Separating signal from noise: the challenge of identifying useful biomarkers in sepsis

Abstract Sepsis diagnosis remains based largely on clinical presentation despite significant advances in the understanding of underlying pathophysiology and host-pathogen interactions. The systematic review article by Zonneveld and colleagues in the previous issue of Critical Care describes another potential avenue of study for using biomarkers for sepsis diagnosis and prognostication. Soluble leukocyte adhesion molecules and their associated sheddase enzymes vary in detectable levels and activity in patients in relation to immunologic status, age, and systemic inflammation, including in the setting of sepsis. Unfortunately, studies of these molecules as diagnostic or prognostic aids (or both) in sepsis have thus far been disappointing. Zonneveld and colleagues propose two potential avenues to enhance the performance characteristics of soluble adhesion molecules and their sheddases in sepsis diagnosis and prognosis: (a) identifying age-adjusted normal values for soluble leukocyte adhesion molecules and their sheddases and (b) investigating simultaneous measurement of both soluble adhesion molecules and sheddases in integrated sepsis evaluation schema. This commentary discusses the proposed solutions of Zonneveld and colleagues in more detail and outlines additional considerations that should be addressed in order to develop robust and valid diagnostic and prognostic tools for clinicians managing patients with sepsis.Peer Reviewe

Integrating Quality of Life and Survival Outcomes in Cardiovascular Clinical Trials.

Background Survival and health status (eg, symptoms and quality of life) are key outcomes in clinical trials of heart failure treatment. However, health status can only be recorded on survivors, potentially biasing treatment effect estimates when there is differential survival across treatment groups. Joint modeling of survival and health status can address this bias. Methods and Results We analyzed patient-level data from the PARTNER 1B trial (Placement of Aortic Transcatheter Valves) of transcatheter aortic valve replacement versus standard care. Health status was quantified with the Kansas City Cardiomyopathy Questionnaire (KCCQ) at randomization, 1, 6, and 12 months. We compared hazard ratios for survival and mean differences in KCCQ scores at 12 months using several models: the original growth curve model for KCCQ scores (ignoring death), separate Bayesian models for survival and KCCQ scores, and a Bayesian joint longitudinal-survival model fit to either 12 or 30 months of survival follow-up. The benefit of transcatheter aortic valve replacement on 12-month KCCQ scores was greatest in the joint-model fit to all survival data (mean difference, 33.7 points; 95% credible intervals [CrI], 24.2-42.4), followed by the joint-model fit to 12 months of survival follow-up (32.3 points; 95% CrI, 22.5-41.5), a Bayesian model without integrating death (30.4 points; 95% CrI, 21.4-39.3), and the original growth curve model (26.0 points; 95% CI, 18.7-33.3). At 12 months, the survival benefit of transcatheter aortic valve replacement was also greater in the joint model (hazard ratio, 0.50; 95% CrI, 0.32-0.73) than in the nonjoint Bayesian model (0.54; 95% CrI, 0.37-0.75) or the original Kaplan-Meier estimate (0.55; 95% CI, 0.40-0.74). Conclusions In patients with severe symptomatic aortic stenosis and prohibitive surgical risk, the estimated benefits of transcatheter aortic valve replacement on survival and health status compared with standard care were greater in joint Bayesian models than other approaches

Bayesian Meta-analysis of Multiple Continuous Treatments with Individual Participant-Level Data: An Application to Antipsychotic Drugs.

Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in nonadherence and dropout. We develop a Bayesian method to flexibly model the dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and nonlinear dose-response curves. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second-generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical trials. We define exposure as total cumulative dose (daily dose × duration) and convert to units equivalent to 100 mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, the median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared with 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% credible interval: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7), respectively. Moreover, compared with nonblack participants, black participants had a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses

Why are There so Few Female Computer Scientists?

This report examines why women pursue careers in computer science and related fields far less frequently than men do. In 1990, only 13% of PhDs in computer science went to women, and only 7.8% of computer science professors were female. Causes include the different ways in which boys and girls are raised, the stereotypes of female engineers, subtle biases that females face, problems resulting from working in predominantly male environments, and sexual biases in language. A theme of the report is that women's underrepresentation is not primarily due to direct discrimination but to subconscious behavior that perpetuates the status quo

Incorporating development of a patient-reported outcome instrument in a clinical drug development program: examples from a heart failure program.

BackgroundPatient-reported outcome (PRO) measures can be used to support label claims if they adhere to US Food & Drug Administration guidance. The process of developing a new PRO measure is expensive and time-consuming. We report the results of qualitative studies to develop new PRO measures for use in clinical trials of omecamtiv mecarbil (a selective, small molecule activator of cardiac myosin) for patients with heart failure (HF), as well as the lessons learned from the development process.MethodsConcept elicitation focus groups and individual interviews were conducted with patients with HF to identify concepts for the instrument. Cognitive interviews with HF patients were used to confirm that no essential concepts were missing and to assess patient comprehension of the instrument and items.ResultsDuring concept elicitation, the most frequently reported HF symptoms were shortness of breath, tiredness, fluid retention, fatigue, dizziness/light-headedness, swelling, weight fluctuation, and trouble sleeping. Two measures were developed based on the concepts: the Heart Failure Symptom Diary (HF-SD) and the Heart Failure Impact Scale (HFIS). Findings from cognitive interviews suggested that the items in the HF-SD and HFIS were relevant and well understood by patients. Multiple iterations of concept elicitation and cognitive interviews were needed based on FDA request for a broader patient population in the qualitative study. Lessons learned from the omecamtiv mecarbil PRO/clinical development program are discussed, including challenges of qualitative studies, patient recruitment, expected and actual timelines, cost, and engagement with various stakeholders.ConclusionDevelopment of a new PRO measure to support a label claim requires significant investment and early planning, as demonstrated by the omecamtiv mecarbil program

Stylish Risk-Limiting Audits in Practice

Risk-limiting audits (RLAs) can use information about which ballot cards contain which contests (card-style data, CSD) to ensure that each contest receives adequate scrutiny, without examining more cards than necessary. RLAs using CSD in this way can be substantially more efficient than RLAs that sample indiscriminately from all cast cards. We describe an open-source Python implementation of RLAs using CSD for the Hart InterCivic Verity voting system and the Dominion Democracy Suite(R) voting system. The software is demonstrated using all 181 contests in the 2020 general election and all 214 contests in the 2022 general election in Orange County, CA, USA, the fifth-largest election jurisdiction in the U.S., with over 1.8 million active voters. (Orange County uses the Hart Verity system.) To audit the 181 contests in 2020 to a risk limit of 5% without using CSD would have required a complete hand tally of all 3,094,308 cast ballot cards. With CSD, the estimated sample size is about 20,100 cards, 0.65% of the cards cast--including one tied contest that required a complete hand count. To audit the 214 contests in 2022 to a risk limit of 5% without using CSD would have required a complete hand tally of all 1,989,416 cast cards. With CSD, the estimated sample size is about 62,250 ballots, 3.1% of cards cast--including three contests with margins below 0.1% and 9 with margins below 0.5%