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Use of In vivo Imaging to Monitor the Progression of Experimental Mouse Cytomegalovirus Infection in Neonates
Published on: July 6, 2013
Development, Testing, and Calibration of LINCS: A New Microsimulation Model of Maternal and Fetal Cytomegalovirus
Aaron S Wu1, Elif Coskun1, Malavika Prabhu2,3
1Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA.
Background. Congenital cytomegalovirus (cCMV) is a leading cause of birth defects and the most common cause of nongenetic sensorineural hearing loss in children. There is a lack of decision-modeling frameworks that can project cytomegalovirus (CMV)-related patient outcomes and inform health policy. We created, tested, and calibrated a model of CMV acquisition and transmission in pregnancy using linked mother-infant dyads. Methods. We developed the Linking INfants and Mothers in Cytomegalovirus Simulation (LINCS) dyad-level Monte Carlo microsimulation model of CMV infection among pregnant people and fetuses throughout pregnancy. We parameterized the model with data from North America and Europe from the existing literature, implemented rigorous code-testing procedures, and calibrated a key set of parameters to match the model output to external data on cCMV prevalence and symptom risk. Results. A fully parameterized model for CMV among pregnant people and fetuses was developed, and the model code was confirmed to perform as specified. The calibration procedure identified parameter sets that generated model output closely matching the target values from the available data on cCMV prevalence and symptom risk. Conclusions. The LINCS model's ability to simulate the natural history of CMV infection during pregnancy was described and demonstrated, and the model was tested and calibrated to ensure proper functioning. Base-case parameters were derived for CMV infection natural history to be used in future decision analyses of CMV testing and treatment strategies.
Background. Congenital cytomegalovirus (cCMV) is a leading cause of birth defects and the most common cause of nongenetic sensorineural hearing loss in children. There is a lack of decision-modeling frameworks that can project cytomegalovirus (CMV)-related patient outcomes and inform health policy. We created, tested, and calibrated a model of CMV acquisition and transmission in pregnancy using linked mother-infant dyads. Methods. We developed the Linking INfants and Mothers in Cytomegalovirus Simulation (LINCS) dyad-level Monte Carlo microsimulation model of CMV infection among pregnant people and fetuses throughout pregnancy. We parameterized the model with data from North America and Europe from the existing literature, implemented rigorous code-testing procedures, and calibrated a key set of parameters to match the model output to external data on cCMV prevalence and symptom risk. Results. A fully parameterized model for CMV among pregnant people and fetuses was developed, and the model code was confirmed to perform as specified. The calibration procedure identified parameter sets that generated model output closely matching the target values from the available data on cCMV prevalence and symptom risk. Conclusions. The LINCS model's ability to simulate the natural history of CMV infection during pregnancy was described and demonstrated, and the model was tested and calibrated to ensure proper functioning. Base-case parameters were derived for CMV infection natural history to be used in future decision analyses of CMV testing and treatment strategies.
Highlights:
Congenital cytomegalovirus (cCMV) is a leading cause of birth defects, but there are limited empirical data available and conflicting guidelines around the best approach to prevention, screening, and treatment. The Linking INfants and Mothers in Cytomegalovirus Simulation (LINCS) model is a comprehensive decision analytic model for cCMV, capturing the full dynamics of the incidence and transmission of CMV among both pregnant people and infants using a microsimulation model centered around the mother-infant dyad.Despite limited data around cCMV, key parameters in the LINCS model were successfully calibrated to available estimates of cCMV prevalence and symptom risk.The LINCS model can be used to project outcomes under available strategies to prevent, screen for, and treat cCMV, providing a robust framework to conduct decision analyses, including cost-effectiveness and comparative effectiveness analyses, and inform better policy and clinical guidelines to reduce the impact of cCMV.
