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Rationale & Objective: Severe hypertension (sHTN) that develops after hospital admission is prevalent in 10% of patients admitted for reasons other than HTN. sHTN is commonly treated with intravenous (IV) antihypertensives and is associated with a greater risk of acute kidney injury (AKI). Our goal was to explore whether there is heterogeneity in IV antihypertensives' effect on AKI incidence among patients who develop sHTN during hospitalization., Study Design: Heterogeneity of treatment effect analysis., Settings & Participants: Patients who developed sHTN, systolic blood pressure (BP) >180, or diastolic BP >110 mmHg during hospitalization and did not have kidney failure., Exposure: Treatment with IV antihypertensives within 3 hours of BP elevation., Outcome: Time to develop AKI., Analytical Approach: An accelerated failure time Bayesian Additive Regression Trees (BART) model to capture the association between the time to develop AKI and predictors. Individual treatment effects estimated for each participant using a counterfactual outcome framework and these estimates were used to identify patient characteristics associated with treatment effect heterogeneity in response to IV antihypertensives., Results: We included 11,951 patients who developed sHTN, 741 were treated with IV antihypertensives, and 11,210 were not, of which 18% and 13% developed AKI, respectively. Most patients would have been harmed from IV antihypertensive treatment except for a small subset of 317 patients who were White, had an SBP on admission ≥156 mmHg, an eGFR ≥70.7 ml/min/1.73m 2 , and a serum bicarbonate <21.7 mmol/L., Limitations: Data-driven, hypothesis-generating approach. Findings were not validated with external data sources., Conclusion: These exploratory findings suggest that most patients who develop sHTN will not benefit from IV antihypertensive treatment. Future studies should assess for heterogeneity when identifying treatment options, if any are needed, for sHTN., (Copyright © 2024. Published by Elsevier Inc.)

Background: We sought to estimate whether a lower mean arterial blood pressure target, compared with a higher mean arterial blood pressure target, reduced 90-day all-cause mortality among critically ill adult patients with vasodilatory shock., Methods: We conducted an individual patient data meta-analysis of randomized controlled trials that evaluated the effect of distinct thresholds of mean arterial blood pressure to guide vasopressor support among critically ill adults identified in a systematic literature search. The main exposure was a lower mean arterial pressure target compared with a higher mean arterial pressure target (including usual care). The primary outcome was 90-day all-cause mortality. We used a Bayesian random effects log-binomial model to estimate risk ratios with 95% credible intervals (CrIs)., Results: Between 2010 and 2019, 3352 patients were randomly assigned in three trials (SEPSISPAM, OVATION pilot trial, and 65-Trial) across 103 hospitals from the United Kingdom, France, and Canada. When compared with a higher mean arterial blood pressure target or usual care, the risk ratio for 90-day all-cause mortality associated with a lower blood pressure target was 0.93 (95% CrI, 0.76 to 1.07; low certainty, posterior probability of benefit 87%). Results were consistent across multiple secondary and sensitivity analyses, including adjustment for prognostically important baseline covariates and alternative modeling techniques. Multiple approaches to evaluate the heterogeneity of treatment effect did not identify any subgroups that may potentially benefit from higher mean arterial blood pressure targets., Conclusions: Targeting a lower mean arterial blood pressure for vasopressor therapy in critically ill patients with vasodilatory shock possibly reduced 90-day all-cause mortality. However, the certainty of evidence is low, and this analysis does not exclude the possibility that lower targets may cause harm overall.

