OBJECTIVES: 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 CI51.09-2.44) compared with other ED patients sufficiently ill to require admission. Follow-up confirmation by a study coordinator increased the risk to 3.06 (95% CI52.11-4.44). The sensitivity of the strategy overall wasmodest (14%), but patients identified were likely to have an infectious diagnosis (specificity 598%). 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.
OBJECTIVES: 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 CI51.09-2.44) compared with other ED patients sufficiently ill to require admission. Follow-up confirmation by a study coordinator increased the risk to 3.06 (95% CI52.11-4.44). The sensitivity of the strategy overall wasmodest (14%), but patients identified were likely to have an infectious diagnosis (specificity 598%). 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.
Authors: Joshua Rolnick; N Lance Downing; John Shepard; Weihan Chu; Julia Tam; Alexander Wessels; Ron Li; Brian Dietrich; Michael Rudy; Leon Castaneda; Lisa Shieh Journal: Appl Clin Inform Date: 2016-06-22 Impact factor: 2.342
Authors: Devida Long; Muge Capan; Susan Mascioli; Danielle Weldon; Ryan Arnold; Kristen Miller Journal: Crit Care Nurse Date: 2018-08 Impact factor: 1.708
Authors: Zhongheng Zhang; Nathan J Smischney; Haibo Zhang; Sven Van Poucke; Panagiotis Tsirigotis; Jordi Rello; Patrick M Honore; Win Sen Kuan; Juliet June Ray; Jiancang Zhou; You Shang; Yuetian Yu; Christian Jung; Chiara Robba; Fabio Silvio Taccone; Pietro Caironi; David Grimaldi; Stefan Hofer; George Dimopoulos; Marc Leone; Sang-Bum Hong; Mabrouk Bahloul; Laurent Argaud; Won Young Kim; Herbert D Spapen; Jose Rodolfo Rocco Journal: J Thorac Dis Date: 2016-09 Impact factor: 2.895
Authors: Laura Schubel; Danielle L Mosby; Joseph Blumenthal; Muge Capan; Ryan Arnold; Rebecca Kowalski; F Jacob Seagull; Ken Catchpole; J Sanford Schwartz; Ella Franklin; Robin Littlejohn; Kristen E Miller Journal: Health Informatics J Date: 2019-05-13 Impact factor: 2.681
Authors: Michael M Liao; Dennis Lezotte; Steven R Lowenstein; Kevin Howard; Zachary Finley; Zipei Feng; Richard L Byyny; Jeffrey D Sankoff; Ivor S Douglas; Jason S Haukoos Journal: Am J Emerg Med Date: 2014-08-08 Impact factor: 2.469