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UNSTRUCTURED Coronary artery calcium (CAC) is a key indicator of atherosclerosis, a condition marked by the accumulation of plaque in the coronary arteries, which is a major risk factor for coronary artery disease (CAD) and cardiovascular events. Detecting CAC is critical for identifying patients at risk of CAD. To enhance the detection of CAC, Nanox. AI has developed HealthCCSng, an AI-based solution that identifies CAC categories in patients undergoing non-cardiac dedicated chest CT exams. This tool highlights patients with moderate to high CAC values, providing secondary capture images that can be seamlessly integrated into radiology reports. A pilot implementation of the Nanox. AI CAC measurement tool was conducted at Einstein Medical Center, part of Jefferson Health. The initiative was a collaborative effort between the radiology and cardiology departments. Radiologists were trained to assess the AI-generated secondary capture images from non-gated, non-contrast chest CT scans. Meanwhile, the cardiology team informed primary care providers (PCPs) in their network about this innovative approach for opportunistically screening patients for CAD. A dedicated nurse was appointed to review the identified cases to ensure they met the eligibility criteria for follow-up, such as no known CAD and having an affiliated PCP. The AI solution was configured to flag patients aged 30 or older with a CAC score above 100 Agatston units (AU), a threshold that most guidelines suggest warrants medical intervention. Among the 757 patients identified by the AI tool, 179 met all the criteria for further evaluation by the cardiology department. Exclusion criteria included patients with known cardiovascular disease (CVD) and those whose PCPs were outside the Jefferson Health network. Out of the 179 patients flagged, notifications were sent to their PCPs through an automated process in the electronic medical record (EMR) system. Consequently, 97 patients returned to Einstein Medical Center for various cardiac-related consultations and procedures. These 97 patients underwent a total of 308 touchpoints with the cardiology department, including 78 echocardiograms, 11 SPECT stress tests, 42 general cardiac-related procedures, and 18 cardiac-specific interventions. This activity generated approximately 130, 000 in revenue for the hospital, with only 46% of flagged patients returning for follow-up care. The revenue is expected to increase as more patients come back to the hospital for further clinical workup and treatment. Overall, the implementation of this AI-driven CAC screening solution demonstrates significant clinical and economic benefits by enabling the early detection and management of CAD in a large patient population.
Nicholas Lim (Tue,) studied this question.