Publications by authors named "Michael Harhay"

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.

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  • The study aimed to determine if setting a lower mean arterial blood pressure (MAP) target could reduce 90-day all-cause mortality in critically ill patients with vasodilatory shock compared to a higher MAP target.
  • Researchers performed a meta-analysis of randomized controlled trials involving 3,352 patients across several countries, using statistical models to analyze the data.
  • The results suggested that a lower MAP target might be associated with a slight reduction in mortality risk, but the evidence is uncertain, indicating that it does not rule out the possibility of potential harm from lower targets.
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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.

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  • Patients with acute hypoxemic respiratory failure face high mortality and long ventilation times, so effective intervention analysis methods are needed to optimize clinical trials for these outcomes.
  • This study compared various statistical methods for analyzing the composite outcome of "days alive and free of ventilation," assessing their performance using simulated patient data under different treatment effects.
  • Results showed that while all methods maintained good control of false positives, the effectiveness of each analytical approach varied based on treatment impacts on mortality and ventilation duration, suggesting that the choice of method should align with the specific outcomes being analyzed.
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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.

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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.

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  • Estimands help clarify treatment effects in research, especially in cluster-randomised trials where additional factors must be defined.
  • The paper defines estimands using potential outcomes notation and examines the differences between them along with associated estimators and their assumptions.
  • A re-analysis of a published cluster-randomised trial illustrates that different estimands and estimators can significantly influence the interpretation of results and treatment effect estimates.
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  • The study analyzes the effects of lung transplant allocation policies in the USA and France, focusing on urgent lung transplants and their impact on patient outcomes.
  • Using data from 2007 to 2017, the research found that urgent lung transplants had a higher risk of death, with survival rates significantly better in the US compared to France.
  • The conclusion suggests that while urgent lung transplants are risky in both countries, the US score-based allocation system leads to better post-transplant survival rates, indicating a complex interplay of factors influencing outcomes.
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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.

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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.

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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%.

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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.

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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.

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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. To develop an acceptable approach to measure quality-weighted HFDs using patient-reported outcomes.

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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.

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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.

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  • Assessing treatment effects in causal inference is crucial for clinical decisions, especially when outcomes are affected by events like death, which makes it hard to observe results for some participants.
  • A new Bayesian machine learning approach was developed to estimate causal effects specifically for always-survivor patients, using data from the ARMA trial on respiratory management.
  • The study revealed that low tidal volume treatment was beneficial overall, but the effects varied significantly based on factors like sex and initial lung function, suggesting potential improvements for future trials.
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  • The study investigates the differences in mortality rates between children and adults with acute respiratory distress syndrome (ARDS), revealing lower mortality in children despite similar severity of illness.
  • Using data from various cohorts, researchers found a nonlinear relationship between age and 90-day mortality, with risk increasing more rapidly for individuals aged 11 to 65 years.
  • The study concluded that while pediatric ARDS has a lower mortality rate (19%) compared to adults (33% and 67% in different cohorts), age plays a significant role in mortality risk for both children and adults.
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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.

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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.

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