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Purpose: The hypothesis of this study was that single-legged horizontal hop test ratios would correlate with IKDC, KOOS, and Marx activity level scores in patients 2 years after primary ACL reconstruction.Methods: Individual patient-reported outcome tools and hop test ratios on 69 ACL reconstructed patients were compared using correlations and multivariable modeling. Correlations between specific questions on the IKDC and KOOS concerning the ability to jump and hop ratios were also performed.Results: The triple-hop ratio was moderately but significantly correlated with the IKDC, KOOS Sports and Recreation subscale, and the KOOS Knee Related Quality of Life subscale, as well as with the specific questions related to jumping. Similar but weaker relationship patterns were found for the single-hop ratio and timed hop. No significant correlations were found for the Marx activity level or crossover-hop ratio. Multivariable modeling showed almost no significant additional contribution to predictability of the IKDC or KOOS subscores by gender, BMI, or the number of faults on either leg.Conclusions: The triple-hop test is most significantly correlated with patient-reported outcome scores. Multivariable modeling indicates that less than a quarter of the variability in outcome scores can be explained by hop test results. This indicates that neither test can serve as a direct proxy for the other; however, assessment of patient physical function by either direct report using validated outcome tools or by the hop test will provide relatively comparable data. [ABSTRACT FROM AUTHOR]

Background: Growth hormone secretion and muscle mass decline from midpuberty throughout life, culminating in sarcopenia, frailty, decreased function, and loss of independence. The decline of growth hormone in the development of sarcopenia is one of many factors, and its etiologic role needs to be demonstrated.Objective: To determine whether MK-677, an oral ghrelin mimetic, increases growth hormone secretion into the young-adult range without serious adverse effects, prevents the decline of fat-free mass, and decreases abdominal visceral fat in healthy older adults.Design: 2-year, double-blind, randomized, placebo-controlled, modified-crossover clinical trial.Setting: General clinical research center study performed at a university hospital.Participants: 65 healthy adults (men, women receiving hormone replacement therapy, and women not receiving hormone replacement therapy) ranging from 60 to 81 years of age.Intervention: Oral administration of MK-677, 25 mg, or placebo once daily.Measurements: Growth hormone and insulin-like growth factor I levels. Fat-free mass and abdominal visceral fat were the primary end points after 1 year of treatment. Other end points were body weight, fat mass, insulin sensitivity, lipid and cortisol levels, bone mineral density, limb lean and fat mass, isokinetic strength, function, and quality of life. All end points were assessed at baseline and every 6 months.Results: Daily administration of MK-677 significantly increased growth hormone and insulin-like growth factor I levels to those of healthy young adults without serious adverse effects. Mean fat-free mass decreased in the placebo group but increased in the MK-677 group (change, -0.5 kg [95% CI, -1.1 to 0.2 kg] vs. 1.1 kg [CI, 0.7 to 1.5 kg], respectively; P < 0.001), as did body cell mass, as reflected by intracellular water (change, -1.0 kg [CI, -2.1 to 0.2 kg] vs. 0.8 kg [CI, -0.1 to 1.6 kg], respectively; P = 0.021). No significant differences were observed in abdominal visceral fat or total fat mass; however, the average increase in limb fat was greater in the MK-677 group than the placebo group (1.1 kg vs. 0.24 kg; P = 0.001). Body weight increased 0.8 kg (CI, -0.3 to 1.8 kg) in the placebo group and 2.7 kg (CI, 2.0 to 3.5 kg) in the MK-677 group (P = 0.003). Fasting blood glucose level increased an average of 0.3 mmol/L (5 mg/dL) in the MK-677 group (P = 0.015), and insulin sensitivity decreased. The most frequent side effects were an increase in appetite that subsided in a few months and transient, mild lower-extremity edema and muscle pain. Low-density lipoprotein cholesterol levels decreased in the MK-677 group relative to baseline values (change, -0.14 mmol/L [CI, -0.27 to -0.01 mmol/L]; -5.4 mg/dL [CI, -10.4 to -0.4 mg/dL]; P = 0.026); no differences between groups were observed in total or high-density lipoprotein cholesterol levels. Cortisol levels increased 47 nmol/L (CI, 28 to 71 nmol/L (1.7 microg/dL [CI, 1.0 to 2.6 microg/dL]) in MK-677 recipients (P = 0.020). Changes in bone mineral density consistent with increased bone remodeling occurred in MK-677 recipients. Increased fat-free mass did not result in changes in strength or function. Two-year exploratory analyses confirmed the 1-year results.Limitation: Study power (duration and participant number) was insufficient to evaluate functional end points in healthy elderly persons.Conclusion: Over 12 months, the ghrelin mimetic MK-677 enhanced pulsatile growth hormone secretion, significantly increased fat-free mass, and was generally well tolerated. Long-term functional and, ultimately, pharmacoeconomic, studies in elderly persons are indicated. [ABSTRACT FROM AUTHOR]

Objective: To examine the association between the use of right heart catheterization (RHC) during the first 24 hours of care in the intensive care unit (ICU) and subsequent survival, length of stay, intensity of care, and cost of care.Design: Prospective cohort study.Setting: Five US teaching hospitals between 1989 and 1994.Subjects: A total of 5735 critically ill adult patients receiving care in an ICU for 1 of 9 prespecified disease categories.Main Outcome Measures: Survival time, cost of care, intensity of care, and length of stay in the ICU and hospital, determined from the clinical record and from the National Death Index. A propensity score for RHC was constructed using multivariable logistic regression. Case-matching and multivariable regression modeling techniques were used to estimate the association of RHC with specific outcomes after adjusting for treatment selection using the propensity score. Sensitivity analysis was used to estimate the potential effect of an unidentified or missing covariate on the results.Results: By case-matching analysis, patients with RHC had an increased 30-day mortality (odds ratio, 1.24; 95% confidence interval, 1.03-1.49). The mean cost (25th, 50th, 75th percentiles) per hospital stay was $49 300 ($17 000, $30 500, $56 600) with RHC and $35 700 ($11 300, $20 600, $39 200) without RHC. Mean length of stay in the ICU was 14.8 (5, 9, 17) days with RHC and 13.0 (4, 7, 14) days without RHC. These findings were all confirmed by multivariable modeling techniques. Subgroup analysis did not reveal any patient group or site for which RHC was associated with improved outcomes. Patients with higher baseline probability of surviving 2 months had the highest relative risk of death following RHC. Sensitivity analysis suggested that a missing covariate would have to increase the risk of death 6-fold and the risk of RHC 6-fold for a true beneficial effect of RHC to be misrepresented as harmful.Conclusion: In this observational study of critically ill patients, after adjustment for treatment selection bias, RHC was associated with increased mortality and increased utilization of resources. The cause of this apparent lack of benefit is unclear. The results of this analysis should be confirmed in other observational studies. These findings justify reconsideration of a randomized controlled trial of RHC and may guide patient selection for such a study. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a simple prognostic scoring system to identify patients in nontraumatic coma at high risk for poor outcomes using data available early in the hospital course.Design: Prospective cohort study.Setting: Five geographically diverse academic medical centers.Patients: A total of 596 patients in nontraumatic coma enrolled in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT), including 247 in the model derivation set and 349 in the model validation set.Main Outcome Measures: Death and severe disability by 2 months.Main Results: For the 596 patients studied (median age, 67 years; 52% female), the primary cause of coma was cardiac arrest in 31% and cerebral infarction or intracerebral hemorrhage in 36%. At 2 months 69% had died, 20% had survived with known severe disability, 8% were known to have survived without severe disability, and 3% survived with unknown functional status. Five clinical variables available on day 3 after enrollment were associated independently with 2-month mortality: abnormal brain stem response (adjusted odds ratio [OR] = 3.2; 95% confidence interval [CI], 1.3 to 8.1), absent verbal response (OR = 4.6; 95% CI, 1.8 to 11.7), absent withdrawal response to pain (OR = 4.3; 95% CI, 1.7 to 10.8), creatinine level greater than or equal to 132.6 mumol/L (1.5 mg/dL) (OR = 4.5; 95% CI, 1.8 to 11.0), and age of 70 years or older (OR = 5.1; 95% CI, 2.2 to 12.2). Mortality at 2 months for patients with four or five of these risk factors was 97% (58/60; 95% CI, 88% to 100%) in the validation set. Brain stem and motor responses best predicted death or severe disability by 2 months. For patients with either an abnormal brain stem response or absent motor response to pain, the rate of death or severe disability at 2 months was 96% (185/193; 95% CI, 92% to 98%) in the validation set.Conclusions: Five readily available clinical variables identify a large subgroup of patients in nontraumatic coma at high risk for poor outcomes. This risk stratification approach offers physicians, patients, and patients' families information that may prove useful in patient care decisions and resource allocation. [ABSTRACT FROM AUTHOR]

