Agents intervening against delirium in the intensive care unit trial-Protocol for a secondary Bayesian analysis

Background Delirium is highly prevalent in the intensive care unit (ICU) and is associated with high morbidity and mortality. The antipsychotic haloperidol is the most frequently used agent to treat delirium although this is not supported by solid evidence. The agents intervening against delirium in the intensive care unit (AID-ICU) trial investigates the effects of haloperidol versus placebo for the treatment of delirium in adult ICU patients. Methods This protocol describes the secondary, pre-planned Bayesian analyses of the primary and secondary outcomes up to day 90 of the AID-ICU trial. We will use Bayesian linear regression models for all count outcomes and Bayesian logistic regression models for all dichotomous outcomes. We will adjust for stratification variables (site and delirium subtype) and use weakly informative priors supplemented with sensitivity analyses using sceptical priors. We will present results as absolute differences (mean differences and risk differences) and relative differences (ratios of means and relative risks). Posteriors will be summarised using median values as point estimates and percentile-based 95% credibility intervals. Probabilities of any benefit/harm, clinically important benefit/harm and clinically unimportant differences will be presented for all outcomes. Discussion The results of this secondary, pre-planned Bayesian analysis will complement the primary frequentist analysis of the AID-ICU trial and facilitate a nuanced and probabilistic interpretation of the trial results.Peer reviewe

Agents intervening against delirium in the intensive care unit (AID-ICU) - Protocol for a randomised placebo-controlled trial of haloperidol in patients with delirium in the ICU

Background Delirium among patients in the intensive care unit (ICU) is a common condition associated with increased morbidity and mortality. Haloperidol is the most frequently used pharmacologic intervention, but its use is not supported by firm evidence. Therefore, we are conducting Agents Intervening against Delirium in the Intensive Care Unit (AID‐ICU) trial to assess the benefits and harms of haloperidol for the treatment of ICU‐acquired delirium. Methods AID‐ICU is an investigator‐initiated, pragmatic, international, randomised, blinded, parallel‐group, trial allocating adult ICU patients with manifest delirium 1:1 to haloperidol or placebo. Trial participants will receive intravenous 2.5 mg haloperidol three times daily or matching placebo (isotonic saline 0.9%) if they are delirious. If needed, a maximum of 20 mg/daily haloperidol/placebo is given. An escape protocol, not including haloperidol, is part of the trial protocol. The primary outcome is days alive out of the hospital within 90 days post‐randomisation. Secondary outcomes are number of days without delirium or coma, serious adverse reactions to haloperidol, usage of escape medication, number of days alive without mechanical ventilation; mortality, health‐related quality‐of‐life and cognitive function at 1‐year follow‐up. A sample size of 1000 patients is required to detect a 7‐day improvement or worsening of the mean days alive out of the hospital, type 1 error risk of 5% and power 90%. Perspective The AID‐ICU trial is based on gold standard methodology applied to a large sample of clinically representative patients and will provide pivotal high‐quality data on the benefits and harms of haloperidol for the treatment ICU‐acquired delirium

<em>Aspergillus nidulans</em> Synthesize Insect Juvenile Hormones upon Expression of a Heterologous Regulatory Protein and in Response to Grazing by <em>Drosophila melanogaster</em> Larvae.

Secondary metabolites are known to serve a wide range of specialized functions including communication, developmental control and defense. Genome sequencing of several fungal model species revealed that the majority of predicted secondary metabolite related genes are silent in laboratory strains, indicating that fungal secondary metabolites remain an underexplored resource of bioactive molecules. In this study, we combine heterologous expression of regulatory proteins in Aspergillus nidulans with systematic variation of growth conditions and observe induced synthesis of insect juvenile hormone-III and methyl farnesoate. Both compounds are sesquiterpenes belonging to the juvenile hormone class. Juvenile hormones regulate developmental and metabolic processes in insects and crustaceans, but have not previously been reported as fungal metabolites. We found that feeding by Drosophila melanogaster larvae induced synthesis of juvenile hormone in A. nidulans indicating a possible role of juvenile hormone biosynthesis in affecting fungal-insect antagonisms

The COACH risk engine: a multistate model for predicting survival and hospitalization in patients with heart failure

