Race and sex differences in response to endothelin receptor antagonists for pulmonary arterial hypertension

Background Recently studied therapies for pulmonary arterial hypertension (PAH) have improved outcomes among populations of patients, but little is known about which patients are most likely to respond to specific treatments. Differences in endothelin-1 biology between sexes and between whites and blacks may lead to differences in patients' responses to treatment with endothelin receptor antagonists (ERAs). Methods We conducted pooled analyses of deidentified, patient-level data from six randomized placebo-controlled trials of ERAs submitted to the US Food and Drug Administration to elucidate heterogeneity in treatment response. We estimated the interaction between treatment assignment (ERA vs placebo) and sex and between treatment and white or black race in terms of the change in 6-min walk distance from baseline to 12 weeks. Results Trials included 1,130 participants with a mean age of 49 years; 21% were men, 74% were white, and 6% were black. The placebo-adjusted response to ERAs was 29.7 m (95% CI, 3.7-55.7 m) greater in women than in men (P = .03). The placebo-adjusted response was 42.2 m for whites and −1.4 m for blacks, a difference of 43.6 m (95% CI, −3.5-90.7 m) (P = .07). Similar results were found in sensitivity analyses and in secondary analyses using the outcome of absolute distance walked. Conclusions Women with PAH obtain greater responses to ERAs than do men, and whites may experience a greater treatment benefit than do blacks. This heterogeneity in treatment-response may reflect pathophysiologic differences between sexes and races or distinct disease phenotypes

A Randomized Trial of Nighttime Physician Staffing in an Intensive Care Unit

Background Increasing numbers of intensive care units (ICUs) are adopting the practice of nighttime intensivist staffing despite the lack of experimental evidence of its effectiveness. Methods We conducted a 1-year randomized trial in an academic medical ICU of the effects of nighttime staffing with in-hospital intensivists (intervention) as compared with nighttime coverage by daytime intensivists who were available for consultation by telephone (control). We randomly assigned blocks of 7 consecutive nights to the intervention or the control strategy. The primary outcome was patients’ length of stay in the ICU. Secondary outcomes were patients’ length of stay in the hospital, ICU and in-hospital mortality, discharge disposition, and rates of readmission to the ICU. For length-of-stay outcomes, we performed time-to-event analyses, with data censored at the time of a patient’s death or transfer to another ICU. Results A total of 1598 patients were included in the analyses. The median Acute Physiology and Chronic Health Evaluation (APACHE) III score (in which scores range from 0 to 299, with higher scores indicating more severe illness) was 67 (interquartile range, 47 to 91), the median length of stay in the ICU was 52.7 hours (interquartile range, 29.0 to 113.4), and mortality in the ICU was 18%. Patients who were admitted on intervention days were exposed to nighttime intensivists on more nights than were patients admitted on control days (median, 100% of nights [interquartile range, 67 to 100] vs. median, 0% [interquartile range, 0 to 33]; P\u3c0.001). Nonetheless, intensivist staffing on the night of admission did not have a significant effect on the length of stay in the ICU (rate ratio for the time to ICU discharge, 0.98; 95% confidence interval [CI], 0.88 to 1.09; P=0.72), ICU mortality (relative risk, 1.07; 95% CI, 0.90 to 1.28), or any other end point. Analyses restricted to patients who were admitted at night showed similar results, as did sensitivity analyses that used different definitions of exposure and outcome. Conclusions In an academic medical ICU in the United States, nighttime in-hospital intensivist staffing did not improve patient outcomes. (Funded by University of Pennsylvania Health System and others; ClinicalTrials.gov number, NCT01434823.

Dealing with heterogeneity of treatment effects: is the literature up to the challenge?

<p>Abstract</p> <p>Background</p> <p>Some patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses.</p> <p>Methods</p> <p>Articles were selected through a probability sample of randomized controlled trials (RCTs) published in <it>Annals of Internal Medicine</it>, <it>BMJ</it>, <it>JAMA</it>, <it>The Lancet</it>, and <it>NEJM </it>during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting.</p> <p>Results</p> <p>319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively).</p> <p>Conclusion</p> <p>HTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine.</p

Active Choice Intervention Increases Advance Directive Completion: A Randomized Trial

Background. Many people recognize the potential benefits of advance directives (ADs), yet few actually complete them. It is unknown whether an active choice intervention influences AD completion. Methods. New employees were randomized to an active choice intervention (n = 642) or usual care (n = 637). The active choice intervention asked employees to complete an AD, confirm prior AD completion, or fill out a declination form. In usual care, participants could complete an AD, confirm prior completion, or skip the task. We used multivariable logistic regression to assess the relationship between the intervention arm and both AD completion online and the return of a signed AD by mail, as well as assess interactions between intervention group and age, gender, race, and clinical degree status. Results. Participants assigned to the active choice intervention more commonly completed ADs online (35.1% v. 20.4%, P 0.10). Limitations. A young and healthy participant may not benefit from AD completion as an older or seriously ill patient would. Conclusions. The active choice intervention significantly increased the proportion of participants completing an AD without changing the choices in ADs. This relationship was especially strong among men and may be a useful method to increase AD completion rates without altering choices

