Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.
Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualizatio...
| Published in: | Journal of Clinical Monitoring & Computing Vol. 33; no. 4; pp. 703 - 712 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
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Springer Nature
Aug2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137276595&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137276595 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Aug2019 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137276595 137276595 NLM30121744 10.1007/s10877-018-0194-4 NLM30121744 137276595 ppf: 703 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. aug: au: Ruminski, Caroline M. Clark, Matthew T. Lake, Douglas E. Kitzmiller, Rebecca R. Keim-Malpass, Jessica Robertson, Matthew P. Simons, Theresa R. Moorman, J. Randall Calland, J. Forrest affil: University of Virginia School of Medicine, P.O. Box 800158, 22908, Charlottesville, VA, USA sug: subj: Monitoring, Physiologic Equipment and Supplies Critical Care Methods Signal Processing, Computer Assisted Intensive Care Units Multivariate Analysis Prospective Studies Aged Male Female Shock, Septic Pathology Relative Risk Retrospective Design Middle Age APACHE (Acute Physiology and Chronic Health Evaluation) Medical Informatics Monitoring, Physiologic Methods Hemorrhage Impact of Events Scale Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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