Background: People diagnosed with a co-occurring serious mental illness (SMI; ie, major depressive disorder, bipolar disorder, or schizophrenia) but hospitalized for a nonpsychiatric condition experience higher rates of readmissions and other adverse outcomes, in part due to poorly coordinated care transitions. Current hospital-to-home transitional care programs lack a focus on the integrated social, medical, and mental health needs of these patients. The Thrive clinical pathway provides transitional care support for patients insured by Medicaid with multiple chronic conditions by focusing on posthospitalization medical concerns and the social determinants of health. This study seeks to evaluate an adapted version of Thrive that also meets the needs of patients with co-occurring SMI discharged from a nonpsychiatric hospitalization., Objective: This study aimed to (1) engage staff and community advisors in participatory implementation processes to adapt the Thrive clinical pathway for all Medicaid-insured patients, including those with SMI; (2) examine utilization outcomes (ie, Thrive referral, readmission, emergency department [ED], primary, and specialty care visits) for Medicaid-insured individuals with and without SMI who receive Thrive compared with usual care; and (3) evaluate the acceptability, appropriateness, feasibility, and cost-benefit of an adapted Thrive clinical pathway that is tailored for Medicaid-insured patients with co-occurring SMI., Methods: This study will use a prospective, type I hybrid effectiveness-implementation, stepped-wedge, cluster randomized controlled trial design. We will randomize the initiation of Thrive referrals at the unit level. Data collection will occur over 24 months. Inclusion criteria for Thrive referral include individuals who (1) are Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) reside in Philadelphia; (3) are admitted for a medical diagnosis for over 24 hours at the study hospital; (4) are planned for discharge to home; (5) agree to receive home care services; and (6) are aged ≥18 years. Primary analyses will use a mixed-effects negative binomial regression model to evaluate readmission and ED utilization, comparing those with and without SMI who receive Thrive to those with and without SMI who receive usual care. Using a convergent parallel mixed methods design, analyses will be conducted simultaneously for the survey and interview data of patients, clinicians, and health care system leaders. The cost of Thrive will be calculated from budget monitoring data for the research budget, the cost of staff time, and average Medicaid facility fee payments., Results: This research project was funded in October 2023. Data collection will occur from April 2024 through December 2025. Results are anticipated to be published in 2025-2027., Conclusions: We anticipate that patients with and without co-occurring SMI will benefit from the adapted Thrive clinical pathway. We also anticipate the adapted version of Thrive to be deemed feasible, acceptable, and appropriate by patients, clinicians, and health system leaders., Trial Registration: ClinicalTrials.gov NCT06203509; https://clinicaltrials.gov/ct2/show/NCT06203509., International Registered Report Identifier (irrid): DERR1-10.2196/64575., (©J Margo Brooks Carthon, Heather Brom, Kelvin Eyram Amenyedor, Michael O Harhay, Marsha Grantham-Murillo, Jacqueline Nikpour, Karen B Lasater, Daniela Golinelli, Pamela Z Cacchione, Amanda P Bettencourt. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 12.11.2024.)

Background: Patients with acute hypoxemic respiratory failure are at high risk of death and prolonged time on the ventilator. Interventions often aim to reduce both mortality and time on the ventilator. Many methods have been proposed for analyzing these endpoints as a single composite outcome (days alive and free of ventilation), but it is unclear which analytical method provides the best performance. Thus, we aimed to determine the analysis method with the highest statistical power for use in clinical trials., Methods: Using statistical simulation, we compared multiple methods for analyzing days alive and free of ventilation: the t, Wilcoxon rank-sum, and Kryger Jensen and Lange tests, as well as the proportional odds, hurdle-Poisson, and competing risk models. We compared 14 scenarios relating to: 1) varying baseline distributions of mortality and duration of ventilation, which were based on data from a registry of patients with acute hypoxemic respiratory failure and 2) the varying effects of treatment on mortality and duration of ventilation., Results and Conclusions: All methods have good control of type 1 error rates (i.e., avoid false positive findings). When data are simulated using a proportional odds model, the t test and ordinal models have the highest relative power (92% and 90%, respectively), followed by competing risk models. When the data are simulated using survival models, the competing risk models have the highest power (100% and 92%), followed by the t test and a ten-category ordinal model. All models struggled to detect the effect of the intervention when the treatment only affected one of mortality and duration of ventilation. Overall, the best performing analytical strategy depends on the respective effects of treatment on survival and duration of ventilation and the underlying distribution of the outcomes. The evaluated models each provide a different interpretation for the treatment effect, which must be considered alongside the statistical power when selecting analysis models., Competing Interests: Dr. Harhay is funded by the Patient-Centered Outcomes Research Institute (Award ME-2020C1-19220) and the U.S. National Institutes of Health/National Heart, Lung, and Blood Institute (grant numbers R00-HL141678 and R01-HL168202). Dr. Urner is supported by a Scholarship of the Interdepartmental Division of Critical Care Medicine, University of Toronto. Dr. Heath is funded by a Canada Research Chair in Statistical Trial Design; Natural Sciences and Engineering Research Council of Canada (award No. RGPIN-2021 03366). Dr. Goligher is funded by an Early Career Investigator Award from the National Sanitarium Association. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Frequentist and Bayesian statistics represent two differing paradigms for the analysis of data. Frequentism became the dominant mode of statistical thinking in medical practice during the 20th century. The advent of modern computing has made Bayesian analysis increasingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including medical research. Rather than conceiving of probability as the expected frequency of an event (purported to be measurable and objective), Bayesian thinking conceives of probability as a measure of strength of belief (an explicitly subjective concept). Bayesian analysis combines previous information (represented by a mathematical probability distribution, the prior) with information from the study (the likelihood function) to generate an updated probability distribution (the posterior) representing the information available for clinical decision making. Owing to its fundamentally different conception of probability, Bayesian statistics offers an intuitive, flexible, and informative approach that facilitates the design, analysis, and interpretation of clinical trials. In this Review, we provide a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research., Competing Interests: Declaration of interests ECG is supported by an Early Career Health Research Award from the National Sanitarium Association. MOH is supported by grant number R01-HL168202 from the National Heart, Lung, and Blood Institute (National Institutes of Health). AH is supported by a Canada Research Chair in Statistical Trial Design and the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada (RGPIN-2021–03366). ECG receives fees for speaking or consulting from Vyaire, BioAge, Stimit, Lungpacer Medical, Getinge, Draeger, Heecap, and Zoll. He serves on the clinical advisory board for Getinge and previously served on the advisory board for Lungpacer Medical. He has received in-kind support for research from Timpel Medical, Lungpacer Medical, and Getinge. MOH has received statistical consulting fees from Unlearn. AI, Guidepoint Global, and the Berkeley Research Group; fees for editorial services from Elsevier and the American Thoracic Society; fees for serving on a data safety monitoring board from the University of California, San Francisco, and the University of Pittsburgh; and fees for pilot grant reviews from Brown University and New York University., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi-stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non-informative priors resembled those from designs with more aggressive adaptation using weakly-to-moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously., (© 2024 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Background: Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes., Methods: This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings., Results: Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity., Conclusion: We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.L.W. has received consulting fees from Pfizer and Revelar Biotherapeutics, Inc. for work unrelated to the topics in this manuscript.

