Safety and Efficacy of Liraglutide, 3.0 mg, Once Daily vs Placebo in Patients With Poor Weight Loss Following Metabolic Surgery: The BARI-OPTIMISE Randomized Clinical Trial.

Importance: Metabolic surgery leads to weight loss and improved health, but these outcomes are highly variable. Poor weight loss is associated with lower circulating levels of glucagon-like peptide-1 (GLP-1). Objective: To assess the efficacy and safety of the GLP-1 receptor agonist, liraglutide, 3.0 mg, on percentage body weight reduction in patients with poor weight loss and suboptimal GLP-1 response after metabolic surgery. Design, Setting, and Participants: The Evaluation of Liraglutide 3.0 mg in Patients With Poor Weight Loss and a Suboptimal Glucagon-Like Peptide-1 Response (BARI-OPTIMISE) randomized placebo-controlled trial recruited adult patients at least 1 year after metabolic surgery who had experienced 20% or less body weight loss from the day of surgery and a suboptimal nutrient-stimulated GLP-1 response from 2 hospitals in London, United Kingdom, between October 2018 and November 2019. Key exclusion criteria were type 1 diabetes; severe concomitant psychiatric, gastrointestinal, cardiac, kidney or metabolic disease; and use of insulin, GLP-1 receptor analogues, and medication that can affect weight. The study period was 24 weeks followed by a 4-week follow-up period. Last participant follow-up was completed in June 2020. All participants and clinical study personnel were blinded to treatment allocation. Of 154 assessed for eligibility, 70 met trial criteria and were included in the study, and 57 completed follow-up. Interventions: Liraglutide, 3.0 mg, once daily or placebo as an adjunct to lifestyle intervention with a 500-kcal daily energy deficit for 24 weeks, on a 1:1 allocation by computer-generated randomization sequence, stratified by surgery type (Roux-en-Y gastric bypass [RYGB] or sleeve gastrectomy [SG]) and type 2 diabetes status. Main Outcome and Measures: The primary outcome was change in percentage body weight from baseline to the end of the 24-week study period based on an intention-to-treat analysis. Participant safety was assessed through monitoring of biochemical parameters, including kidney and liver function, physical examination, and assessment for adverse events. Results: A total of 70 participants (mean [SD] age, 47.6 [10.7] years; 52 [74%] female) with a poor weight loss response following RYGB or SG were randomized to receive 3.0-mg liraglutide (n = 35) or placebo (n = 35). All participants received at least 1 dose of the trial drug. Eight participants discontinued treatment (4 per group), and 2 in the 3.0-mg liraglutide group and 1 in the placebo group were lost to follow-up. Due to COVID-19 restrictions, 3 participants in the 3.0-mg liraglutide group and 7 in the placebo group were unable to attend their final in-person assessment. Estimated change in mean (SD) percentage body weight from baseline to week 24 was -8.82 (4.94) with liraglutide, 3.0 mg (n = 31), vs -0.54 (3.32) with placebo (n = 26). The mean difference in percentage body weight change for liraglutide, 3.0 mg, vs placebo was -8.03 (95% CI, -10.39 to -5.66; P < .001). Adverse events, predominantly gastrointestinal, were more frequent with liraglutide, 3.0 mg (28 events [80%]), than placebo (20 events [57%]). There were no serious adverse events and no treatment-related deaths. Conclusion and Relevance: These findings support the use of adjuvant liraglutide, 3.0 mg, for weight management in patients with poor weight loss and suboptimal GLP-1 response after metabolic surgery. Trial Registration: ClinicalTrials.gov Identifier: NCT03341429.

Outlier identification and monitoring of institutional or clinician performance: an overview of statistical methods and application to national audit data.

