Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Linear Risk Prediction Models and Their Limitations for Durable MCS
3.2. AI Models for Durable MCS and Their Limitations
3.3. Linear Risk Prediction Models for HT
3.4. AI Models of HT
(A) | |||||
Study (Authors and Reference #) | Study Type | Study Method | Subjects | Duration (Months) | Conclusion of Study |
Cowger et al., 2013 [38] | Multicenter, prospective | Logistic regression, HMII Risk Score | 1122 patients enrolled into HMII bridge to transplantation and destination therapy trials | 3-month mortality | Stratifies mortality risk in HMII candidates; AUC 0.71, 95% CI: 0.66–0.75. |
Atluri et al., 2013 [35] | Retrospective, single center | Multivariable logistic regression, CRITT score | 218 patients who underwent VAD implant: LVAD = 167, BIVAD = 51 | Patients between 2003 and 2011 | 5-variable risk stratification score to determine suitability for uni- or bi- ventricular support; NPV 93%, AUC: 0.80 ± 0.04. |
Fitzpatrick et al., 2008 [18] | Retrospective, single center | Logistic regression, PENN score | 266 LVAD recipients | Patients between 1995 and 2007 | Most significant predictors for RVAD need were creatinine level, prior cardiac surgery, systolic blood pressure, stroke work index, severe pre-operative RV dysfunction; showed >80% sensitivity and specificity. |
(B) | |||||
Study (Authors and Reference #) | Study Type | Study Method | Subjects | Duration (Months) | Conclusion of Study |
Kilic et al., 2021 [2] | Retrospective, multicenter | Extreme gradient: XG Boost and logistic regression | Adults aged 19 years or older undergoing primary durable LVAD implantation as part of the INTERMACS database (16,120) | 3 and 12 months | ML was associated with a statistically significant improvement in discriminatory performance for both 90-day and 1-year mortality; ML can be used independently and as an adjunct to logistic regression. |
Kanwar et al., 2018 [42] | Retrospective, multicenter | Bayesian models, Cardiac Outcomes Risk Assessment (CORA) | Adults over 18 who received an initial primary continuous flow LVAD or LVAD and right ventricular assist device (RVAD) in combination (10,277) | 1, 3, and 12 months | Accuracy of all Bayesian models was between 76% and 87%, with an area under the receiver operative characteristics curve between 0.70 and 0.71. |
Shad et al., 2021 [4] | Retrospective, 3 contributing centers | Three-dimensional convolutional neural network, built using the Keras framework with a TensorFlow 2.1 backend and Python | 18 years or older with at least one pre-operative transthoracic echocardiogram undergoing LVAD placement (941) | Implant to MCS-ARC definition of post-operative RV failure | A video AI system trained to predict post-operative RVF in the setting of MCS can outperform human experts on the same task evaluation (AUC 0.729). |
Loghmanpour et al., 2016 [5] | Retrospective, multicenter | Bayesian models, CORA models | Continuous flow LVAD as the primary implant and age ≥18 years (10,909) | Acute (<48 h), early (48 h to 14 days), and late (>14 days) | Three separate Bayesian models for acute, early, and late RVF substantially outperformed the existing linear risk scores in their ability to predict the risk of RV failure. |
Loghmanpour et al., 2015 [41] | Retrospective, multicenter | Bayesian models, CORA models | Continuous flow LVAD patients over 19 years (8050) | 1, 3, 6, 12, and 24 months | Bayesian models predicting mortality at 5 time points out performed HeartMate II Risk Score (HMRS); preimplant interventions, ECMO, and ventilators were major risk factors. |
Misumi et al., 2019 [47] | Retrospective, single center | ML | Acoustic spectra from 4 patients with HeartMate II CF-LVAD who developed CVA during 1-year follow -up; 81 sound signals from 4 patients | 12 months | ML model predicted cerebrovascular accident in patients with a VAD using acoustic spectra with AUC 0.98, F-measure 0.89. |
Misumi et al., 2021 [48] | Prospective, single center | ML | Acoustic spectra from 13 adults with Jarvik2000 LVAD; 245 spectra from 13 patients | 24 months | ML trained on acoustic spectra offers a novel modality for prediction of aortic regurgitation in LVAD patients. |
