Real-time identification of serious infection in geriatric patients using clinical information system surveillance

J Am Geriatr Soc. 2009 Jan;57(1):40-5. doi: 10.1111/j.1532-5415.2008.02094.x.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Emergency Service, Hospital
  • Female
  • Health Services for the Aged
  • Hospital Information Systems*
  • Humans
  • Infections / complications
  • Infections / diagnosis*
  • Male
  • Population Surveillance
  • Prospective Studies
  • Systemic Inflammatory Response Syndrome / etiology*