BACKGROUND: Institutions or clinicians (units) are often compared according to a performance indicator such as in-hospital mortality. Several approaches have been proposed for the detection of outlying units, whose performance deviates from the overall performance. METHODS: We provide an overview of three approaches commonly used to monitor institutional performances for outlier detection. These are the common-mean model, the 'Normal-Poisson' random effects model and the 'Logistic' random effects model. For the latter we also propose a visualisation technique. The common-mean model assumes that the underlying true performance of all units is equal and that any observed variation between units is due to chance. Even after applying case-mix adjustment, this assumption is often violated due to overdispersion and a post-hoc correction may need to be applied. The random effects models relax this assumption and explicitly allow the true performance to differ between units, thus offering a more flexible approach. We discuss the strengths and weaknesses of each approach and illustrate their application using audit data from England and Wales on Adult Cardiac Surgery (ACS) and Percutaneous Coronary Intervention (PCI). RESULTS: In general, the overdispersion-corrected common-mean model and the random effects approaches produced similar p-values for the detection of outliers. For the ACS dataset (41 hospitals) three outliers were identified in total but only one was identified by all methods above. For the PCI dataset (88 hospitals), seven outliers were identified in total but only two were identified by all methods. The common-mean model uncorrected for overdispersion produced several more outliers. The reason for observing similar p-values for all three approaches could be attributed to the fact that the between-hospital variance was relatively small in both datasets, resulting only in a mild violation of the common-mean assumption; in this situation, the overdispersion correction worked well. CONCLUSION: If the common-mean assumption is likely to hold, all three methods are appropriate to use for outlier detection and their results should be similar. Random effect methods may be the preferred approach when the common-mean assumption is likely to be violated.

The accuracy of clinician predictions of survival in the Prognosis in Palliative care Study II (PiPS2): A prospective observational study.

BACKGROUND: Prognostic information is important for patients with cancer, their families, and clinicians. In practice, survival predictions are made by clinicians based on their experience, judgement, and intuition. Previous studies have reported that clinicians' survival predictions are often inaccurate. This study reports a secondary analysis of data from the Prognosis in Palliative care Study II (PiPS2) to assess the accuracy of survival estimates made by doctors and nurses. METHODS AND FINDINGS: Adult patients (n = 1833) with incurable, locally advanced or metastatic cancer, recently referred to palliative care services (community teams, hospital teams, and inpatient palliative care units) were recruited. Doctors (n = 431) and nurses (n = 777) provided independent prognostic predictions and an agreed multi-professional prediction for each patient. Clinicians provided prognostic estimates in several formats including predictions about length of survival and probability of surviving to certain time points. There was a minimum follow up of three months or until death (whichever was sooner; maximum follow-up 783 days). Agreed multi-professional predictions about whether patients would survive for days, weeks or months+ were accurate on 61.9% of occasions. The positive predictive value of clinicians' predictions about imminent death (within one week) was 77% for doctors and 79% for nurses. The sensitivity of these predictions was low (37% and 35% respectively). Specific predictions about how many weeks patients would survive were not very accurate but showed good discrimination (patients estimated to survive for shorted periods had worse outcomes). The accuracy of clinicians' probabilistic predictions (assessed using Brier's scores) was consistently better than chance, improved with proximity to death and showed good discrimination between groups of patients with different survival outcomes. CONCLUSIONS: Using a variety of different approaches, this study found that clinicians predictions of survival show good discrimination and accuracy, regardless of whether the predictions are about how long or how likely patients are to survive. Accuracy improves with proximity to death. Although the positive predictive value of estimates of imminent death are relatively high, the sensitivity of such predictions is relatively low. Despite limitations, the clinical prediction of survival should remain the benchmark against which any innovations in prognostication are judged. STUDY REGISTRATION: ISRCTN13688211. http://www.isrctn.com/ISRCTN13688211.

The role of the electrocardiographic phenotype in risk stratification for sudden cardiac death in childhood hypertrophic cardiomyopathy.

AIMS: The 12-lead electrocardiogram (ECG) is routinely performed in children with hypertrophic cardiomyopathy (HCM). An ECG risk score has been suggested as a useful tool for risk stratification, but this has not been independently validated. This aim of this study was to describe the ECG phenotype of childhood HCM in a large, international, multi-centre cohort and investigate its role in risk prediction for arrhythmic events. METHODS AND RESULTS: Data from 356 childhood HCM patients with a mean age of 10.1 years (±4.5) were collected from a retrospective, multi-centre international cohort. Three hundred and forty-seven (97.5%) patients had ECG abnormalities at baseline, most commonly repolarization abnormalities (n = 277, 77.8%); left ventricular hypertrophy (n = 240, 67.7%); abnormal QRS axis (n = 126, 35.4%); or QT prolongation (n = 131, 36.8%). Over a median follow-up of 3.9 years (interquartile range 2.0-7.7), 25 (7%) had an arrhythmic event, with an overall annual event rate of 1.38 (95% CI 0.93-2.04). No ECG variables were associated with 5-year arrhythmic event on univariable or multivariable analysis. The ECG risk score threshold of >5 had modest discriminatory ability [C-index 0.60 (95% CI 0.484-0.715)], with corresponding negative and positive predictive values of 96.7% and 6.7. CONCLUSION: In a large, international, multi-centre cohort of childhood HCM, ECG abnormalities were common and varied. No ECG characteristic, either in isolation or combined in the previously described ECG risk score, was associated with 5-year sudden cardiac death risk. This suggests that the role of baseline ECG phenotype in improving risk stratification in childhood HCM is limited.

