Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

BACKGROUND: Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. METHODS: Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. RESULTS: Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. CONCLUSION: Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions

A Visual Query System for the Specification and Scientific Analysis of Continual Queries

The lack of a facility that would allow nonprogrammers to easily formulate temporal ad hoc analyses over a network of heterogeneous, constantly-updated data sources has been a significant impediment to research, particularly in the scientific community. In this paper we describe WebFormulate, an Internet-based system which facilitates the development of analyses using information obtained from databases on the Internet. The main distinction between this system and existing Internet facilities to retrieve information and assimilate it into computations is that WebFormulate provides the necessary facilities to perform continual queries, developing and maintaining dynamic links such that computations and reports automatically maintain themselves. A further distinction is that this system is specifically designed for users of spreadsheet-level ability, rather than professional programmers

Neurons in the Dorsomedial Hypothalamus Promote, Prolong, and Deepen Torpor in the Mouse

Torpor is a naturally occurring, hypometabolic, hypothermic state engaged by a wide range of animals in response to imbalance between the supply and demand for nutrients. Recent work has identified some of the key neuronal populations involved in daily torpor induction in mice, in particular, projections from the preoptic area of the hypothalamus to the dorsomedial hypothalamus (DMH). The DMH plays a role in thermoregulation, control of energy expenditure, and circadian rhythms, making it well positioned to contribute to the expression of torpor. We used activity-dependent genetic TRAPing techniques to target DMH neurons that were active during natural torpor bouts in female mice. Chemogenetic reactivation of torpor-TRAPed DMH neurons in calorie-restricted mice promoted torpor, resulting in longer and deeper torpor bouts. Chemogenetic inhibition of torpor-TRAPed DMH neurons did not block torpor entry, suggesting a modulatory role for the DMH in the control of torpor. This work adds to the evidence that the preoptic area of the hypothalamus and the DMH form part of a circuit within the mouse hypothalamus that controls entry into daily torpor

Are multiple primary outcomes analysed appropriately in randomised controlled trials? A review

To review how multiple primary outcomes are currently considered in the analysis of randomised controlled trials. We briefly describe the methods available to safeguard the inferences and to raise awareness of the potential problems caused by multiple outcomes

Tocilizumab in COVID-19: a meta-analysis, trial sequential analysis, and meta-regression of randomized-controlled trials

PURPOSE: Interleukin-6 (IL-6) levels discriminate between patients with mild and severe COVID-19, making IL-6 inhibition an attractive therapeutic strategy. We conducted a systematic review, meta-analysis, trial sequential analysis (TSA), and meta-regression of randomized-controlled trials to ascertain the benefit of IL-6 blockade with tocilizumab for COVID-19. METHODS: We included randomized-controlled trials (RCTs) allocating patients with COVID-19 to tocilizumab. Our control group included standard care or placebo. Trials co-administering other pharmacological interventions for COVID-19 were not excluded. Primary outcome was 28–30 day mortality. Secondary outcomes included progression-to-severe disease defined as need for mechanical ventilation, intensive-care unit (ICU) admission, or a composite. RESULTS: We identified 10 RCTs using tocilizumab, 9 of which reported primary outcome data (mortality), recruiting 6493 patients with 3358 (52.2%) allocated to tocilizumab. Tocilizumab may be associated with an improvement in mortality (24.4% vs. 29.0%; OR 0.87 [0.74–1.01]; p = 0.07; I2 = 10%; TSA adjusted CI 0.66–1.14). Meta-regression suggested a relationship between treatment effect and mortality risk, with benefit at higher levels of risk (logOR vs %risk beta = −0.018 [−0.037 to −0.002]; p = 0.07). Tocilizumab did reduce the need for mechanical ventilation and was associated with a benefit in the composite secondary outcome but did not reduce ICU admission. CONCLUSIONS: For hospitalized COVID-19 patients, there is some evidence that tocilizumab use may be associated with a short-term mortality benefit, but further high-quality data are required. Its benefits may also lie in reducing the need for mechanical ventilation

