Blockchain Papers

Alexandre Tran, David Granton, Eddy Fan, Bram Rochwerg

Clinical practice guidelines (CPGs) are used by critical care clinicians to guide practice and inform best care. According to the GRADE framework, evidence synthesis should preferentially rely on randomized controlled trials (RCTs) because they minimize bias and establish causality.1 Despite challenges, critical care is well-suited to randomized studies given its (1) high incidence of acute conditions, (2) protocolized interventions, (3) standardized outcomes, and (4) strong data infrastructure and trial networks.2,3 Despite the advantages, RCTs are often unavailable for CPGs or leave knowledge gaps, particularly for subgroup effects or patient-important outcomes like long-term quality of life related to heterogeneous populations, urgent interventions, and recruitment constraints.4,5 Physicians may hesitate to apply RCT results because (1) enrolled patients differ from real-world populations, (2) key outcomes may be unmeasured, (3) effect estimates may be imprecise, and (4) subgroup analyses may be lacking.6 When RCT evidence is insufficient, high-quality non-randomized studies of interventions (NRSI) can complement trials by approximating causal inference—estimating exposure effects while separating systematic bias from random error.7 High-quality NRSI ­require large, well-validated datasets with minimal missingness and adequate temporal resolution. Without these, even advanced analytics cannot yield credible estimates. NRSI often emulate target trials, aligning eligibility, time zero, and predefined interventions and outcomes.8,9 Design must reflect strong knowledge of confounders and time-varying biases, addressed through advanced data and statistical methods. When based on explicit and credible assumptions (eg, exchangeability, no residual confounding), NRSI can yield valid and generalizable estimates, though such assumptions cannot be proven and still require caution in interpretation.10 Most NRSI are retrospective and lack safeguards standard in RCTs such as trial registration or prespecified outcomes. In target-trial emulation (Table 1), preregistration before data access is critical to prevent selective reporting and analytic flexibility, mirroring RCT practice. These limitations are especially relevant in critical care, given dynamic physiology, urgent decisions, and substantial clinical heterogeneity. These factors complicate exposure timing, increase time-varying confounding, and challenge stability assumptions in target-trial designs. Rigorous cohort definition and analytic strategy are essential when applying NRSI in this context. As causal-inference methods such as target-trial emulation spread, cautious application with methodological rigor and transparency is essential to avoid poorly executed, misleading, or irreproducible NRSI. High-quality NRSI depend not only on analytical sophistication but also on careful data acquisition, explicit protocolization, and transparency in prespecifying exposures, outcomes, and analytic plans—principles that mirror RCT standards. Target trial (ideal RCT) versus emulation. 1. Treatment with ECMO therapy if PaO2/FiO2 < 80 mmHg 2. Treatment with conventional mechanical ventilation without the use of ECMO therapy Adapted from: National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Developing a Protocol to Evaluate the Concomitant Prescribing of Opioids and Benzodiazepine Medications and Veteran Deaths and Suicides. An Approach to Evaluate the Effects of Concomitant Prescribing of Opioids and Benzodiazepines on Veteran Deaths and Suicides. Washington (DC): National Academies Press (U.S.); 2019 Sep 24. 2, Specifying the Target Trial. Available from: https://www.ncbi.nlm.nih.gov/books/NBK547516/. Case example: Venovenous extracorporeal membrane oxygenation in patients with acute covid-19 associated respiratory failure: comparative effectiveness study.22 This commentary examines the evolving role of NRSI in developing critical care CPGs. We outline key challenges in conducting and synthesizing critical care research, then describe how high-quality NRSI can complement randomized evidence by (1) aligning effect estimates with RCTs, (2) informing certainty of evidence (CoE), and (3) guiding clinical practice recommendations. We propose practical strategies for CPG panels and domain experts to maximize the utility of NRSI while maintaining methodological rigor. Our goal is to support CPG panelists, researchers, and clinicians in interpreting recommendations that integrate NRSI. These recommendations align with evolving GRADE guidance, operationalizing its principles for critical care applications. GRADE provides a structured approach for rating CoE, the confidence that an estimated effect is close to the truth.11 When ­developing guidelines, the GRADE Evidence-to-Decision (EtD) framework translates synthesized evidence into recommendations by weighing intervention effects, CoE, patient-valued outcomes, and contextual factors such as resource use, equity, acceptability, and feasibility.12 These contextual judgments ensure that evidence is interpreted through a patient- and system-centered lens, recognizing that even high-certainty data require value-based consideration before adoption into practice. A review of critical care CPGs showed reasonable uptake of GRADE, with