Coronary sinus reducer for the treatment of refractory angina (ORBITA-COSMIC): a randomised, placebo-controlled trial.

BACKGROUND: The coronary sinus reducer (CSR) is proposed to reduce angina in patients with stable coronary artery disease by improving myocardial perfusion. We aimed to measure its efficacy, compared with placebo, on myocardial ischaemia reduction and symptom improvement. METHODS: ORBITA-COSMIC was a double-blind, randomised, placebo-controlled trial conducted at six UK hospitals. Patients aged 18 years or older with angina, stable coronary artery disease, ischaemia, and no further options for treatment were eligible. All patients completed a quantitative adenosine-stress perfusion cardiac magnetic resonance scan, symptom and quality-of-life questionnaires, and a treadmill exercise test before entering a 2-week symptom assessment phase, in which patients reported their angina symptoms using a smartphone application (ORBITA-app). Patients were randomly assigned (1:1) to receive either CSR or placebo. Both participants and investigators were masked to study assignment. After the CSR implantation or placebo procedure, patients entered a 6-month blinded follow-up phase in which they reported their daily symptoms in the ORBITA-app. At 6 months, all assessments were repeated. The primary outcome was myocardial blood flow in segments designated ischaemic at enrolment during the adenosine-stress perfusion cardiac magnetic resonance scan. The primary symptom outcome was the number of daily angina episodes. Analysis was done by intention-to-treat and followed Bayesian methodology. The study is registered with ClinicalTrials.gov, NCT04892537, and completed. FINDINGS: Between May 26, 2021, and June 28, 2023, 61 patients were enrolled, of whom 51 (44 [86%] male; seven [14%] female) were randomly assigned to either the CSR group (n=25) or the placebo group (n=26). Of these, 50 patients were included in the intention-to-treat analysis (24 in the CSR group and 26 in the placebo group). 454 (57%) of 800 imaged cardiac segments were ischaemic at enrolment, with a median stress myocardial blood flow of 1·08 mL/min per g (IQR 0·77-1·41). Myocardial blood flow in ischaemic segments did not improve with CSR compared with placebo (difference 0·06 mL/min per g [95% CrI -0·09 to 0·20]; Pr(Benefit)=78·8%). The number of daily angina episodes was reduced with CSR compared with placebo (OR 1·40 [95% CrI 1·08 to 1·83]; Pr(Benefit)=99·4%). There were two CSR embolisation events in the CSR group, and no acute coronary syndrome events or deaths in either group. INTERPRETATION: ORBITA-COSMIC found no evidence that the CSR improved transmural myocardial perfusion, but the CSR did improve angina compared with placebo. These findings provide evidence for the use of CSR as a further antianginal option for patients with stable coronary artery disease. FUNDING: Medical Research Council, Imperial College Healthcare Charity, National Institute for Health and Care Research Imperial Biomedical Research Centre, St Mary's Coronary Flow Trust, British Heart Foundation.

Would You Rather: Quantifying Traumatic Brain Injury Survivor Perceptions of Functional Status Through Their Surrogates.

OBJECTIVE: To quantify health utilities of the Glasgow Outcome Scale-Extended (GOSE) states after actual traumatic brain injury (TBI). BACKGROUND: Recovery after TBI is measured using the GOSE, a validated clinical trial endpoint. A recent public survey quantified the health utilities of some GOSE states after hypothetical TBI as worse than death. However, no health utilities exist for disability after actual TBI. METHODS: This national computer-adaptive survey followed Enhancing the Quality and Transparency of Health Research-Checklist for Reporting Results of Internet E-Surveys guidelines and recruited adult TBI survivors (injury >1 year prior) through their available surrogates. Using a standard gamble approach in randomized order, participants gave preferences for post-TBI categorical health states ranging from GOSE 2 to GOSE 8. We calculated median (interquartile range) health utilities for each GOSE state, from -1 (worse than death) to 1 (full health), with 0 as reference (death, GOSE 1). RESULTS: Of 515 eligible, 298 surrogates (58%) consented and completed the scenarios on TBI survivors' behalf. TBI survivors had a current median GOSE 5 (3-7). GOSE 2, GOSE 3, and GOSE 4 were rated worse than death by 89%, 64%, and 38%, respectively. The relationship was nonlinear, and intervals were unequal between states, with a bimodal distribution for GOSE 4. CONCLUSIONS: In this index study of actual post-TBI disability, poor neurological outcomes represented by GOSE 2 to GOSE 4 were perceived as worse than death by at least one in 3 survivors. Similar to previously reported public perceptions after a hypothetical TBI, these long-term perceptions may inform earlier post-TBI shared decision-making, as well as help shape value-based research and quality of care. LEVEL OF EVIDENCE: Level II-economic and value-based evaluations.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Health-Status Outcomes with Invasive or Conservative Care in Coronary Disease.

