Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems.

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions. We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging. This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

Clinician preference instrumental variable analysis of the effectiveness of magnesium supplementation for atrial fibrillation prophylaxis in critical care.

Atrial fibrillation is a frequently encountered condition in critical illness and causes adverse effects including haemodynamic decompensation, stroke and prolonged hospital stay. It is a common practice in critical care to supplement serum magnesium for the purpose of preventing episodes of atrial fibrillation. However, no randomised studies support this practice in the non-cardiac surgery critical care population, and the effectiveness of magnesium supplementation is unclear. We sought to investigate the effectiveness of magnesium supplementation in preventing the onset of atrial fibrillation in a mixed critical care population. We conducted a single centre retrospective observational study of adult critical care patients. We utilised a natural experiment design, using the supplementation preference of the bedside critical care nurse as an instrumental variable. Using routinely collected electronic patient data, magnesium supplementation opportunities were defined and linked to the bedside nurse. Nurse preference for administering magnesium was obtained using multilevel modelling. The results were used to define "liberal" and "restrictive" supplementation groups, which were inputted into an instrumental variable regression to obtain an estimate of the effect of magnesium supplementation. 9114 magnesium supplementation opportunities were analysed, representing 2137 critical care admissions for 1914 patients. There was significant variation in magnesium supplementation practices attributable to the individual nurse, after accounting for covariates. The instrumental variable analysis showed magnesium supplementation was associated with a 3% decreased relative risk of experiencing an atrial fibrillation event (95% CI - 0.06 to - 0.004, p = 0.03). This study supports the strategy of routine supplementation, but further work is required to identify optimal serum magnesium targets for atrial fibrillation prophylaxis.

Embedded point of care randomisation for evaluating comparative effectiveness questions: PROSPECTOR-critical care feasibility study protocol.

INTRODUCTION: Many routinely administered treatments lack evidence as to their effectiveness. When treatments lack evidence, patients receive varying care based on the preferences of clinicians. Standard randomised controlled trials are unsuited to comparisons of different routine treatment strategies, and there remains little economic incentive for change.Integrating clinical trial infrastructure into electronic health record systems offers the potential for routine treatment comparisons at scale, through reduced trial costs. To date, embedded trials have automated data collection, participant identification and eligibility screening, but randomisation and consent remain manual and therefore costly tasks.This study will investigate the feasibility of using computer prompts to allow flexible randomisation at the point of clinical decision making. It will compare the effectiveness of two prompt designs through the lens of a candidate research question-comparing liberal or restrictive magnesium supplementation practices for critical care patients. It will also explore the acceptability of two consent models for conducting comparative effectiveness research. METHODS AND ANALYSIS: We will conduct a single centre, mixed-methods feasibility study, aiming to recruit 50 patients undergoing elective surgery requiring postoperative critical care admission. Participants will be randomised to either 'Nudge' or 'Preference' designs of electronic point-of-care randomisation prompt, and liberal or restrictive magnesium supplementation.We will judge feasibility through a combination of study outcomes. The primary outcome will be the proportion of prompts displayed resulting in successful randomisation events (compliance with the allocated magnesium strategy). Secondary outcomes will evaluate the acceptability of both prompt designs to clinicians and ascertain the acceptability of pre-emptive and opt-out consent models to patients. ETHICS AND DISSEMINATION: This study was approved by Riverside Research Ethics Committee (Ref: 21/LO/0785) and will be published on completion. TRIAL REGISTRATION NUMBER: NCT05149820.

Causal inference in perioperative medicine observational research: part 1, a graphical introduction.

Graphical models have emerged as a tool to map out the interplay between multiple measured and unmeasured variables, and can help strengthen the case for a causal association between exposures and outcomes in observational studies. In Part 1 of this methods series, we will introduce the reader to graphical models for causal inference in perioperative medicine, and set the framework for Part 2 of the series involving advanced methods for causal inference.

Causal inference in perioperative medicine observational research: part 2, advanced methods.

Although RCTs represent the gold standard in clinical research, most clinical questions cannot be answered using this technique, because of ethical considerations, time, and cost. The goal of observational research in clinical medicine is to gain insight into the relationship between a clinical exposure and patient outcome, in the absence of evidence from RCTs. Observational research offers additional benefit when compared with data from RCTs: the conclusions are often more generalisable to a heterogenous population, which may be of greater value to everyday clinical practice. In Part 2 of this methods series, we will introduce the reader to several advanced methods for supporting the case for causality between an exposure and outcome, including: mediation analysis, natural experiments, and joint effects methods.

