Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study
Name
Reis-2009-Longitudinal histories as predictors of future diagnoses of domesti
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294.15 KB
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Author(s)
Kohane, Isaac
Mandl, Kenneth D.
Reis, Ben Y.
Date Issued
May 2009
Journal
BMJ: British medical journal
Publisher
British Medical Association
Citation
Reis, Ben Y., Isaac S. Kohane and Kenneth D. Mandl. "Longitudinal histories as predictors of future diagnoses of
domestic abuse: modelling study." BMJ 2009;339:b3677. © 2009 British Medical Association.
Version
Final published version
Abstract
Objective To determine whether longitudinal data in
patients’ historical records, commonly available in
electronic health record systems, can be used to predict a
patient’s future risk of receiving a diagnosis of domestic
abuse.
Design Bayesian models, known as intelligent histories,
used to predict a patient’s risk of receiving a future
diagnosis of abuse, based on the patient’s diagnostic
history. Retrospective evaluation of the model’s
predictions using an independent testing set.
Setting A state-wide claims database covering six years of
inpatient admissions to hospital, admissions for
observation, and encounters in emergency departments.
Population All patients aged over 18 who had at least four
years between their earliest and latest visits recorded in
the database (561 216 patients).
Main outcome measures Timeliness of detection,
sensitivity, specificity, positive predictive values, and
area under the ROC curve.
Results 1.04% (5829) of the patients met the narrow case
definition for abuse, while 3.44% (19 303) met the
broader case definition for abuse. The model achieved
sensitive, specific (area under the ROC curve of 0.88), and
early (10-30 months in advance, on average) prediction of
patients’ future risk of receiving a diagnosis of abuse.
Analysis of model parameters showed important
differences between sexes in the risks associated with
certain diagnoses.
Conclusions Commonly available longitudinal diagnostic
data can be useful for predicting a patient’s future risk of
receiving a diagnosis of abuse. This modelling approach
could serve as the basis for an early warning system to
help doctors identify high risk patients for further
screening.
Description
http://www.bmj.com/content/339/bmj.b3677
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
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