Differential Clinical Profiles, Exercise Responses, and Outcomes Associated With Existing HFpEF Definitions.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is common, yet there is currently no consensus on how to define HFpEF according to various society and clinical trial criteria. How clinical and hemodynamic profiles of patients vary across definitions is unclear. We sought to determine clinical characteristics, as well as physiologic and prognostic implications of applying various criteria to define HFpEF. METHODS: We examined consecutive patients with chronic exertional dyspnea (New York Heart Association class II to IV) and ejection fraction ≥50% referred for comprehensive cardiopulmonary exercise testing with invasive hemodynamic monitoring. We applied societal and clinical trial HFpEF definitions and compared clinical profiles, exercise responses, and cardiovascular outcomes. RESULTS: Of 461 patients (age 58±15 years, 62% women), 416 met American College of Cardiology/American Heart Association (ACC/AHA), 205 met European Society of Cardiology (ESC), and 55 met Heart Failure Society of America (HFSA) criteria for HFpEF. Clinical profiles and exercise capacity varied across definitions, with peak oxygen uptake of 16.2±5.2 (ACC/AHA), 14.1±4.2 (ESC), and 12.7±3.1 mL·kg-1·min-1 (HFSA). A total of 243 patients had hemodynamic evidence of HFpEF (abnormal rest or exercise filling pressures), of whom 222 met ACC/AHA, 161 met ESC, and 41 met HFSA criteria. Over a mean follow-up of 3.8 years, the incidence of cardiovascular outcomes ranged from 75 (ACC/AHA) to 298 events per 1000 person-years (HFSA). Application of clinical trial definitions of HFpEF similarly resulted in distinct patient classification and prognostication. CONCLUSIONS: Use of different HFpEF classifications variably enriches for future cardiovascular events, but at the expense of not including up to 85% of individuals with physiologic evidence of HFpEF. Comprehensive phenotyping of patients with suspected heart failure highlights the limitations and heterogeneity of current HFpEF definitions and may help to refine HFpEF subgrouping to test therapeutic interventions.

Current clinical trials on breast cancer in China: A systematic literature review.

With rapid advancement in clinical research, clinical trials on breast cancer in China have made great progress and are increasingly receiving worldwide recognition. Oncologists have been provided with an unprecedented opportunity to conduct clinical trials that offer both advantages and challenges. Investigator-initiated trials (IITs) and trials on domestic innovative drugs are still in the initial stages, with plenty of room to grow. The goal of this study was to systematically review time trends of the changing landscape of clinical drug development in China over the course of the last decade (from 2009 to 2018). The number of clinical trials specific to breast cancer has increased in a span of 10 years, from 36 trials in 2009 to 113 in 2018, and this trend is accompanied by an increase in publications, from 13 in 2009 to 52 in 2015. A total of 593 trials were conducted in breast cancer between 2009 and 2018. The distribution pattern of trial phases shows that phase 2 trials accounted for 34% of the total, followed by phase 3 trials at 21% and phase 1 trials at 20%. Academic trials or IITs were found to be the major sponsors, with 52% of trials being sponsored by them followed by pharmaceutical companies as a secondary sponsor (38%). Additionally, trials on chemotherapeutic agents constituted 50% of the trials followed by trials on targeted therapy (31%). The review provides insight on the effectiveness of the pharmaceutical industry and identify unmet clinical needs of stakeholders. With accumulated experience of Chinese oncologists and increasing support from the Chinese government, greater success could be anticipated in the near future.

Analysis of registered cancer clinical trials in Latin America and the Caribbean, 2007-2013.

Objective To characterize cancer clinical trials in Latin America and the Caribbean (LAC), with a focus on registration and enrollment trends. Methods Data were collected from 1 285 active cancer clinical trials registered up until 31 May 2014 in the World Health Organization's International Clinical Trial Registry Platform (ICTRP). The trials were categorized by six characteristics of the continuum of cancer control and care: 1) control and planning, 2) prevention, 3) detection and screening, 4) diagnosis, 5) treatment, and 6) survivorship and palliative care. The search strategy protocol included the use of optimized keywords combined with the names of the 43 countries selected for a descriptive analysis. Results A total of 973 registered and 972 enrolled cancer clinical trials between January 2007 and December 2013 were identified. Trends of growth were observed for both registration and enrollment of cancer treatment clinical trials; for other types of cancer clinical trials, trends for registration and enrollment varied in direction. Conclusions Growth trends in the registration of cancer treatment clinical trials indicate incremental adherence to cancer research reporting and improvements in cancer research transparency. The higher proportion of cancer treatment trials versus other types of cancer clinical trials indicates an imbalance in cancer research in the LAC region and suggests the need for more funding and incentives for other areas of research in order to achieve a more comprehensive approach to gaining knowledge on cancer issues.