BACKGROUND: In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS: We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS: At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS: In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).

Initial Invasive or Conservative Strategy for Stable Coronary Disease.

BACKGROUND: Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS: We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS: Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, -1.8 percentage points; 95% CI, -4.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS: Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used. (Funded by the National Heart, Lung, and Blood Institute and others; ISCHEMIA ClinicalTrials.gov number, NCT01471522.).

National Institutes of Health Stroke Scale as an Outcome in Stroke Research: Value of ANCOVA Over Analyzing Change From Baseline.

National Institutes of Health Stroke Scale (NIHSS), measured a few hours to days after stroke onset, is an attractive outcome measure for stroke research. NIHSS at the time of presentation (baseline NIHSS) strongly predicts the follow-up NIHSS. Because of the need to account for the baseline NIHSS in the analysis of follow-up NIHSS as an outcome measure, a common and intuitive approach is to define study outcome as the change in NIHSS from baseline to follow-up (ΔNIHSS). However, this approach has important limitations. Analyzing ΔNIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis. ANCOVA allows for the analysis of follow-up NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model and addresses several challenges of using ΔNIHSS outcome using simple bivariate comparisons (eg, a t test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke research. In this article, we use clinical trial simulations to illustrate that variability in NIHSS outcome is less when follow-up NIHSS is adjusted for baseline compared to ΔNIHSS and how a reduction in this variability improves the power. We outline additional, important clinical and statistical arguments to support the superiority of ANCOVA using the final measurement of the NIHSS adjusted for baseline over, and caution against using, the simple bivariate comparison of absolute NIHSS change (ie, delta).

Model-assisted analyses of longitudinal, ordinal outcomes with absorbing states.

Studies of critically ill, hospitalized patients often follow participants and characterize daily health status using an ordinal outcome variable. Statistically, longitudinal proportional odds models are a natural choice in these settings since such models can parsimoniously summarize differences across patient groups and over time. However, when one or more of the outcome states is absorbing, the proportional odds assumption for the follow-up time parameter will likely be violated, and more flexible longitudinal models are needed. Motivated by the VIOLET Study (Ginde et al), a parallel-arm, randomized clinical trial of Vitamin D3 in critically ill patients, we discuss and contrast several treatment effect estimands based on time-dependent odds ratio parameters, and we detail contemporary modeling approaches. In VIOLET, the outcome is a four-level ordinal variable where the lowest "not alive" state is absorbing and the highest "at-home" state is nearly absorbing. We discuss flexible extensions of the proportional odds model for longitudinal data that can be used for either model-based inference, where the odds ratio estimator is taken directly from the model fit, or for model-assisted inferences, where heterogeneity across cumulative log odds dichotomizations is modeled and results are summarized to obtain an overall odds ratio estimator. We focus on direct estimation of cumulative probability model (CPM) parameters using likelihood-based analysis procedures that naturally handle absorbing states. We illustrate the modeling procedures, the relative precision of model-based and model-assisted estimators, and the possible differences in the values for which the estimators are consistent through simulations and analysis of the VIOLET Study data.

Using Bayesian Methods to Augment the Interpretation of Critical Care Trials. An Overview of Theory and Example Reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial.

