Risk factors and markers of sepsis development

SIRS+: From a static to a dynamic description of the systemic inflammatory response Syndrome

Distinction of systemic inflammatory response syndrome (SIRS) due to an infection, classically defined as sepsis, from non-infectious SIRS is challenging in the intensive care unit (ICU) due to high SIRS-prevalence. We developed a computerized algorithm for detection of SIRS-criteria (tachycardia, tachypnea, leukocytosis and fever). We applied the algorithm to each minute of every patient stay in the electronic medical records of our surgical ICU. The average prevalence of conventional SIRS (≥2 criteria met concomitantly) for all admissions 2006-2011 was 43.3%.

We analyzed all consecutive polytrauma admissions in this period, one third of which were diagnosed with sepsis according to retrospective clinical expert adjudication. We evaluated the predictive and diagnostic performance of 3 dynamic SIRS-descriptors. Compared with the conventional dichotomous and static SIRS-classification, combined SIRS-criteria average and first-to-last minute difference over a 24-hour time window enhanced accuracies for sepsis prediction and particularly diagnosis, rivaling established biomarkers, in our polytrauma cohort.

These results suggest that temporal changes in signs of systemic inflammation reflect an early, clinically meaningful step along the sepsis continuum. A dynamic appraisal of SIRS (SIRS+) in clinical practice may have diagnostic potential for sepsis detection.

Statistical models of SIRS and sepsis

We use data from the electronic medical records of our surgical ICU to operationalize clinical concepts of critical illness for epidemiological and biostatistical analyses to uncover physiological changes along the sepsis continuum. The resulting statistical models will inform risk specific patient enrollment in prospective clinical studies and support clinical decision making.

Collaboration & Support

This work is carried out in collaboration with

• Prof. Dr. Christel Weiß, Department of Medical Statistics, Medical Faculty Mannheim

and supported by

• the Klaus Tschira Stiftung within the SCIDATOS research alliance.

Publications

Lindner HA, Schamoni S, Kirschning T, Worm C, Hahn B, Centner FS, Schoettler JJ, Hagmann M, Krebs J, Mangold D, Nitsch S, Riezler S, Thiel M, Schneider-Lindner V.
Ground truth labels challenge the validity of sepsis consensus definitions in critical illness.
J Transl Med. 2022; 20(27).
https://doi.org/10.1186/s12967-022-03228-7

Schefzik R, Boland L, Hahn B, Kirschning T, Lindner HA, Thiel M, Schneider-Lindner V.
Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine. Front. Physiol. 2022; 12:801622.
https://doi.org/10.3389/fphys.2021.801622

Schoettler JJ, Kirschning T, Hagmann M, Hahn B, Fairley AM, Centner FS, Schneider-Lindner V, Herrle F, Tzatzarakis E, Thiel M, Krebs J.
Maintaining oxygen delivery is crucial to prevent intestinal ischemia in critical ill patients.
PLoS One. 2021 Jul 9; 16(7):e0254352.
Abstract on PubMed

Centner FS, Schoettler JJ, Fairley AM, Lindner HA, Schneider-Lindner V, Weiss C, Thiel M, Hagmann M.
Impact of different consensus definition criteria on sepsis diagnosis in a cohort of critically ill patients-Insights from a new mathematical probabilistic approach to mortality-based validation of sepsis criteria.
PLoS One. 2020 Sep 8; 15(9):e0238548.
Abstract on PubMed

Lindner HA, Kirschning T, Fairley AM, Oster ME, Weiß C, Schneider-Lindner V.
Earlier Recognition of Sepsis and Septic Shock With Sepsis-3 Criteria- It's Still Early Days!
Shock. 2020 Jun;53(6):779-780.
Abstract on PubMed

Schamoni S, Lindner HA, Schneider-Lindner V, Thiel M, Riezler S.
Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.
Artif Intell Med. 2019 Sep; 100:101725.
Abstract on PubMed

Lindner HA, Thiel M, Schneider-Lindner V.
Automated dynamic sepsis surveillance with routine data: opportunities and challenges.
Ann Transl Med. 2017 Feb; 5(3):66.
Abstract on PubMed

Lindner HA, Balaban Ü, Sturm T, Weiß C, Thiel M, Schneider-Lindner V. 2016. An Algorithm for Systemic Inflammatory Response Syndrome Criteria-Based Prediction of Sepsis in a Polytrauma Cohort. Crit Care Med. doi: 10.1097/CCM.0000000000001955
Abstract on PubMed

Schneider-Lindner V, Lindner HA, Thiel M. 2016. Definitions for Sepsis and Septic Shock. JAMA. 316(4):457
Abstract on PubMed