[Same words, different meanings: How epidemiological terminology struggles with population health intervention research]. / À mêmes mots, sens différents--les difficultés de la terminologie épidémiologique avec la recherche en interventions en santé des populations.

Public health research differs from clinical epidemiological research in that its focus is primarily on the population level social and structural determinants of individual health and the interventions that might ameliorate them, rather than having a primary focus on individual-level risks. It is typically concerned with the proximal and distal causes of health problems, and their location within complex systems, more than with single exposures. Thus, epidemiological terms and concepts may have very different implications when used in the context of population health. This paper considers some key differences in relation to terms like 'population', 'baseline', 'control group' 'outcome' and 'adverse effects'. Even the concept of an 'intervention' often needs careful handling. The paper concludes that there is a need for an expanded, and more realistic use of these terms in the population health intervention research context.

Clinical Outcome Assessments: Conceptual Foundation-Report of the ISPOR Clinical Outcomes Assessment - Emerging Good Practices for Outcomes Research Task Force.

An outcome assessment, the patient assessment used in an endpoint, is the measuring instrument that provides a rating or score (categorical or continuous) that is intended to represent some aspect of the patient's health status. Outcome assessments are used to define efficacy endpoints when developing a therapy for a disease or condition. Most efficacy endpoints are based on specified clinical assessments of patients. When clinical assessments are used as clinical trial outcomes, they are called clinical outcome assessments (COAs). COAs include any assessment that may be influenced by human choices, judgment, or motivation. COAs must be well-defined and possess adequate measurement properties to demonstrate (directly or indirectly) the benefits of a treatment. In contrast, a biomarker assessment is one that is subject to little, if any, patient motivational or rater judgmental influence. This is the first of two reports by the ISPOR Clinical Outcomes Assessment - Emerging Good Practices for Outcomes Research Task Force. This report provides foundational definitions important for an understanding of COA measurement principles. The foundation provided in this report includes what it means to demonstrate a beneficial effect, how assessments of patients relate to the objective of showing a treatment's benefit, and how these assessments are used in clinical trial endpoints. In addition, this report describes intrinsic attributes of patient assessments and clinical trial factors that can affect the properties of the measurements. These factors should be considered when developing or refining assessments. These considerations will aid investigators designing trials in their choice of using an existing assessment or developing a new outcome assessment. Although the focus of this report is on the development of a new COA to define endpoints in a clinical trial, these principles may be applied more generally. A critical element in appraising or developing a COA is to describe the treatment's intended benefit as an effect on a clearly identified aspect of how a patient feels or functions. This aspect must have importance to the patient and be part of the patient's typical life. This meaningful health aspect can be measured directly or measured indirectly when it is impractical to evaluate it directly or when it is difficult to measure. For indirect measurement, a concept of interest (COI) can be identified. The COI must be related to how a patient feels or functions. Procedures are then developed to measure the COI. The relationship of these measurements with how a patient feels or functions in the intended setting and manner of use of the COA (the context of use) could then be defined. A COA has identifiable attributes or characteristics that affect the measurement properties of the COA when used in endpoints. One of these features is whether judgment can influence the measurement, and if so, whose judgment. This attribute defines four categories of COAs: patient reported outcomes, clinician reported outcomes, observer reported outcomes, and performance outcomes. A full description as well as explanation of other important COA features is included in this report. The information in this report should aid in the development, refinement, and standardization of COAs, and, ultimately, improve their measurement properties.

A pilot test of the new Swiss regulatory procedure for categorizing clinical trials by risk: A randomized controlled trial.

