Document Detail


Linking surveillance to action: incorporation of real-time regional data into a medical decision rule.
MedLine Citation:
PMID:  17213492     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: Broadly, to create a bidirectional communication link between public health surveillance and clinical practice. Specifically, to measure the impact of integrating public health surveillance data into an existing clinical prediction rule. We incorporate data about recent local trends in meningitis epidemiology into a prediction model differentiating aseptic from bacterial meningitis.
DESIGN AND MEASUREMENTS: Retrospective analysis of a cohort of all 696 children with meningitis admitted to a large urban pediatric hospital from 1992 to 2000. We modified a published bacterial meningitis score by adding a new epidemiological context adjustor variable. We examined 540 possible rules for this adjustor, varying both the number of aseptic meningitis cases that needed to be seen, and the recent time window in which they were seen. We performed sensitivity analyses with each of 540 possibilities in order to identify the optimal rule--namely, the one that included the most cases of aseptic meningitis without missing additional cases of bacterial meningitis, as compared with the published prediction model. We used bootstrap methods to validate this new score.
RESULTS: The optimal rule was found to be: "at least four cases of aseptic meningitis in the previous 10 days." The epidemiological context adjustor based on surveillance of recent cases of meningitis allowed the correct identification of an additional 47 cases (7%) of aseptic meningitis without missing any additional cases of bacterial meningitis. The epidemiological context adjustor was validated, showing significance in 84% of 1,000 bootstrap samples.
CONCLUSION: Epidemiological contextual information can improve the performance of a clinical prediction rule. We provide a methodological framework for leveraging regional surveillance data to improve medical decision-making.
Authors:
Andrew M Fine; Lise E Nigrovic; Ben Y Reis; E Francis Cook; Kenneth D Mandl
Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, P.H.S.     Date:  2007-01-09
Journal Detail:
Title:  Journal of the American Medical Informatics Association : JAMIA     Volume:  14     ISSN:  1067-5027     ISO Abbreviation:  J Am Med Inform Assoc     Publication Date:    2007 Mar-Apr
Date Detail:
Created Date:  2007-03-05     Completed Date:  2007-04-19     Revised Date:  2011-12-06    
Medline Journal Info:
Nlm Unique ID:  9430800     Medline TA:  J Am Med Inform Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  206-11     Citation Subset:  IM    
Affiliation:
Division of Emergency Medicine, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA. Andrew.Fine@childrens.harvard.edu
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MeSH Terms
Descriptor/Qualifier:
Child
Cohort Studies
Computer Systems
Decision Making
Decision Support Techniques*
Diagnosis, Differential
Epidemiologic Methods
Humans
Logistic Models
Meningitis, Aseptic / diagnosis*,  epidemiology
Meningitis, Bacterial / diagnosis*,  epidemiology
Population Surveillance*
Retrospective Studies
Sensitivity and Specificity
Grant Support
ID/Acronym/Agency:
1 R01 LM007677-01/LM/NLM NIH HHS; P01 CD000260-01/CD/CDC HHS; T32 HD40128-01/HD/NICHD NIH HHS
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