Background: Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference., Methods: We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design., Results: Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size., Conclusions: We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific , and whether they are participant- or cluster-average . In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 ( p  = 0.17) to 1.83 ( p  = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Context: Dyslipidemia is common, and resultant endothelial dysfunction may impact reproductive outcomes. No prospective study has examined the effect of preconception lipid parameters in both female and male partners or their interaction on live birth., Objective: To determine whether live birth is associated with preconception lipids in both partners by planned fertility treatment., Design: Secondary analysis of the Folic Acid and Zinc Supplementation Trial, conducted between June 2013-December 2017. Couples were followed for nine months after randomization and until delivery., Setting: Multicenter study., Participants: Couples seeking fertility treatment (n = 2370; females 18-45 years, males ≥18 years)., Exposures: Female, male, and couple abnormal versus normal preconception lipid concentrations (total cholesterol [TC], low-density lipoprotein [LDL], high-density lipoprotein [HDL], triglycerides [TG])., Main Outcome Measures: Live birth., Results: Among 2370 couples, most males (84%) and females (76%) had at least one abnormal lipid parameter. Males planning in vitro fertilization (IVF, n = 373) with elevated LDL had lower probability of live birth than those with normal levels (47.4% vs. 59.7%, aRR 0.79, 95% CI 0.65-0.98). In couples planning IVF where both partners had elevated TC or LDL, live birth was lower than those with normal levels (TC: 32.4% vs. 58.0%, aRR 0.53, 95% CI 0.36-0.79; and LDL: 41.9% vs. 63.8%, aRR 0.69, 95% CI 0.55-0.85). Lipid parameters were not associated with live birth for couples planning non-IVF treatments., Conclusions: Couples planning IVF where both partners had elevated TC or LDL and males planning IVF with elevated LDL had decreased probability of live birth. These findings may support lipid screening in patients seeking fertility treatment for prognostic information for reproductive outcomes., (Published by Oxford University Press on behalf of the Endocrine Society 2024.)

Rationale: Hospital-free days (HFDs), a measure of the number of days alive spent outside the hospital, is increasingly used as an endpoint in studies of patients with acute respiratory failure (ARF) or other critical and serious illnesses. Current approaches to measuring HFDs do not account for decrements in functional status or quality of life that ARF survivors and family members value. Objectives: To develop an acceptable approach to measure quality-weighted HFDs using patient-reported outcomes. Methods: We conducted a four-round modified Delphi process among ARF experts: those with lived or professional experience. Experts rated survivorship domains, instrument and data collection characteristics, and methods to translate responses into quality-weighted HFDs. The consensus threshold was that ⩾70% of respondents rated an item "totally acceptable" or "acceptable" and ⩽15% of respondents rated the item "totally unacceptable," "unacceptable," or "slightly unacceptable." Results: Fifty-seven experts participated in round 1. Response rates were 82-93% for subsequent rounds. Priority survivorship domains were physical function and health-related quality of life. Participants reached a consensus that data collection during ARF recovery should take less than 15 minutes per assessment, allow surrogate completion when patients are unable, and continue for at least 24 months of follow-up. Using the EuroQol-5 Dimensions (EQ-5D) questionnaire to quality weight HFDs met consensus criteria for acceptability. A majority of panelists preferred quality-weighted HFDs to unweighted HFDs or survival for use in future ARF studies. Conclusions: Quality-weighting HFDs using patient and/or surrogate responses to the EQ-5D captured stakeholder priorities and was acceptable to this Delphi panel.