BACKGROUND/AIMS: Several countries are working to adapt clinical trial regulations to align the approval process to the level of risk for trial participants. The optimal framework to categorize clinical trials according to risk remains unclear, however. Switzerland is the first European country to adopt a risk-based categorization procedure in January 2014. We assessed how accurately and consistently clinical trials are categorized using two different approaches: an approach using criteria set forth in the new law (concept) or an intuitive approach (ad hoc). METHODS: This was a randomized controlled trial with a method-comparison study nested in each arm. We used clinical trial protocols from eight Swiss ethics committees approved between 2010 and 2011. Protocols were randomly assigned to be categorized in one of three risk categories using the concept or the ad hoc approach. Each protocol was independently categorized by the trial's sponsor, a group of experts and the approving ethics committee. The primary outcome was the difference in categorization agreement between the expert group and sponsors across arms. Linear weighted kappa was used to quantify agreements, with the difference between kappas being the primary effect measure. RESULTS: We included 142 of 231 protocols in the final analysis (concept=78; ad hoc=64). Raw agreement between the expert group and sponsors was 0.74 in the concept and 0.78 in the ad hoc arm. Chance-corrected agreement was higher in the ad hoc (kappa: 0.34 (95% confidence interval=0.10-0.58)) than in the concept arm (0.27 (0.06-0.50)), but the difference was not significant (p=0.67). LIMITATIONS: The main limitation was the large number of protocols excluded from the analysis mostly because they did not fit with the clinical trial definition of the new law. CONCLUSION: A structured risk categorization approach was not better than an ad hoc approach. Laws introducing risk-based approaches should provide guidelines, examples and templates to ensure correct application.

[Key aspects in interpreting clinical trials in radiology]. / El ensayo clínico en Radiología: claves para su interpretación.

A clinical trial is an experimental study to evaluate the efficacy and safety of a treatment or diagnostic technique in human beings. To ensure the methodological quality of a clinical trial and the validity of its results, various checklists have been elaborated to identify biases that could invalidate its conclusions. This article focuses on the points we need to consider in the critical evaluation of a clinical trial. We can usually find this information in the "materials and methods" and "results" sections of articles. Randomization, follow-up (or analysis of losses), blinding, and equivalence between groups (apart from the intervention itself) are some key aspects related to design. In the "results" section, we need to consider what measures of clinical efficacy were used (relative risk, odds ratio, or number needed to treat, among others) and the precision of the results (confidence intervals). Once we have confirmed that the clinical trial fulfills these criteria, we need to determine whether the results can be applied in our environment and whether the benefits obtained justify the risks and costs involved.

The American College of Rheumatology and the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus in two multiethnic cohorts: a commentary.

The authors offer some comments on the advantages and possible drawbacks of using the SLICC criteria in longitudinal observational studies and clinical trials after applying and comparing them to the ACR criteria in two multinational, multiethnic lupus cohorts.

Clustering clinical trials with similar eligibility criteria features.

OBJECTIVES: To automatically identify and cluster clinical trials with similar eligibility features. METHODS: Using the public repository ClinicalTrials.gov as the data source, we extracted semantic features from the eligibility criteria text of all clinical trials and constructed a trial-feature matrix. We calculated the pairwise similarities for all clinical trials based on their eligibility features. For all trials, by selecting one trial as the center each time, we identified trials whose similarities to the central trial were greater than or equal to a predefined threshold and constructed center-based clusters. Then we identified unique trial sets with distinctive trial membership compositions from center-based clusters by disregarding their structural information. RESULTS: From the 145,745 clinical trials on ClinicalTrials.gov, we extracted 5,508,491 semantic features. Of these, 459,936 were unique and 160,951 were shared by at least one pair of trials. Crowdsourcing the cluster evaluation using Amazon Mechanical Turk (MTurk), we identified the optimal similarity threshold, 0.9. Using this threshold, we generated 8806 center-based clusters. Evaluation of a sample of the clusters by MTurk resulted in a mean score 4.331±0.796 on a scale of 1-5 (5 indicating "strongly agree that the trials in the cluster are similar"). CONCLUSIONS: We contribute an automated approach to clustering clinical trials with similar eligibility features. This approach can be potentially useful for investigating knowledge reuse patterns in clinical trial eligibility criteria designs and for improving clinical trial recruitment. We also contribute an effective crowdsourcing method for evaluating informatics interventions.

Survival analysis of time-to-event data in respiratory health research studies.

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

An overview of characteristics of registered studies on dental implants.

