Literature DB >> 9613690

Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study.

T G Emori1, J R Edwards, D H Culver, C Sartor, L A Stroud, E E Gaunt, T C Horan, R P Gaynes.   

Abstract

OBJECTIVE: To assess the accuracy of nosocomial infections data reported on patients in the intensive-care unit by nine hospitals participating in the National Nosocomial Infections Surveillance (NNIS) System.
DESIGN: A pilot study was done in two phases to review the charts of selected intensive-care-unit patients who had nosocomial infections reported to the NNIS System. The charts of selected high- and low-risk patients in the same cohort who had no infections reported to the NNIS System also were included. In phase I, trained data collectors reviewed a sample of charts for nosocomial infections. Retrospectively detected infections that matched with previously reported infections were deemed to be true infections. In phase II, two Centers for Disease Control and Prevention (CDC) epidemiologists reexamined a sample of charts for which a discrepancy existed. Each sampled infection either was confirmed or disallowed by the epidemiologists. Confirmed infections also were deemed to be true infections. True infections from both phases were used to estimate the accuracy of reported NNIS data by calculating the predictive value positive, sensitivity, and specificity at each major infection site and the "other sites."
RESULTS: The data collectors examined a total of 1,136 patients' charts in phase I. Among these charts were 611 infections that the study hospitals had reported to the CDC. The data collectors retrospectively matched 474 (78%) of the prospectively identified infections, but also detected 790 infections that were not reported prospectively. Phase II focused on the discrepant infections: the 137 infections that were identified prospectively and reported but not detected retrospectively, and the 790 infections that were detected retrospectively but not reported previously. The CDC epidemiologists examined a sample of 113 of the discrepant reported infections and 369 of the discrepant detected infections, and estimated that 37% of all discrepant reported infections and 43% of all discrepant detected infections were true infections. The predictive value positive for reported bloodstream infections, pneumonia, surgical-site infection, urinary tract infection, and other sites was 87%, 89%, 72%, 92%, and 80%, respectively; the sensitivity was 85%, 68%, 67%, 59%, and 30%, respectively; and the specificity was 98.3%, 97.8%, 97.7%, 98.7%, and 98.6%, respectively.
CONCLUSIONS: When the NNIS hospitals in the study reported a nosocomial infection, the infection most likely was a true infection, and they infrequently reported conditions that were not infections. The hospitals also identified and reported most of the nosocomial infections that occurred in the patients they monitored, but accuracy varied by infection site. Primary bloodstream infection was the most accurately identified and reported site. Measures that will be taken to improve the quality of the infection data reported to the NNIS System include reviewing the criteria for definitions of infections and other data fields, enhancing communication between the CDC and NNIS hospitals, and improving the training of surveillance personnel in NNIS hospitals.

Entities:  

Mesh:

Year:  1998        PMID: 9613690     DOI: 10.1086/647820

Source DB:  PubMed          Journal:  Infect Control Hosp Epidemiol        ISSN: 0899-823X            Impact factor:   3.254


  27 in total

1.  Does using a laparoscopic approach to cholecystectomy decrease the risk of surgical site infection?

Authors:  Chesley Richards; Jonathan Edwards; David Culver; T Grace Emori; James Tolson; Robert Gaynes
Journal:  Ann Surg       Date:  2003-03       Impact factor: 12.969

2.  Adoption of policies to prevent catheter-associated urinary tract infections in United States intensive care units.

Authors:  Laurie J Conway; Monika Pogorzelska; Elaine Larson; Patricia W Stone
Journal:  Am J Infect Control       Date:  2012-02-07       Impact factor: 2.918

3.  Effect of guideline implementation on costs of hand hygiene.

Authors:  Patricia W Stone; Sumya Hasan; Dave Quiros; Elaine L Larson
Journal:  Nurs Econ       Date:  2007 Sep-Oct       Impact factor: 1.085

4.  Dissemination of the CDC's Hand Hygiene Guideline and impact on infection rates.

Authors:  Elaine L Larson; Dave Quiros; Susan X Lin
Journal:  Am J Infect Control       Date:  2007-12       Impact factor: 2.918

5.  Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection.

Authors:  Bala Hota; Michael Lin; Joshua A Doherty; Tara Borlawsky; Keith Woeltje; Kurt Stevenson; Yosef Khan; Jeremy Young; Robert A Weinstein; William Trick
Journal:  J Am Med Inform Assoc       Date:  2010 Jan-Feb       Impact factor: 4.497

6.  An electronic surveillance tool for catheter-associated urinary tract infection in intensive care units.

Authors:  Heather E Hsu; Erica S Shenoy; Douglas Kelbaugh; Winston Ware; Hang Lee; Pearl Zakroysky; David C Hooper; Rochelle P Walensky
Journal:  Am J Infect Control       Date:  2015-03-31       Impact factor: 2.918

7.  Central line-associated infections as defined by the Centers for Medicare and Medicaid Services' Hospital-acquired condition versus standard infection control surveillance: why hospital compare seems conflicted.

Authors:  Rebekah W Moehring; Russell Staheli; Becky A Miller; Luke Francis Chen; Daniel John Sexton; Deverick John Anderson
Journal:  Infect Control Hosp Epidemiol       Date:  2013-01-18       Impact factor: 3.254

8.  An agent-based model for evaluating surveillance methods for catheter-related bloodstream infection.

Authors:  Michael A Rubin; Jeanmarie Mayer; Tom Greene; Brian C Sauer; Bala Hota; William Trick; William E Trick; John A Jernigan; Matthew H Samore
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

9.  Feeding back surveillance data to prevent hospital-acquired infections.

Authors:  R Gaynes; C Richards; J Edwards; T G Emori; T Horan; J Alonso-Echanove; S Fridkin; R Lawton; G Peavy; J Tolson
Journal:  Emerg Infect Dis       Date:  2001 Mar-Apr       Impact factor: 6.883

10.  Using automated health plan data to assess infection risk from coronary artery bypass surgery.

Authors:  Richard Platt; Ken Kleinman; Kristin Thompson; Rachel S Dokholyan; James M Livingston; Andrew Bergman; John H Mason; Teresa C Horan; Robert P Gaynes; Steven L Solomon; Kenneth E Sands
Journal:  Emerg Infect Dis       Date:  2002-12       Impact factor: 6.883

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.