External validation of the HCM Risk-Kids model for predicting sudden cardiac death in childhood hypertrophic cardiomyopathy.

AIMS: Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM). The newly developed HCM Risk-Kids model provides clinicians with individualized estimates of risk. The aim of this study was to externally validate the model in a large independent, multi-centre patient cohort. METHODS AND RESULTS: A retrospective, longitudinal cohort of 421 patients diagnosed with HCM aged 1-16 years independent of the HCM Risk-Kids development and internal validation cohort was studied. Data on HCM Risk-Kids predictor variables (unexplained syncope, non-sustained ventricular tachycardia, maximal left ventricular wall thickness, left atrial diameter, and left ventricular outflow tract gradient) were collected from the time of baseline clinical evaluation. The performance of the HCM Risk-Kids model in predicting risk at 5 years was assessed. Twenty-three patients (5.4%) met the SCD end-point within 5 years, with an overall incidence rate of 2.03 per 100 patient-years [95% confidence interval (CI) 1.48-2.78]. Model validation showed a Harrell's C-index of 0.745 (95% CI 0.52-0.97) and Uno's C-index 0.714 (95% 0.58-0.85) with a calibration slope of 1.15 (95% 0.51-1.80). A 5-year predicted risk threshold of ≥6% identified 17 (73.9%) SCD events with a corresponding C-statistic of 0.702 (95% CI 0.60-0.81). CONCLUSIONS: This study reports the first external validation of the HCM Risk-Kids model in a large and geographically diverse patient population. A 5-year predicted risk of ≥6% identified over 70% of events, confirming that HCM Risk-Kids provides a method for individualized risk predictions and shared decision-making in children with HCM.

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.

Study recruitment factors in advanced cancer: the Prognosis in Palliative care Study II (PiPS2) - a multicentre, prospective, observational cohort project.

OBJECTIVES: The Prognosis in Palliative care Study II (PiPS2) was a large multicentre observational study validating prognostic tools in patients with advanced cancer. Many palliative care studies fail to reach their recruitment target. To inform future studies, PiPS2 rigorously monitored and identified any potential recruitment barriers. METHODS: Key recruitment stages (ie, whether patients were eligible for the study, approached by the researchers and whether consent was obtained for enrolment) were monitored via comprehensive screening logs at participating sites (inpatient hospices, hospitals and community palliative care teams). The reasons for patients' ineligibility, inaccessibility or decision not to consent were documented. RESULTS: 17 014 patients were screened across 27 participating sites over a 20-month recruitment period. Of those, 4642 (27%) were ineligible for participation in the study primarily due to non-cancer diagnoses. Of 12 372 eligible patients, 9073 (73%) were not approached, the most common reason being a clinical decision not to do so. Other reasons included patients' death or discharge before they were approached by the researchers. Of the 3299 approached patients, 1458 (44%) declined participation mainly because of feeling too unwell, experiencing severe distress or having other competing priorities. 11% (n=1841/17 014) of patients screened were enrolled in the study, representing 15% (n=1841/12 372) of eligible patients. Different recruitment patterns were observed across inpatient hospice, hospital and community palliative care teams. CONCLUSIONS: The main barrier to recruitment was 'accessing' potentially eligible patients. Monitoring key recruitment stages may help to identify barriers and facilitators to enrolment and allow results to be put into better context. TRIAL REGISTRATION NUMBER: ISRCTN13688211.

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study.

BACKGROUND: The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'. OBJECTIVES: The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use. DESIGN: This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals. SETTING: Community, hospital and hospice palliative care services across England and Wales. PARTICIPANTS: For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals. MAIN OUTCOME MEASURES: For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models. RESULTS: For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation. LIMITATIONS: The results may not be generalisable to other populations. CONCLUSIONS: PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice. FUTURE WORK: A study to evaluate the impact of introducing PiPS into routine clinical practice is needed. TRIAL REGISTRATION: Current Controlled Trials ISRCTN13688211. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.