Background: Medical treatment decisions should be based on the preferences of informed patients or their proxies and on the expected outcomes of treatment. Because seriously ill patients are at risk for cardiac arrest, examination of do-not-resuscitate (DNR) practices affecting them provides useful insights into the associations between various factors and medical decision making.Objective: To examine the association between patients' preferences for resuscitation (along with other patient and physician characteristics) and the frequency and timing of DNR orders.Design: Prospective cohort study.Setting: 5 teaching hospitals.Patients: 6802 seriously ill hospitalized patients enrolled in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment (SUPPORT) between 1989 and 1994.Measurements: Patients and their surrogates were interviewed about patients' cardiopulmonary resuscitation preferences, medical records were reviewed to determine disease severity, and a multivariable regression model was constructed to predict the time to the first DNR order.Results: The patients' preference for cardiopulmonary resuscitation was the most important predictor of the timing of DNR orders, but only 52% of patients who preferred not to be resuscitated actually had DNR orders written. The probability of surviving for 2 months was the next most important predictor of the timing of DNR orders. Although DNR orders were not linearly related to the probability of surviving for 2 months, they were written earlier and more frequently for patients with a 50% or lower probability of surviving for 2 months. Orders were written more quickly for patients older than 75 years of age, regardless of prognosis. After adjustment for these and other influential patient characteristics, the use and timing of DNR orders varied significantly among physician specialties and among hospitals.Conclusions: Patients' preferences and short-term prognoses are associated with the timing of DNR orders. However, the substantial variation seen among hospital sites and among physician specialties suggests that there is room for improvement. In this study, DNR orders were written earlier for patients older than 75 years of age, regardless of prognosis. This finding suggests that physicians may be using age in a way that is inconsistent with the reported association between age and survival. The process for making decisions about DNR orders needs to be improved if such orders are to routinely and accurately reflect patients' preferences and probable outcomes. [ABSTRACT FROM AUTHOR]

Objective: To develop a model estimating the probability of an adult patient having severe functional limitations 2 months after being hospitalized with one of nine serious illnesses.Design: Prospective cohort study.Setting: Five teaching hospitals in the United States.Participants: 1746 patients (model development) who survived 2 months and completed an interview, selected from 4301 patients in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT); independent validation sample of 2478 patients.Measurements and Main Outcomes: Patient function 2 months after admission categorized as absence or presence of severe functional limitations (defined as Sickness Impact Profile scores > or = 30 or as activities of daily living scores > or = 4 [levels that require near-constant personal assistance]). A logistic regression model was constructed to predict severe functional limitation.Results: One third (n = 590) of patients who were interviewed at 2 months had severe functional limitations. Changes in functional status were common: Of those with no baseline dependencies (not dependent on personal assistance), 21% were severely limited at 2 months; of those with 4 or more baseline limitations, 30% had improved. The patient's ability to do activities of daily living was the most important predictor of functional status. Physiologic abnormalities, diagnosis, days in hospital, age, quality of life, and previous exercise capacity also contributed substantially. Model performance, assessed using receiver-operating characteristic curves, was 0.79 for the development sample and 0.75 for the validation sample. The model was well calibrated for the entire risk range.Conclusions: Functional outcome varied substantially after hospitalization for a serious illness. A small amount of readily available clinical information can estimate the probability of severe functional limitations. [ABSTRACT FROM AUTHOR]

The Bayesian analytical framework is clinically intuitive, characterized by the incorporation of previous evidence into the analysis, and allowing an estimation treatment effects and their associated uncertainties. The application of Bayesian statistical inference is not new to the cardiovascular field, as illustrated by various recent randomized trials that applied a primary Bayesian analysis. Given the guideline-shaping character of trials, a thorough understanding of the concepts and technical details of Bayesian statistical methodology is of utmost importance to the modern practicing cardiovascular physician. Therefore, this Review aims to present a step-by-step guide to interpreting and performing a Bayesian (re-)analysis of cardiovascular clinical trials, while highlighting the main advantages of Bayesian inference for the clinical reader. After an introduction of the concepts of frequentist and Bayesian statistical inference and reasons to apply Bayesian methods, key steps for performing a Bayesian analysis are presented, including: the verification of the clinical appropriateness of the research question, the quality and completeness of the trial design, as well as the adequate elicitation of the prior (i.e., ones belief towards a certain treatment before the current evidence becomes available), identification of the likelihood, and their combination into a posterior distribution. Examination of this posterior distribution offers the possibility of not only determining the probability of treatment superiority, but also the probability of exceeding any chosen minimal clinically important difference. Multiple priors should be transparently prespecified, limiting post-hoc manipulations. Using this guide, three cardiovascular randomized controlled trials are re-analysed, demonstrating the clarity and versatility of Bayesian inference., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The Placebo-controlled Trial of Percutaneous Coronary Intervention for the Relief of Stable Angina (ORBITA-2) provided evidence for the role of percutaneous coronary intervention (PCI) for angina relief in stable coronary artery disease (CAD). Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are often used to guide PCI, however their ability to predict placebo-controlled angina improvement is unknown., Methods: Participants with angina, ischemia, and stable CAD were enrolled and antianginal medications were stopped. Participants reported angina episodes daily for 2 weeks using the ORBITA-app. At the research angiogram, FFR and iFR were measured. After sedation and auditory isolation, participants were randomized to PCI or placebo, before entering a 12-week blinded follow-up phase with daily angina reporting. The ability of FFR and iFR, analyzed as continuous variables, to predict the placebo-controlled effect of PCI, was tested using Bayesian proportional odds modelling., Results: Invasive physiology data were available in 279 patients (140 PCI and 139 placebo). The median (IQR) age was 65 years (59.0 to 70.5) and 223 (79.9%) were male. Median FFR was 0.60 (0.46 to 0.73) and median iFR was 0.76 (0.50 to 0.86). The lower the FFR or iFR, the greater the placebo-controlled improvement with PCI across all endpoints. There was strong evidence that a patient with an FFR at the lower quartile would have a greater placebo-controlled improvement in angina symptom score with PCI than a patient at the upper quartile (FFR 0.46 vs. 0.73: OR 2.01, 95% CrI 1.79 to 2.26, Pr(Interaction)>99.9%). Similarly, there was strong evidence that a patient with an iFR at the lower quartile would have a greater placebo controlled improvement in angina symptom score with PCI than a patient with an iFR at the upper quartile (iFR 0.50 vs. 0.86: OR 2.13, 95% CrI 1.87 to 2.45, Pr(Interaction) >99.9%). The relationship between benefit and physiology was seen in both Rose angina and Rose nonangina., Conclusions: Physiological stenosis severity, as measured by FFR and iFR, predicts placebo-controlled angina relief from PCI. Invasive coronary physiology can be used to target PCI to those patients who are most likely to experience benefit.

