Handling missing continuous outcome data in a Bayesian network meta-analysis

Background: A Bayesian network meta-analysis (NMA) model is a statistical method aimed at estimating the relative effects of multiple interventions against the same disease. The method has recently gained prominence, leading to the synthesis of the evidence regarding rank probabilities for each treatment. In several cases, an NMA is performed excluding incomplete data of studies retrieved through a systematic review, resulting in a loss of precision and power.  Methods: There are several methods for handling missing or incomplete data in an NMA framework, especially for continuous outcomes. In certain cases, only baseline and follow-up measurements are available; in this framework, to obtain data regarding mean changes, it is necessary to consider the pre-post study correlation. In this context, in a Bayesian setting, several authors suggest imputation strategies for pre-post correlation. In other cases, a variability measure associated with a mean change score might be unavailable. Different imputation methods have been suggested, such as those based on maximum standard deviation imputation. The purpose of this study is to verify the robustness of Bayesian NMA models concerning different imputation strategies through simulations.  Results: Simulation results show that the bias is notably small for every scenario, confirming that rankings provided by models are robust concerning different imputation methods in several heterogeneity-correlation settings.  Conclusions: This NMA method seems to be more robust to missing data imputation when data reported in different studies are generated in a low-heterogeneity scenario. The NMA method seems to be more robust to missing value imputation if the expectation of the prior distribution, defined on the heterogeneity parameter, approaches the true value of the variability across studies.&nbsp

Comparative Effectiveness of DPP-4 Inhibitors Versus Sulfonylurea for the Treatment of Type 2 Diabetes in Routine Clinical Practice: A Retrospective Multicenter Real-World Study

Introduction: DPP-4 inhibitors (DPP4i) and sulfonylureas are popular second-line therapies for type 2 diabetes (T2D), but there is a paucity of real-world studies comparing their effectiveness in routine clinical practice. Methods: This was a multicenter retrospective study on diabetes outpatient clinics comparing the effectiveness of DPP4i versus gliclazide extended release. The primary endpoint was change from baseline in HbA1c. Secondary endpoints were changes in fasting plasma glucose, body weight, and systolic blood pressure. Automated software extracted data from the same clinical electronic chart system at all centers. Propensity score matching (PSM) was used to generate comparable cohorts to perform outcome analysis. Results: We included data on 2410 patients starting DPP4i and 1590 patients starting gliclazide (mainly 30–60 mg/day). At baseline, the two groups differed in disease duration, body weight, blood pressure, HbA1c, fasting glucose, HDL cholesterol, triglycerides, liver enzymes, eGFR, prevalence of microangiopathy, and use of metformin. Among DPP4i molecules, no difference in glycemic effectiveness was detected. In matched cohorts (n = 1316/group), patients starting DPP4i, as compared with patients starting gliclazide, experienced greater reductions in HbA1c (− 0.6% versus − 0.4%; p < 0.001), fasting glucose (− 14.1 mg/dl versus − 8.8 mg/dl; p = 0.007), and body weight (− 0.4 kg versus − 0.1 kg; p = 0.006) after an average 6 months follow-up. DPP4i improved glucose control more than gliclazide, especially in patients who had failed with other glucose-lowering medications or were on basal insulin. Conclusions: This large retrospective real-world study shows that, in routine clinical practice, starting a DPP4i allows better glycemic control than starting low-dose gliclazide. Funding: The Italian Diabetes Society, with external support from AstraZeneca

Use and effectiveness of dapagliflozin in routine clinical practice. An Italian multicenter retrospective study

In randomized controlled trials (RCTs), SGLT-2 inhibitors (SGLT2i) showed glycaemic and extra-glycaemic benefits. The DARWIN-T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes) was a multicenter retrospective study designed to evaluate baseline characteristics of patients receiving dapagliflozin versus selected comparators (DPP-4 inhibitors, gliclazide, or GLP-1 receptor agonists), and drug effectiveness in routine clinical practice. From a population of 281,217 patients, the analysis included 17,285 initiating dapagliflozin or comparator glucose lowering medications (GLM), 6751 of whom had a follow-up examination. At baseline, patients starting dapagliflozin were younger, had a longer disease duration, higher HbA1c, and a more complex history of previous GLM use, but the clinical profile of patients receiving dapagliflozin was changing during the study period. Dapagliflozin reduced HbA1c by 0.7%, body weight by 2.7 kg, and systolic blood pressure by 3.0 mm Hg. Effects of comparator GLM were also within the expected range based on RCTs. This real-world study demonstrates an initial channelling of dapagliflozin to difficult-to-treat patients. Nonetheless, dapagliflozin provided significant benefits on glucose control, body weight, and blood pressure that were in line with findings from RCTs