Acute respiratory distress syndrome (ARDS) is associated with long-term impairments in brain and muscle function that significantly impact the quality of life of those who survive the acute illness. The mechanisms underlying these impairments are not yet well understood, and evidence-based interventions to minimize the burden on patients remain unproved. The NHLBI of the NIH assembled a workshop in April 2023 to review the state of the science regarding ARDS-associated brain and muscle dysfunction, to identify gaps in current knowledge, and to determine priorities for future investigation. The workshop included presentations by scientific leaders across the translational science spectrum and was open to the public as well as the scientific community. This report describes the themes discussed at the workshop as well as recommendations to advance the field toward the goal of improving the health and well-being of ARDS survivors.

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Rationale: The epidemiology, management, and outcomes of acute respiratory distress syndrome (ARDS) differ between children and adults, with lower mortality rates in children despite comparable severity of hypoxemia. However, the relationship between age and mortality is unclear. Objective: We aimed to define the association between age and mortality in ARDS, hypothesizing that it would be nonlinear. Methods: We performed a retrospective cohort study using data from two pediatric ARDS observational cohorts ( n  = 1,236), multiple adult ARDS trials ( n  = 5,547), and an adult observational ARDS cohort ( n  = 1,079). We aligned all datasets to meet Berlin criteria. We performed unadjusted and adjusted logistic regression using fractional polynomials to assess the potentially nonlinear relationship between age and 90-day mortality, adjusting for sex, Pa O 2 /Fi O 2 , immunosuppressed status, year of study, and observational versus randomized controlled trial, treating each individual study as a fixed effect. Measurements and Main Results: There were 7,862 subjects with median ages of 4 years in the pediatric cohorts, 52 years in the adult trials, and 61 years in the adult observational cohort. Most subjects (43%) had moderate ARDS by Berlin criteria. Ninety-day mortality was 19% in the pediatric cohorts, 33% in the adult trials, and 67% in the adult observational cohort. We found a nonlinear relationship between age and mortality, with mortality risk increasing at an accelerating rate between 11 and 65 years of age, after which mortality risk increased more slowly. Conclusions: There was a nonlinear relationship between age and mortality in pediatric and adult ARDS.

Background: There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities., Purpose: To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities., Data Sources: Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023., Study Selection: Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms., Data Extraction: Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension., Data Synthesis: Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques., Limitation: Results are mostly based on modeling studies and may be highly context-specific., Conclusion: Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes., Primary Funding Source: Agency for Healthcare Quality and Research., Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-2960.

Background: Disparities in posthospitalization outcomes for people with chronic medical conditions and insured by Medicaid are well documented, yet interventions that mitigate them are lacking. Prevailing transitional care interventions narrowly target people aged 65 years and older, with specific disease processes, or limitedly focus on individual-level behavioral change such as self-care or symptom management, thus failing to adequately provide a holistic approach to ensure an optimal posthospital care continuum. This study evaluates the implementation of THRIVE-an evidence-based, equity-focused clinical pathway that supports Medicaid-insured individuals with multiple chronic conditions transitioning from hospital to home by focusing on the social determinants of health and systemic and structural barriers in health care delivery. THRIVE services include coordinating care, standardizing interdisciplinary communication, and addressing unmet clinical and social needs following hospital discharge., Objective: The study's objectives are to (1) examine referral patterns, 30-day readmission, and emergency department use for participants who receive THRIVE support services compared to those receiving usual care and (2) evaluate the implementation of the THRIVE clinical pathway, including fidelity, feasibility, appropriateness, and acceptability., Methods: We will perform a sequential randomized rollout of THRIVE to case managers at the study hospital in 3 steps (4 in the first group, 4 in the second, and 5 in the third), and data collection will occur over 18 months. Inclusion criteria for THRIVE participation include (1) being Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) residing in Philadelphia; (3) having experienced a hospitalization at the study hospital for more than 24 hours with a planned discharge to home; (4) agreeing to home care at partner home care settings; and (5) being aged 18 years or older. Qualitative data will include interviews with clinicians involved in THRIVE, and quantitative data on health service use (ie, 30-day readmission, emergency department use, and primary and specialty care) will be derived from the electronic health record., Results: This project was funded in January 2023 and approved by the institutional review board on March 10, 2023. Data collection will occur from March 2023 to July 2024. Results are expected to be published in 2025., Conclusions: The THRIVE clinical pathway aims to reduce disparities and improve postdischarge care transitions for Medicaid-insured patients through a system-level intervention that is acceptable for THRIVE participants, clinicians, and their teams in hospitals and home care settings. By using our equity-focused case management services and leveraging the power of the electronic medical record, THRIVE creates efficiencies by identifying high-need patients, improving communication across acute and community-based sectors, and driving evidence-based care coordination. This study will add important findings about how the infusion of equity-focused principles in the design and evaluation of evidence-based interventions contributes to both implementation and effectiveness outcomes., International Registered Report Identifier (irrid): DERR1-10.2196/54211., Trial Registration: ClinicalTrials.gov NCT05714605; https://clinicaltrials.gov/ct2/show/NCT05714605., (©J Margo Brooks Carthon, Heather Brom, Marsha Grantham-Murrillo, Kathy Sliwinski, Aleigha Mason, Mindi Roeser, Donna Miles, Dianne Garcia, Jovan Bennett, Michael O Harhay, Emilia Flores, Kelvin Amenyedor, Rebecca Clark. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 26.03.2024.)

Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials., (© 2024. The Author(s).)

Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and degree of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field.

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials., (© 2023 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Importance: Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking., Objective: To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes., Design, Setting, and Participants: A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019., Intervention: Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care., Main Outcomes and Measures: The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality., Results: Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08])., Conclusions and Relevance: Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes., Trial Registration: ClinicalTrials.gov Identifier: NCT02505035.

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Cluster-randomized trials (CRTs) involve randomizing entire groups of participants-called clusters-to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Objective: It is unknown whether weight change or physical fitness is associated with chronic kidney disease (CKD) risk among nondiabetic adults with obesity., Methods: This was a prospective, longitudinal cohort study of adults with obesity without baseline CKD or diabetes enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA). Linear mixed-effects and multistate models were adjusted for demographics, time-varying covariates including blood pressure, and comorbidities these were used to examine associations of weight change and slow walking pace (<2 miles/h) with (i) rate of annual estimated glomerular filtration rate (eGFR) decline and (ii) incident CKD, defined as eGFR Cr-Cys  < 60 mL/min/1.73 m 2 , and tested for interaction by baseline hypertension status., Results: Among 1208 included MESA participants (median BMI 33.0 kg/m 2 [interquartile range 31.2-35.9]), 15% developed CKD. Slow walking pace was associated with eGFR decline (-0.27 mL/min/1.73 m 2 /year; 95% CI: -0.42 to -0.12) and CKD risk (adjusted hazard ratio 1.48; 95% CI: 1.08 to 2.01). Weight gain was associated with CKD risk (adjusted hazard ratio 1.34; 95% CI: 1.02 to 1.78 per 5 kg weight gain from baseline). There was no significant interaction by baseline hypertension status., Conclusions: Slow walking pace and weight gain were associated with CKD risk among adults with obesity who did not have diabetes at baseline., (© 2023 The Obesity Society.)

The clinical trajectory of survivors of critical illness after hospital discharge can be complex and highly unpredictable. Assessing long-term outcomes after critical illness can be challenging because of possible competing events, such as all-cause death during follow-up (which precludes the occurrence of an event of particular interest). In this perspective, we explore challenges and methodological implications of competing events during the assessment of long-term outcomes in survivors of critical illness. In the absence of competing events, researchers evaluating long-term outcomes commonly use the Kaplan-Meier method and the Cox proportional hazards model to analyze time-to-event (survival) data. However, traditional analytical and modeling techniques can yield biased estimates in the presence of competing events. We present different estimands of interest and the use of different analytical approaches, including changes to the outcome of interest, Fine and Gray regression models, cause-specific Cox proportional hazards models, and generalized methods (such as inverse probability weighting). Finally, we provide code and a simulated dataset to exemplify the application of the different analytical strategies in addition to overall reporting recommendations.

