2016
DOI: 10.1097/ccm.0000000000001738
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Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation*

Abstract: Objective Patients in intensive care units are susceptible to subacute, potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early, subclinical signatures of incipient life-threatening illness. Design We… Show more

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Cited by 83 publications
(136 citation statements)
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“…The methodology employed for the development of all these scoring systems all assume that the data sets utilized sufficiently represent the overall set of behaviors possible, which the current study demonstrates is not the case. In fact, given the “bowtie structure” of biological signaling and regulatory networks [39], dynamic physiology-based prediction systems [34, 40, 41] likely have a greater chance of success in refining individual patient trajectories, but at the cost of not providing insight into the mechanistic drivers that would be potential targets for therapeutic control. Future work with simulation-based characterization of sepsis will likely involve mapping between computationally-generated behavioral landscapes and finer-grained temporal clinical phenotype characterization using advanced physiological metrics to guide discovery of mechanistic determinants of individual patient trajectories.…”
Section: 0 Discussionmentioning
confidence: 99%
“…The methodology employed for the development of all these scoring systems all assume that the data sets utilized sufficiently represent the overall set of behaviors possible, which the current study demonstrates is not the case. In fact, given the “bowtie structure” of biological signaling and regulatory networks [39], dynamic physiology-based prediction systems [34, 40, 41] likely have a greater chance of success in refining individual patient trajectories, but at the cost of not providing insight into the mechanistic drivers that would be potential targets for therapeutic control. Future work with simulation-based characterization of sepsis will likely involve mapping between computationally-generated behavioral landscapes and finer-grained temporal clinical phenotype characterization using advanced physiological metrics to guide discovery of mechanistic determinants of individual patient trajectories.…”
Section: 0 Discussionmentioning
confidence: 99%
“…The use of pediatric clinical scoring systems in clinical care and research has exploded in the last decade. Clinical scoring systems appeal to multiple stakeholders because they are quantitative, can be validated and improve patient outcomes . Pediatric asthma is no exception; as the most common chronic disease of childhood, development of clinical scores and guidelines have helped to streamline and improve pediatric asthma care delivery …”
Section: Introductionmentioning
confidence: 99%
“…A dynamic decision support system that accurately predicts sepsis could focus limited resources, avoid 'alert fatigue' that may accompany decision support systems, and decrease procedural complications for patients. Indeed, work from multiple groups, key among them Moorman and collaborators, has focused on the clinical prognostic value of dynamic assessment of physiologic waveforms (e.g., heart rate variability) in the setting of pediatric sepsis (24,25).…”
Section: Conclusion and Future Perspectivementioning
confidence: 99%