Pulmonary embolism (PE) is a major source of morbidity and mortality. The presentation of acute PE varies, ranging from few or no symptoms to sudden death. Patient outcome depends on how well the right ventricle can sustain the increased afterload caused by the embolic burden. Careful risk stratification is critical, and the PE response team (PERT) concept offers a rapid and multidisciplinary approach. Anticoagulation is essential unless contraindicated; thrombolysis, surgical embolectomy, and catheter-directed approaches are also available. Clinical consensus statements have been published that offer a guide to PE management, but areas remain for which the evidence is inadequate. Although the management of low-risk and high-risk patients is more straightforward, optimal management of intermediate-risk patients remains controversial. In this document, we offer a case-based approach to PE management, beginning with diagnosis and risk stratification, followed by therapeutic alternatives, and finishing with follow-up care.

Transthoracic echocardiography is the standard of care in anatomic and functional cardiovascular assessment; however, focused cardiac ultrasound (FoCUS) performed with portable ultrasound equipment is increasingly being used as an adjunct to comprehensive history and physical examination. FoCUS assessments, unlike formal echocardiography, are intended to assist physicians in answering explicit clinical questions with a narrow differential diagnosis in real time. Over the past decade, a growing body of literature has repeatedly shown the value that FoCUS adds to clinical evaluation. Specifically, FoCUS improves point-of-care diagnostic accuracy, which in turn modifies treatment plans, decreases time to diagnosis, and reduces resource utilization. Although less robust, there is also evidence showing improvement in clinical outcomes. Based on this evidence, clinicians, training programs, and clinical societies have embraced FoCUS as a tool to complement bedside patient evaluation. Herein, we review the evidence for FoCUS in clinical practice, specifically evaluating the diagnostic accuracy, the impact on clinical decision-making, and the effect on clinical outcomes.

Liberal oxygen supplementation is often used in acute illness but has, in some studies, been associated with harm.

The recent pandemic highlights the essential nature of optimizing the use of invasive mechanical ventilation (IMV) in complex critical care settings. This review of reviews maps evidence-based practices (EBPs) that are associated with better outcomes among adult patients with acute respiratory failure or ARDS on the continuum of care, from intubation to liberation.

The coronavirus disease 2019 pandemic will be remembered for the rapidity with which it spread, the morbidity and mortality associated with it, and the paucity of evidence-based management guidelines. One of the major concerns of hospitals was to limit spread of infection to health-care workers. Because the virus is spread mainly by respiratory droplets and aerosolized particles, procedures that may potentially disperse viral particles, the so-called "aerosol-generating procedures" were avoided whenever possible. Included in this category were noninvasive ventilation (NIV), high-flow nasal cannula (HFNC), and awake (nonintubated) proning. Accordingly, at many health-care facilities, patients who had increasing oxygen requirements were emergently intubated and mechanically ventilated to avoid exposure to aerosol-generating procedures. With experience, physicians realized that mortality of invasively ventilated patients was high and it was not easy to extubate many of these patients. This raised the concern that HFNC and NIV were being underutilized to avoid intubation and to facilitate extubation. In this article, we attempt to separate fact from fiction and perception from reality pertaining to the aerosol dispersion with NIV, HFNC, and awake proning. We describe precautions that hospitals and health-care providers must take to mitigate risks with these devices. Finally, we take a practical approach in describing how we use the three techniques, including the common indications, contraindications, and practical aspects of application.

When a review is performed following predefined steps (ie, systematically) and its results are quantitatively analyzed, it is called meta-analysis. Publication of meta-analyses has increased exponentially in pubmed.gov; using the key word "meta-analysis," 1,473 titles were yielded in 2007 and 176,704 on January 2020. Well-designed and reported meta-analyses provide valuable information for clinicians, researchers, and policymakers. The aim of this study was to provide CHEST peer reviewers, as well as authors and researchers in training, with tools that can help to improve the quality and timeliness of journal reviews, as well as the quality of the meta-analyses submitted. This article also is intended to be a practical guide to inform authors about the key features of meta-analyses to be considered when producing their review.

