tetano
Editor, Senior Moderator
Emerg Health Threats J. 2011 Apr 20;4:7108. doi: 10.3402/ehtj.v4i0.7108.
Automated detection of influenza-like illness using clinical surveillance markers at a Department of Veterans Affairs Medical Center.
Park LP, Rao S, Nabity SA, Abbott D, Frederick J, Woods CW.
Source
Department of Medicine, Duke University School of Medicine, Durham, NC, USA; Larry.Park@duke.edu.
Abstract
Background: Using demographic and clinical measures from emergency department evaluations, we developed an automated surveillance system for influenza-like illness (ILI). Methods: We selected a random sample of patients who were seen at the Durham, NC Veterans Affairs Medical Center between May 2002 and October 2009 with fever or a respiratory ICD-9 diagnosis code and divided this into subsets for system development and validation. Comprehensive chart reviews identified patients who met a standard case definition for ILI. Logistic regression models predicting ILI were fit in the development sample. We applied the parameter estimates from these models to the validation sample and evaluated their utility using receiver-operator characteristic analysis. Results: The models discriminated ILI very well in the validation sample; the C-statistics were >0.89. Conclusions: Risk estimates based on statistical models can be incorporated into electronic medical records systems to assist clinicians and could be used in real-time surveillance for disease outbreaks.
KEYWORDS:
influenza-like illness, population surveillance, predictive modeling
PMID:
24149026
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24149026
Automated detection of influenza-like illness using clinical surveillance markers at a Department of Veterans Affairs Medical Center.
Park LP, Rao S, Nabity SA, Abbott D, Frederick J, Woods CW.
Source
Department of Medicine, Duke University School of Medicine, Durham, NC, USA; Larry.Park@duke.edu.
Abstract
Background: Using demographic and clinical measures from emergency department evaluations, we developed an automated surveillance system for influenza-like illness (ILI). Methods: We selected a random sample of patients who were seen at the Durham, NC Veterans Affairs Medical Center between May 2002 and October 2009 with fever or a respiratory ICD-9 diagnosis code and divided this into subsets for system development and validation. Comprehensive chart reviews identified patients who met a standard case definition for ILI. Logistic regression models predicting ILI were fit in the development sample. We applied the parameter estimates from these models to the validation sample and evaluated their utility using receiver-operator characteristic analysis. Results: The models discriminated ILI very well in the validation sample; the C-statistics were >0.89. Conclusions: Risk estimates based on statistical models can be incorporated into electronic medical records systems to assist clinicians and could be used in real-time surveillance for disease outbreaks.
KEYWORDS:
influenza-like illness, population surveillance, predictive modeling
PMID:
24149026
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/24149026