Quantifying the risks of non-oncology phase I research in healthy volunteers:Meta-analysis of phase I studies

Objective To quantify the frequency and seriousness of adverse events in non-oncology phase I studies with healthy participants. Design Meta-analysis of individual, healthy volunteer level data. Setting Phase I studies with healthy volunteers conducted between September 2004 and March 2011 at Pfizer’s three dedicated phase I testing sites in Belgium, Singapore, and the United States. These included studies in which drug development was terminated. Participants 11 028 participants who received the study drug in 394 distinct non-oncology phase I studies, which involved 4620 unique individuals. A total of 2460 (53.2%) participants were involved in only one study, whereas others participated in two or more studies. Main outcome measures Adverse events classified as mild, moderate, and severe as well as serious adverse events—defined by the Food and Drug Administration as events that result in death, a life threatening event, admission to hospital, prolongation of existing hospital stay, a persistent or major disability, or a congenital anomaly or birth defect. Pfizer researchers of phase I trials determined adverse events, and serious adverse events were those filed with the FDA. Results Overall, 4000 (36.3%) participants who received the study drug experienced no adverse events and 7028 (63.7%) experienced 24 643 adverse events. Overall, 84.6% (n=20 840) of adverse events were mild and 1.0% (n=255) were severe. 34 (0.31%) serious adverse events occurred among the 11 028 participants who received the study agent, with no deaths or life threatening events. Of the 34 serious adverse events, 11 were related to the study drug and seven to study procedures, whereas 16 were unrelated to a study drug or procedure, including four that occurred when the participant was receiving a placebo. Overall, 24.1% (n=5947) of adverse events were deemed to be unrelated to the study drug. With a total of 143 (36%) studies involving placebo, 10.3% (n=2528) of all adverse events occurred among participants receiving placebo. The most common adverse events were headache (12.2%, n=3017), drowsiness (9.8%, n=2410), and diarrhea (6.9%, n=1698). Research on drugs for neuropsychiatric indications had the highest frequency of adverse events (3015 per 1000 participants). Conclusion Among 11 028 healthy participants who received study drug in non-oncology phase I studies, the majority (85%) of adverse events were mild. 34 (0.31%) serious adverse events occurred, with no life threatening events or deaths. Half of all adverse events were related to the study drug or to procedures. Extrapolation of these data to other types of phase I studies, especially with biological agents, may not be warranted

Dealing with heterogeneity of treatment effects: is the literature up to the challenge?

BackgroundSome patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses.MethodsArticles were selected through a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine, BMJ, JAMA, The Lancet, and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting.Results319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p &lt; 0.05 and p &lt; 0.001, respectively).ConclusionHTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine

What's behind the white coat: Potential mechanisms of physician-attributable variation in critical care.

BackgroundCritical care intensity is known to vary across regions and centers, yet the mechanisms remain unidentified. Physician behaviors have been implicated in the variability of intensive care near the end of life, but physician characteristics that may underlie this association have not been determined.PurposeWe sought to identify behavioral attributes that vary among intensivists to generate hypotheses for mechanisms of intensivist-attributable variation in critical care delivery.MethodsWe administered a questionnaire to intensivists who participated in a prior cohort study in which intensivists made prognostic estimates. We evaluated the degree to which scores on six attribute measures varied across intensivists. Measures were selected for their relevance to preference-sensitive critical care: a modified End-of-Life Preferences (EOLP) scale, Life Orientation Test-Revised (LOT-R), Jefferson Scale of Empathy (JSE), Physicians' Reactions to Uncertainty (PRU) scale, Collett-Lester Fear of Death (CLFOD) scale, and a test of omission bias. We conducted regression analyses assessing relationships between intensivists' attribute scores and their prognostic accuracy, as physicians' prognostic accuracy may influence preference-sensitive decisions.Results20 of 25 eligible intensivists (80%) completed the questionnaire. Intensivists' scores on the EOLP, LOT-R, PRU, CLFOD, and omission bias measures varied considerably, while their responses on the JSE scale did not. There were no consistent associations between attribute scores and prognostic accuracy.ConclusionsIntensivists vary in feasibly measurable attributes relevant to preference-sensitive critical care delivery. These attributes represent candidates for future research aimed at identifying mechanisms of clinician-attributable variation in critical care and developing effective interventions to reduce undue variation