Importance: Patients' expectations for future health guide their decisions and enable them to prepare, adapt, and cope. However, little is known about how inaccurate expectations may affect patients' illness outcomes., Objective: To assess the association between patients' expectation inaccuracies and health-related quality of life., Design, Setting, and Participants: This cohort study of patients with severe chronic obstructive pulmonary disease (COPD) was conducted from 2017 to 2021, which included a 24-month follow-up period. Eligible participants received outpatient primary care at pulmonary clinics of a single large US health system. Data were analyzed between 2021 and 2023., Exposure: Expectation accuracy, measured by comparing patients' self-reported expectations of their symptom burden with their actual physical and emotional symptoms 3, 12, and 24 months in the future., Main Outcome and Measure: Health-related quality of life, measured by the St George's Respiratory Questionnaire-COPD at 3, 12, and 24 months., Results: A total of 207 participants were included (median age, 65.5 years [range, 42.0-86.0 years]; 120 women [58.0%]; 118 Black [57.0%], 79 White [38.2%]). The consent rate among approached patients was 80.0%. Most patients reported no or only limited discussions of future health and symptom burdens with their clinicians. Across physical and emotional symptoms and all 3 time points, patients' expectations were more optimistic than their experiences. There were no consistent patterns of measured demographic or behavioral characteristics associated with expectation accuracy. Regression models revealed that overoptimistic expectations of future burdens of dyspnea (linear regression estimate, 4.68; 95% CI, 2.68 to 6.68) and negative emotions (linear regression estimate, -3.04; 95% CI, -4.78 to 1.29) were associated with lower health-related quality of life at 3 months after adjustment for baseline health-related quality of life, forced expiratory volume over 1 second, and interval clinical events (P < .001 for both). Similar patterns were observed at 12 months (dyspnea: linear regression estimate, 2.41; 95% CI, 0.45 to 4.37) and 24 months (negative emotions: linear regression estimate, -2.39; 95% CI, -4.67 to 0.12; dyspnea: linear regression estimate, 3.21; 95% CI, 0.82 to 5.60), although there was no statistically significant association between expectation of negative emotions and quality of life at 12 months., Conclusions and Relevance: In this cohort study of patients with COPD, we found that patients are overoptimistic in their expectations about future negative symptom burdens, and such inaccuracies were independently associated with worse well-being over time. Developing and implementing strategies to improve patients' symptom expectations may improve patient-centered outcomes.

