008-2012. Machine learning techniques [support vector machine (SVM) and random forests] were applied to innovatively leverage longitudinal data for robust HIV risk behaviour prediction. In this study, we focused on two adolescent risk behaviours: had ever had sex and had multiple sex partners. Twenty percent of the data were withheld for model testing. RESULTS: The SVM model with cost-sensitive learning achieved the highest sensitivity, at 79.1%, specificity of 75.4% with AUC of 0.86 in predicting multiple sex partners on the training data (10-fold cross-validation), and sensitivity of 79.7%, specificity of 76.5% with AUC of 0.86 on the testing data. The random forest model obtained the best performance in predicting had ever had sex, yielding the sensitivity of 78.5%, specificity of 73.1% with AUC of 0.84 on the training data and sensitivity of 82.7%, specificity of 75.3% with AUC of 0.87 on the testing data. CONCLUSION: Machine learning methods can be used to build effective prediction model(s) to identify adolescents who are likely to engage in HIV risk behaviours. This study builds a foundation for targeted intervention strategies and informs precision prevention efforts in school-setting

Maintaining Outstanding Outcomes Using Response- and Biology-Based Therapy for Intermediate-Risk Neuroblastoma: A Report From the Children's Oncology Group Study ANBL0531.

PurposeThe primary objective of the Children's Oncology Group study ANBL0531 (ClinicalTrials.gov identifier: NCT00499616) was to reduce therapy for subsets of patients with intermediate-risk neuroblastoma using a biology- and response-based algorithm to assign treatment duration while maintaining a 3-year overall survival (OS) of 95% or more for the entire cohort.Patients and methodsChildren younger than age 12 years with intermediate-risk stage 2A/2B or stage 3 tumors with favorable histology; infants younger than age 365 days with stage 3, 4 or 4S disease; and toddlers from 365 to younger than 547 days with favorable histology, hyperdiploid stage 4, or unfavorable histology stage 3 tumors were eligible. Patients with MYCN-amplified tumors were excluded. Patients were assigned to initially receive two (group 2), four (group 3), or eight (group 4) cycles of chemotherapy with or without surgery on the basis of prognostic markers, including allelic status of chromosomes 1p and 11q; ultimate duration of therapy was determined by overall response.ResultsBetween 2007 and 2011, 404 evaluable patients were enrolled. Compared with legacy Children's Oncology Group studies, subsets of patients had a reduction in treatment. The 3-year event-free survival and OS rates were 83.2% (95% CI, 79.4% to 87.0%) and 94.9% (95% CI, 92.7% to 97.2%), respectively. Infants with stage 4 tumors with favorable biology (n = 61) had superior 3-year event-free survival compared with patients with one or more unfavorable biologic features (n = 47; 86.9% [95% CI, 78.3% to 95.4%] v 66.8% [95% CI, 53.1% to 80.6%]; P = .02), with a trend toward OS advantage (95.0% [95% CI, 89.5% to 100%] v 86.7% [95% CI, 76.6% to 96.7%], respectively; P = .08). OS for patients with localized disease was 100%.ConclusionExcellent survival was achieved with this treatment algorithm, with reduction of therapy for subsets of patients. More-effective treatment strategies still are needed for infants with unfavorable biology stage 4 disease