Table 1.

Summary of current evidence in the literature supporting the future application of AI in the setting of Impella support.

Manuscript	Study domain	Key finding
AI‐predictive model of Impella therapy outcomes
Rüschen et al. 2019 14	Pre‐clinical animal model of induced CS and Impella support	The total cardiac output can be readily estimated from the signals provided by the optical pressure sensors of the Impella pump Online, reliable estimation of the total cardiac output can offer immediate and physiologically relevant feedback regarding optimal pump setting, enhancing positive therapeutic outcomes (e.g., pump speed, P‐level, etc.)
Unoki et al. 2022 15	Clinical study on ECPELLA support	Existence of a direct relationship between the motor current of the Impella pump and the degree of LV unloading at every P‐level, also in the case of multidevice support strategies (e.g., ECPELLA) Potential for the development of new algorithms for automated control of Impella operation (i.e., pump speed, P‐level, etc.) based on data recorded by the AIC
https://www.abiomed.com/about‐us/news‐and‐media/press‐releases/fda‐approves‐data‐streaming‐impella‐console‐setting‐stage‐artificial‐intelligence 19	Clinical data retrieved from the Impella console	AI‐based algorithm for estimating future evolution and trends of patients' haemodynamic parameters based on the prior 5 min of Impella console data
AI‐driven Impella digital twin
Jelenc et al. 2022 21	Simulation/computer model	Combining available AIC data with data from computer simulation models can provide predictions of outcomes in Impella‐supported patients
Di Molfetta et al. 2020 22
Donker et al. 2019 23	Simulation/computer model	Real‐time computer simulations can provide quantitative and patient‐specific clinical measures of LV overload The model is sensitive to the degree and type of MCS support (IABP, Impella, VA‐ECMO, IABP + VA‐ECMO vs. left atrial/ventricular venting)
De Lazzari et al. 2023 24
Contarino et al. 2022 25	Simulation/computer model	Computer simulation of the cardiovascular system in patients with CS and Impella support can provide reliable data for clinical decision‐making (e.g., predicting the outcome of different possible therapeutic approaches in a patient‐specific environment)
Pladet et al. 2023 26	Simulation/computer model	Computational simulations provide accurate predictive assessments of MCS‐related risks and benefits, thus improving complex clinical decisions surrounding MCS allocation and management
AI‐driven evaluation of optimal Impella position
Baldetti et al. 2023 27	Clinical study on Impella‐supported patients	The optimal Impella position can be evaluated by integrating data from the AIC with those from multimodal imaging techniques
Prevention/rational management of haemocompatibility‐related adverse events
Van Edom et al. 2023 30	Clinical study on Impella‐supported patients	Development of a rationale algorithm to standardize anticoagulation management in the case of adverse events (haemolysis, bleeding) while on Impella support
Early stratification of patients according to the chance of native heart recovery
Luo et al. 2022 31	Clinical study on patients with HF/review article	Integration of AI and clinical data to classify patients according to predicted different outcomes and guide individualized clinical decisions
Gutman et al. 2022 32
Sardar et al. 2019 33
Manlhiot et al. 2022 34
Kapur 2023 35	Clinical study on patients with CS	Mortality in CS patients can be predicted by AI‐based models

Abbreviations: AI, artificial intelligence; AIC, Automated Impella Controller; CS, cardiogenic shock; ECPELLA, extracorporeal membrane oxygenation + Impella therapy; HF, heart failure; IABP, intra‐aortic balloon pump; LV, left ventricular; VA‐ECMO, veno‐arterial extracorporeal membrane oxygenation.