Outcome measures for clinical research in sepsis: A report of the 2nd Cambridge Colloquium of the International Sepsis Forum
Bibliographic record
Abstract
Background and Objectives: Sepsis is the leading cause of morbidity and mortality for patients admitted to an intensive care unit. The evaluation of new therapies has been hampered by the underdevelopment of outcome measures used to detect biological activity and patient-centered benefit in a complex and highly heterogeneous patient population. We sought to evaluate existing approaches and to draw on insights from other disciplines to propose a comprehensive approach to outcome evaluation in sepsis clinical trials. Methods: An expert colloquium organized by the International Sepsis Forum brought together sepsis researchers, clinical epidemiologists, and experts in the development and implementation of outcome measures in rheumatology, neurology, and oncology. Results: The translation of an evolving understanding of the biology of sepsis into effective new therapies for critically ill patients requires a reevaluation of the end points used to determine response to intervention. These represent a continuum that measures biological activity against the target at one end and sustained improvement in survival or quality of life at the other. Early phase research should determine whether an intervention works in vivo, using measures that are responsive and informative to provide proof of principle, to aid in selecting optimal patient populations for study, and to gain insights into optimal dose and duration of therapy. After in vivo biology has been demonstrated and the possibility of efficacy inferred by plausible improvements in surrogate physiologic measures, definitive studies should seek robust evidence of benefit using end points that measure important, patient-centered benefit, including intermediate and longer term survival and health-related quality of life. Nonmortal measures of benefit assume particular importance for populations, such as children, whose mortality risk is low, or who have significant rates of comorbidities that independently limit survival. Composite measures that integrate morbidity and mortality effects may provide the most meaningful information about therapeutic efficacy. Conclusions: The development of explicit, hypothesis-driven, and iterative approaches to outcome measure development, patterned on approaches used in the fields of rheumatology and oncology, may improve the conduct of clinical studies in the critically ill. LEARNING OBJECTIVES On completion of this article, the reader should be able to: Describe direct measures of outcomes in sepsis. Describe surrogate measures of outcomes in sepsis. Use this information in a clinical setting. Dr. Marshall has disclosed that he was formerly a consultant for Edwards and Wyeth-Ayerst and is currently a consultant for BRAHMS Diagnostics, and GlaxoSmithKline. Dr. Bernard has disclosed that he has been the recipient of grant/research funds from Eli Lilly, Novo Nordisk, and Takeda Pharmaceuticals. Dr. Bonbardier has disclosed that she has been the recipient of grant/research funds from Abbott and Amgen, is a consultant/advisor for and is on the speakers bureau of Merck, Pfizer, Schering-Plough Corp., and Wyeth. Dr. Calandra has disclosed that he is the recipient of direct grant/research funding from Baxter, Wako, and Merck and was the recipient of direct grant/research funding from Pfizer, Natimmune, Bristol-Myers Squibb in the past; a consultant/advisor for Baxter, Pfizer, Merck, GlaxoSmithKline, CAT, Roche; and on the speakers bureau of Pfizer and Merck and was formerly on the speakers bureau of GlaxoSmithKline. The remaining authors have disclosed that they have no financial relationships or interest in any commercial companies pertaining to this educational activity. Wolters Kluwer Health has identified and resolved all faculty conflicts of interests regarding this educational activity. Visit the Critical Care Medicine Web site (www.ccmjournal.org) for information on obtaining continuing medical education credit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.328 | 0.226 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".