Economic evaluations, including cost-effectiveness analyses, are frameworks for decision-making. They help to illustrate tradeoffs between selecting one choice over another. This form of analysis is of great power and value to the health-care system. Health-care decisions are complex; they require synthesis of a myriad of data variables and sources, and the impact of the choices made is significant. Given this importance and the increasing demand and complexity of health decisions, it is imperative to ensure that economic evaluations are of high quality, comprehensive, and follow the guidelines and recommendations of experts in the field. This article provides an overview of the types of economic evaluations and their role in decision-making. It also discusses key study design considerations, including methods, scope, results, and reporting. Links to published checklists are provided along with additional sources of information, including a glossary of terms (Appendix), to guide the researcher to produce high-quality economic evaluations and guide the reviewer to provide high-quality feedback during the review process.

Randomized controlled trials (RCTs) are considered the highest level of evidence to establish causal associations in clinical research. There are many RCT designs and features that can be selected to address a research hypothesis. Designs of RCTs have become increasingly diverse as new methods have been proposed to evaluate increasingly complex scientific hypotheses. This article reviews the principles and general concepts behind many common RCT designs and introduces newer designs that have been proposed, such as adaptive and cluster randomized trials. A focus on the many choices for randomization within an RCT is described, along with their potential tradeoffs. To illustrate their diversity, examples of RCTs from the literature are provided. Statistical considerations, such as power and type I error rates, are discussed with the intention of providing practical guidance about how to specify study hypotheses that address the scientific question while being statistically appropriate. Finally, the freely available Consolidated Standards of Reporting Trials guidelines and US Food and Drug Administration guidance documents are introduced, along with a set of guidelines one should consider when planning an RCT or reviewing RCTs submitted for publication in peer-reviewed academic journals.

Cohort studies are types of observational studies in which a cohort, or a group of individuals sharing some characteristic, are followed up over time, and outcomes are measured at one or more time points. Cohort studies can be classified as prospective or retrospective studies, and they have several advantages and disadvantages. This article reviews the essential characteristics of cohort studies and includes recommendations on the design, statistical analysis, and reporting of cohort studies in respiratory and critical care medicine. Tools are provided for researchers and reviewers.

Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.

Case-control studies are one of the major observational study designs for performing clinical research. The advantages of these study designs over other study designs are that they are relatively quick to perform, economical, and easy to design and implement. Case-control studies are particularly appropriate for studying disease outbreaks, rare diseases, or outcomes of interest. This article describes several types of case-control designs, with simple graphical displays to help understand their differences. Study design considerations are reviewed, including sample size, power, and measures associated with risk factors for clinical outcomes. Finally, we discuss the advantages and disadvantages of case-control studies and provide a checklist for authors and a framework of considerations to guide reviewers' comments.

Self-reported measures of health-related quality of life (HRQOL) are increasingly used in clinical management and evaluation of patient outcomes. HRQOL measures are used to monitor patient progress and treatment response, investigate effects of medical interventions, and provide patient-based data for quality improvement initiatives and policy decisions. Given the importance of HRQOL, it is imperative that the instruments used to assess HRQOL are precise, valid, reliable, and responsive, and that the HRQOL data are appropriately collected, analyzed, and presented. This article reviews the key attributes of studies involving HRQOL data, discusses best practices for selecting appropriate instruments, and provides guidelines for the assessment, analysis, and presentation of these data. A checklist and a reviewer guide are included to serve as templates for authors and reviewers when submitting and reviewing studies involving HRQOL.