Most randomized trials are designed and analyzed using frequentist statistical approaches such as null hypothesis testing and P values. Conceptually, P values are cumbersome to understand, as they provide evidence of data incompatibility with a null hypothesis (e.g., no clinical benefit) and not direct evidence of the alternative hypothesis (e.g., clinical benefit). This counterintuitive framework may contribute to the misinterpretation that the absence of evidence is equal to evidence of absence and may cause the discounting of potentially informative data. Bayesian methods provide an alternative, probabilistic interpretation of data. The reanalysis of completed trials using Bayesian methods is becoming increasingly common, particularly for trials with effect estimates that appear clinically significant despite P values above the traditional threshold of 0.05. Statistical inference using Bayesian methods produces a distribution of effect sizes that would be compatible with observed trial data, interpreted in the context of prior assumptions about an intervention (called "priors"). These priors are chosen by investigators to reflect existing beliefs and past empirical evidence regarding the effect of an intervention. By calculating the likelihood of clinical benefit, a Bayesian reanalysis can augment the interpretation of a trial. However, if priors are not defined a priori, there is a legitimate concern that priors could be constructed in a manner that produces biased results. Therefore, some standardization of priors for Bayesian reanalysis of clinical trials may be desirable for the critical care community. In this Critical Care Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduce a framework that researchers can use to select priors for a Bayesian reanalysis, and demonstrate how to apply our proposal by conducting a novel Bayesian trial reanalysis.

Worse Than Death: Survey of Public Perceptions of Disability Outcomes After Hypothetical Traumatic Brain Injury.

OBJECTIVE: The aim of this study was to determine the health utility states of the most commonly used traumatic brain injury (TBI) clinical trial endpoint, the Extended Glasgow Outcome Scale (GOSE). SUMMARY BACKGROUND DATA: Health utilities represent the strength of one's preferences under conditions of uncertainty. There are insufficient data to indicate how an individual would value levels of disability after a TBI. METHODS: This was a cross-sectional web-based online convenience sampling adaptive survey. Using a standard gamble approach, participants evaluated their preferences for GOSE health states 1 year after a hypothetical TBI. The categorical GOSE was studied from vegetative state (GOSE2) to upper good recovery (GOSE8). Median (25th percentile, 75th percentile) health utility values for different GOSE states after TBI, ranging from -1 (worse than death) to 1 (full health), with 0 as reference (death). RESULTS: Of 3508 eligible participants, 3235 (92.22%) completed the survey. Participants rated lower GOSE states as having lower utility, with some states rated as worse than death, though the relationship was nonlinear and intervals were unequal between health states. Over 75% of participants rated a vegetative state (GOSE2, absence of awareness and bedridden) and about 50% rated lower severe disability (GOSE3, housebound needing all-day assistance) as conditions worse than death. CONCLUSIONS: In the largest investigation of public perceptions about post-TBI disability, we demonstrate unequally rated health states, with some states perceived as worse than death. Although limited by selection bias, these results may guide future comparative-effectiveness research and shared medical decision-making after neurologic injury.

Development and Validation of a Model to Predict Neonatal Abstinence Syndrome.

OBJECTIVES: To develop and validate clinical risk prediction tools for neonatal abstinence syndrome (NAS). STUDY DESIGN: We developed prediction models for NAS based on a set of 30 demographic and antenatal exposure covariates collected during pregnancy. Data (outpatient prescription, vital, and administrative records), were obtained from enrollees in the Tennessee Medicaid Program from 2009 to 2014. Models were created using logistic regression and backward selection based on improvement in the Akaike information criterion, and internally validated using bootstrap cross-validation. RESULTS: A total of 218 020 maternal and infant dyads met inclusion criteria, of whom 3208 infants were diagnosed with NAS. The general population model included age, hepatitis C virus infection, days of opioid used by type, number of cigarettes used daily, and the following medications used in the last 30 day of pregnancy: bupropion, antinausea medicines, benzodiazepines, antipsychotics, and gabapentin. Infant characteristics included birthweight, small for gestational age, and infant sex. A high-risk model used a smaller number of predictive variables. Both models discriminated well with an area under the curve of 0.89 and were well-calibrated for low-risk infants. CONCLUSIONS: We developed 2 predictive models for NAS based on demographics and antenatal exposure during the last 30 days of pregnancy that were able to risk stratify infants at risk of developing the syndrome.

Dobutamine Stress Echocardiography Ischemia as a Predictor of the Placebo-Controlled Efficacy of Percutaneous Coronary Intervention in Stable Coronary Artery Disease: The Stress Echocardiography-Stratified Analysis of ORBITA.