Impact of a natural versus commercial enteral-feeding on the occurrence of diarrhea in critically ill cardiac surgery patients. A retrospective cohort study

Diarrhea is an important complication in critically ill patients undergoing enteral feeding. The occurrence of diarrhea may lead to systemic and local complications and negatively impacts on nursing workload and patient's wellbeing. An enteral feeding based on blenderized natural food could be beneficial in reducing the risk of diarrhea. No study has compared natural and commercial enteral feedings in critically ill cardiac surgery patients

Whole exome sequencing enhanced imputation identifies 85 metabolite associations in the Alpine CHRIS cohort

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with \u3e5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriche

“Clinical Stability” and Propensity Score Matching in Cardiac Surgery: is the clinical evaluation of treatment efficacy algorithmdependent in small sample size settings?

Background: Propensity score matching represents one of the most popular techniques to deal with treatment allocation bias in observational studies. However, when the number of enrolled patients is very low, the creation of matched set of subjects may highly depend on the model used to estimate individual propensity scores, undermining the stability of consequential clinical findings. In this study, we investigate the potential issues related to the stability of the matched sets created by different propensity score models and we propose some diagnostic tools to evaluate them. Methods: Matched groups of patients were created using five different methods: Logistic Regression, Classification and Regression Trees, Bagging, Random Forest and Generalized Boosted Model. Differences between subjects in the matched sets were evaluated by comparing both pre-treatment covariates and propensity score distributions. We applied our proposal to a cardio-surgical observational study that aims to compare two different procedures of cardiac valve replacement. Results: Both baseline characteristics and propensity score distributions were systematically different across matched samples of patients created with different models used to estimate propensity score. The most relevant differences were observed for the matched set created by estimating individual propensity scores with Classification and Regression Trees algorithm. Conclusion: Clinical stability of matched samples created with different statistical methods should always be evaluated to ensure reliability of final estimates. This work opens the door for future investigations that fully assess the implications of this finding

Machine learning in clinical and epidemiological research: isn't it time for biostatisticians to work on it?

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques

Similar effectiveness of dapagliflozin and GLP-1 receptor agonists concerning combined endpoints in routine clinical practice: A multicentre retrospective study

Aims According to cardiovascular outcome trials, some sodium-glucose contransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) are recommended for secondary cardiovascular prevention in type 2 diabetes (T2D). In this real-world study, we compared the simultaneous reductions in HbA1c, body weight and systolic blood pressure after initiation of dapagliflozin or GLP-1RA as second or a more advanced line of therapy. Materials and methods DARWIN-T2D was a retrospective multi-centre study conducted at diabetes specialist clinics in Italy that compared T2D patients who initiated dapagliflozin or GLP-1RA (exenatide once weekly or liraglutide). Data were collected at baseline and at the first follow-up visit after 3 to 12 months. The primary endpoint was the proportion of patients achieving a simultaneous reduction in HbA1c, body weight and systolic blood pressure. To reduce confounding, we used multivariable adjustment (MVA) or propensity score matching (PSM). Results Totals of 473 patients initiating dapagliflozin and 336 patients initiating GLP-1RA were included. The two groups differed in age, diabetes duration, HbA1c, weight and concomitant medications. The median follow-up was 6 months in both groups. Using MVA or PSM, the primary endpoint was observed in 30% to 32% of patients, with no difference between groups. Simultaneous reduction of HbA1c, BP and SBP by specific threshold, as well as achievement of final goals, did not differ between groups. GLP-1RA reduced HbA1c by 0.3% more than the reduction achieved with dapagliflozin. Conclusion In routine specialist care, initiation of dapagliflozin can be as effective as initiation of a GLP-1RA for attainment of combined risk factor goals

Food Composition Impacts the Accuracy of Wearable Devices When Estimating Energy Intake from Energy-Dense Food

The present study aimed to assess the feasibility and reliability of an a3utomatic food intake measurement device in estimating energy intake from energy-dense foods. Eighteen volunteers aged 20−36 years were recruited from the University of Padova. The device used in the present study was the Bite Counter (Bite Technologies, Pendleton, USA). The rationale of the device is that the wrist movements occurring in the act of bringing food to the mouth present unique patterns that are recognized and recorded by the Bite Counter. Subjects were asked to wear the Bite Counter on the wrist of the dominant hand, to turn the device on before the first bite and to turn it off once he or she finished his or her meal. The accuracy of caloric intake was significantly different among the methods used. In addition, the device’s accuracy in estimating energy intake varied according to the type and amount of macronutrients present, and the difference was independent of the number of bites recorded. Further research is needed to overcome the current limitations of wearable devices in estimating caloric intake, which is not independent of the food being eaten