Background: Targeting short-term improvements in multicomponent risk scores for mortality in patients with pulmonary arterial hypertension (PAH) could result in improved long-term outcomes. We aimed to determine whether PAH risk scores were adequate surrogates for clinical worsening or mortality outcomes in PAH randomised clinical trials (RCTs)., Methods: We performed an individual participant data meta-analysis of RCTs selected from PAH trials provided by the US Food and Drug Administration (FDA). We calculated predicted risk using the COMPERA, COMPERA 2.0, non-invasive FPHR, REVEAL 2.0, and REVEAL Lite 2 risk scores. The primary outcome of interest was time to clinical worsening, a composite endpoint composed of any of the following events: all-cause death, hospitalisation for worsening PAH, lung transplantation, atrial septostomy, discontinuation of study treatment (or study withdrawal) for worsening PAH, initiation of parenteral prostacyclin analogue therapy, or decrease of at least 15% in 6-min walk distance from baseline, combined with either worsening of WHO functional class from baseline or the addition of an approved PAH treatment. The secondary outcome of interest was time to all-cause mortality. We assessed the surrogacy of these risk scores, parameterised as attainment of low-risk status by 16 weeks, for improvement in long-term clinical worsening and survival using mediation and meta-analysis frameworks., Findings: Of 28 trials received from the FDA, three RCTs (AMBITION, GRIPHON, and SERAPHIN; n=2508) had the data necessary to assess long-term surrogacy. The mean age was 49 years (SD 16), 1956 (78%) participants were women, 1704 (68%) were classified as White, and 280 (11%) were Hispanic or Latino. 1388 (55%) of 2503 participants with available data had idiopathic PAH and 776 (31%) of 2503 had PAH associated with connective tissue disease. In a mediation analysis, the proportions of treatment effects explained by attainment of low-risk status ranged only from 7% to 13%. In a meta-analysis of trial-regions, the treatment effects on low-risk status were not predictive of the treatment effects on time to clinical worsening (R 2 values 0·01-0·19) nor the treatment effects on time to all-cause mortality (R 2 values 0-0·2). A leave-one-out analysis suggested that the use of these risk scores as surrogates might lead to biased inferences regarding the effect of therapies on clinical outcomes in PAH RCTs. Results were similar when using absolute risk scores at 16 weeks as the potential surrogates., Interpretation: Multicomponent risk scores have utility for the prediction of outcomes in patients with PAH. Clinical surrogacy for long-term outcomes cannot be inferred from observational studies of outcomes. Our analyses of three PAH trials with long-term follow-up suggest that further study is necessary before using these or other scores as surrogate outcomes in PAH RCTs or clinical care., Funding: Cardiovascular Medical Research and Education Fund, US National Institutes of Health., Competing Interests: Declaration of interests CEV received institutional funding for participation in a randomised trial sponsored by Altavant Sciences; personal fees from Regeneron, Merck, Janssen, Altavant Sciences, and Acceleron/Merck; personal fees from Gather-ED for continuing medical education; and support and personal fees for editorial work and for travel from the American Thoracic Society. JF received institutional funding for participation in a randomised trial sponsored by United Therapeutics. JAM received personal fees for serving on a data safety monitoring board for United Therapeutics, Merck, and Janssen. SMK received consulting fees from Janssen, Regeneron, and Morphic; travel support from Aerovate; fees for serving on a data safety monitoring board from United Therapeutics; fees for serving on committees for Acceleron, Vivus, Aerivate, and Proteo Biotech; editorial fees from the European Respiratory Journal; has stock or stock options in Verve Therapeutics; and has received in-kind remote monitoring equipment from PhysIQ. MOH received statistical consulting fees from Unlearn. AI and Berkeley Research Group; fees for editorial services from Elsevier and the American Thoracic Society; fees for serving on a data safety monitoring board from the University of California, San Francisco, and the University of Pittsburgh; and fees for pilot grant reviews from Brown University and New York University. All other authors declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Lack of insurance has been independently associated with an increased risk of in-hospital mortality after abdominal aortic aneurysm repair, possibly due to worse control of comorbidities and delays in diagnosis and treatment. Medicaid expansion has improved insurance rates and access to care, potentially benefiting these patients. We sought to assess the association between Medicaid expansion and outcomes after abdominal aortic aneurysm repair., Methods: A retrospective analysis of Healthcare Cost and Utilization Project State Inpatient Databases data from 14 states between 2012 and 2018 was conducted. The sample was restricted to first-record abdominal aortic aneurysm repairs in adults under age 65 in states that expanded Medicaid on January 1, 2014 (Medicaid expansion group) or had not expanded before December 31, 2018 (non-expansion group). The Medicaid expansion and non-expansion groups were compared between pre-expansion (2012-2013) and post-expansion (2014-2018) time periods to assess baseline demographic and operative differences. We used difference-in-differences multivariable logistic regression adjusted for patient factors, open vs endovascular repair, and standard errors clustered by state. Our primary outcome was in-hospital mortality. Outcomes were stratified by insurance type., Results: We examined 8995 patients undergoing abdominal aortic aneurysm repair, including 3789 (42.1%) in non-expansion states and 5206 (57.9%) in Medicaid expansion states. Rates of Medicaid insurance were unchanged in non-expansion states but increased in Medicaid expansion states post-expansion (non-expansion: 10.9% to 9.8%; P = .346; expansion: 9.7% to 19.7%; P < .001). One in 10 patients from both non-expansion and Medicaid expansion states presented with ruptured aneurysms, which did not change over time. Rates of open repair decreased in both non-expansion and Medicaid expansion states over time (non-expansion: 25.1% to 19.2%; P < .001; expansion: 25.2% to 18.4%; P < .001). On adjusted difference-in-differences analysis between expansion and non-expansion states pre-to post-expansion, Medicaid expansion was associated with a 1.02% absolute reduction in in-hospital mortality among all patients (95% confidence interval, -1.87% to -0.17%; P = .019). Additionally, among patients who were either on Medicaid or were uninsured (ie, the patients most likely to be impacted by Medicaid expansion), a larger 4.17% decrease in in-hospital mortality was observed (95% confidence interval, -6.47% to -1.87%; P < .001). In contrast, no significant difference-in-difference in mortality was observed for privately insured patients., Conclusions: Medicaid expansion was associated with decreased in-hospital mortality after abdominal aortic aneurysm repair among all patients and particularly among patients who were either on Medicaid or were uninsured. Our results provide support for improved access to care for patients undergoing abdominal aortic aneurysm repair through Medicaid expansion., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time-to-event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model-free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within-cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster-robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small-sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias-corrected sandwich variance estimators to reduce the negative bias of the cluster-robust sandwich variance estimator. We study the finite-sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials., (© 2022 Wiley-VCH GmbH.)