recommendation strength generally aligned with CoE.13 However, strong recommendations are still often made from low or very low-certainty evidence, often related to evidence gaps in RCTs. This highlights the need to integrate high-quality NRSI into CPG development to strengthen evidence synthesis and uptake. Critical care CPG panels should consistently apply GRADE principles, incorporating all high-quality evidence, including NRSI to augment situations where RCT data may be limited or absent. RCTs are resource-intensive and difficult to conduct in critical care.1 To maintain feasibility, investigators often overestimate effect sizes, leading to underpowered studies that may miss true effects.14,15 Reviews of critical care RCTs show that predicted treatment effects are often exaggerated—nearly 10-fold higher than observed, and that few trials sufficiently justify their sample-size targets.16 Similar overestimation has been reported in sepsis, stroke, and trauma trials.17–19 Among high-profile publications, fewer than half of trials had reproducible results.20 Moreover, a meta-epidemiologic review of more than 600 critical care trials found that only 1 in 16 was at low risk of bias, with little improvement over 4 decades.21 These findings suggest that RCTs alone may not provide sufficient high-quality evidence to inform strong guideline recommendations. Critical care populations are highly heterogeneous, encompassing subgroups with different baseline risks and treatment ­responses. RCTs often target broad syndromes such as sepsis or acute respiratory distress syndrome (ARDS), which likely contributes to many “negative” trials unable to detect differences in outcome.22 Because these studies estimate average treatment effects (ATEs) across diverse patients, potential subgroup benefits can be obscured when other subgroups experience harm.23 This variability, termed heterogeneity of treatment effect (HTE), reflects non-random differences in benefit or harm linked to patient characteristics.24 Understanding HTE (Table 2) is central to precision medicine: treatments that appear neutral on average may conceal offsetting benefit and harm across biologically or contextually distinct subgroups. Explicit exploration of these differences can refine trial design, improve interpretation, and guide targeted recommendations. Methods for assessing heterogeneity of treatment effect. Case example: Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19.19 Causal forest and other machine-learning approaches allow for non-linear and interactive modeling of treatment effect heterogeneity but are more susceptible to overfitting and typically require larger sample sizes and external validation. In contrast, regression-based risk modeling approaches are generally more interpretable but may oversimplify interaction effects. RCTs typically assess HTE using pairwise subgroup analyses, but these are often underpowered, rely on arbitrary subgroup thresholds (eg, age <65 vs ≥65), and cannot capture complex interactions.25 The American Thoracic Society (ATS) and European Respiratory Society (ERS) guideline on non-invasive ventilation illustrates these limitations: subgroup evidence for conditions such as acute hypoxemic respiratory failure or ARDS came mostly from small or secondary analyses, yielding sparse data and very low certainty.26 These challenges highlight the need for improved data science approaches to identify and characterize HTE: a priority emphasized in the recent ATS research agenda for sepsis and ARDS.27 Data-driven subgroups (subphenotypes) can integrate multiple patient characteristics to assess effect modification and estimate individualized treatment effects.28,29 These models require rigorous derivation and validation to avoid overfitting, yet no consensus framework currently guides their validation or clinical use. Critical care trialists should adopt realistic effect size and recruitment targets and predefine strategies to evaluate clinically relevant HTE. When RCT evidence is insufficient, we propose strategies for CPG panels to integrate NRSI within the GRADE framework to complement RCTs and strengthen recommendations. In accordance with GRADE guidance, if the CoE from RCTs is judged to be high then the role for NRSI is minimal for the specific comparison and outcome of interest.7 However, RCTs often do not report certain patient-important outcomes such as adverse events, quality of life, or longer-term morbidity or mortality. Even if a particular question and outcome of interest have RCT evidence, the estimates of treatment effect are often limited by imprecision due to aforementioned recruitment and sample size concerns. Treatment effects are often assessed in highly selected populations; trial participants typically represent a small fraction of those screened and even meta-analyses may yield low certainty due to imprecision or inconsistency.30,31 In these situations, guideline panels should consider high-quality NRSI, defined by adherence to TARGET (Transparent Reporting of Observational Studies Emulating a Target Trial) standards, acceptable risk of bias, and robust sensitivity analyses, to supplement RCT evidence.7 Target-trial emulation exemplifies this approach: investigators first design a hypothetical randomized trial addressing the question of interest, then emulate it using