Background: The relative contributions of common patient-reported social determinants of health on 30- and 90-day post-discharge outcomes among patients with acute coronary syndromes (ACS) is unclear., Objective: The aim of this article is to examine the independent associations of social determinants with readmission or death, accounting for medical history., Methods: Participants included adults who were hospitalized with ACS at an academic medical center. Domains measured were social support, health literacy/numeracy, and socioeconomic status (SES) (including education and difficulty paying bills). We employed multivariable Cox proportional hazard models to study associations with time to all-cause readmission or death, up to 30 or 90 days after discharge, and adjusted for demographics and medical history (prior admissions and Elixhauser comorbidity index)., Key Results: Among 1,168 patients with ACS and no history of heart failure, more prior admissions, and higher comorbidity index (the medical history domain) were associated with higher rates of 30- and 90-day readmission or death (domain p values <.01 and <.0001, respectively). The social support domain was not associated with outcomes. Higher health literacy and numeracy were associated with lower rates of 30- and 90-day readmission or death (domain p values .016 and .002, respectively). Higher education and less difficulty paying bills (the SES domain) was marginally associated with lower rates of 90-day readmission or death (domain, p = .052)., Conclusions: In addition to medical history, the domain of health literacy and numeracy was independently associated with readmission or death of patients with ACS during the 90 days after hospital discharge. [ HLRP: Health Literacy Research and Practice . 2024;8(4):e212-e223. ]., Competing Interests: Disclosure: ASM has served as a data safety and monitoring board member for the Clinical Science Research and Development Veterans Affairs Merit Grant: Personalizing Veteran Lung Cancer Screening and Diagnosis. RLR has received support for travel through the Engel Research Award from the Academy for Communication in Healthcare; and has received grants from the National Institutes of Health, Patient-Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, Duke Clinical Research Institute/Novartis, Centers for Disease Control and Prevention Global Health Foundation, Centers for Medicare and Medicaid Services/Center for Medicare and Medicaid Innovation, paid to the institution. EEV has received grants from the National Heart, Lung, and Blood Institute and National Institute of Nursing Research (UG3HL165740), and the National Institutes of Health/National Institute on Aging (R01-AG065249), and served on a COVID-19 Advisory Board for Gilead Sciences. LSM has received grants from the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases through the University of Utah (R01 DK124719), the Helmsley Charitable Trust (R-2203-05822), and the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK119282 and R01 DK119282-S), paid to the institution. SPB has received grants from the National Institutes of Health/National Institute of Aging (1K23AG048347-A101), National Institutes of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12-HD043483), paid to the institution. SK has received grants from the National Institutes of Health, Patient-Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services, and Esperion Therapeutics, paid to the institution; and has served as a consultant to Gilead Sciences and Bayer. The remaining authors have disclosed no potential conflicts of interest, financial or otherwise.