Background: Many patients hospitalized for COVID-19 experience prolonged symptoms months after discharge. Little is known abou t patients' personal experiences recovering from COVID-19 in the United States (US), where medically underserved populations are at particular risk of adverse outcomes., Objective: To explore patients' perspectives on the impact of COVID-19 hospitalization and barriers to and facilitators of recovery 1 year after hospital discharge in a predominantly Black American study population with high neighborhood-level socioeconomic disadvantage., Design: Qualitative study utilizing individual, semi-structured interviews., Participants: Adult patients hospitalized for COVID-19 approximately 1 year after discharge home who were engaged in a COVID-19 longitudinal cohort study., Approach: The interview guide was developed and piloted by a multidisciplinary team. Interviews were audio-recorded and transcribed. Data were coded and organized into discrete themes using qualitative content analysis with constant comparison techniques., Key Results: Of 24 participants, 17 (71%) self-identified as Black, and 13 (54%) resided in neighborhoods with the most severe neighborhood-level socioeconomic disadvantage. One year after discharge, participants described persistent deficits in physical, cognitive, or psychological health that impacted their current lives. Repercussions included financial suffering and a loss of identity. Participants reported that clinicians often focused on physical health over cognitive and psychological health, an emphasis that posed a barrier to recovering holistically. Facilitators of recovery included robust financial or social support systems and personal agency in health maintenance. Spirituality and gratitude were common coping mechanisms., Conclusions: Persistent health deficits after COVID-19 resulted in downstream consequences in participants' lives. Though participants received adequate care to address physical needs, many described persistent unmet cognitive and psychological needs. A more comprehensive understanding of barriers and facilitators for COVID-19 recovery, contextualized by specific healthcare and socioeconomic needs related to socioeconomic disadvantage, is needed to better inform intervention delivery to patients that experience long-term sequelae of COVID-19 hospitalization., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions., Methods: We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero-one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity., Results: In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation., Conclusions: This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies., Trial Registration: COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731., (© 2023. The Author(s).)

Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death-that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e., the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased., Methods: Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa)., Results: We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings., Conclusion: A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not.

Background: The availability of asthma biologics may not benefit all patients equally., Objective: We sought to identify patient characteristics associated with asthma biologic prescribing, primary adherence, and effectiveness., Methods: A retrospective, observational cohort study of 9,147 adults with asthma who established care with a Penn Medicine asthma subspecialist was conducted using Electronic Health Record data from January 1, 2016, to October 18, 2021. Multivariable regression models were used to identify factors associated with (1) receipt of a new biologic prescription; (2) primary adherence, defined as receiving a dose in the year after receiving the prescription, and (3) oral corticosteroid (OCS) bursts in the year after the prescription., Results: Factors associated with a new prescription, which was received by 335 patients, included being a woman (odds ratio [OR] 0.66; P = .002), smoking currently (OR 0.50; P = .04), having an asthma hospitalization in the prior year (OR 2.91; P < .001), and having 4+ OCS bursts in the prior year (OR 3.01; P < .001). Reduced primary adherence was associated with Black race (incidence rate ratio 0.85; P < .001) and Medicaid insurance (incidence rate ratio 0.86; P < .001), although most in these groups, 77.6% and 74.3%, respectively, still received a dose. Nonadherence was associated with patient-level barriers in 72.2% of cases and health insurance denial in 22.2%. Having more OCS bursts after receiving a biologic prescription was associated with Medicaid insurance (OR 2.69; P = .047) and biologic days covered (OR 0.32 for 300-364 d vs 14-56 d; P = .03)., Conclusions: In a large health system, primary adherence to asthma biologics varied by race and insurance type, whereas nonadherence was primarily explained by patient-level barriers., (Copyright © 2023. Published by Elsevier Inc.)

Background: Intraoperative hypotension is associated with postoperative complications. The use of vasopressors is often required to correct hypotension but the best vasopressor is unknown., Methods: A multicentre, cluster-randomised, crossover, feasibility and pilot trial was conducted across five hospitals in California. Phenylephrine (PE) vs norepinephrine (NE) infusion as the first-line vasopressor in patients under general anaesthesia alternated monthly at each hospital for 6 months. The primary endpoint was first-line vasopressor administration compliance of 80% or higher. Secondary endpoints were acute kidney injury (AKI), 30-day mortality, myocardial injury after noncardiac surgery (MINS), hospital length of stay, and rehospitalisation within 30 days., Results: A total of 3626 patients were enrolled over 6 months; 1809 patients were randomised in the NE group, 1817 in the PE group. Overall, 88.2% received the assigned first-line vasopressor. No drug infiltrations requiring treatment were reported in either group. Patients were median 63 yr old, 50% female, and 58% white. Randomisation in the NE group vs PE group did not reduce readmission within 30 days (adjusted odds ratio=0.92; 95% confidence interval, 0.6-1.39), 30-day mortality (1.01; 0.48-2.09), AKI (1.1; 0.92-1.31), or MINS (1.63; 0.84-3.16)., Conclusions: A large and diverse population undergoing major surgery under general anaesthesia was successfully enrolled and randomised to receive NE or PE infusion. This pilot and feasibility trial was not powered for adverse postoperative outcomes and a follow-up multicentre effectiveness trial is planned., Clinical Trial Registration: NCT04789330 (ClinicalTrials.gov)., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)