BACKGROUND: Dobutamine stress echocardiography is widely used to test for ischemia in patients with stable coronary artery disease. In this analysis, we studied the ability of the prerandomization stress echocardiography score to predict the placebo-controlled efficacy of percutaneous coronary intervention (PCI) within the ORBITA trial (Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina). METHODS: One hundred eighty-three patients underwent dobutamine stress echocardiography before randomization. The stress echocardiography score is broadly the number of segments abnormal at peak stress, with akinetic segments counting double and dyskinetic segments counting triple. The ability of prerandomization stress echocardiography to predict the placebo-controlled effect of PCI on response variables was tested by using regression modeling. RESULTS: At prerandomization, the stress echocardiography score was 1.56±1.77 in the PCI arm (n=98) and 1.61±1.73 in the placebo arm (n=85). There was a detectable interaction between prerandomization stress echocardiography score and the effect of PCI on angina frequency score with a larger placebo-controlled effect in patients with the highest stress echocardiography score (Pinteraction=0.031). With our sample size, we were unable to detect an interaction between stress echocardiography score and any other patient-reported response variables: freedom from angina (Pinteraction=0.116), physical limitation (Pinteraction=0.461), quality of life (Pinteraction=0.689), EuroQOL 5 quality-of-life score (Pinteraction=0.789), or between stress echocardiography score and physician-assessed Canadian Cardiovascular Society angina class (Pinteraction=0.693), and treadmill exercise time (Pinteraction=0.426). CONCLUSIONS: The degree of ischemia assessed by dobutamine stress echocardiography predicts the placebo-controlled efficacy of PCI on patient-reported angina frequency. The greater the downstream stress echocardiography abnormality caused by a stenosis, the greater the reduction in symptoms from PCI. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02062593.

Impact of a Follow-up Telephone Call Program on 30-Day Readmissions (FUTR-30): A Pragmatic Randomized Controlled Real-world Effectiveness Trial.

BACKGROUND: Telephone call programs are a common intervention used to improve patients' transition to outpatient care after hospital discharge. OBJECTIVE: To examine the impact of a follow-up telephone call program as a readmission reduction initiative. RESEARCH DESIGN: Pragmatic randomized controlled real-world effectiveness trial. SUBJECTS: We enrolled and randomized all patients discharged home from a hospital general medicine service to a follow-up telephone call program or usual care discharge. Patients discharged against medical advice were excluded. The intervention was a hospital program, delivering a semistructured follow-up telephone call from a nurse within 3-7 days of discharge, designed to assess understanding and provide education, and assistance to support discharge plan implementation. MEASURES: Our primary endpoint was hospital inpatient readmission within 30 days identified by the electronic health record. Secondary endpoints included observation readmission, emergency department revisit, and mortality within 30 days, and patient experience ratings. RESULTS: All 3054 patients discharged home were enrolled and randomized to the telephone call program (n=1534) or usual care discharge (n=1520). Using a prespecified intention-to-treat analysis, we found no evidence supporting differences in 30-day inpatient readmissions [14.9% vs. 15.3%; difference -0.4 (95% confidence interval, 95% CI), -2.9 to 2.1; P=0.76], observation readmissions [3.8% vs. 3.6%; difference 0.2 (95% CI, -1.1 to 1.6); P=0.74], emergency department revisits [6.1% vs. 5.4%; difference 0.7 (95% CI, -1.0 to 2.3); P=0.43], or mortality [4.4% vs. 4.9%; difference -0.5 (95% CI, -2.0 to 1.0); P=0.51] between telephone call and usual care groups. CONCLUSIONS: We found no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program.

Graphical calibration curves and the integrated calibration index (ICI) for survival models.

In the context of survival analysis, calibration refers to the agreement between predicted probabilities and observed event rates or frequencies of the outcome within a given duration of time. We aimed to describe and evaluate methods for graphically assessing the calibration of survival models. We focus on hazard regression models and restricted cubic splines in conjunction with a Cox proportional hazards model. We also describe modifications of the Integrated Calibration Index, of E50 and of E90. In this context, this is the average (respectively, median or 90th percentile) absolute difference between predicted survival probabilities and smoothed survival frequencies. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and under different types of model mis-specification. We illustrate the utility of calibration curves and the three calibration metrics by using them to compare the calibration of a Cox proportional hazards regression model with that of a random survival forest for predicting mortality in patients hospitalized with heart failure. Under a correctly specified regression model, differences between the two methods for constructing calibration curves were minimal, although the performance of the method based on restricted cubic splines tended to be slightly better. In contrast, under a mis-specified model, the smoothed calibration curved constructed using hazard regression tended to be closer to the true calibration curve. The use of calibration curves and of these numeric calibration metrics permits for a comprehensive comparison of the calibration of competing survival models.