Background: Obesity and metabolic dysregulation (MetD) have increasing prevalence and adversely affect asthma morbidity and therapeutic response., Objective: To determine the role of weight and MetD on incident asthma in adulthood., Methods: In a retrospective, longitudinal cohort of patients, we performed a time-to-asthma diagnosis analysis after a 3-year landmark period (t 0 -t 3 ) during which weight and MetD components were evaluated. We assessed incident asthma risk with MetD components and weight., Results: In total, 90,081 patients met the inclusion criteria, with 836 cases (0.93%) of incident asthma in our primary cohort. Diabetes present at t 0 , but no other MetD components, was associated with increased risk of asthma (adjusted hazard ratio = 1.85, 95% CI: 1.27-2.71, P = .0002). The effect of weight on asthma risk, independent of other MetD components, identified individuals with overweight or obesity as having a 10-year attributable risk of 15.4%. Metformin was prescribed more frequently, and hemoglobin A1c levels were lower in patients with diabetes in whom asthma did not develop (P < .0001)., Conclusion: Weight and diabetes prevention and management represent modifiable risk factors for adult asthma development., Competing Interests: Disclosures Dr Bacharier reports personal fees from GlaxoSmithKline, Genentech/Novartis, Merck, DBV Technologies, Teva, Boehringer Ingelheim, AstraZeneca, Avillion, WebMD/Medscape, Sanofi/Regeneron, Vectura, Circassia, OM Pharma, Elsevier, Kinaset, and Vertex outside the submitted work. Dr Cahill served on scientific advisory boards for AstraZeneca, Sanofi, Genentech, Regeneron, Novartis, and GlaxoSmithKline; served as a consultant for Ribon Therapeutics, Third Harmonic Bio, and Verantos; reports royalties from UpToDate; and reports research support from Novo Nordisk. The remaining authors have no conflicts of interest to report., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Competing Interests: Declaration of conflicting interestsThe author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Placebo-controlled evidence from ORBITA-2 (Objective Randomised Blinded Investigation with Optimal Medical Therapy of Angioplasty in Stable Angina-2) found that percutaneous coronary intervention (PCI) in stable coronary artery disease with little or no antianginal medication relieved angina, but residual symptoms persisted in many patients. The reason for this was unclear., Objectives: This ORBITA-2 secondary analysis investigates the relationship between presenting symptoms and disease severity (anatomic, noninvasive, and invasive ischemia) and the ability of symptoms to predict the placebo-controlled efficacy of PCI., Methods: Prerandomization symptom severity and nature were assessed using the ORBITA smartphone application and symptom and quality of life questionnaires including the World Health Organization Rose angina questionnaire (Rose). Disease severity was assessed using quantitative coronary angiography, stress echocardiography, fractional flow reserve, and instantaneous wave-free ratio. Bayesian ordinal regression was used., Results: At prerandomization, the median number of daily angina episodes was 0.8 (Q1-Q3: 0.4-1.6), 64% had Rose angina, quantitative coronary angiography diameter stenosis was 61% (Q1-Q3: 49%-74%), stress echocardiography score was 1.0 (Q1-Q3: 0.0-2.7), fractional flow reserve was 0.63 (Q1-Q3: 0.49-0.75), and instantaneous wave-free ratio was 0.78 (Q1-Q3: 0.55-0.87). There was little relationship between symptom severity and nature and disease severity: angina symptom score with quantitative coronary angiography ordinal correlation coefficient: 0.06 (95% credible interval [CrI]: 0.00-0.08); stress echocardiography: 0.09 (95% CrI: 0.02-0.10); fractional flow reserve: 0.04 (95% CrI: -0.03 to 0.07); and instantaneous wave-free ratio: 0.04 (95% CrI: -0.01 to 0.07). However, Rose angina and guideline-based typical angina were strong predictors of placebo-controlled PCI efficacy (angina symptom score: OR: 1.9; 95% CrI: 1.6-2.1; probability of interaction [Pr Interaction ] = 99.9%; and OR: 1.8; 95% CrI: 1.6-2.1; Pr Interaction  = 99.9%, respectively)., Conclusions: Although symptom severity and nature were poorly associated with disease severity, the nature of symptoms powerfully predicted the placebo-controlled efficacy of PCI., Competing Interests: Funding Support and Author Disclosures ORBITA-2 was an investigator-initiated trial sponsored by Imperial College London. The trial was funded by grants from National Institute for Health and Care Research Imperial Biomedical Research Centre, Medical Research Council, British Heart Foundation, National Institute for Health and Care Research, and the Imperial Coronary Flow Trust. Philips Volcano supplied the coronary pressure wires. Dr Simader has received a sponsorship from Servier Pharmaceuticals. Dr Rajkumar has received speaker fees from Menarini; and has received consulting fees from Philips. Dr Foley has received speaker fees from Menarini and Philips. Dr Khokhar has received speaker fees and travel support from Boston Scientific and Abbott. Dr Davies has received grants from Medtronic and Abbott; has received sponsorship from Vascular Perspectives, Boston Scientific, Medtronic and Abbott; and has received speaker fees from AstraZeneca, Pfizer, Bristol Myers Squibb, and Novartis. Dr Keeble has served on advisory boards for Abbott Vascular and SMT; and has received institutional research funding from Terumo, Medtronic, Boston Scientific, Abbott Vascular, Philips Volcano, and Cardionovum. Dr O’Kane has received speaker fees from Abbott Vascular, Biosensors, Boston Scientific, Heartflow, Medtronic, Philips, Shockwave, and Terumo. Dr Kotecha has received honoraria from Bayer and Janssen. Dr Nijjer has received speaker fees from Philips Volcano, Pfizer, Bayer, AstraZeneca, Boehringer Ingelheim, and Amarin. Dr Spratt has received speaker fees from Boston Scientific Corporation and Shockwave Medical, Inc. Dr Sen has received speaker and consulting fees from Philips, Medtronic, Recor, and AstraZeneca. Dr Curzen has received grants from Beckman Coulter, Inc, Boston Scientific Corporation, Haemonetics Corporation, and HeartFlow Inc; and has received speaker fees from Heartflow. Dr Howard has received shares in Mycardium AI; and has received a grant from the British Heart Foundation. Dr Cole has received shares in Mycardium AI. Dr Al-Lamee has served on advisory boards for Janssen Pharmaceuticals, Abbott, and Philips; and has received speaker fees from Abbott, Philips, Medtronic, Servier, Omniprex, and Menarini. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Background: In stable coronary artery disease, 30% to 60% of patients remain symptomatic despite successful revascularization. Perhaps not all symptoms reported by a patient with myocardial ischemia are, in fact, angina., Objectives: This study sought to determine whether independent symptom verification using a placebo-controlled ischemic stimulus could distinguish which patients achieve greatest symptom relief from percutaneous coronary intervention (PCI)., Methods: ORBITA-STAR was a multicenter, n-of-1, placebo-controlled study in patients undergoing single-vessel PCI for stable symptoms. Participants underwent 4 episodes (60 seconds each) of low-pressure balloon occlusion across their coronary stenosis, randomly paired with 4 episodes of placebo inflation. Following each episode, patients reported the similarity of the induced symptom in comparison with their usual symptom. The similarity score ranged from -10 (placebo replicated the symptom more than balloon occlusion) to +10 (balloon occlusion exactly replicated the symptom). The primary endpoint was the ability of the similarity score to predict symptom relief with PCI., Results: Fifty-one patients were recruited, aged 62.9 ± 8.6 years. The median fractional flow reserve was 0.68 (Q1-Q3: 0.57-0.79), and the instantaneous wave-free ratio was 0.80 (Q1-Q3: 0.48-0.89). The median similarity score was 3 (Q1-Q3: 0.875-5.25). The similarity score was a strong predictor of symptom improvement following PCI: a patient with an upper quartile similarity score of 5.25 was significantly more likely to have lower angina frequency at follow-up (OR: 8.01; 95% credible interval: 2.39-15.86) than a patient with a lower quartile similarity score of 0.875 (OR: 1.31; 95% credible interval: 0.71-1.99), Pr (difference) >99.9%., Conclusions: Similarity score powerfully predicted symptom improvement from PCI. These data lay the foundation for independent symptom mapping to target PCI to those patients most likely to benefit. (Systematic Trial of Angina Assessment Before Revascularization [ORBITA-STAR]; NCT04280575)., Competing Interests: Funding Support and Author Disclosures This work has received support from the National Institute for Health and Care Research Imperial Biomedical Research Centre and the Imperial College British Heart Foundation Centre of Research Excellence. Dr Rajkumar has received a Clinical Research Training Fellowship from the Medical Research Council; has received speaker fees from Menarini; and has received consulting fees from Philips. Dr Foley has received a Clinical Research Training Fellowship from the Medical Research Council; and has received speaker fees from Menarini and Philips. Dr Ahmed-Jushuf has received a Clinical Research Training Fellowship from the Medical Research Council. Dr Simader has received sponsorship from Servier. Dr Sen has received speaker and consulting fees from Philips, Medtronic, Recor, and AstraZeneca. Dr Petraco has received consulting fees from Philips and Abbott. Dr Nijjer has received speaker fees from Philips Volcano, Pfizer, Bayer, AstraZeneca, Boehringer Ingelheim, and Amarin. Dr Dungu has received grants from Medtronic and Abbott; has received sponsorship from Vascular Perspectives, Boston Scientific, Medtronic, and Abbott; and has received speaker honoraria from AstraZeneca, Pfizer, Bristol Myers Squibb, and Novartis. Dr Kotecha has received honoraria from Bayer and Janssen. Dr Cole holds shares in Mycardium AI. Dr Howard holds shares in Mycardium AI. Dr Keeble has served on the advisory boards of Abbott Vascular and SMT; and has received institutional research funding from Terumo, Medtronic, Boston Scientific, Abbott Vascular, Philips Volcano, and Cardionovum. Dr O’Kane has received speaker fees from Abbott Vascular, Biosensors, Boston Scientific, HeartFlow, Medtronic, Philips, Shockwave, and Terumo. Dr Al-Lamee has received an Intermediate Research Fellowship from the British Heart Foundation [FS/ICRF/22/26051]; has served on the advisory boards of Janssen Pharmaceuticals, Abbott, and Philips; and has received speaker honoraria from Abbott, Philips, Medtronic, Servier, Omniprex, and Menarini. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: With ongoing investigations of the impact of device texturing on breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL), studies have begun comparing complication profiles of tissue expanders. However, there is a paucity of timing and severity data of complications. The aim of this study was to provide a comparative survival analysis of postoperative complications between smooth (STEs) and textured tissue expanders (TTEs) in breast reconstruction., Methods: A single-institution experience with tissue expander breast reconstruction was reviewed for complications up to 1 year after second-stage reconstruction from 2014 to 2020. Demographics, comorbidities, operation-related variables, and complications were evaluated. Kaplan-Meier curves, Cox proportional hazard models, and a consensus-based ordinal logistic regression model were used to compare complication profiles., Results: Of 919 total patients, 600 (65.3%) received TTEs and 319 (34.7%) received STEs. There was increased risk of infection ( P < 0.0001), seroma ( P = 0.046), expander malposition ( P < 0.0001), and wound dehiscence ( P = 0.019) in STEs compared with TTEs. However, there was also a decreased risk of capsular contracture ( P = 0.005) in STEs compared with TTEs. Failure of breast reconstruction ( P < 0.001) and wound dehiscence ( P = 0.018) occurred significantly earlier in STEs compared with TTEs. Predictors for significantly higher severity complications included the following: smooth tissue expander use ( P = 0.007), shorter time to complication ( P < 0.0001), higher body mass index ( P = 0.005), smoking history ( P = 0.025), and nipple-sparing mastectomy ( P = 0.012)., Conclusions: Differences in the timing and severity of complications contribute to the safety profiles of tissue expanders. STEs are associated with increased odds of higher severity and earlier complications. Therefore, tissue expander selection may depend on underlying risk factors and severity predictors., Clinical Question/level of Evidence: Therapeutic, III., (Copyright © 2023 by the American Society of Plastic Surgeons.)