observational data.8,32 For instance, an international study using the COVID-19 Critical Care Consortium dataset estimated the effect of VV-ECMO versus conventional ventilation in patients with severe COVID-19, providing real-world evidence where an RCT was impractical due to complexity and cost.33 Similar emulations have evaluated intubation,34 ventilation,35 and corticosteroid strategies36 in critical care—demonstrating how NRSI can inform practice when trials are unfeasible. Consider the example of drotrecogin alfa (activated protein C, rhAPC). Following the PROWESS RCT,37 which demonstrated benefit of rhAPC in patient with septic shock, the large open-label ENHANCE observational study38 reported a similar reduction in mortality with rhAPC but was the first to raise important concerns about serious bleeding, including intracranial hemorrhage. These observational findings influenced early guideline discussions, tempering enthusiasm for the drug, and subsequent RCTs39,40 confirmed this harm and rhAPC was ultimately withdrawn. This highlights that replication across larger datasets remains essential to confirm findings and ensure generalizability beyond selected RCT populations. This sequence illustrates an iterative process: observational signals can generate early warnings or hypotheses that subsequent RCTs confirm or refute. When results diverge, these contrasts can highlight methodological limitations or context-specific factors that warrant further investigation. The TARGET statement outlines 21 reporting items to standardize eligibility, interventions, outcomes, and analyses, improving transparency and reproducibility of emulated trials.41 Adherence to TARGET helps guideline panels assess NRSI rigor and determine when such evidence can complement or upgrade certainty around RCT findings. Similarly, the RCT-DUPLICATE initiative evaluated whether database-derived emulations can reproduce findings from RCTs across 32 cardiovascular studies, including interventions for anticoagulation, antiplatelet therapy, and chronic disease management. The authors found that effect estimates from well-designed emulations closely mirrored their RCT counterparts in both direction and magnitude, demonstrating that real-world data can yield valid causal inference when study design and analytic methods are rigorous.10 Whether successes from other fields will translate to critical care remains uncertain, given its confounding, physiologic complexity, and HTE. A blinded target-trial emulation in this setting reproduced findings of the PreVent RCT examining bag-mask ventilation and hypoxemia,42,43 providing proof-of-principle that short-term physiologic effects can be predicted from observational data, though its value for longer-term or patient-centered outcomes remains untested. Valid causal inference in NRSI requires adherence to key assumptions: exchangeability (no unmeasured confounding), positivity (each patient could receive any treatment), and consistency (observed outcomes reflect potential outcomes under that treatment).8,9 Meeting these assumptions demands careful cohort design, proper time alignment, and analytic techniques that address confounding, such as target-trial emulation, inverse-probability weighting, or doubly robust estimators.44,45 Studies must also handle time-varying confounding and competing risks (eg, death precluding extubation), which can otherwise bias effect estimates.46 To address these concerns, marginal structural models may be used to estimate the causal effect of a time-varying treatment and address the challenge of estimating treatment effects when confounders are influenced by prior treatment—a situation conventional regression models struggle with. CPG panels should systematically appraise NRSI by verifying TARGET adherence, assessing bias with validated tools such as ROBINS-I, and judging how results affect GRADE domains such as imprecision, inconsistency, and indirectness.41,47 Robust sensitivity analyses, testing alternative models, handling missing data, and probing unmeasured confounding, are essential to confirm result stability and should be clearly reported.48,49 Transparent presentation of assumptions and their plausibility further strengthen credibility. When high-certainty RCT evidence already exists for all relevant target populations, additional NRSI are seldom needed (Figure 1). More often, however, critical care trials involve highly selected populations, making complementary NRSI useful for confirming ­treatment effects in broader or under-represented groups.50,51 When RCT and NRSI results are consistent, guideline panels may consider upgrading certainty and recommendation strength in line with GRADE guidance.7 GRADE also allows rating up observational evidence when large effects, dose-response relationships, or confounding that would only diminish an observed benefit are present.52 Conversely, inconsistent or methodologically weak NRSI such as those with implausible assumptions, poor reporting, or critical bias, should be excluded, with the rationale documented. Expanding use of target-trial emulation is promising but must be paired with training and standards to prevent low-quality proliferation that could erode confidence in observational evidence.48 Framework for incorporating NRSI into critical care CPGs. CPG panels should incorporate