Jacob S Calvert1, Daniel A Price1, Uli K Chettipally2, Christopher W Barton3, Mitchell D Feldman4, Jana L Hoffman5, Melissa Jay1, Ritankar Das1. 1. Dascena Inc., Hayward, CA, United States. 2. Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, United States; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States. 3. Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, United States. 4. Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, United States. 5. Dascena Inc., Hayward, CA, United States. Electronic address: jana@dascena.com.
Abstract
OBJECTIVE: To develop high-performance early sepsis prediction technology for the general patient population. METHODS: Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission. RESULTS: A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patient sepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance. CONCLUSIONS: Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.
OBJECTIVE: To develop high-performance early sepsis prediction technology for the general patient population. METHODS: Retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC II dataset) who were not septic at the time of admission. RESULTS: A sepsis early warning algorithm, InSight, was developed and applied to the prediction of sepsis up to three hours prior to a patient's first five hour Systemic Inflammatory Response Syndrome (SIRS) episode. When applied to a never-before-seen set of test patients, InSight predictions demonstrated a sensitivity of 0.90 (95% CI: 0.89-0.91) and a specificity of 0.81 (95% CI: 0.80-0.82), exceeding or rivaling that of existing biomarker detection methods. Across predictive times up to three hours before a sustained SIRS event, InSight maintained an average area under the ROC curve of 0.83 (95% CI: 0.80-0.86). Analysis of patientsepsis risk showed that contributions from the coevolution of multiple risk factors were more important than the contributions from isolated individual risk factors when making predictions further in advance. CONCLUSIONS:Sepsis can be predicted at least three hours in advance of onset of the first five hour SIRS episode, using only nine commonly available vital signs, with better performance than methods in standard practice today. High-order correlations of vital sign measurements are key to this prediction, which improves the likelihood of early identification of at-risk patients.
Authors: Jennifer C Ginestra; Heather M Giannini; William D Schweickert; Laurie Meadows; Michael J Lynch; Kimberly Pavan; Corey J Chivers; Michael Draugelis; Patrick J Donnelly; Barry D Fuchs; Craig A Umscheid Journal: Crit Care Med Date: 2019-11 Impact factor: 7.598
Authors: Murad Megjhani; Farhad Kaffashi; Kalijah Terilli; Ayham Alkhachroum; Behnaz Esmaeili; Kevin William Doyle; Santosh Murthy; Angela G Velazquez; E Sander Connolly; David Jinou Roh; Sachin Agarwal; Ken A Loparo; Jan Claassen; Amelia Boehme; Soojin Park Journal: Neurocrit Care Date: 2020-02 Impact factor: 3.210