The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support

被引:12
作者
Consolo, Filippo [1 ]
Ursoleo, Jacopo D'Andria [2 ]
Pieri, Marina [1 ,2 ]
Nardelli, Pasquale [2 ]
Cianfanelli, Lorenzo [3 ]
Pazzanese, Vittorio [3 ]
Ajello, Silvia [3 ]
Scandroglio, Anna Mara [2 ]
机构
[1] Univ Vita Salute San Raffaele, Milan, Italy
[2] IRCCS San Raffaele Sci Inst, Dept Anesthesia & Intens Care, Via Olgettina 60, I-20132 Milan, Italy
[3] IRCCS San Raffaele Sci Inst, Dept Cardiol, Milan, Italy
关键词
artificial intelligence; cardiogenic shock; Impella; machine learning; temporary mechanical circulatory support; MANAGEMENT;
D O I
10.1002/ehf2.14865
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aims Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real-time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an 'ideal' source for data-driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI-based tools in the setting of temporary MCS with an Impella device. Methods We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management. Results We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility-related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI-driven insights in the setting of MCS with an Impella device. Conclusions Temporary MCS with an Impella device has great potential to benefit from the integration of AI-based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision-making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.
引用
收藏
页码:2481 / +
页数:977
相关论文
共 35 条
[11]   IABP versus Impella Support in Cardiogenic Shock: "In Silico" Study [J].
De Lazzari, Beatrice ;
Capoccia, Massimo ;
Badagliacca, Roberto ;
Bozkurt, Selim ;
De Lazzari, Claudio .
JOURNAL OF CARDIOVASCULAR DEVELOPMENT AND DISEASE, 2023, 10 (04)
[12]   Left ventricular unloading during extracorporeal membrane oxygenation - Impella versus atrial septal defect: A simulation study [J].
Di Molfetta, Arianna ;
Adachi, Iki ;
Ferrari, Gianfranco ;
Gagliardi, Maria Giulia ;
Perri, Gianluigi ;
Iacobelli, Roberta ;
Qureshi, Athar M. ;
Di Pasquale, Luigi ;
Vera, Rodrigo Zea ;
Guccione, Paolo ;
Di Molfetta, Matteo ;
Chiariello, Giovanni Alfonso ;
Filippelli, Sergio ;
Amodeo, Antonio .
INTERNATIONAL JOURNAL OF ARTIFICIAL ORGANS, 2020, 43 (10) :663-670
[13]  
Dicompyler, about us
[14]   Left Ventricular Unloading During Veno-Arterial ECMO: A Simulation Study [J].
Donker, Dirk W. ;
Brodie, Daniel ;
Henriques, Jose P. S. ;
Broome, Michael .
ASAIO JOURNAL, 2019, 65 (01) :11-20
[15]   The Impella Device: Historical Background, Clinical Applications and Future Directions [J].
Glazier, James J. ;
Kaki, Amir .
INTERNATIONAL JOURNAL OF ANGIOLOGY, 2019, 28 (02) :118-123
[16]   What drives performance in machine learning models for predicting heart failure outcome? [J].
Gutman, Rom ;
Aronson, Doron ;
Caspi, Oren ;
Shalit, Uri .
EUROPEAN HEART JOURNAL - DIGITAL HEALTH, 2023, 4 (03) :175-187
[17]   Safety and feasibility of elective high-risk percutaneous coronary intervention procedures with left ventricular support of the Impella Recover LP 2.5 [J].
Henriques, JPS ;
Remmelink, M ;
Baan, J ;
van der Schaaf, RJ ;
Vis, MM ;
Koch, KT ;
Scholten, EW ;
de Mol, BAJM ;
Tijssen, JGP ;
Piek, JJ ;
de Winter, RJ .
AMERICAN JOURNAL OF CARDIOLOGY, 2006, 97 (07) :990-992
[18]   Left ventricular venting in veno-arterial extracorporeal membrane oxygenation: A computer simulation study [J].
Jelenc, Matija ;
Jelenc, Blaz ;
Novak, Robert ;
Poglajen, Gregor .
INTERNATIONAL JOURNAL OF ARTIFICIAL ORGANS, 2022, 45 (10) :841-848
[19]   Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians [J].
Kanwar, Manreet K. ;
Kilic, Arman ;
Mehra, Mandeep R. .
JOURNAL OF HEART AND LUNG TRANSPLANTATION, 2021, 40 (06) :414-425
[20]  
Kapur N., 2023, MACHINE LEARNING MOR