OBJECTIVE: Clinical trials serve as the empirical basis for clinical decision making. The objective of the current study is to provide an overview of clinical trials examining dental implant outcomes. METHOD: All registered studies on Dental Implants were selected for analysis. The clinicaltrials.gov website was used to query the characteristics of registered studies. The search term used was dental implants. RESULTS: As of the study conduct date (01/01/2014), a total of 205 studies on dental implants were registered. These included 168 interventional and 37 observational studies. Results were available for only 14 studies. All observational studies and 98.8% of interventional studies included both male and female subjects. Close to 60% of studies had sample sizes between 1 and 50. NIH was listed as funding source in only 5 interventional studies and 3 observational studies. 80% of interventional studies were randomized. However, double masking was reported in only 15% of interventional studies with majority being open labeled. CONCLUSION: ClinicalTrials.gov registry was created with the intention of increasing the transparency of conducted or ongoing clinical studies and to minimize publication bias commonly seen with industry-sponsored studies. Results of the current study showed that a predominating number of registered studies are funded by industry and other sources, very few registered studies have made their results public, and the ClinicalTrials.gov registry does not provide sufficient information on the quality of study design and thus precluding the public and researchers to judge on the quality of registered studies and publication bias.

Frequency and impact of bleeding in elective coronary stent clinical trials--utility of three commonly used definitions.

BACKGROUND: Bleeding events are common after percutaneous coronary intervention (PCI) and have been shown to increase mortality in studies of acute coronary syndrome (ACS) and anti-thrombotic therapy. Despite this evidence, bleeding has not been included as a traditional major endpoint in clinical trials of low-risk populations enrolled in PCI clinical trials. Thus, the impact of specific bleeding definitions has not been evaluated fully among these patients. METHODS AND RESULTS: Using patient-level pooled data from sirolimus and zotarolimus drug-eluting stent clinical trials, we identified bleeding events using three common definitions of bleeding, ACUITY, TIMI, and GUSTO, and assessed the impact on mortality and MI at 12 months after PCI. The GUSTO, ACUITY, and TIMI classifications identified bleeding rates of 2.3%, 1.9%, and 2.1%, respectively. The GUSTO criteria classified all 118 suspected bleeding events. There were 22 (18.6%) and 8 (6.8%) suspected bleeding events that did not meet ACUITY and TIMI criteria, respectively. The combined endpoint of all-cause death or myocardial infarction (MI) at 12 months was significantly higher for patients with a bleeding event compared with those who did not bleed [hazard ratio 1.95 (95% CI 1.06-3.60)]. CONCLUSION: There is a substantial variability in the utility and inclusiveness of three widely used bleeding definitions in identifying clinically significant bleeding events in clinical trials of low risk patients undergoing PCI with DES. Patients with bleeding after elective PCI have an increased one-year risk of death or MI compared to those patients who do not bleed.

Statistical analysis and design for estimating accuracy in clinical-center classification of cause-specific clinical events in clinical trials.

BACKGROUND: When cause-specific clinical events (for instance, hospitalization for cardiac disease) are used as the primary or key secondary outcomes, it is important to assess how accurately these events have been classified and estimate the error-corrected treatment effects to ensure validity. However, it may not be feasible to verify cause classification for every event in large clinical trials. PURPOSE: We present statistical methods for the design and analysis of outcome-classification accuracy and for estimating error-corrected treatment effects. METHODS: Using the Hemodialysis (HEMO) Study, in which primary causes were designated for all 7822 hospitalizations by the 15 participating clinical centers - but only two subsets were audited by the Outcome Committee - we applied existing methods to obtain unbiased estimates of the sensitivity and specificity of clinical-center classifications. The multiple imputation method was used to correct for the misclassification of events. We then examined how trial results were affected by three methods of event classification: unaudited, imputed, and adjudicated. RESULTS: We applied a three-step procedure to extend the results for the two subsets of audited events to estimate the sensitivity and specificity for the complete set. Finite population sample size formulas were developed for designing the quality control sample. Based on the HEMO analysis, the estimate of the intervention effect using the unaudited outcome was biased; the bias was reduced using the outcome corrected by imputation. LIMITATIONS: The methods are limited to situations in which there are clinical-center classifications for all clinical events but only partial availability of reference standard classifications, and the verification process does not depend on the true event cause or other unobserved information. CONCLUSIONS: Designing a quality control study to estimate the accuracy of outcome classification is important. The multiple imputation method can be used to correct for errors in outcome classification and to estimate the error-corrected treatment effect. Trial results need to be reexamined using the error-corrected outcome.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

Reporting of randomized factorial trials was frequently inadequate.