SNAP-2 EPICCS: the second Sprint National Anaesthesia Project-EPIdemiology of Critical Care after Surgery: protocol for an international observational cohort study.

INTRODUCTION: The admission of high-risk patients to critical care after surgery is a recommended standard of care. Nevertheless, poor compliance against this recommendation has been repeatedly demonstrated in large epidemiological studies. It is unclear whether this is due to reasons of capacity, equipoise, poor quality clinical care or because hospitals are working creatively to create capacity for augmented care on normal surgical wards. The EPIdemiology of Critical Care after Surgery study aims to address these uncertainties. METHODS AND ANALYSIS: One-week observational cohort study in the UK and Australasia. All patients undergoing inpatient (overnight stay) surgery will be included. All will have prospective data collection on risk factors, surgical procedure and postoperative outcomes including the primary outcome of morbidity (measured using the Postoperative Morbidity Survey on day 7 after surgery) and secondary outcomes including length of stay and mortality. Data will also be collected on critical care referral and admission, surgical cancellations and critical care occupancy. The epidemiology of patient characteristics, processes and outcomes will be described. Inferential techniques (multilevel multivariable regression, propensity score matching and instrumental variable analysis) will be used to evaluate the relationship between critical care admission and postoperative outcome. ETHICS AND DISSEMINATION: The study has received ethical approval from the National Research Ethics Service in the UK and equivalent in Australasia. The collection of patient identifiable data without prior consent has been approved by the Confidentiality Advisory Group (England and Wales) and the Public Privacy and Patient Benefit Panel (Scotland). In these countries, patient identifiable data will be used to link prospectively collected data with national registers of death and inpatient administrative data. The study findings will be disseminated using a multimedia approach with the support of our lay collaborators, to patients, public, policy-makers, clinical and academic audiences.

Relationship between patients' outcomes and the changes in serum creatinine and urine output and RIFLE classification in a large critical care cohort database.

We report the stepwise application of the RIFLE classification in 155,624 admissions in the UK Intensive Care National Audit & Research Centre Case Mix Programme database. The assumptions required to define RIFLE and their relationship with renal replacement therapy (RRT) and ICU mortality were assessed. Previous reports had not explored the method of estimating baseline creatinine, the position of class boundaries, or interactions between urine volume (AKI-U) and the peak/estimated baseline creatinine (AKI-Cr) within 24 h of ICU admission. The risk of developing AKI strongly depended on the assumed GFR increasing from 36 to 58% across the recommended range. AKI-U was often seen without AKI-Cr, and moderate oliguria (under 850 ml/24 h) was a stronger predictor of mortality than any degree of AKI-Cr partly because mortality fell when peak/estimated baseline creatinine ratios exceed fourfold. Mild oliguria (850-1500 ml/24 h) was common (38,928 admissions, 26%) and had a similar association with mortality (relative risk 1.6, 95% CI: 1.5-1.6) as did AKI-Cr defined Failure (risk ratio 1.5, 95% CI: 1.5-1.6). However, AKI-Cr was a strong predictor for RRT, which was used in 17,802 (11%) of admissions. Nearly half (48%) of the Failure patients never received RRT; nonetheless, most (66%) survived critical care. Thus, although the RIFLE classification may be attempted in large population cohorts, there is significant heterogeneity of both renal and, in particular, vital outcomes within each class.

Progress curve analysis for enzyme and microbial kinetic reactions using explicit solutions based on the Lambert W function.

We present a simple method for estimating kinetic parameters from progress curve analysis of biologically catalyzed reactions that reduce to forms analogous to the Michaelis-Menten equation. Specifically, the Lambert W function is used to obtain explicit, closed-form solutions to differential rate expressions that describe the dynamics of substrate depletion. The explicit nature of the new solutions greatly simplifies nonlinear estimation of the kinetic parameters since numerical techniques such as the Runge-Kutta and Newton-Raphson methods used to solve the differential and integral forms of the kinetic equations, respectively, are replaced with a simple algebraic expression. The applicability of this approach for estimating Vmax and Km in the Michaelis-Menten equation was verified using a combination of simulated and experimental progress curve data. For simulated data, final estimates of Vmax and Km were close to the actual values of 1 microM/h and 1 microM, respectively, while the standard errors for these parameter estimates were proportional to the error level in the simulated data sets. The method was also applied to hydrogen depletion experiments by mixed cultures of bacteria in activated sludge resulting in Vmax and Km estimates of 6.531 microM/h and 2.136 microM, respectively. The algebraic nature of this solution, coupled with its relatively high accuracy, makes it an attractive candidate for kinetic parameter estimation from progress curve data.