Survival (time-to-event) analysis is commonly used in clinical research. Key features of performing a survival analysis include checking proportional hazards assumptions, reporting CIs for hazards ratios and relative risks, graphically displaying the findings, and analyzing with consideration of competing risks. This article provides a brief overview of important statistical considerations for survival analysis. Censoring schemes, different methods of survival function estimation, and ways to compare survival curves are described. We also explain competing risk and how to model survival data in the presence of it. Different kinds of bias that influence survival estimation and avenues to model the data under these circumstances are also described. Several analysis techniques are accompanied by graphical representations illustrating proper reporting strategies. We provide a list of guiding statements for researchers and reviewers.

Considerable heterogeneity persists in the conduct and reporting of statistical analyses in the medical literature. Authors submitting manuscripts to CHEST are encouraged to adhere to the following guidelines where possible.

We have entered the era of "big data" and with that the explosion of interest in prediction modeling. With this explosion comes the challenge of evaluating statistical prediction models, both from the standpoint of an author as well as a reviewer. This article provides guidance for the evaluation and critique of a statistical prediction model. Hopefully, this will improve the quality of statistical prediction modeling studies and facilitate their review.

Causal directed acyclic graphs (cDAGs) have become popular tools for researchers to better examine biases related to causal questions. DAGs comprise a series of arrows connecting nodes that represent variables and in doing so can demonstrate the causal relation between different variables. cDAGs can provide researchers with a blueprint of the exposure and outcome relation and the other variables that play a role in that causal question. cDAGs can be helpful in the design and interpretation of observational studies in pulmonary, critical care, sleep, and cardiovascular medicine. They can also help clinicians and researchers to better identify the structure of different biases that can affect the validity of observational studies. Most of the available literature on cDAGs and their function use language that might be unfamiliar to clinicians. This article explains cDAG terminology and the principles behind how they work. We use cDAGs and clinical examples that are mostly focused in the area of pulmonary medicine to describe the structure of confounding, selection bias, overadjustment bias, and detection bias. These principles are then applied to a more complex published case study on the use of statins and COPD mortality. We also introduce readers to other resources for a more in-depth discussion of causal inference principles.

Sample size determination is an essential step in planning a clinical study. It is critical to understand that different study designs need different methods of sample size estimation. Although there is a vast literature discussing sample size estimation, incorrect or improper formulas continue to be applied. This article reviews basic statistical concepts in sample size estimation, discusses statistical considerations in the choice of a sample size for randomized controlled trials and observational studies, and provides strategies for reducing sample size when planning a study. To assist clinical researchers in performing sample size calculations, we have developed an online calculator for common clinical study designs. The calculator is available at http://riskcalc.org:3838/samplesize/. Finally, we offer our recommendations on reporting sample size determination in clinical studies.

Reductions in genotyping costs and improvements in computational power have made conducting genome-wide association studies (GWAS) standard practice for many complex diseases. GWAS is the assessment of genetic variants across the genome of many individuals to determine which, if any, genetic variants are associated with a specific trait. As with any analysis, there are evolving best practices that should be followed to ensure scientific rigor and reliability in the conclusions. This article presents a brief summary for many of the key bioinformatics considerations when either planning or evaluating GWAS. This review is meant to serve as a guide to those without deep expertise in bioinformatics and GWAS and give them tools to critically evaluate this popular approach to investigating complex diseases. In addition, a checklist is provided that can be used by investigators to evaluate whether a GWAS has appropriately accounted for the many potential sources of bias and generally followed current best practices.

Medical tests are procedures intended to detect, diagnose, characterize, or monitor a specific medical condition. Understanding the accuracy of a medical test is a critical part of informed decision-making in patient management, as it allows clinicians to appreciate the types of errors a medical test might be prone to making and how often it makes them. Designing a study to assess the performance of a medical test, however, presents unique challenges, from acquiring a reference standard to dealing with the complexities that arise when the test involves an interpretation by a human reader. This article provides an overview of design considerations in this context, including common biases and how to avoid them, statistical considerations, and reporting guidelines. A short list of questions is also provided, which can serve as a quick reference for anyone designing, implementing, or reviewing a study that intends to assess the performance of a medical test.