Sepsis Subclasses: A Framework for Development and Interpretation.


Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.






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Publication Info

DeMerle, Kimberley M, Derek C Angus, J Kenneth Baillie, Emily Brant, Carolyn S Calfee, Joseph Carcillo, Chung-Chou H Chang, Robert Dickson, et al. (2021). Sepsis Subclasses: A Framework for Development and Interpretation. Critical care medicine, 49(5). pp. 748–759. 10.1097/ccm.0000000000004842 Retrieved from

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Christopher Lindsell

Professor of Biostatistics & Bioinformatics

As Director, Chris Lindsell, PhD leads the visionary strategic planning, development, and execution of state-of-the-art research for DCRI to achieve its scientific goals. He also serves as a member of the Senior Management Team and, along with Dr. Laine Thomas, will partner with Jack Shostak, Director of Statistical Operations, to execute research.

Lindsell has served as the Institute for Clinical and Translational Research Methods program Director, co-Director of the Center for Health Data Science, and professor of biostatistics and biomedical informatics at Vanderbilt University. He is a leader in the application of rigorous methods in the acute care environment, and to the intersection between emergency care and public health. He has led data coordinating centers for numerous multi-center clinical trials, including FDA-regulated trials, and epidemiological studies. His experience spans mechanistic studies, network trials, pragmatic trials, embedded trials, and more.

Lindsell holds patents for using clinical information, biomarkers and transcriptomics for prognosis and prediction in sepsis and septic shock with a goal of precision therapy in critical illness. He has contributed to data standards for CONNECTS, NHLBI’s network of networks for COVID-19 research, and to the DAQCORD guidelines for data collection and curation in observational studies. He has published over 350 peer-reviewed papers, and most recently, he has been leading multiple major data center efforts during the Covid-19 pandemic including TREAT NOW, ACTIV6 and the IVY Network.


Ephraim Tsalik

Adjunct Associate Professor in the Department of Medicine

My research at Duke has focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease.  This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance.

With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of presentation what the underlying cause of illness is.  For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both, or a non-infectious condition such as asthma or allergies.  Given the difficulties in making the diagnosis, most patients are inappropriately given antibacterials.  However, each of these etiologies (bacteria, virus, or something else entirely) leaves a fingerprint embedded in the host’s response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to understand, diagnose, and manage disease.

These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness.  We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes.  These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics.  Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.

These examples are just a small sampling of the breadth of research Dr. Tsalik and his colleagues have conducted.  

In April 2022, Dr. Tsalik has joined Danaher Diagnostics as the VP and Chief Scientific Officer for Infectious Disease, where he is applying this experience in biomarkers and diagnostics to shape the future of diagnostics in ID. 

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