An empirical comparison of two novel transformation models.

Continuous response variables are often transformed to meet modeling assumptions, but the choice of the transformation can be challenging. Two transformation models have recently been proposed: semiparametric cumulative probability models (CPMs) and parametric most likely transformation models (MLTs). Both approaches model the cumulative distribution function and require specifying a link function, which implicitly assumes that the responses follow a known distribution after some monotonic transformation. However, the two approaches estimate the transformation differently. With CPMs, an ordinal regression model is fit, which essentially treats each continuous response as a unique category and therefore nonparametrically estimates the transformation; CPMs are semiparametric linear transformation models. In contrast, with MLTs, the transformation is parameterized using flexible basis functions. Conditional expectations and quantiles are readily derived from both methods on the response variable's original scale. We compare the two methods with extensive simulations. We find that both methods generally have good performance with moderate and large sample sizes. MLTs slightly outperformed CPMs in small sample sizes under correct models. CPMs tended to be somewhat more robust to model misspecification and outcome rounding. Except in the simplest situations, both methods outperform basic transformation approaches commonly used in practice. We apply both methods to an HIV biomarker study.

Machine Learning for Work Disability Prevention: Introduction to the Special Series.

Rapid development in computer technology has led to sophisticated methods of analyzing large datasets with the aim of improving human decision making. Artificial Intelligence and Machine Learning (ML) approaches hold tremendous potential for solving complex real-world problems such as those faced by stakeholders attempting to prevent work disability. These techniques are especially appealing in work disability contexts that collect large amounts of data such as workers' compensation settings, insurance companies, large corporations, and health care organizations, among others. However, the approaches require thorough evaluation to determine if they add value to traditional statistical approaches. In this special series of articles, we examine the role and value of ML in the field of work disability prevention and occupational rehabilitation.

Effect of Hydroxychloroquine on Clinical Status at 14 Days in Hospitalized Patients With COVID-19: A Randomized Clinical Trial.

Importance: Data on the efficacy of hydroxychloroquine for the treatment of coronavirus disease 2019 (COVID-19) are needed. Objective: To determine whether hydroxychloroquine is an efficacious treatment for adults hospitalized with COVID-19. Design, Setting, and Participants: This was a multicenter, blinded, placebo-controlled randomized trial conducted at 34 hospitals in the US. Adults hospitalized with respiratory symptoms from severe acute respiratory syndrome coronavirus 2 infection were enrolled between April 2 and June 19, 2020, with the last outcome assessment on July 17, 2020. The planned sample size was 510 patients, with interim analyses planned after every 102 patients were enrolled. The trial was stopped at the fourth interim analysis for futility with a sample size of 479 patients. Interventions: Patients were randomly assigned to hydroxychloroquine (400 mg twice daily for 2 doses, then 200 mg twice daily for 8 doses) (n = 242) or placebo (n = 237). Main Outcomes and Measures: The primary outcome was clinical status 14 days after randomization as assessed with a 7-category ordinal scale ranging from 1 (death) to 7 (discharged from the hospital and able to perform normal activities). The primary outcome was analyzed with a multivariable proportional odds model, with an adjusted odds ratio (aOR) greater than 1.0 indicating more favorable outcomes with hydroxychloroquine than placebo. The trial included 12 secondary outcomes, including 28-day mortality. Results: Among 479 patients who were randomized (median age, 57 years; 44.3% female; 37.2% Hispanic/Latinx; 23.4% Black; 20.1% in the intensive care unit; 46.8% receiving supplemental oxygen without positive pressure; 11.5% receiving noninvasive ventilation or nasal high-flow oxygen; and 6.7% receiving invasive mechanical ventilation or extracorporeal membrane oxygenation), 433 (90.4%) completed the primary outcome assessment at 14 days and the remainder had clinical status imputed. The median duration of symptoms prior to randomization was 5 days (interquartile range [IQR], 3 to 7 days). Clinical status on the ordinal outcome scale at 14 days did not significantly differ between the hydroxychloroquine and placebo groups (median [IQR] score, 6 [4-7] vs 6 [4-7]; aOR, 1.02 [95% CI, 0.73 to 1.42]). None of the 12 secondary outcomes were significantly different between groups. At 28 days after randomization, 25 of 241 patients (10.4%) in the hydroxychloroquine group and 25 of 236 (10.6%) in the placebo group had died (absolute difference, -0.2% [95% CI, -5.7% to 5.3%]; aOR, 1.07 [95% CI, 0.54 to 2.09]). Conclusions and Relevance: Among adults hospitalized with respiratory illness from COVID-19, treatment with hydroxychloroquine, compared with placebo, did not significantly improve clinical status at day 14. These findings do not support the use of hydroxychloroquine for treatment of COVID-19 among hospitalized adults. Trial Registration: ClinicalTrials.gov: NCT04332991.