well-conducted NRSI to strengthen CoE and adopt structured workflows: (1) verifying TARGET adherence, (2) considering potential risk of bias, and (3) linking NRSI results to GRADE domains to ensure transparent, reproducible use of observational evidence. Critical care RCTs often study heterogeneous syndromes using strict eligibility criteria that limit generalizability and obscure subgroup effects. A multicenter simulation of 15 landmark trials found that over half of real-world ICU patients would have been ineligible,53 and a review of 75 high-impact trials showed that 60% used at least one poorly justified exclusion such as language barriers or lack of insurance—further restricting applicability.54 Most RCTs originate from high-income countries, leaving major evidence gaps for critically ill patients in the Global South.55 For example, a Zambian sepsis RCT found higher mortality with early fluid resuscitation—contradicting prior goal-directed therapy trials.56,57 This discordance may be explained by the fact that these trials enrolled predominantly young, malnourished individuals predisposed to pulmonary edema and respiratory failure in a setting with limited ventilatory support. Beyond generating estimates of effectiveness in underrepresented populations, NRSIs also offer a pathway to address structural inequities in evidence generation and utilization. Conducting RCTs in the Global South is often hindered by logistical, regulatory, and infrastructural challenges—including limited research infrastructure, ethical oversight, or funding mechanisms, which systematically exclude these populations from RCTs.55 Well-designed NRSI can help bridge such gaps by leveraging local data to assess effectiveness, feasibility, and contextual factors in resource-limited settings. They can also identify structural and contextual modifiers such as malnutrition, health-system capacity, and disease epidemiology; thereby supporting more equitable, context-specific guideline recommendations.58 Embedding such evidence from the Global South not only broadens external validity but also enhances the global relevance of CPGs—thereby promoting more equitable and relevant evidence-based decision-making for clinicians practicing in resource-limited settings. NRSI can also inform feasibility, acceptability, and which are key factors in CPG For instance, the ATS guideline on ARDS a recommendation for VV-ECMO based on NRSI substantial in and across and NRSI can RCT findings to real-world which patients benefit or are based on risk or A key is which to assess how RCT results to external populations and to identify contextual effect improving both evidence relevance and trial Causal inference using real-world data can evaluate HTE across broader populations, including and patients typically underrepresented in a systematic review found major in methodological rigor for HTE analyses, particularly in testing and for confounding, the need for standardized methods and In critical care, HTE from secondary analyses of RCT In the modeling showed that patient characteristics predicted benefit from specific oxygenation targets for patients with and higher for those with The subsequent Care Medicine a recommendation higher oxygenation targets based on very low-certainty an of the trial found that even when are machine-learning models can identify clinically subgroups with benefit or the value of HTE modeling in acute respiratory These secondary analyses are and but should be by observational studies to evaluate HTE beyond RCTs. The ARDS cohort illustrates the value of non-randomized showed that patients had mortality with higher while no benefit in the example of HTE using real-world ICU Beyond also a global of guideline adherence, and ARDS outcomes. not its and rigor how observational studies can yield at a RCTs informing international ARDS When developing panels should consider how best to incorporate NRSI in HTE. this requires systematically HTE analyses, particularly for subgroups in the and assessing how these findings complement subgroup no GRADE yet panels should still evaluate whether HTE evidence recommendations or can guide research for or in RCTs. CPG panels should apply well-conducted causal-inference analyses to confirm the generalizability of RCT findings and identify clinically important HTE. RCTs the standard for and but well-designed NRSI can augment both the certainty and of evidence. Critical care CPG panels should integrate observational evidence when while recognizing methodological standardized (1) TARGET for reporting, (2) validated risk of bias and (3) explicit GRADE will ensure use of NRSI across guideline High-quality NRSI can CoE and generalizability beyond selective RCT populations, providing a to evaluate HTE. incorporating such studies into CPG development may improve both the generalizability and of recommendations. such as the dataset highlight how NRSI can HTE not in trials As analytic methods and target-trial NRSI will an important role in addressing evidence gaps in critical care. will rely on close across and to ensure that NRSI are and with the rigor of randomized authors the the authors to the and of the is at American of and Critical Care Medicine the which have been as tools used in this

Anna Kollerup, Sarah Wadmann, Toke Bek, Jakob Kjellberg

No abstract is available for this record.