Biological sex is associated with heterogeneous responses to IL-6 receptor inhibitor treatment in COVID-19—A retrospective cohort study

COVID-19 is associated with higher inflammatory markers, illness severity and mortality in males compared to females. Differences in immune responses to COVID-19 may underpin sex- specific outcome differences. We hypothesised that anti-IL-6 receptor monoclonal antibodies are associated with heterogenous treatment effects between male and female patients. We conducted a retrospective cohort study assessing the interaction between biological sex and anti-IL-6 receptor antibody treatment with respect to hospital mortality or progression of respiratory failure. We used a Cox proportional hazards regression model to adjust for age, ethnicity, steroid use, baseline C-reactive protein, and COVID-19 variant. We included 1274 patients, of which 58% were male and 15% received anti-IL-6 receptor antibodies. There was a significant interaction between sex and anti-IL-6 receptor antibody use on progression to respiratory failure or death (p = 0.05). For patients who did not receive anti-IL-6 receptor antibodies, the risk of death was slightly higher in males (HR = 1.13 (0.72–1.79)), whereas in patients who did receive anti-IL-6 receptor antibodies, the risk was lower in males (HR = 0.65 (0.32–1.33)). There was a heterogenous treatment effect with anti-IL-6 receptor antibodies between males and females; with anti-IL-6 receptor antibody use having a greater benefit in preventing progression to respiratory failure or death in males (p = 0.05)

Protocol for a prospective double-blind, randomised, placebo-controlled feasibility trial of octreotide infusion during liver transplantation

Introduction Liver transplantation is a complex operation that can provide significant improvements in quality of life and survival to the recipients. However, serious complications are common and include major haemorrhage, hypotension and renal failure. Blood transfusion and the development of acute kidney injury lead to both short-term and long-term poor patient outcomes, including an increased risk of death, graft failure, length of stay and reduced quality of life. Octreotide may reduce the incidence of renal dysfunction, perioperative haemorrhage and enhance intraoperative blood pressure. However, octreotide does have risks, including resistant bradycardia, hyperglycaemia and hypoglycaemia and QT prolongation. Hence, a randomised controlled trial of octreotide during liver transplantation is needed to determine the cost-efficacy and safety of its use; this study represents a feasibility study prior to this trial. Methods and analysis We describe a multicentre, double-blind, randomised, placebo-controlled feasibility study of continuous infusion of octreotide during liver transplantation surgery. We will recruit 30 adult patients at two liver transplant centres. A blinded infusion during surgery will be administered in a 2:1 ratio of octreotide:placebo. The primary outcomes will determine the feasibility of this study design. These include the recruitment ratio, correct administration of blinded study intervention, adverse event rates, patient and clinician enrolment refusal and completion of data collection. Secondary outcome measures of efficacy and safety will help shape future trials by assessing potential primary outcome measures and monitoring safety end points. No formal statistical tests are planned. This manuscript represents study protocol number 1.3, dated 2 June 2021. Ethics and dissemination This study has received Research Ethics Committee approval. The main study outcomes will be submitted to an open-access journal. Trial sponsor The Joint Research Office, University College London, UK. Neither the sponsor nor the funder have any role in study design, collection, management, analysis and interpretation of data, writing of the study report or the decision to submit the report for publication. Trial registration The study is registered with ClinicalTrials.gov (NCT04941911) with recruitment due to start in August 2021 with anticipated completion in July 2022. Clinical trials unit Surgical and Interventional Group, Division of Surgery & Interventional Science, University College London

The Abdominal Aortic Aneurysm Statistically Corrected Operative Risk Evaluation (AAA SCORE) for predicting mortality after open and endovascular interventions

BackgroundAccurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data.MethodsUsing data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis.ResultsA total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis.ConclusionsWe have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data