A prognosis is a prediction about how long someone will live after a diagnosis of illness. The Prognosis in Palliative care Study (PiPS) tools [PiPS-A (Prognosis in Palliative care Study ­ All) and PiPS-B (Prognosis in Palliative care Study ­ Blood), respectively] were designed to predict survival in patients with incurable cancer. Previously, they were found to be as accurate as health-care professionals. The purpose of this study was to find out whether PiPS was more accurate at prognosticating than health-care professionals, to evaluate other prognostic tools and to ask patients, their carers and health-care professionals what they thought about using them. We studied 1833 patients with advanced cancer and calculated their PiPS score and other prognostic scores. We asked health-care professionals to estimate how long the patients would live. We then followed up the patients to find out how long they actually lived and if the predictions made by health-care professionals were as accurate as the predictions made by the prognostic tools. We interviewed patients, their carers and health-care professionals to ask them what they thought about using these prognostic tools. We found that PiPS-B was as accurate as the combined wisdom of a doctor and a nurse at predicting whether patients would live for 'days', 'weeks' or 'months+'. We found that PiPS-A predictions were not as accurate as predictions made by health-care professionals. We found that (where direct comparisons could be made) PiPS-B was better than other prognostic tools. Finally, we found that patients, carers and health-care professionals thought that PiPS tools could be helpful in clinical practice because they would be less subjective than clinicians' intuition. This means that PiPS-B could be considered as a tool to support clinician predictions of survival and may lead to patients and families being able to take more control at the end of their lives. Further research will be required to investigate whether or not this approach actually leads to improvements in care.

Estimation of required sample size for external validation of risk models for binary outcomes.

Risk-prediction models for health outcomes are used in practice as part of clinical decision-making, and it is essential that their performance be externally validated. An important aspect in the design of a validation study is choosing an adequate sample size. In this paper, we investigate the sample size requirements for validation studies with binary outcomes to estimate measures of predictive performance (C-statistic for discrimination and calibration slope and calibration in the large). We aim for sufficient precision in the estimated measures. In addition, we investigate the sample size to achieve sufficient power to detect a difference from a target value. Under normality assumptions on the distribution of the linear predictor, we obtain simple estimators for sample size calculations based on the measures above. Simulation studies show that the estimators perform well for common values of the C-statistic and outcome prevalence when the linear predictor is marginally Normal. Their performance deteriorates only slightly when the normality assumptions are violated. We also propose estimators which do not require normality assumptions but require specification of the marginal distribution of the linear predictor and require the use of numerical integration. These estimators were also seen to perform very well under marginal normality. Our sample size equations require a specified standard error (SE) and the anticipated C-statistic and outcome prevalence. The sample size requirement varies according to the prognostic strength of the model, outcome prevalence, choice of the performance measure and study objective. For example, to achieve an SE < 0.025 for the C-statistic, 60-170 events are required if the true C-statistic and outcome prevalence are between 0.64-0.85 and 0.05-0.3, respectively. For the calibration slope and calibration in the large, achieving SE < 0.15 would require 40-280 and 50-100 events, respectively. Our estimators may also be used for survival outcomes when the proportion of censored observations is high.

Community Occupational Therapy for people with dementia and family carers (COTiD-UK) versus treatment as usual (Valuing Active Life in Dementia [VALID]) study: A single-blind, randomised controlled trial.