Background: An angiotensin receptor-neprilysin inhibitor (ARNI) is the preferred renin-angiotensin system (RAS) inhibitor for heart failure with reduced ejection fraction (HFrEF). Among eligible patients, insurance status and prescriber concern regarding out-of-pocket costs may constrain early initiation of ARNI and other new therapies., Objectives: In this study, the authors sought to evaluate the association of insurance and other social determinants of health with ARNI initiation at discharge from HFrEF hospitalization., Methods: The authors analyzed ARNI initiation from January 2017 to June 2020 among patients with HFrEF eligible to receive RAS inhibitor at discharge from hospitals in the Get With The Guidelines-Heart Failure registry. The primary outcome was the proportion of ARNI prescription at discharge among those prescribed RAS inhibitor who were not on ARNI on admission. A logistic regression model was used to determine the association of insurance status, U.S. region, and their interaction, as well as self-reported race, with ARNI initiation at discharge., Results: From 42,766 admissions, 24,904 were excluded for absolute or relative contraindications to RAS inhibitors. RAS inhibitors were prescribed for 16,817 (94.2%) of remaining discharges, for which ARNI was prescribed in 1,640 (9.8%). Self-reported Black patients were less likely to be initiated on ARNI compared to self-reported White patients (OR: 0.64; 95% CI: 0.50-0.81). Compared to Medicare beneficiaries, patients with third-party insurance, Medicaid, or no insurance were less likely to be initiated on ARNI (OR: 0.47 [95% CI: 0.31-0.72], OR: 0.41 [95% CI: 0.25-0.67], and OR: 0.20 [95% CI: 0.08-0.47], respectively). ARNI therapy varied by hospital region, with lowest utilization in the Mountain region. An interaction was demonstrated between the impact of insurance disparities and hospital region., Conclusions: Among patients hospitalized between 2017 and 2020 for HFrEF who were prescribed RAS inhibitor therapy at discharge, insurance status, geographic region, and self-reported race were associated with ARNI initiation., Competing Interests: Funding Support and Author Disclosures This research was partially funded by American Heart Association Strategically Focused Research Network (18SFRN34110369 to Dr Davogustto). Dr Harrell has received funding support from VICTR (The Vanderbilt Institute for Clinical and Translational Research). VICTR is funded by the NCATS (National Center for Advancing Translational Sciences) CTSA (Clinical Translational Science Award) Program, Award Number 5UL1TR002243-03. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The Get With The Guidelines–Heart Failure program is provided by the American Heart Association and sponsored in part by Novartis, Boehringer Ingelheim, Novo Nordisk, AstraZeneca, Bayer, Tylenol, and Alnylam Pharmaceuticals. Dr Greene has received research support from the Duke University Department of Medicine Chair’s Research Award, American Heart Association, Amgen, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Cytokinetics, Merck, Novartis, Pfizer, and Sanofi; has served on advisory boards or as consultant for Amgen, AstraZeneca, Bayer, Boehringer Ingelheim/Lilly, Bristol Myers Squibb, Corteria Pharmaceuticals, CSL Vifor, Cytokinetics, Lexicon, Merck, Roche Diagnostics, Sanofi, scPharmaceuticals, Tricog Health, and Urovant Pharmaceuticals; and has received speaker fees from Bayer, Boehringer Ingelheim, Cytokinetics, Lexicon, and Roche Diagnostics; none of these activities are related to the content of the present work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The primary goals during acute heart failure (AHF) hospitalization are decongestion and guideline-directed medical therapy (GDMT) optimization. Unlike diuretics or other GDMT, early dapagliflozin initiation could achieve both AHF goals., Objectives: The authors aimed to assess the diuretic efficacy and safety of early dapagliflozin initiation in AHF., Methods: In a multicenter, open-label study, 240 patients were randomized within 24 hours of hospital presentation for hypervolemic AHF to dapagliflozin 10 mg once daily or structured usual care with protocolized diuretic titration until day 5 or hospital discharge. The primary outcome, diuretic efficiency expressed as cumulative weight change per cumulative loop diuretic dose, was compared across treatment assignment using a proportional odds model adjusted for baseline weight. Secondary and safety outcomes were adjudicated by a blinded committee., Results: For diuretic efficiency, there was no difference between dapagliflozin and usual care (OR: 0.65; 95% CI: 0.41-1.02; P = 0.06). Dapagliflozin was associated with reduced loop diuretic doses (560 mg [Q1-Q3: 260-1,150 mg] vs 800 mg [Q1-Q3: 380-1,715 mg]; P = 0.006) and fewer intravenous diuretic up-titrations (P ≤ 0.05) to achieve equivalent weight loss as usual care. Early dapagliflozin initiation did not increase diabetic, renal, or cardiovascular safety events. Dapagliflozin was associated with improved median 24-hour natriuresis (P = 0.03) and urine output (P = 0.005), expediting hospital discharge over the study period., Conclusions: Early dapagliflozin during AHF hospitalization is safe and fulfills a component of GDMT optimization. Dapagliflozin was not associated with a statistically significant reduction in weight-based diuretic efficiency but was associated with evidence for enhanced diuresis among patients with AHF. (Efficacy and Safety of Dapagliflozin in Acute Heart Failure [DICTATE-AHF]; NCT04298229)., Competing Interests: Funding Support and Author Disclosures This study was funded by AstraZeneca as an investigator-initiated study. The authors designed the protocol, conducted the trial according to the protocol and statistical analysis plan, analyzed the data, and attest to the accuracy and completeness of the data. The REDCap database is supported by a grant from National Center for Advancing Translational Sciences/National Institutes of Health (NIH) (UL1 TR000445). Dr Cox has received research funding from AstraZeneca; and has received consulting fees from Roche and Translational Catalyst. Dr Collins has performed consulting for Boehringer Ingelheim, Reprieve Cardiovascular, Siemens, and Abbott; and has received research support from the Patient-Centered Outcomes Research Institute, NIH, U.S. Department of Defense, Agency for Healthcare Research and Quality, and Beckman Coulter. Dr Lindsell has received funding from AstraZeneca, bioMerieux, Entegrion, Endpoint Health, NIH, the U.S. Department of Defense, and the Centers for Disease Control and Prevention. Dr Stubblefield has received funding from the NIH/National Heart, Lung, and Blood Institute. Dr Lindenfeld has received research funding from AstraZeneca, Volumetrix, and NIH; and has received consulting fees from AstraZeneca, Abbott, Alleviant, Axon, Boston Scientific, CVRx, Edwards Lifesciences, Medtronic, Merck, Vascular Dynamics, VWave, and Whiteswell. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The coronary sinus Reducer (CSR) is an hourglass-shaped device which creates an artificial stenosis in the coronary sinus. Whilst placebo-controlled data show an improvement in angina, these results are unreplicated and are the subject of further confirmatory research. The mechanism of action of this unintuitive therapy is unknown. The Coronary Sinus Reducer Objective Impact on Symptoms, MRI Ischaemia, and Microvascular Resistance (ORBITA-COSMIC) trial is a randomised, placebo-controlled, double-blind trial investigating the efficacy of the CSR. Patients with (i) established epicardial coronary artery disease, (ii) angina on maximally tolerated antianginal medication, (iii) evidence of myocardial ischaemia and (iv) no further options for percutaneous coronary intervention or coronary artery bypass grafting will be enrolled. Upon enrolment, angina and quality-of-life questionnaires, treadmill exercise testing and quantitative stress perfusion cardiac magnetic resonance (CMR) imaging will be performed. Participants will record their symptoms daily on a smartphone application throughout the trial. After a 2-week symptom assessment phase, participants will be randomised in the cardiac catheterisation laboratory to CSR or a placebo procedure. After 6 months of blinded follow-up, all prerandomisation tests will be repeated. A prespecified subgroup will undergo invasive coronary physiology assessment at prerandomisation and follow-up. The primary outcome is stress myocardial blood flow on CMR. Secondary outcomes include angina frequency, quality of life and treadmill exercise time. (ClinicalTrials.gov: NCT04892537).

Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/and declare: support from Cancer Research UK and the Medical Research Council for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. GSC and RDR are statistical editors for The BMJ.

Objective and Background: We assessed current understandings in interpretation of Bayesian and traditional statistical results within the clinical researcher (non-statistician) community., Methods: Within a 22-question survey, including demographics and experience and comfort levels with Bayesian analyses, we included questions on how to interpret both Bayesian and traditional statistical outputs. We also assessed whether Bayesian or traditional interpretations are considered more useful., Results: Among the 323 respondent clinicians, 42.4% and 36.5% chose the correct interpretations of the posterior probability and 95% credible interval, respectively. Only 11.5% of respondents interpreted the p-value correctly and 23.5% interpreted the 95% confidence interval correctly., Conclusions: Based on these survey results, we conclude that most of these clinicians face uncertainty when attempting to interpret results from both Bayesian and traditional statistical outputs. When presented with accurate interpretations, clinicians generally conclude that Bayesian results are more useful than conventional ones. We believe there is a need for education of clinicians in statistical interpretation in ways that are customized to this audience., (© 2022. The Author(s), under exclusive licence to The Drug Information Association, Inc.)

Objective and Background: The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them., Methods: We developed a 22-question survey submitted to medical researchers (non-statisticians) from industry, academia, and regulatory agencies. Question areas included demographics, experience, comfort levels with Bayesian analyses, perceived barriers to these analyses, and preferences for increasing familiarity with Bayesian methods., Results: Of the 323 respondents, most were affiliated with pharmaceutical companies (33.4%), clinical research organizations (29.7%), and regulatory agencies (18.6%). The rest represented academia, medical practice, or other. Over 56% of respondents expressed little to no comfort in interpreting Bayesian analyses. "Insufficient knowledge of Bayesian approaches" was ranked the most important perceived barrier to implementing Bayesian methods by a plurality (48%). Of the approaches listed, in-person training was the most preferred for gaining comfort with Bayesian methods., Conclusions: Based on these survey results, we recommend that introductory level training on Bayesian statistics be presented in an in-person workshop that could also be broadcast online with live Q&A. Other approaches such as online training or collaborative projects may be better suited for higher-level trainings where instructors may assume a baseline understanding of Bayesian statistics. Increased coverage of Bayesian methods at medical conferences and medical school trainings would help improve comfort and overcome the substantial knowledge barriers medical researchers face when implementing these methods., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

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 D 3 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., (© 2022 John Wiley & Sons Ltd.)

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods perform better than statistical learning methods in a specific setting. We evaluated six learning methods: stochastic gradient boosting machines using trees as the base learners, random forests, artificial neural networks, the lasso, ridge regression, and linear regression estimated using ordinary least squares (OLS). Our simulations were informed by empirical analyses in patients with acute myocardial infarction (AMI) and congestive heart failure (CHF) and used six data-generating processes, each based on one of the six learning methods, to simulate continuous outcomes in the derivation and validation samples. The outcome was systolic blood pressure at hospital discharge, a continuous outcome. We applied the six learning methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples. The primary observation was that neural networks tended to result in estimates with worse predictive accuracy than the other five methods in both disease samples and across all six data-generating processes. Boosted trees and OLS regression tended to perform well across a range of scenarios., (© 2022. The Author(s).)

Importance: Awake prone positioning may improve hypoxemia among patients with COVID-19, but whether it is associated with improved clinical outcomes remains unknown., Objective: To determine whether the recommendation of awake prone positioning is associated with improved outcomes among patients with COVID-19-related hypoxemia who have not received mechanical ventilation., Design, Setting, and Participants: This pragmatic nonrandomized controlled trial was conducted at 2 academic medical centers (Vanderbilt University Medical Center and NorthShore University HealthSystem) during the COVID-19 pandemic. A total of 501 adult patients with COVID-19-associated hypoxemia who had not received mechanical ventilation were enrolled from May 13 to December 11, 2020., Interventions: Patients were assigned 1:1 to receive either the practitioner-recommended awake prone positioning intervention (intervention group) or usual care (usual care group)., Main Outcomes and Measures: Primary outcome analyses were performed using a bayesian proportional odds model with covariate adjustment for clinical severity ranking based on the World Health Organization ordinal outcome scale, which was modified to highlight the worst level of hypoxemia on study day 5., Results: A total of 501 patients (mean [SD] age, 61.0 [15.3] years; 284 [56.7%] were male; and most [417 (83.2%)] were self-reported non-Hispanic or non-Latinx) were included. Baseline severity was comparable between the intervention vs usual care groups, with 170 patients (65.9%) vs 162 patients (66.7%) receiving oxygen via standard low-flow nasal cannula, 71 patients (27.5%) vs 62 patients (25.5%) receiving oxygen via high-flow nasal cannula, and 16 patients (6.2%) vs 19 patients (7.8%) receiving noninvasive positive-pressure ventilation. Nursing observations estimated that patients in the intervention group spent a median of 4.2 hours (IQR, 1.8-6.7 hours) in the prone position per day compared with 0 hours (IQR, 0-0.7 hours) per day in the usual care group. On study day 5, the bayesian posterior probability of the intervention group having worse outcomes than the usual care group on the modified World Health Organization ordinal outcome scale was 0.998 (posterior median adjusted odds ratio [aOR], 1.63; 95% credibility interval [CrI], 1.16-2.31). However, on study days 14 and 28, the posterior probabilities of harm were 0.874 (aOR, 1.29; 95% CrI, 0.84-1.99) and 0.673 (aOR, 1.12; 95% CrI, 0.67-1.86), respectively. Exploratory outcomes (progression to mechanical ventilation, length of stay, and 28-day mortality) did not differ between groups., Conclusions and Relevance: In this nonrandomized controlled trial, prone positioning offered no observed clinical benefit among patients with COVID-19-associated hypoxemia who had not received mechanical ventilation. Moreover, there was substantial evidence of worsened clinical outcomes at study day 5 among patients recommended to receive the awake prone positioning intervention, suggesting potential harm., Trial Registration: ClinicalTrials.gov Identifier: NCT04359797.