Christopher R. Carpenter, Lucas Oliveira J. e Silva, Suneel Upadhye, Joshua Broder · 5 authors

Emergency medicine is often a specialty defined by diagnostic uncertainty when worried patients present with constellations of symptoms seeking explanation and relief. Abdominal pain is a common chief complaint among adult emergency department (ED) patients, with recurrent symptoms in the subsequent days, weeks, months, and even years, sometimes prompting repeat evaluations.1 The differential diagnosis is broad and diverse, including multiple organs and systems and extraabdominal causes. Ideally, clinical practice guidelines (CPGs) synthesize the entirety of evidence for questions relevant to an explicitly defined patient population and outcomes, but until now no CPG existed for the scenario of recurrent abdominal pain. Consequently, significant practice variation exists in the diagnostic and therapeutic approach to this clinical condition.2 The Society for Academic Emergency Medicine (SAEM) second "Guidelines for Reasonable and Appropriate Care in the Emergency Department 2 (GRACE-2)" article provides that CPG with adherence to Grading of Recommendations, Assessment, Development and Evaluations (GRADE) methodology including incorporation of patient priorities and external stakeholders.3 Through adherence to the GRADE methodology we aimed to create rigorous and trustworthy guidelines. The GRACE-2 writing team deliberated to select topics and questions and to explicitly define a clinically meaningful population, ultimately settling on definitions of recurrence within 30 days and adults with "low-risk" abdominal pain. Identifying no well-accepted or validated definition of "low risk" (as opposed to a risk model like the HEART score for chest pain4), the GRACE-2 writing team devised a definition of "low risk" that resonated with our clinical intuition and excluded populations that emergency physicians would routinely identify as moderate or high risk. Subsequently, the GRACE-2 writing team worked with medical librarians to focus searches based on the patient-intervention-control-outcome-time (PICOT) template5 and completed systematic reviews for each question before developing the recommendations.6, 7 The PICOT-oriented literature search revealed no studies that aligned with our definition of low risk and few that defined recurrence within our predefined time frame. However, that does not mean that our search rendered zero published evidence around which GRACE-2 could contemplate actionable recommendations via the GRADE Evidence to Decision (EtD) framework.8, 9 We had to make a decision about how to classify and incorporate the published research that we did identify. GRADE provides a framework for evidence synthesis and development of clinical guidelines and recommends inclusion of both direct and indirect evidence into CPGs, while providing guidance around the distinction between the two.8-12 Therefore, we decided to classify evidence as "direct" if each element of the PICOT question matched the study's inclusion criteria and outcomes assessed. "Indirect" evidence was defined by deviation from any component of the PICOT question (Table 1). These definitions are necessary for guideline developers who need to evaluate the domain of indirectness when rating the certainty of evidence.10, 12 Since the overall value of CPGs rests upon the rigor and transparency of the evidentiary search, quality assessment, and synthesis in conjunction with an explicit and representative assessment of anticipated benefits or potential harms of the subsequent recommendations, weighing the pros and cons of including indirect evidence is merited while considering if and how to incorporate the GRACE-2 recommendations into ED practice. Not emergency department Not adult Not recurrent Not undifferentiated abdominal pain Not low risk Initial CT identified explanatory pathology like kidney stone Repeat CT >12 months after initial CT GRADE recognizes indirectness as a key domain in the assessment of certainty of evidence.10, 12 Whenever systematic review authors or guideline developers identify important indirectness issues from a body of evidence that deviates from the original PICOT question, the certainty of evidence must be downgraded by one or two levels.10, 12 Despite such guidance from GRADE, conceptualization of what type of indirect evidence could be synthesized and used by CPGs remains debatable, especially when there is insufficient direct evidence.13 Also, a second layer of complexity is added when guideline developers need to evaluate the directness of research evidence for all criteria of the GRADE EtD framework including