Title: Incorporating Epidemiologic Context into a Clinical Prediction Model for Pertussis
1Incorporating Epidemiologic Context into a
Clinical Prediction Model for Pertussis
Andrew M. Fine, Ben Y. Reis, Lise Nigrovic, Don
Goldmann, Karen L. Olson, Tracy LaPorte, Kenneth
D. Mandl December 4, 2008 International Society
for Disease Surveillance Raleigh, NC
2Bordetella Pertussis
- Incidence
- Impact
- Difficult to diagnose early
- Delayed test results
- Outbreaks have temporal and geographic
variability
3Research Approach
- Develop model to adjust standard clinical risk
with epidemiological context
Epidemiological Context
Standard Clinical Predictors
Contextualized Risk
4Low risk
No recent cases
1 week of cough
Low to normal risk
5Elevated contextualized risk
Recent of local cases exceeds threshold
1 week of cough
Normal to elevated risk
6Weekly Proportion Positive
Pertussis season
7Pertussis cases in Massachusetts
8Objective
- To improve identification of pertussis cases by
developing a decision model that incorporates
data on recent, local population-level disease
incidence
9Methods
- Retrospective cohort study
- Patient population
- Under 12 months old
- Presented to a single urban tertiary care
pediatric emergency department and were tested
for pertussis by culture - 2003-2007
10Data Sources
- Clinical data
- Hospital electronic medical records
- Demographics, historical features, physical exam
findings, laboratory data, treatment,
disposition, outcome - Public health surveillance data
- State laboratory query of all pertussis culture
results 2003-07 (all ages) - Date sent and test result
11Definitions
- Pertussis positive or negative
- Based on culture results
- PCR, serology, DFA not used in definition
12Clinical Data (443 infants)
- Demographics
- Age
- Gender
- Visit date
- Signs/ symptoms
- Cough duration
- Fever duration
- History of apnea
- History of post-tussive emesis
- History of cyanosis
- History of seizure
- History of pertussis contact
13Surveillance Data
- 19,907 culture results (2003-07)
- Created aggregate variables for state data
- Total tests sent
- Number positives
- Proportion positive
- OVER range of time 1, 2, 3, 4 weeks prior to
each study date - Each date from Jan 1, 2003 Dec 31, 2007 was
assigned aggregate surveillance variables
14Weekly Proportion Positive
Pertussis season
15Weekly Proportion Positive
Week of Sept 9. 2005 6/45 0.13
Pertussis season
16Weekly Proportion Positive
Week of Sept 9. 2005 6/45 0.13
Pertussis season
Week of Nov 24, 2006 10/285 0.035
17Weekly Proportion Positive
Week of Sept 9. 2005 6/45 0.13
Pertussis season
Week of Nov 24, 2006 10/285 0.035
Week of Jan 5, 2007 6/188 0.032
18Two Decision Models
- Clinical model
- -- Demographics and clinical data
- Contextualized model
- -- All clinical and PH surveillance variables
19Statistical Methods
- All 443 infants were used to derive models with
bootstrap validation - Univariate analysis to determine variables
significantly associated with pertussis - Multivariate logistic regression to identify
independent predictors of pertussis
20Performance Metrics
- Sensitivity, Specificity, PPV, NPV, AROC
- Best model defined as greatest specificity among
those with highest sensitivity - Also compared actual performance by clinical
experts from Childrens Hospital Boston emergency
department with the 2 models - Percent correct classification (utilization or
omission of antibiotics)
21PertussisResults
n 443
n 19,908
22Notable ResultsUnivariate analysis
23Logistic RegressionClinical only model
24Logistic RegressionContextualized model
25Performance of Decision Models
p0.04
plt0.001
P0.02
26Misclassification Tables (1)Missed Opportunities
27Misclassification Tables (2)Negative cultures
but treated
28Limitations
- Retrospective
- Single site
- Culture only
- Other potential predictors immunization, cough
descriptors, lymphocytosis
29Discussion
- Incorporating PH surveillance data improved
ability of decision model to correctly classify
patients with and without pertussis - Supports idea of bidirectional exchange between
clinicians and public health - Supports further exploration of methods for
integrating large PH data sets with rich clinical
data sets to improve decision making and PH
30Idealized Scenario
- Jerry, a 4 mo M, presents to the ED with 2 days
of cough and runny nose. Initially, the
clinician thinks Jerry has a common cold. Upon
opening the electronic chart, his doctor is
presented with Jerrys chief complaint and
standard VS, but based on recent epidemiological
trends, he also is presented with a message
there has been an increased incidence of
pertussis in Jerrys county over the past month.
Based on awareness of recent epidemiological
trends in the area, the physician now considers
obtaining pertussis tests, initiating antibiotics
on the patient and close contacts, and reporting
to public health.
31Acknowledgements
- Mentor Ken Mandl
- Funder Centers for Disease Control (K01)
- Childrens Hospital Boston - Ben Reis, John
Brownstein, Lise Nigrovic, Karen Olson, Don
Goldmann - MA DPH - Jim Daniel, Tracy Laporte, Gillian
Haney, Linda Han, Stephanie Schauer, Susan Lett