BACKGROUND: We aimed to estimate the clinical effectiveness of Community Occupational Therapy for people with dementia and family carers-UK version (Community Occupational Therapy in Dementia-UK version [COTiD-UK]) relative to treatment as usual (TAU). We hypothesised that COTiD-UK would improve the ability of people with dementia to perform activities of daily living (ADL), and family carers' sense of competence, compared with TAU. METHODS AND FINDINGS: The study design was a multicentre, 2-arm, parallel-group, assessor-masked, individually randomised controlled trial (RCT) with internal pilot. It was conducted in 15 sites across England from September 2014 to January 2018. People with a diagnosis of mild to moderate dementia living in their own home were recruited in pairs with a family carer who provided domestic or personal support for at least 4 hours per week. Pairs were randomised to either receive COTiD-UK, which comprised 10 hours of occupational therapy delivered over 10 weeks in the person with dementia's home or TAU, which comprised the usual local service provision that may or may not include standard occupational therapy. The primary outcome was the Bristol Activities of Daily Living Scale (BADLS) score at 26 weeks. Secondary outcomes for the person with dementia included the following: the BADLS scores at 52 and 78 weeks, cognition, quality of life, and mood; and for the family carer: sense of competence and mood; plus the number of social contacts and leisure activities for both partners. Participants were analysed by treatment allocated. A total of 468 pairs were recruited: people with dementia ranged from 55 to 97 years with a mean age of 78.6 and family carers ranged from 29 to 94 with a mean of 69.1 years. Of the people with dementia, 74.8% were married and 19.2% lived alone. Of the family carers, 72.6% were spouses, and 22.2% were adult children. On randomisation, 249 pairs were assigned to COTiD-UK (62% people with dementia and 23% carers were male) and 219 to TAU (52% people with dementia and 32% carers were male). At the 26 weeks follow-up, data were available for 364 pairs (77.8%). The BADLS score at 26 weeks did not differ significantly between groups (adjusted mean difference estimate 0.35, 95% CI -0.81 to 1.51; p = 0.55). Secondary outcomes did not differ between the groups. In total, 91% of the activity-based goals set by the pairs taking part in the COTiD-UK intervention were fully or partially achieved by the final COTiD-UK session. Study limitations include the following: Intervention fidelity was moderate but varied across and within sites, and the reliance on primarily proxy data focused on measuring the level of functional or cognitive impairment which may not truly reflect the actual performance and views of the person living with dementia. CONCLUSIONS: Providing community occupational therapy as delivered in this study did not improve ADL performance, cognition, quality of life, or mood in people with dementia nor sense of competence or mood in family carers. Future research should consider measuring person-centred outcomes that are more meaningful and closely aligned to participants' priorities, such as goal achievement or the quantity and quality of activity engagement and participation. TRIAL REGISTRATION: Current Controlled Trials ISRCTN10748953.

A comparison of methods for analysing multiple outcome measures in randomised controlled trials using a simulation study.

Multiple primary outcomes are sometimes collected and analysed in randomised controlled trials (RCTs), and are used in favour of a single outcome. By collecting multiple primary outcomes, it is possible to fully evaluate the effect that an intervention has for a given disease process. A simple approach to analysing multiple outcomes is to consider each outcome separately, however, this approach does not account for any pairwise correlations between the outcomes. Any cases with missing values must be ignored, unless an additional imputation step is performed. Alternatively, multivariate methods that explicitly model the pairwise correlations between the outcomes may be more efficient when some of the outcomes have missing values. In this paper, we present an overview of relevant methods that can be used to analyse multiple outcome measures in RCTs, including methods based on multivariate multilevel (MM) models. We perform simulation studies to evaluate the bias in the estimates of the intervention effects and the power of detecting true intervention effects observed when using selected methods. Different simulation scenarios were constructed by varying the number of outcomes, the type of outcomes, the degree of correlations between the outcomes and the proportions and mechanisms of missing data. We compare multivariate methods to univariate methods with and without multiple imputation. When there are strong correlations between the outcome measures (ρ > .4), our simulation studies suggest that there are small power gains when using the MM model when compared to analysing the outcome measures separately. In contrast, when there are weak correlations (ρ < .4), the power is reduced when using univariate methods with multiple imputation when compared to analysing the outcome measures separately.

Seroprevalence of SARS-CoV-2 antibodies in people with an acute loss in their sense of smell and/or taste in a community-based population in London, UK: An observational cohort study.

