Introduction to the special issue: papers from the Society for Complex Acute Illness (SCAI)

This is the first special issue of articles contributed by members of the Society for Complex Acute Illness (SCAI, http://www. scai-med.org). Since its founding in 2003, SCAI has focused on bringing acute care clinicians, experimentalists, mathematicians, physicists, and engineers together to tackle refractory problems in critical care. The multidisciplinary society emphasizes quantitative scientific approaches for understanding critical illness and translation of these findings into practice. Over time, the mathematical toolbox discussed by the group has broadened and evolved, from primarily nonlinear timeseries analysis techniques and mechanistic mathematical modeling to include emerging tools such as machine learning methods. SCAI members have also addressed developments relevant to the goals of the society such as innovative applications of acute care electronic health records and novel sensor and imaging technology, prompting the society’s recent name change from ‘‘Society for Complexity in Acute Illness’’ to ‘‘Society for Complex Acute Illness’’. The concept for this special issue was hatched at SCAI’s International Conference on Complexity in Acute Illness (ICCAI, http://www.iccai.org) in September 2011 in Bonn, Germany, the 10th SCAI annual meeting. After an open invitation to SCAI members and past ICCAI contributors to submit relevant manuscripts, 20 contributions were subjected to rigorous peer review, of which 12 were finally accepted for publication in this special issue. The accepted articles reflect the society’s goals and scope, covering a broad range of quantitative methodologies and clinical applications across all of acute care medicine. Advanced timeseries analysis techniques are successfully brought to bear on clinical problems in neurocritical care in the original contributions of Kvandal et al. [1], Park et al. [2], and Soehle et al. [3], using input data available in standard clinical settings. Scheff et al. [4] review applications of similar methodologies in the context of clinical endotoxemia. In another contribution applying timeseries analysis, Dorantes Mendez et al. [5] study the effects of propofol anesthesia induction on baroreflex sensitivity. In Csete and Hunt [6], a pathway towards adapting sensor technology not currently used routinely in acute care for clinical applications is outlined, while Hoog Antink et al. [7] describe and evaluate an algorithm that may help to improve the utility of existing imaging technology in acute care. Advanced applications of electronic medical records in the acute care setting are reviewed by Herasevich et al. [8]. Data driven modeling approaches are well-represented by Guiza et al. [9], in a review of predictive data mining approaches to intensive care data, and the original contribution by Engoren et al. [10], which applies different data driven modeling approaches in predicting perioperative readmission rates and compares their performance. Mechanistic mathematical modeling is applied to physiological processes highly relevant to critical illness by Bagci et al. [11] in an in depth theoretical investigation of possible determinants of interindividual variability in response to proand antiapoptotic interventions. Podziemski and _ Zebrowski [12] present a reduced model of the right atrium with possible applications to further understanding arrhythmias. Finally, the editor would like to thank the numerous anonymous reviewers, without whose dedication and expertise this special issue would not have been possible. S. Zenker (&) Applied Mathematical Physiology (AMP) Laboratory, Department of Anesthesiology and Intensive Care Medicine, Universitatsklinikum Bonn, University of Bonn, Bonn, Germany e-mail: Sven.Zenker@ukb.uni-bonn.de