This task trainer allows for repetitive surgical opening of the median sternotomy and stimulates direct, manual cardiac massage. Open chest cardiac massage (OCCM) is a critical emergency intervention performed after sternotomy when external compressions are inadequate. Although requiring an emergent chest reopening is a rare event, it requires immediate and decisive action. Current training options are limited: cadaver-based practice is costly and non-reusable, while available simulators focus on thoracotomy approaches and fail to replicate sternotomy anatomy. The healthcare simulation market is projected to reach between $3.19 and $7.7 billion by 2027, with a compound annual growth rate of 14.6% to 17.8%.
Researchers at the University of Florida have developed a reusable OCCM task trainer for accurately simulating the human chest cavity and heart. The trainer combines deformable tissues, a cuttable sternum, and fluid-flow simulation, supported by embedded sensors that measure compression characteristics and provide real-time feedback. This technology enables safe, repeatable, and quantitative training, addressing the limitations of cadaver-based methods and inadequate existing models.
Reusable simulator for training medical professionals in open chest cardiac massage following sternotomy, with real-time performance feedback
The OCCM task trainer consists of a lifelike torso model containing a molded chest cavity with a rib cage, lungs, diaphragm, and heart. The sternum is constructed with surgical wire and covered with a multi-layer rejuvenable ballistics gel to mimic human skin and subcutaneous tissue, allowing for repeated surgical incisions and resealing through thermal or chemical manipulation for multiple cycles of practice. The heart component integrates a linear displacement sensor capable of capturing compression rate and depth. The sensor array is linked to onboard analysis circuitry, which processes raw data and transmits real-time performance metrics to a digital display, including compression suitability indicators, cardiac output, and stroke volume. The trainer is designed for integration with external computing devices and cloud-based platforms, supporting data storage, remote assessment, and customizable feedback protocols for scalable medical education environments. Additionally, the real-time capabilities of the technology allow trainees to receive immediate, actionable feedback on their performance.