BACKGROUND: Loss of smell and taste are commonly reported symptoms associated with coronavirus disease 2019 (COVID-19); however, the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in people with acute loss of smell and/or taste is unknown. The study aimed to determine the seroprevalence of SARS-CoV-2 antibodies in a community-based population with acute loss of smell and/or taste and to compare the frequency of COVID-19 associated symptoms in participants with and without SARS-CoV-2 antibodies. It also evaluated whether smell or taste loss are indicative of COVID-19 infection. METHODS AND FINDINGS: Text messages, sent via primary care centers in London, United Kingdom, invited people with loss of smell and/or taste in the preceding month, to participate. Recruitment took place between 23 April 2020 and 14 May 2020. A total of 590 participants enrolled via a web-based platform and responded to questions about loss of smell and taste and other COVID-19-related symptoms. Mean age was 39.4 years (SD ± 12.0) and 69.1% (n = 392) of participants were female. A total of 567 (96.1%) had a telemedicine consultation during which their COVID-19-related symptoms were verified and a lateral flow immunoassay test that detected SARS-CoV-2 immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies was undertaken under medical supervision. A total of 77.6% of 567 participants with acute smell and/or taste loss had SARS-CoV-2 antibodies; of these, 39.8% (n = 175) had neither cough nor fever. New loss of smell was more prevalent in participants with SARS-CoV-2 antibodies, compared with those without antibodies (93.4% versus 78.7%, p < 0.001), whereas taste loss was equally prevalent (90.2% versus 89.0%, p = 0.738). Seropositivity for SARS-CoV-2 was 3 times more likely in participants with smell loss (OR 2.86; 95% CI 1.27-6.36; p < 0.001) compared with those with taste loss. The limitations of this study are the lack of a general population control group, the self-reported nature of the smell and taste changes, and the fact our methodology does not take into account the possibility that a population subset may not seroconvert to develop SARS-CoV-2 antibodies post-COVID-19. CONCLUSIONS: Our findings suggest that recent loss of smell is a highly specific COVID-19 symptom and should be considered more generally in guiding case isolation, testing, and treatment of COVID-19. TRIALS REGISTRATION: ClinicalTrials.gov NCT04377815.

Empowering Better End-of-Life Dementia Care (EMBED-Care): A mixed methods protocol to achieve integrated person-centred care across settings.

OBJECTIVES: Globally, the number of people with dementia who have palliative care needs will increase fourfold over the next 40 years. The Empowering Better End-of-Life Dementia Care (EMBED-Care) Programme aims to deliver a step change in care through a large sequential study, spanning multiple work streams. METHODS: We will use mixed methods across settings where people with dementia live and die: their own homes, care homes, and hospitals. Beginning with policy syntheses and reviews of interventions, we will develop a conceptual framework and underpinning theory of change. We will use linked data sets to explore current service use, care transitions, and inequalities and predict future need for end-of-life dementia care. Longitudinal cohort studies of people with dementia (including young onset and prion dementias) and their carers will describe care transitions, quality of life, symptoms, formal and informal care provision, and costs. Data will be synthesised, underpinned by the Knowledge-to-Action Implementation Framework, to design a novel complex intervention to support assessment, decision making, and communication between patients, carers, and inter-professional teams. This will be feasibility and pilot tested in UK settings. Patient and public involvement and engagement, innovative work with artists, policymakers, and third sector organisations are embedded to drive impact. We will build research capacity and develop an international network for excellence in dementia palliative care. CONCLUSIONS: EMBED-Care will help us understand current and future need, develop novel cost-effective care innovations, build research capacity, and promote international collaborations in research and practice to ensure people live and die well with dementia.

A contingency management intervention to reduce cannabis use and time to relapse in early psychosis: the CIRCLE RCT.

