An in silico framework for integrating epidemiologic and genetic evidence with health care applications: ventilation-related pneumothorax as a case illustration

Abstract

Abstract Objective 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. Material and methods 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. Results 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. Conclusion 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.