Fractional Flow Reserve and Instantaneous Wave-Free Ratio as Predictors of the Placebo-Controlled Response to Percutaneous Coronary Intervention in Stable Single-Vessel Coronary Artery Disease.

BACKGROUND: There are no data on how fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are associated with the placebo-controlled efficacy of percutaneous coronary intervention (PCI) in stable single-vessel coronary artery disease. METHODS: We report the association between prerandomization invasive physiology within ORBITA (Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina), a placebo-controlled trial of patients who have stable angina with angiographically severe single-vessel coronary disease clinically eligible for PCI. Patients underwent prerandomization research FFR and iFR assessment. The operator was blinded to these values. Assessment of response variables, treadmill exercise time, stress echocardiography score, symptom frequency, and angina severity were performed at prerandomization and blinded follow-up. Effects were calculated by analysis of covariance. The ability of FFR and iFR to predict placebo-controlled changes in response variables was tested by using regression modeling. RESULTS: Invasive physiology data were available in 196 patients (103 PCI and 93 placebo). At prerandomization, the majority had Canadian Cardiovascular Society class II or III symptoms (150/196, 76.5%). Mean FFR and iFR were 0.69±0.16 and 0.76±0.22, respectively; 97% had ≥1 positive ischemia tests. The estimated effect of PCI on between-arm prerandomization-adjusted total exercise time was 20.7 s (95% confidence interval [CI], -4.0 to 45.5; P=0.100) with no interaction of FFR ( Pinteraction=0.318) or iFR ( Pinteraction=0.523). PCI improved stress echocardiography score more than placebo (1.07 segment units; 95% CI, 0.70-1.44; P<0.00001). The placebo-controlled effect of PCI on stress echocardiography score increased progressively with decreasing FFR ( Pinteraction<0.00001) and decreasing iFR ( Pinteraction<0.00001). PCI did not improve angina frequency score significantly more than placebo (odds ratio, 1.64; 95% CI, 0.96-2.80; P=0.072) with no detectable evidence of interaction with FFR ( Pinteraction=0.849) or iFR ( Pinteraction=0.783). However, PCI resulted in more patient-reported freedom from angina than placebo (49.5% versus 31.5%; odds ratio, 2.47; 95% CI, 1.30-4.72; P=0.006) but neither FFR ( Pinteraction=0.693) nor iFR ( Pinteraction=0.761) modified this effect. CONCLUSIONS: In patients with stable angina and severe single-vessel disease, the blinded effect of PCI was more clearly seen by stress echocardiography score and freedom from angina than change in treadmill exercise time. Moreover, the lower the FFR or iFR, the greater the magnitude of stress echocardiographic improvement caused by PCI. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02062593.

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

When designing a study to develop a new prediction model with binary or time-to-event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox-Snell R2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.

Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes.

In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value of n should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9; (ii) small absolute difference of ≤ 0.05 in the apparent and adjusted R2 ; (iii) precise estimation (a margin of error ≤ 10% of the true value) of the model's residual standard deviation; and similarly, (iv) precise estimation of the mean predicted outcome value (model intercept). The criteria require prespecification of the user's chosen p and the model's anticipated R2 as informed by previous studies. The value of n that meets all four criteria provides the minimum sample size required for model development. In an applied example, a new model to predict lung function in African-American women using 25 predictor parameters requires at least 918 subjects to meet all criteria, corresponding to at least 36.7 subjects per predictor parameter. Even larger sample sizes may be needed to additionally ensure precise estimates of key predictor effects, especially when important categorical predictors have low prevalence in certain categories.