An in silico framework for integrating epidemiologic and genetic evidence with health care applications: ventilation-related pneumothorax as a case illustration.
To illustrate an in silico integration of epidemiologic and genetic evidence that is being developed at the Center for Devices and Radiological Health/US Food and Drug Administration as part of regulatory research on postmarket device performance. In addition to using conventional epidemiologic evidence from registries, this innovative approach explores the vast potential of open-access omics databases for identifying genetic evidence pertaining to devices.



A retrospective analysis of Agency for Healthcare Research and Quality (AHRQ)/Healthcare Cost and Utilization Project (HCUPNet) data (2002-2011) was focused on the ventilation-related iatrogenic pneumothorax (Vent-IP) outcome in discharges with mechanical ventilation (MV) and continuous positive airway pressure (CPAP). The derived epidemiologic evidence was analyzed in conjunction with pre-existing genomic data from Gene Expression Omnibus/National Center for Biotechnology Information and other databases.



AHRQ/HCUPNet epidemiologic evidence showed that annual occurrence of Vent-IP did not decrease over a decade. While the Vent-IP risk associated with noninvasive CPAP comprised about 0.5%, the Vent-IP risk due to longer-term MV reached 2%. Along with MV posing an independent risk for Vent-IP, female sex and white race were found to be effect modifiers, resulting in the highest Vent-IP risk among mechanically ventilated white females. The Vent-IP risk was also potentiated by comorbidities associated with spontaneous pneumothorax (SP) and fibrosis. Consistent with the epidemiologic evidence, expression profiling in a number of animal models showed that the expression of several collagens and other SP/fibrosis-related genes was modified by ventilation settings.



Integration of complementary genetic evidence into epidemiologic analysis can lead to a cost- and time-efficient discovery of the risk predictors and markers and thus can facilitate more efficient marker-based evaluation of medical product performance.
Division of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA yelizaveta.torosyan@fda.hhs.gov.
Yelizaveta
Y
Torosyan
Division of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Columbia University Mailman School of Public Health, New York, NY, USA.
Yuzhi
Y
Hu
Division of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Sarah
S
Hoffman
Division of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Qianlai
Q
Luo
Pharmaceutical Outcomes Programme, BC Children's Hospital; Division of Translational Therapeutics, Department of Pediatrics, Child and Family Research Institute, University of British Columbia, Vancouver, Canada.
Bruce
B
Carleton
Division of Epidemiology, Center for Devices and Radiological Health, CDRH/FDA, Silver Spring, MD, USA.
Danica
D
Marinac-Dabic
D003198:Computer Simulation; D004812:Epidemiologic Methods; D005006:Ethnicity; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D010865:Pilot Projects; D011030:Pneumothorax / Q000453:epidemiology / Q000209:etiology / Q000235:genetics*; D012121:Respiration, Artificial / Q000009:adverse effects*; D012189:Retrospective Studies
In silico evidence integration using big data approaches; Medical device safety biomarkers; Repurposing and re-utilization of pre-existing genetic data; Translational Epidemiology