Aims: Several models for predicting the prognosis of heart failure (HF) patients have been developed, but all of them focus on a single outcome variable, such as all-cause mortality. The purpose of this study was to develop a multistate model for simultaneously predicting survival and HF-related hospitalization in patients discharged alive from hospital after recovery from acute HF. &lt;p/&gt;Methods and results: The model was derived in the COACH (Coordinating Study Evaluating Outcomes of Advising and Counseling in Heart Failure) cohort, a multicentre, randomized controlled trial in which 1023 patients were enrolled after hospitalization because of HF. External validation was attained with the FINN-AKVA (Finish Acute Heart Failure Study) cohort, a prospective, multicentre study with 620 patients hospitalized due to acute HF. The observed vs. predicted 18-month survival was 72.1% vs. 72.3% in the derivation cohort and 71.4% vs. 71.2% in the validation cohort. The corresponding values of the c statistic were 0.733 [95% confidence interval (CI) 0.705–0.761] and 0.702 (95% CI 0.663–0.744), respectively. The model's accuracy in predicting HF hospitalization was excellent, with predicted values that closely resembled the values observed in the derivation cohort. &lt;p/&gt;Conclusion: The COACH risk engine accurately predicted survival and various measures of recurrent hospitalization in (acute) HF patients. It may therefore become a valuable tool in improving and personalizing patient care and optimizing the use of scarce healthcare resources

The PTPN22 C1858T gene variant is associated with proinsulin in new-onset type 1 diabetes

<p>Abstract</p> <p>Background</p> <p>The protein tyrosine phosphatase nonreceptor type 2 (<it>PTPN22</it>) has been established as a type 1 diabetes susceptibility gene. A recent study found the C1858T variant of this gene to be associated with lower residual fasting C-peptide levels and poorer glycemic control in patients with type 1 diabetes. We investigated the association of the C1858T variant with residual beta-cell function (as assessed by stimulated C-peptide, proinsulin and insulin dose-adjusted HbA_1c), glycemic control, daily insulin requirements, diabetic ketoacidosis (DKA) and diabetes-related autoantibodies (IA-2A, GADA, ICA, ZnT8Ab) in children during the first year after diagnosis of type 1 diabetes.</p> <p>Methods</p> <p>The C1858T variant was genotyped in an international cohort of children (n = 257 patients) with newly diagnosed type 1 diabetes during 12 months after onset. We investigated the association of this variant with liquid-meal stimulated beta-cell function (proinsulin and C-peptide) and antibody status 1, 6 and 12 months after onset. In addition HbA_1cand daily insulin requirements were determined 1, 3, 6, 9 and 12 months after diagnosis. DKA was defined at disease onset.</p> <p>Results</p> <p>A repeated measurement model of all time points showed the stimulated proinsulin level is significantly higher (22%, p = 0.03) for the T allele carriers the first year after onset. We also found a significant positive association between proinsulin and IA levels (est.: 1.12, p = 0.002), which did not influence the association between <it>PTPN22 </it>and proinsulin (est.: 1.28, p = 0.03).</p> <p>Conclusions</p> <p>The T allele of the C1858T variant is positively associated with proinsulin levels during the first 12 months in newly diagnosed type 1 diabetes children.</p

Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood : An individual participant data meta-analysis

Background Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these effects differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact. Methods and findings We conducted an individual participant data meta-analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges, with the risks of overweight/obesity in early (2.0-5.0 years), mid (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal sociodemographic and lifestylerelated characteristics. We observed that higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (odds ratios [ORs] for overweight/obesity in early, mid, and late childhood, respectively: OR 1.66 [95% CI: 1.56, 1.78], OR 1.91 [95% CI: 1.85, 1.98], and OR 2.28 [95% CI: 2.08, 2.50] for maternal overweight; OR 2.43 [95% CI: 2.24, 2.64], OR 3.12 [95% CI: 2.98, 3.27], and OR 4.47 [95% CI: 3.99, 5.23] for maternal obesity; and OR 1.39 [95% CI: 1.30, 1.49], OR 1.55 [95% CI: 1.49, 1.60], and OR 1.72 [95% CI: 1.56, 1.91] for excessive gestational weight gain). The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2% to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (p-values for interactions of maternal BMI with gestational weight gain: p = 0.038, p <0.001, and p = 0.637 in early, mid, and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North America, and Australia, results need to be interpreted with caution with respect to other populations. Conclusions In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.Peer reviewe