values, resources, cost-effectiveness, equity, acceptability, and feasibility. This cognitive framework can quickly diffuse into uncertainties. Nonetheless, indirect evidence is not fundamentally or inevitably flawed. For example, a study might be rated as indirect because the age range of enrolled subjects does not perfectly match the intended population or because the time frame of follow-up slightly exceeds the desired target. Such evidence may provide a very reasonable estimate of the range of expected outcomes in the intended population. In addition, many randomized controlled trials employ so many exclusion criteria and carefully controlled experimental conditions that the results may not predict outcomes in a broader ED population. GRADE attempts to distill value from the broad range of available evidence, rather than taking a nihilistic attitude that rejects evidence on the basis of any imperfection. Nihilism suggests "we know nothing" and have no basis for any decision—but emergency medicine requires that physicians make the best decision possible based on the available, if imperfect, evidence in a more pragmatic approach. GRADE recommends that if a large body of indirect evidence that can be convincingly linked to the PICOT support the management strategy, recommendations should be made.14 Specifically, GRADE states that "clinicians will rarely explore the evidence as thoroughly as a guideline panel, nor devote as much thought to the trade-offs, or the possible underlying values and preferences in the population. We therefore encourage panels to deal with their discomfort and to make recommendations even when confidence in effect estimate is low and/or desirable and undesirable consequences are closely balanced."15 The ideal strategy to dealing with absent direct evidence for a certain question during CPG development is not fully established among guideline developers, and each panel along with their methodologists need to individualize such decisions according to resource availability (e.g., methodological expertise, funding) and feasibility. Murad et al.,13 for example, suggest five strategies for supplementing systematic review findings when evidence on benefits or harms is found to be insufficient, including: (1) reconsider eligible study designs, (2) summarize indirect evidence, (3) summarize contextual and implementation evidence, (4) consider modeling, and (5) incorporate unpublished health system data in the evidence synthesis. In GRACE-2, summary of indirect evidence was chosen as the main approach and different "bodies of indirect evidence" were systematically synthesized.6, 7 As three out of four questions in GRACE-2 were related to diagnostic tests, we also faced the reality that direct evidence evaluating the impact of different testing strategies on patient-important outcomes (i.e., diagnostic randomized trials) seldom exists, which ultimately leads to the use of different types of indirect evidence such as diagnostic accuracy.16-18 Even within the adaptive framework of GRADE, indirect evidence may leave the PICOT question unanswered, and subsequent recommendations are often necessarily weak or nonexistent. Strong and definitive recommendations necessitate multiple studies evaluating the identical patient population and diagnostic approach quantifying the same outcomes in similar time frames with minimal concerns about health inequities, resource consumption, imprecision, or feasibility. Indirect evidence alone is unlikely to justify strong recommendations, but does provide substantive proof of the scope of the problem relative to the paucity of evidence. If the indirect evidence was excluded, CPG stakeholders would not be cognizant that this research was identified and reviewed in developing the recommendations. Clinicians, educators, and researchers would remain unaware of how little empiric evidence exists around which to shape decision making or how future investigators could more directly address the knowledge void. GRACE-2 is not alone in identifying a painfully surprising gap between what emergency medicine thinks is common knowledge and high-quality research to justify those beliefs. One reflection of this is that the majority of American College of Emergency Physician Clinical Policy recommendations are not level A.19 Ultimately, concerns about the directness of evidence for CPGs represent an existential crisis for emergency medicine. As society's safety net for potentially life-threatening medical, surgical, or psychiatric illness, emergency medicine's breadth of knowledge must remain broad and open-ended as clinical science continues to expand the horizons