Objective: To develop and validate a prediction model for postdischarge opioid use in patients undergoing cesarean birth., Methods: We conducted a prospective cohort study of patients undergoing cesarean birth. Patients were enrolled postoperatively, and they completed pain and opioid use questionnaires 14 days after cesarean birth. Clinical data were abstracted from the electronic health record (EHR). Participants were prescribed 30 tablets of hydrocodone 5 mg-acetaminophen 325 mg at discharge and were queried about postdischarge opioid use. The primary outcome was total morphine milligram equivalents used. We constructed three proportional odds predictive models of postdischarge opioid use: a full model with 34 predictors available before hospital discharge, an EHR model that excluded questionnaire data, and a reduced model. The reduced model used forward selection to sequentially add predictors until 90% of the full model performance was achieved. Predictors were ranked a priori based on data from the literature and prior research. Predictive accuracy was estimated using discrimination (concordance index)., Results: Between 2019 and 2020, 459 participants were enrolled and 279 filled the standardized study prescription. Of the 398 with outcome measurements, participants used a median of eight tablets (interquartile range 1-18 tablets) after discharge, 23.5% used no opioids, and 23.0% used all opioids. Each of the models demonstrated high accuracy predicting postdischarge opioid use (concordance index range 0.74-0.76 for all models). We selected the reduced model as our final model given its similar model performance with the fewest number of predictors, all obtained from the EHR (inpatient opioid use, tobacco use, and depression or anxiety)., Conclusion: A model with three predictors readily found in the EHR-inpatient opioid use, tobacco use, and depression or anxiety-accurately estimated postdischarge opioid use. This represents an opportunity for individualizing opioid prescriptions after cesarean birth., Competing Interests: Financial Disclosure Stephen Bruehl disclosed receiving funding from NeuroBo. Carlos G. Grijalva reports consultancy fees from Pfizer, Merck, and Sanofi-Pasteur, and grants from Campbell Alliance/Syneos Health, CDC, NIH, the Food and Drug Administration, AHRQR, and Sanofi, outside the submitted work. The other authors did not report any potential conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Aims: The traditional approach to documenting angina outcomes in clinical trials is to ask the patient to recall their symptoms at the end of a month. With the ubiquitous availability of smartphones and tablets, daily contemporaneous documentation might be possible., Methods and Results: The ORBITA-2 symptom smartphone app was developed with a user-centred iterative design and testing cycle involving a focus group of previous ORBITA participants. The feasibility and acceptability were assessed in an internal pilot of participants in the ongoing ORBITA-2 trial. Seven days of app entries by ORBITA-2 participants were compared with subsequent participant recall at the end of the 7-day period. The design focus group tested a prototype app. They reported that the final version captured their symptoms and was easy to use. In the completion assessment group, 141 of 142 (99%) completed the app in full and 47 of 141 (33%) without reminders. In the recall assessment group, 29 of 29 (100%) participants said they could recall the previous day's symptoms, and 82% of them recalled correctly. For 2 days previously, 88% said they could recall and of those, 87% recalled correctly. The proportion saying they could recall their symptoms fell progressively thereafter: 89, 67, 61, 50%, and at 7 days, 55% ( P  < 0.001 for trend). The proportion of recalling correctly also fell progressively to 55% at 7 days ( P  = 0.04 for trend)., Conclusion: Episode counts of angina are difficult to recall after a few days. For trials such as ORBITA-2 focusing on angina, daily symptom collection via a smartphone app will increase the validity of the results., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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).

Objectives: In a recent paper we suggest that the relative risk (RR) be replaced with the odds ratio (OR) as the effect measure of choice in clinical epidemiology. In response, Chu, and colleagues raise several points that argue for the status quo. In this paper, we respond to their response., Study Designs and Settings: We use the same examples given by Chu and colleagues to recompute estimates of effect and demonstrate the problem with the RR., Results: We reaffirm the following findings: a) the OR and RR measure different things and their numerical difference is only important if misinterpreted b) this potential misinterpretation is a trivial issue compared to the lack of portability of the RR c) the same examples reaffirm non-portability of the RR and demonstrate how misleading the results might be in contrast to the OR, which is independent of the baseline risk d) the concept of non-collapsibility for the OR should be expected in the presence of a non-confounding risk factor, and is not a bias e) the log link in regression models that generate RRs as well as the use of RRs in meta-analysis is shown to be problematic using the same examples., Conclusion: The OR should replace the RR in clinical research and meta-analyses though there should be conversion of the end product into ratios or differences of risk, solely, for interpretation. To this end we provide a Stata module (logittorisk) for this purpose., (Copyright © 2021. Published by Elsevier Inc.)

Despite numerous promising therapeutic targets, there are no proven medical treatments for calcific aortic stenosis (AS). Multiple stakeholders need to come together and several scientific, operational, and trial design challenges must be addressed to capitalize on the recent and emerging mechanistic insights into this prevalent heart valve disease. This review briefly discusses the pathobiology and most promising pharmacologic targets, screening, diagnosis and progression of AS, identification of subgroups that should be targeted in clinical trials, and the need to elicit the patient voice earlier rather than later in clinical trial design and implementation. Potential trial end points and tools for assessment and approaches to implementation and design of clinical trials are reviewed. The efficiencies and advantages offered by a clinical trial network and platform trial approach are highlighted. The objective is to provide practical guidance that will facilitate a series of trials to identify effective medical therapies for AS resulting in expansion of therapeutic options to complement mechanical solutions for late-stage disease., Competing Interests: Funding Support and Author Disclosures Dr Lindman has served on the scientific advisory board for Roche Diagnostics; and has received research grants from Edwards Lifesciences and Roche Diagnostics. Dr Madhavan has received support from an institutional grant by the National Institutes of Health/National Heart, Lung, and Blood Institute to Columbia University Irving Medical Center (grant T32 HL007854). Dr Arsenault has received investigator-initiated research contracts from Pfizer and Ionis Pharmaceuticals; and has served as a consultant for Novartis and Silence Therapeutics. Dr Coylewright has received research grants from Edwards Lifesciences and Boston Scientific; and has served on consulting/advisory boards for Abbott, Medtronic, and Alleviant. Dr Merryman has received support from the National Institutes of Health (grant R35-HL135790) and Fondation Leducq. Dr Harrell has received support from the Clinical and Translational Science Awards (award UL1 TR002243) from the National Center for Advancing Translational Sciences. Dr Mack has served as coprimary investigator for the PARTNER trial for Edwards Lifesciences and the COAPT trial for Abbott; and has served as study chair for the APOLLO trial for Medtronic. Dr Leon has received institutional research support from Edwards Lifesciences, Medtronic, Boston Scientific, and Abbott; and has served on consulting/advisory boards for Medtronic, Boston Scientific, Gore, Meril Lifescience, and Abbott. Dr Pibarot has received research grants from Edwards Lifesciences and Medtronic for echo core lab analyses in transcatheter aortic valve replacement. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: Body mass index (BMI) is the most commonly used predictor of weight-related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology., Methods: Use of BMI or height-weight interaction in prediction models for established weight-related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high-density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2 , and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps., Results: Compared to BMI, the maximally flexible height-weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2 , and Somers' Dxy rank correlation, for event-free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range., Conclusions: Because alternative measures of body composition such as waist-to-hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk., Competing Interests: J. Beckman declares potential conflicts with Amgen, Bayer, Glaxo Smith Kline, Janssen, Novartis, and Sanofi in the past 12months. All other authors declare no conflicts of interest., (© 2021 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

