Use of Sepsis Clinical Prediction Models to Improve Patient Care

Mogri, Murtaza; Grant, Richard W.; Liu, Vincent X

doi:10.1001/jamainternmed.2022.7164

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Without standards for diagnosing infection, sepsis, dysregulated host response, or organ dysfunction that are both accurate and generalizable, it will be difficult to create them. Despite the interest and growth in automated sepsis screening models, high-quality evidence of benefit over routine care is lacking (33). A Cochrane review of automated monitoring vs. standard care identified only very low-quality evidence that precluded drawing any meaningful conclusions (34).…”

Section: Resultsmentioning

confidence: 99%

Surviving Sepsis Campaign Research Priorities 2023

De Backer,

Deutschman,

Hellman

et al. 2024

Critical Care Medicine

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OBJECTIVES: To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock. DESIGN: Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities. METHODS: Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article. RESULTS: A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis? CONCLUSIONS: Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

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Section: Resultsmentioning

confidence: 99%

Surviving Sepsis Campaign Research Priorities 2023

De Backer,

Deutschman,

Hellman

et al. 2024

Critical Care Medicine

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“…In 2016, a third fellowship position was added to focus on applied clinical informatics in recognition of the increasingly important role of data science and the advances in clinical prediction modeling methods within care delivery systems. Fellows accepted into this DSF/Informatics track gain advanced skills in developing clinical prediction models and in barriers and facilitators to their clinical implementation 19,20 …”

Section: Implementing a Delivery Science Fellowship Within A Learning...mentioning

confidence: 99%

Training the next generation of delivery science researchers: 10‐year experience of a post‐doctoral research fellowship program within an integrated care system

Grant

Schmittdiel

Liu

et al. 2023

Learning Health Systems

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IntroductionLearning health systems require a workforce of researchers trained in the methods of identifying and overcoming barriers to effective, evidence‐based care. Most existing postdoctoral training programs, such as NIH‐funded postdoctoral T32 awards, support basic and epidemiological science with very limited focus on rigorous delivery science methods for improving care. In this report, we present the 10‐year experience of developing and implementing a Delivery Science postdoctoral fellowship embedded within an integrated health care delivery system.MethodsIn 2012, the Kaiser Permanente Northern California Division of Research designed and implemented a 2‐year postdoctoral Delivery Science Fellowship research training program to foster research expertise in identifying and addressing barriers to evidence‐based care within health care delivery systems.ResultsSince 2014, 20 fellows have completed the program. Ten fellows had PhD‐level scientific training, and 10 fellows had clinical doctorates (eg, MD, RN/PhD, PharmD). Fellowship alumni have graduated to faculty research positions at academic institutions (9), and research or clinical organizations (4). Seven alumni now hold positions in Kaiser Permanente's clinical operations or medical group (7).ConclusionsThis delivery science fellowship program has succeeded in training graduates to address delivery science problems from both research and operational perspectives. In the next 10 years, additional goals of the program will be to expand its reach (eg, by developing joint research training models in collaboration with clinical fellowships) and strengthen mechanisms to support transition from fellowship to the workforce, especially for researchers from underrepresented groups.

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Use of Sepsis Clinical Prediction Models to Improve Patient Care

Cited by 2 publications

References 8 publications

Surviving Sepsis Campaign Research Priorities 2023

Surviving Sepsis Campaign Research Priorities 2023

Training the next generation of delivery science researchers: 10‐year experience of a post‐doctoral research fellowship program within an integrated care system

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