BACKGROUND: Cannabis is the most prevalent illicit substance among people with psychosis, and its use is associated with poorer clinical and social outcomes. However, so far, there has been limited evidence that any treatment is effective for reducing use. Contingency management (CM) is an incentive-based intervention for substance misuse that has a substantial evidence base across a range of substances and cohorts. However, to date there have been no randomised controlled trials (RCTs) of CM as a treatment for cannabis use specifically in psychosis. OBJECTIVE: To conduct a RCT investigating the clinical effectiveness and cost-effectiveness of CM in reducing cannabis use among Early Intervention in Psychosis (EIP) service users. DESIGN: The CIRCLE (Contingency Intervention for Reduction of Cannabis in Early Psychosis) trial was a rater-blinded, multicentre RCT with two arms. Participants were randomised 1 : 1 to either an CM arm, in which participants received CM for cannabis use alongside an optimised treatment-as-usual programme including structured psychoeducation, or a control arm in which participants received the treatment as usual only. SETTING: EIP services across the Midlands and the south-east of England. PARTICIPANTS: The main eligibility criteria were EIP service users with a history of psychosis, aged 18-36 years, and having used cannabis at least once per week during 12 of the previous 24 weeks. INTERVENTION: The CM intervention offered financial incentives (i.e. shopping vouchers) for cannabis abstinence over 12 once-weekly sessions, confirmed using urinalysis. The maximum value in vouchers that participants could receive was £240. MAIN OUTCOME MEASURES: The main outcome was time to relapse, operationalised as admission to an acute mental health service or hospital. The primary outcome was assessed at 18 months post inclusion using electronic patient records. Secondary outcomes assessed the clinical effectiveness and cost-effectiveness of the intervention, for which data were collected at 3 and 18 months. RESULTS: A total of 278 participants were randomised to the CM arm and 273 were randomised to the control arm. In total, 530 (96%) participants were followed up for the primary outcome. There was no significant difference in time to admission between trial arms by 18 months following consent (hazard ratio 1.03, 95% confidence interval 0.76 to 1.40). There were no statistically significant differences in most secondary outcomes, including cannabis use, at either follow-up assessment. There were 58 serious adverse events, comprising 52 inpatient episodes, five deaths and one arrest. LIMITATIONS: Participant retention was low at 18 months, limiting the assessment of secondary outcomes. A different CM intervention design or reward level may have been effective. CONCLUSIONS: The CM intervention did not appear to be effective in reducing cannabis use and acute relapse among people with early psychosis and problematic cannabis use. FUTURE WORK: Cannabis use is still a significant clinical concern in this population. A pressing need remains to identify suitable treatments. A wider perspective on the social circumstances of young people with psychosis may be needed for a successful intervention to be found. TRIAL REGISTRATION: Current Controlled Trials ISRCTN33576045. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 45. See the NIHR Journals Library website for further project information.

A large proportion of people with psychosis use cannabis, despite the negative impact that it has on their recovery. So far, a clearly effective way of helping young people in the early stages of psychosis to cut down their cannabis use has not been found. The CIRCLE trial investigated if an approach known as contingency management (CM) would be beneficial for this group. This approach involves offering voucher rewards for not using cannabis. It has been effective in addressing drug use problems in general, but there is not much evidence about its effects on cannabis use in those with psychosis. A total of 551 service users with psychosis who used cannabis agreed to enter the trial. Half of the sample group was chosen by a chance method to receive CM. The other half formed a comparison group. The CM group received shopping vouchers if urine samples showed that they had not used cannabis for the previous week, measured over 12 weekly sessions. Participants could obtain £240-worth of vouchers if they did not use cannabis during the treatment period. The participants in both groups were also offered a six-session psychoeducation programme about the pros and cons of cannabis use and ways to reduce use of it. The main comparison in the trial was the average length of time in each group before a relapse of psychosis occurred, which was recorded for each participant over 18 months after they joined the trial. The results found no difference between the two trial groups in this measure. Furthermore, there were no differences found between the groups in terms of the levels of cannabis use, clinical symptoms, or engagement with work or education. However, a cost-effectiveness analysis found an 85% chance of CM being more effective than the treatment-as-usual psychoeducation package, which appears to be because of the lower use of inpatient services by those receiving CM. However, it is difficult to understand why this was, because there was no drop in cannabis use. The results suggest that CM is unlikely to be clinically effective and that alternative treatments are still needed.

Clinical and cost-effectiveness of contingency management for cannabis use in early psychosis: the CIRCLE randomised clinical trial.

