Predictive Modeling of Left Ventricular Assist Device Complications
Collectively, the University Medical Center Utrecht, Utrecht University, and Abbott will develop a novel prediction model of LVAD (mechanical heart pump) clinical outcomes based on unique state-of-the-art technology, clinical input, and data mining.
LVAD therapy has greatly improved the survival of patients with end-stage heart failure. However, major adverse events are common (approximately 20% of the patients) and often occur suddenly and unpredictably. It is technically possible to retrieve a wealth of data from the LVADs itself, as well as from patients on LVAD support, but these data are not routinely retrieved because appropriate analysis models are lacking and thus the clinical value of these data are unknown. A prediction model is required that can combine input from multiple sources, handle repeated measurements and missing data.
By exploiting existing big data from the device, itself and the patient, a prediction model of clinical outcomes will be developed. Data in a custom-made database will be retrieved and combined, data analysis and machine learning will be applied, and finally replicated in alternative sample datasets.
Within the short-term objective a novel prediction model will be developed based on a unique approach using state-of-the-art technology, clinical input, and data mining (enabling technologies); thus delivering an innovative product for health care. On the longer term, the use and comprehension of “big data” from LVADs and patients on LVAD support will lead to better outcomes for LVAD patients, by optimising settings and early detection of complications. In addition, this knowledge may advance the development of the next generation LVADs.