of possibility. The hierarchy of evidence-based medicine places CPGs at the top of the information pyramid, yet guidelines for common syndromic presentations like acute abdominal pain do not exist in emergency medicine. Certainly, our specialty could await others to create CPGs for these conditions, but those organizations are most likely to view patient encounters through the lens of an established or highly suspected diagnosis. In contrast, emergency physicians confront undifferentiated patients with constellations of signs and symptoms in a chaotic environment. We navigate the challenges of accurate and timely diagnosis, often with imperfect data. Paradoxically, we face expectations of constraint with testing—often without evidence-based guidance for when to safely limit workups—while avoiding critical misses. Experience with other organization's CPGs has shown that ED clinicians are often noncompliant with their recommendations, which commonly do not account for the real-life conditions of emergency care. The consequence is an unfair judgment that emergency physicians engage in inferior, non–evidence-based, and excessively costly health care by external stakeholders.20-23 The Association of Academic Chairs of Emergency Medicine's 2030 research goals focus on increasing the proportion of NIH R01-funded emergency medicine investigators, who will someday close the knowledge gap for prevalent conditions that currently remain underinvestigated.24 The absence of direct evidence and the imperfection of indirect evidence for high-priority emergency medicine CPGs illustrates the importance of forming a National Institute of Emergency Care. In addition to supporting the next generation of independent health-outcomes researchers, a central prioritizing body could ensure that the most pertinent questions affecting patient care on a daily basis are the focus of funding opportunities.19, 25 Until that day, GRACE-2 provides a synthesis of evidence and corresponding recommendations derived using GRADE methodology upon which to guide imaging and therapy decisions for adults with low-risk and recurrent abdominal pain. Medicine is equal parts science and art with clinical judgment based on hermeneutic thinking to generate a holistic understanding of an individual patient's current condition.26 Our vision is for the inclusion of direct and indirect evidence in this CPG to ground that decision making in a realistic understanding of our current state of knowledge, while catalyzing more pertinent research in the near future.

A. Mina

Abstract Introduction/Objective My role model when I was a medical technologist intern was a chief pathologist who taught me to speak up when something is unsafe and to serve willingly, do what is right, be fair and excellent in work. His character impacted my whole life and career. Every moment matters; life is precious and something to protect. The patient and their care teams depend upon accurate, safe and high-quality clinical laboratory tests result to achieve positive patient outcomes. Methods/Case Report Clinical Practice Results (if a Case Study enter NA) We can do better. Everybody can be surveyors with greater understanding of pathophysiologic processes in disease, extensive experience working in laboratories and in-depth knowledge about complaince, quality, mistake-proofing care, patient-focused and laboratory management. Diagnostic testing would be the method to screen for disease, confirm disease, and monitor disease in hopes of secondary prevention - to identify latent disease to “catch it early.” The screening tests could be anything from newborn screening for inborn metabolism errors, adult screening tests (like mammograms, pap smears, and colonoscopies), to high-risk population screenings to detect HIV, RPR, and gonorrhea. The disease must have a high prevalence to justify the expense of therapy and should be detectable before symptoms arise. The test must not have many false positives but extremely high sensitivity. The results of these tests could lead to the three different methods of prevention. Primary prevention would reduce the risk of developing cardiovascular disease or stroke. Secondary prevention would detect the disease early to prevent progression of the disease. Tertiary prevention would reduce disabilty and promotion of rehabilitation from the disease like strokes or rehab programs. Conclusion In conclusion, this information provides clinicians with a laboratory test menu guidelines to improve clinical practice. We can all learn well to focus on what really matters. Together, we can make healthcare better, more patient-centered, less costly, and safer.