OBJECTIVES: Factorial designs can allow efficient evaluation of multiple treatments within a single trial. We evaluated the design, analysis, and reporting in a sample of factorial trials. STUDY DESIGN AND SETTING: Review of 2 × 2 factorial trials evaluating health-related interventions and outcomes in humans. Using Medline, we identified articles published between January 2015 and March 2018. We randomly selected 100 articles for inclusion. RESULTS: Most trials (78%) did not provide a rationale for using a factorial design. Only 63 trials (63%) assessed the interaction for the primary outcome, and 39/63 (62%) made a further assessment for at least one secondary outcome. 12/63 trials (19%) identified a significant interaction for the primary outcome and 16/39 trials (41%) for at least one secondary outcome. Inappropriate methods of analysis to protect against potential negative effects from interactions were common, with 18 trials (18%) choosing the analysis method based on a preliminary test for interaction, and 13% (n = 10/75) of those conducting a factorial analysis including an interaction term in the model. CONCLUSION: Reporting of factorial trials was often suboptimal, and assessment of interactions was poor. Investigators often used inappropriate methods of analysis to try to protect against adverse effects of interactions.

Clinical trials for deep brain stimulation: Current state of affairs.

BACKGROUND: Deep brain stimulation (DBS) is a surgical neuromodulation procedure with a historically wide range of possible therapeutic indications, including movement disorders, neuropsychiatric conditions, and cognitive disorders. Ongoing research in this field is critical to gain further insights into the mechanisms of DBS, to discover novel brain targets for new and existing indications, and to refine targeting and post-operative programming techniques for the optimization of therapeutic outcomes. OBJECTIVE: To update on the state of DBS-related clinical human research by cataloging and summarizing clinical trials that have been completed or are currently ongoing in this field worldwide. METHODS: A search was conducted for clinical trials pertaining to DBS, currently listed on the ClinicalTrials.gov database. Trials were analyzed to generate a detailed overview of ongoing DBS-related research. Specifically, trials were categorized by trial start date, study completion status, clinical phase, projected subject enrollment, disorder, brain target, country of origin, device manufacturer, funding source, and study topic. RESULTS: In total, 384 relevant clinical trials were identified. The trials spanned 28 different disorders across 26 distinct brain targets, with almost 40% of trials being for conditions other than movement disorders. The majority of DBS trials have been US-based (41.9% of studies) but many countries are becoming increasingly active. The ratio of investigator-sponsored to industry-sponsored trials was 3:1. Emphasizing the need to better understand the mechanism of action of DBS, one-third of the studies predominantly focus on imaging or electrophysiological changes associated with DBS. CONCLUSIONS: This overview of current DBS-related clinical trials provides insight into the status of DBS research and what we can anticipate in the future concerning new brain targets, indications, techniques, and developing a better understanding of the mechanisms of action of DBS.

Study design: what's in a name?

The name of the study should properly reflect the actual conduct and analysis of the study. This short paper provides guidance on how to properly name the study design. The first distinction is between a trial (intervention given to patients to study its effect) and an observational study. For observational studies, it should further be decided whether it is cross-sectional or whether follow-up time is taken into account (cohort or case-control study). The distinction prospective-retrospective has two disadvantages: prospective is often seen as marker of higher quality, which is not necessarily true; there is no unifying definition that makes a proper distinction between retrospective and prospective possible.

[Immunity against SARS-CoV-2: walking to the vaccination]. / Inmunidad frente a SARS-CoV-2: caminando hacia la vacunación.

The coronavirus are a wide group of viruses among that the SARS-CoV-2 is included (family Coronaviridae, subfamily Coronavirinae, genus Betacoronavirus and subgenus Sarbecovirus). Its main structural proteins are the membrane (M), the envelope (E), the nucleocapsid (N) and spike (S). The immune response to SARS-CoV-2 involves the cellular and the humoral sides, with neutralizing antibodies fundamentally directed against the S antigen. Although the seroprevalence data are frequently assumed as protection markers, no necessarily they are. In Spain, it is estimated that, to assure the herd immunity, at least four-fifths of the population should be immunoprotected. Due the high fatality rate of COVID-19, the acquisition of the protection only by the natural infection it not assumable and other measures as the mass immunization are required. Currently, there are several vaccine prototypes (including life virus, viral vectors, peptides and proteins and nucleic acid) in different phase of clinical evaluation. Foreseeably, some of these news vaccines would be soon commercially available. In this text, aspects related to these issues are reviewed.