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Decision models of prediabetes populations: a systematic review.With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarise and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, evaluate their potential use for the assessment of novel prevention strategies and discuss the knowledge gaps, challenges and opportunities. We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardised form, and double extraction, four reviewers each extracted 50% of identified studies. Twenty-nine published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n=8) and type 2 diabetes (n=17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n=7) and accounted for heterogeneity amongst individuals with prediabetes (n=7). Current prediabetes decision models have considerable limitations in terms of their quality and validity and are not equipped to evaluate stratified strategies using novel biomarkers highlighting a clear need for more comprehensive prediabetes decision models. This article is protected by copyright. All rights reserved.
One-carbon metabolism biomarkers and risk of urothelial cell carcinoma in the European prospective investigation into cancer and nutrition.Published associations between dietary folate and bladder cancer risk are inconsistent. Biomarkers may provide more accurate measures of nutrient status. This nested case-control analysis within the European Prospective Investigation into Cancer and Nutrition (EPIC) investigated associations between pre-diagnostic serum folate, homocysteine, vitamins B6 and B12 and the risk of urothelial cell carcinomas of the bladder (UCC). A total of 824 patients with newly diagnosed UCC were matched with 824 cohort members. Serum folate, homocysteine, and vitamins B6 and B12 were measured. Odds ratios (OR) and 95% confidence intervals (CI) for total, aggressive, and non-aggressive UCC were estimated using conditional logistic regression with adjustment for smoking status, smoking duration and intensity, and other potential confounders. Additionally, statistical interaction with smoking status was assessed. A halving in serum folate concentrations was moderately associated with risk of UCC (OR: 1.18; 95% CI: 0.98-1.43), in particular aggressive UCC (OR: 1.34; 95% CI: 1.02-1.75; p-heterogeneity = 0.19). Compared to never smokers in the highest quartile of folate concentrations, this association seemed only apparent among current smokers in the lowest quartile of folate concentrations (OR: 6.26; 95% CI: 3.62-10.81, p-interaction = 0.07). Dietary folate was not associated with aggressive UCC (OR: 1.26; 95% CI: 0.81-1.95; p-heterogeneity = 0.14). No association was observed between serum homocysteine, vitamins B6 and B12 and risk of UCC. This study suggests that lower serum folate concentrations are associated with increased UCC risk, in particular aggressive UCC. Residual confounding by smoking cannot be ruled out and these findings require confirmation in future studies with multiple measurements.
Perioperative proADM-change is associated with the development of acute respiratory distress syndrome in critically ill cardiac surgery patients: a prospective cohort study.Aim: Biomarkers of acute respiratory distress syndrome (ARDS) after cardiac-surgery may help risk-stratification and management. Preoperative single-value proADM increases predictive capacity of scoring-system EuroSCORE. To include the impact of surgery, we aim to assess the predictive value of the perioperative proADM-change on development of ARDS in 40 cardiac-surgery patients. Materials & methods: ProADM was measured in nine sequential blood samples. The Berlin definition of ARDS was used. For data-analyses, a multivariate model of EuroSCORE and perioperative proADM-change, linear mixed models and logistic regression were used. Results: Perioperative proADM-change was associated with ARDS after cardiac-surgery, and it was superior to EuroSCORE. A perioperative proADM-change >1.5 nmol/l could predict ARDS. Conclusion: Predicting post-surgery ARDS with perioperative proADM-change enables clinicians to intensify lung-protective interventions and individualized fluid therapy to minimize secondary injury.