Real-Time Identification of Serious Infection in Geriatric Patients Using Clinical Information System Surveillance
dc.contributor.author | Meurer, William J. | en_US |
dc.contributor.author | Smith, Barbara L. | en_US |
dc.contributor.author | Losman, Eve D. | en_US |
dc.contributor.author | Sherman, Diana | en_US |
dc.contributor.author | Yaksich, Joseph D. | en_US |
dc.contributor.author | Jared, Jeremy D. | en_US |
dc.contributor.author | Malani, Preeti N. | en_US |
dc.contributor.author | Younger, John G. | en_US |
dc.date.accessioned | 2010-04-01T15:33:06Z | |
dc.date.available | 2010-04-01T15:33:06Z | |
dc.date.issued | 2009-01 | en_US |
dc.identifier.citation | Meurer, William J.; Smith, Barbara L.; Losman, Eve D.; Sherman, Diana; Yaksich, RN, Joseph D.; Jared, Jeremy D.; Malani, Preeti N.; Younger, John G. (2009). "Real-Time Identification of Serious Infection in Geriatric Patients Using Clinical Information System Surveillance." Journal of the American Geriatrics Society 57(1): 40-45. <http://hdl.handle.net/2027.42/66036> | en_US |
dc.identifier.issn | 0002-8614 | en_US |
dc.identifier.issn | 1532-5415 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/66036 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19170788&dopt=citation | en_US |
dc.description.abstract | To develop and characterize an automated syndromic surveillance mechanism for early identification of older emergency department (ED) patients with possible life-threatening infection. DESIGN : Prospective, consecutive-enrollment, single-site observational study. SETTING : A large university medical center with an annual ED census of 75,273. PARTICIPANTS : Patients aged 70 and older admitted to the ED and having two or more systemic inflammatory response syndrome (SIRS) criteria during their ED stay. MEASUREMENTS : A search algorithm was developed to screen the census of the ED through its clinical information system. A study coordinator confirmed all patients electronically identified as having a probable infectious explanation for their visit. RESULTS : Infection accounted for 28% of ED and 34% of final hospital diagnoses. Identification using the software tool alone carried a 1.63 relative risk of infection (95% confidence interval CI=1.09–2.44) compared with other ED patients sufficiently ill to require admission. Follow-up confirmation by a study coordinator increased the risk to 3.06 (95% CI=2.11–4.44). The sensitivity of the strategy overall was modest (14%), but patients identified were likely to have an infectious diagnosis (specificity=98%). The most common SIRS criterion triggering the electronic notification was the combination of tachycardia and tachypnea. CONCLUSION : A simple clinical informatics algorithm can detect infection in elderly patients in real time with high specificity. The utility of this tool for research and clinical care may be substantial. | en_US |
dc.format.extent | 114757 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Inc | en_US |
dc.rights | Journal compilation 2009 The American Geriatrics Society/Wiley Periodicals, Inc. | en_US |
dc.subject.other | Sepsis | en_US |
dc.subject.other | Aged | en_US |
dc.subject.other | Sentinel Surveillance | en_US |
dc.subject.other | Informatics | en_US |
dc.title | Real-Time Identification of Serious Infection in Geriatric Patients Using Clinical Information System Surveillance | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Geriatrics | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Infectious Disease, Department of Internal Medicine, Ann Arbor Veterans Affairs Healthcare System, Geriatric Research, Education and Clinical Center, Ann Arbor, Michigan; and | en_US |
dc.contributor.affiliationum | Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, Michigan. | en_US |
dc.contributor.affiliationother | Department of Emergency Medicine and Divisions of | en_US |
dc.contributor.affiliationother | Geriatrics and | en_US |
dc.contributor.affiliationother | Department of Anesthesiology and | en_US |
dc.identifier.pmid | 19170788 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/66036/1/j.1532-5415.2008.02094.x.pdf | |
dc.identifier.doi | 10.1111/j.1532-5415.2008.02094.x | en_US |
dc.identifier.source | Journal of the American Geriatrics Society | en_US |
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dc.identifier.citedreference | Glickman SW, Anstrom KJ, Lin L et al. Challenges in enrollment of minority, pediatric, and geriatric patients in emergency and acute care clinical research. Ann Emerg Med 2008 ; 51 : 775 – 780.e3. | en_US |
dc.identifier.citedreference | Quinn J, Durski K. A real-time tracking, notification, and web-based enrollment system for emergency department research. Acad Emerg Med 2004 ; 11 : 1245 – 1248. | en_US |
dc.identifier.citedreference | Goldhill DR, McNarry AF, Mandersloot G et al. A physiologically-based early warning score for ward patients : The association between score and outcome. Anaesthesia 2005 ; 60 : 547 – 553. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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