BACKGROUND: Cannabis is the most commonly used illicit substance amongst people with psychosis. Continued cannabis use following the onset of psychosis is associated with poorer functional and clinical outcomes. However, finding effective ways of intervening has been very challenging. We examined the clinical and cost-effectiveness of adjunctive contingency management (CM), which involves incentives for abstinence from cannabis use, in people with a recent diagnosis of psychosis. METHODS: CIRCLE was a pragmatic multi-centre randomised controlled trial. Participants were recruited via Early Intervention in Psychosis (EIP) services across the Midlands and South East of England. They had had at least one episode of clinically diagnosed psychosis (affective or non-affective); were aged 18 to 36; reported cannabis use in at least 12 out of the previous 24 weeks; and were not currently receiving treatment for cannabis misuse, or subject to a legal requirement for cannabis testing. Participants were randomised via a secure web-based service 1:1 to either an experimental arm, involving 12 weeks of CM plus a six-session psychoeducation package, or a control arm receiving the psychoeducation package only. The total potential voucher reward in the CM intervention was £240. The primary outcome was time to acute psychiatric care, operationalised as admission to an acute mental health service (including community alternatives to admission). Primary outcome data were collected from patient records at 18 months post-consent by assessors masked to allocation. The trial was registered with the ISRCTN registry, number ISRCTN33576045. RESULTS: Five hundred fifty-one participants were recruited between June 2012 and April 2016. Primary outcome data were obtained for 272 (98%) in the CM (experimental) group and 259 (95%) in the control group. There was no statistically significant difference in time to acute psychiatric care (the primary outcome) (HR 1.03, 95% CI 0.76, 1.40) between groups. By 18 months, 90 (33%) of participants in the CM group, and 85 (30%) of the control groups had been admitted at least once to an acute psychiatric service. Amongst those who had experienced an acute psychiatric admission, the median time to admission was 196 days (IQR 82, 364) in the CM group and 245 days (IQR 99, 382) in the control group. Cost-effectiveness analyses suggest that there is an 81% likelihood that the intervention was cost-effective, mainly resulting from higher mean inpatient costs for the control group compared with the CM group; however, the cost difference between groups was not statistically significant. There were 58 adverse events, 27 in the CM group and 31 in the control group. CONCLUSIONS: Overall, these results suggest that CM is not an effective intervention for improving the time to acute psychiatric admission or reducing cannabis use in psychosis, at least at the level of voucher reward offered.

Development of a Novel Risk Prediction Model for Sudden Cardiac Death in Childhood Hypertrophic Cardiomyopathy (HCM Risk-Kids).

Importance: Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk. Objective: To develop and validate an SCD risk prediction model that provides individualized risk estimates. Design, Setting, and Participants: A prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017. Exposures: The model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping. Main Outcomes and Measures: A composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise). Results: Of the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years. Conclusions and Relevance: This new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations.

Correction to: Methods to adjust for multiple comparisons in the analysis and sample size calculation of randomised controlled trials with multiple primary outcomes.

In the original publication of this article [1], "=1 - = 1 - ≈ s" was mistakenly added after the sentence " In this method, the unadjusted p-values pj are multiplied by the number of primary outcome" in the Methods section, and should be deleted.

Methods to adjust for multiple comparisons in the analysis and sample size calculation of randomised controlled trials with multiple primary outcomes.

BACKGROUND: Multiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it's important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p-values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It's also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: 'disjunctive power', the probability of detecting at least one true intervention effect across all the outcomes or 'marginal power' the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power. METHODS: We use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data. RESULTS: The Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.

A validation study of the European Society of Cardiology guidelines for risk stratification of sudden cardiac death in childhood hypertrophic cardiomyopathy.

AIMS: Sudden cardiac death (SCD) is the most common cause of death in children with hypertrophic cardiomyopathy (HCM). The European Society of Cardiology (ESC) recommends consideration of an implantable cardioverter-defibrillator (ICD) if two or more clinical risk factors (RFs) are present, but this approach to risk stratification has not been formally validated. METHODS AND RESULTS: Four hundred and eleven paediatric HCM patients were assessed for four clinical RFs in accordance with current ESC recommendations: severe left ventricular hypertrophy, unexplained syncope, non-sustained ventricular tachycardia, and family history of SCD. The primary endpoint was a composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate ICD therapy, or sustained ventricular tachycardia), defined as a major arrhythmic cardiac event (MACE). Over a follow-up period of 2890 patient years (median 5.5 years), MACE occurred in 21 patients (7.5%) with 0 RFs, 19 (16.8%) with 1 RFs, and 3 (18.8%) with 2 or more RFs. Corresponding incidence rates were 1.13 [95% confidence interval (CI) 0.7-1.73], 2.07 (95% CI 1.25-3.23), and 2.52 (95% CI 0.53-7.35) per 100 patient years at risk. Patients with two or more RFs did not have a higher incidence of MACE (log-rank test P = 0.34), with a positive and negative predictive value of 19% and 90%, respectively. The C-statistic was 0.62 (95% CI 0.52-0.72) at 5 years. CONCLUSIONS: The incidence of MACE is higher for patients with increasing numbers of clinical RFs. However, the current ESC guidelines have a low ability to discriminate between high- and low-risk individuals.