Machine learning approaches are increasingly suggested as tools to improve prediction of clinical outcomes. We aimed to identify when machine learning methods perform better than a classical learning method. We hereto examined the impact of the data-generating process on the relative predictive accuracy of six machine and statistical learning methods: bagged classification trees, stochastic gradient boosting machines using trees as the base learners, random forests, the lasso, ridge regression, and unpenalized logistic regression. We performed simulations in two large cardiovascular datasets which each comprised an independent derivation and validation sample collected from temporally distinct periods: patients hospitalized with acute myocardial infarction (AMI, n  = 9484 vs. n  = 7000) and patients hospitalized with congestive heart failure (CHF, n  = 8240 vs. n  = 7608). We used six data-generating processes based on each of the six learning methods to simulate outcomes in the derivation and validation samples based on 33 and 28 predictors in the AMI and CHF data sets, respectively. We applied six prediction methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples according to c-statistic, generalized R 2 , Brier score, and calibration. While no method had uniformly superior performance across all six data-generating process and eight performance metrics, (un)penalized logistic regression and boosted trees tended to have superior performance to the other methods across a range of data-generating processes and performance metrics. This study confirms that classical statistical learning methods perform well in low-dimensional settings with large data sets.

Background: Studies across the healthcare spectrum consistently show that sharing and comparing data across institutions improves the quality of patient care. Whether comparing data about healthcare ethics consultation (HCEC) would similarly improve quality is unknown due to the lack of research on HCEC data sharing and comparison. Methods: To explore this possibility, we analyzed data from two academic medical centers in the Central-Southern United States that both employ a shared, robust coding system for ethics consultations (N = 703 cases total over 2.5 years) using descriptive and chi-square statistics, correlation coefficients and logistic regressions. Results: Our findings relate to patient age, care location, requestor role, and ethical themes, which together contribute to an improved evidence base for explanatory analyses and quality improvement initiatives. Conclusions: We conclude it is possible to analyze and compare HCEC activities across separate institutions using a standardized approach to data gathering, that this approach is consistent with concurrent narrative case review and assessment, and that cross-institutional comparisons are meaningful. Our results suggest future comparative analyses will require additional standardization of advanced measures for describing and analyzing HCEC activities.

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.

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., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Aims: Impaired physical function is common in patients undergoing transcatheter aortic valve replacement (TAVR) and associated with worse outcomes. Participation in centre-based cardiac rehabilitation (CR) after cardiovascular procedures is sub-optimal. We aimed to test a home-based mobile health exercise intervention as an alternative or complementary approach., Methods and Results: At five centres, after a run-in period, eligible individuals treated with TAVR were randomized 1:1 at their 1-month post-TAVR visit to an intervention group [activity monitor (AM) with personalized daily step goal and resistance exercises] or a control group for 6 weeks. Among 50 participants, average age was 76 years, 34% were female, average STS score was 2.91.8, and 40% had Short Physical Performance Battery (SPPB) 9. Daily compliance with wearing the AM and performing exercises averaged 8590%. In the intention to treat population, there was no evidence that the intervention improved the co-primary endpoints: daily steps +769 (95% CI 244 to +1783); SPPB +0.68 (0.27 to 1.53); and Kansas City Cardiomyopathy Questionnaire 1.7 (9.1 to 7.1). The intervention did improve secondary physical activity parameters, including moderate-to-intense daily active minutes ( P <0.05). In a pre-specified analysis including participants who did not participate in CR ( n =30), the intervention improved several measures of physical activity: +1730 (1003360) daily steps; +66 (28105) daily active minutes; +53 (2780) moderate-to-intense active minutes; and 157 (265 to 50) sedentary minutes., Conclusion: Among selected participants treated with TAVR, this study did not provide evidence that a pragmatic home-based mobile health exercise intervention improved daily steps, physical performance or QoL for the overall cohort. However, the intervention did improve several measures of daily activity, particularly among individuals not participating in CR., Trial Registry: Clinicaltrials.gov NCT03270124., (The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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.

Study Design: Retrospective analysis of prospectively collected registry data., Objective: To develop and validate prediction models for 12-month patient-reported outcomes of disability, pain, and myelopathy in patients undergoing elective cervical spine surgery., Summary of Background Data: Predictive models have the potential to be utilized preoperatively to set expectations, adjust modifiable characteristics, and provide a patient-centered model of care., Methods: This study was conducted using data from the cervical module of the Quality Outcomes Database. The outcomes of interest were disability (Neck Disability Index:), pain (Numeric Rating Scale), and modified Japanese Orthopaedic Association score for myelopathy. Multivariable proportional odds ordinal regression models were developed for patients with cervical radiculopathy and myelopathy. Patient demographic, clinical, and surgical covariates as well as baseline patient-reported outcomes scores were included in all models. The models were internally validated using bootstrap resampling to estimate the likely performance on a new sample of patients., Results: Four thousand nine hundred eighty-eight patients underwent surgery for radiculopathy and 2641 patients for myelopathy. The most important predictor of poor postoperative outcomes at 12-months was the baseline Neck Disability Index score for patients with radiculopathy and modified Japanese Orthopaedic Association score for patients with myelopathy. In addition, symptom duration, workers' compensation, age, employment, and ambulatory and smoking status had a statistically significant impact on all outcomes (P < 0.001). Clinical and surgical variables contributed very little to predictive models, with posterior approach being associated with higher odds of having worse 12-month outcome scores in both the radiculopathy and myelopathy cohorts (P < 0.001). The full models overall discriminative performance ranged from 0.654 to 0.725., Conclusions: These predictive models provide individualized risk-adjusted estimates of 12-month disability, pain, and myelopathy outcomes for patients undergoing spine surgery for degenerative cervical disease. Predictive models have the potential to be used as a shared decision-making tool for evidence-based preoperative counselling., Level of Evidence: 2.