Antonio Sitges‐Serra

The knowledge imposes a pattern, and falsifies, for the pattern is new in every moment and every moment is a new and shocking valuation of all that we have been. — East Coker , T.S. EliotMedicine is not a science: it is a cultural product. As such, the way it is practiced and conceived is much affected by cultural contexts, academic traditions, politics, personal interests, the health industry, experts’ and medical bodies’ opinions, journalists and medical publishing companies. Obviously, science and research have played and will continue to play a key role in the development and progress of medical knowledge. Science, however, proceeds slowly, requires the test of time and relies on strict methodological principles and in personal integrity; briefly, good science is at stake in a world dominated by technolatry1: a self-imposed commitment for continuous innovation within an industrial culture dominated by planned obsolescence and profit increase for the myriad companies that live on the global health market. Evidence-based medicine was launched to encourage a scientific and proof-based approach to medical practice.2 As an ideological movement, it has had a significant impact on how doctors read the medical literature, how clinical research should be planned and how new concepts and therapies are scrutinised before being implemented. However, because medicine, in opposition to science, requires bedside decision making, it cannot rely only on hard data. First, because in many domains, such hard, class A data are not available in many instances. Second, the robustness of the data may be challenged by new findings. Third, because the clinical setting is much more complex than the scenario created by clinical trials that, in order to obtain meaningful conclusions, oversimplifiy the decision-making process through strict inclusion and exclusion criteria. Fourth, because the acquisition and implementation of new knowledge is …

C. C. Anyaegbu

Background Over the past two decades the quest for safe blood supply has led to tremendous growth in the content and scope of the science and practice of Transfusion Medicine (TM). The desire by the various stakeholders in the blood establishment for zero‐risk blood transfusion has not only stretched the level of quality expected of the Transfusion Medicine Service to apical height, but has also resulted in the demand by these stakeholders for concrete proofs that expected degrees of quality have been met or preferably exceeded. Quality indicators (QIs) are a Quality Management System (QMS) tool that are instituted in an organization with intent on not just providing this much needed proof of the level of quality performance tenable in the organization, but they are also intent on utilizing the information gained to seek improving the quality of performance in the organization. In the last decade, huge efforts have been exerted by government and non‐government hospital‐based blood banks, national and international organizations to collate, select, establish and analyze quality QIs for several dimensions of healthcare quality. Notable contributions have been made by such eminent bodies as the Agency for Healthcare Research and Quality (AHRQ), the Organization for Economic Cooperation and Development (OECD) and the College of American Pathologists (CAP). Unfortunately these efforts have only sparsely addressed quality indicators for TM. A year ago, the International Society of Blood Transfusion (ISBT) instituted its Working Party on Quality Management (WPQM). Quality indicators for TM are of priority to the WPQM which presumably is intent on redressing this imbalance and providing robust and valid quality indicators to monitor quality performances in key processes and outcomes in TM practice, globally. Objective This paper therefore highlights some basic essentials for successful monitoring of quality of transfusion medicine service through appropriate selection and implementation of quality indicator projects. It is hoped the information shared through this medium might be of value to Transfusion Medicine practitioners as well as to the ISBT WPQM as they thinker with quality indicators for Transfusion Medicine. Material and Method Considerable review of published literature in the form of journal articles, books and online publications on quality management and quality indicators in Healthcare/Transfusion Medicine was performed with the view to extract essentials elements that could be considered building blocks for the selection, implementation, analysis and utilization of quality indicators in Transfusion Medicine. Result Elements deemed as essential building blocks that were appraised included, but were not limited to: understanding Transfusion Medicine terrain; concept of quality and quality indicators selection criteria; choice of improvement model, data analysis and manner of communication of findings as well as the role of effective education and leadership. Conclusion Quality indicators are indispensible tools which various stakeholders in the Blood Transfusion establishment now demand to adjudge and improve on quality performance. Practitioners as well as policy makers in Transfusion Medicine need to ensure that the quality indicators they institute are appropriately selected and analyzed to be effective and efficient monitors of quality. Knowledge of basic building blocks discussed here is therefore a fundamental prerequisite.

J.-Y. Reginster

No abstract is available for this record.