Study (Authors and Reference #) | Study Type | Study Method | Subjects | Duration (Months) | Conclusion of Study |
---|---|---|---|---|---|
Hong et al., 2011 [53] | Multicenter (UNOS), retrospective; | Multivariable logistic regression | 11,703 | 12-month graft failure | The risk stratification score (RSS) model found that pre-transplant recipient variables influence early and late graft failure; the strongest negative predictors of 1-year graft failure were RVAD only, ECMO, renal failure, LVAD, total artificial heart, and advanced age; the 1-year survival for the low risk, intermediate risk, moderate risk, elevated risk, and high-risk groups were 93.8, 89.2, 81.3, 67, and 47%, respectively. |
Aaronson et al., 1997 [51] | Single center, prognostic | Multivariable proportional hazard survival models | 286 patients with advanced heart failure | 12-month survival | Determined 1-year survival in low-risk (93, 88%), medium-risk (72, 60%), and high-risk (43, 35%) patients; Medium- and high-risk patients are likely to die or require transplantation within one year; transplantation can be deferred in the low-risk group. |
Weiss et al., 2012 [54] | Multicenter (UNOS), retrospective | Multivariate logistic regression | 22,252 | 12-month survival | The donor risk index (DRI) model is a 15-point scoring system incorporating ischemic time, donor age, race mismatching, and BUN/creatinine ratio; each point increases the risk of 1-year death by 9%; it also predicted the 30-day mortality (OR = 0.11 [1.08 to 0.14], p < 0.001). |
Weiss et al., 2011 [52] | Multicenter, prospective | Multivariable logistic regression | 21,378 | 12-month mortality post-transplant | A 50-point IMPACT scoring system incorporated 12 recipient-specific variables to accurately predict the mortality, with a c-statistic of 0.65. |
Segovia et al., 2011 [24] | Single center, prospective | Multivariate stepwise logistic regression model | 621 | Post-transplant | 6 multivariate risk factors of PGF (RA pressure >10 mmHg, recipient age >60, diabetes, inotrope dependence, donor age >30 years, and ischemic time >40 min: RADIAL); rates of actual and predicted PCG incidence showed good correlation (c-statistic 0.74). |
4. Discussion
4.1. Superiority of AI and ML Predictive Models
4.2. Future Directions for AI Risk Prediction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Agasthi, P.; Buras, M.R.; Smith, S.D.; Golafshar, M.A.; Mookadam, F.; Anand, S.; Rosenthal, J.L.; Hardaway, B.W.; DeValeria, P.; Arsanjani, R. Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant. Gen. Thorac. Cardiovasc. Surg. 2020, 68, 1369–1376. [Google Scholar] [CrossRef]
- Kilic, A.; Dochtermann, D.; Padman, R.; Miller, J.K.; Dubrawski, A. Using machine learning to improve risk prediction in durable left ventricular assist devices. PLoS ONE 2021, 16, e0247866. [Google Scholar] [CrossRef] [PubMed]
- Medved, D.; Ohlsson, M.; Höglund, P.; Andersson, B.; Nugues, P.; Nilsson, J. Improving prediction of heart transplantation outcome using deep learning techniques. Sci. Rep. 2018, 8, 3613. [Google Scholar] [CrossRef]
- Shad, R.; Quach, N.; Fong, R.; Kasinpila, P.; Bowles, C.; Castro, M.; Guha, A.; Suarez, E.E.; Jovinge, S.; Lee, S.; et al. Predicting post-operative right ventricular failure using video-based deep learning. Nat. Commun. 2021, 12, 5192. [Google Scholar] [CrossRef] [PubMed]
- Loghmanpour, N.A.; Kormos, R.L.; Kanwar, M.K. A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy. JACC Heart Fail. 2016, 4, 711–721. [Google Scholar] [CrossRef]
- Kanwar, M.K.; Kilic, A.; Mehra, M.R. Machine learning, artificial intelligence, and mechanical circulatory support: A primer for clinicians. J. Heart Lung Transplant. 2021, 40, 414–425. [Google Scholar] [CrossRef]
- Kelly, C.J.; Karthikesalingam, A.; Suleyman, M.; Corrado, G.; King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019, 17, 195. [Google Scholar] [CrossRef] [PubMed]
- Averbuch, T.; Sullivan, K.; Sauer, A.; Mamas, M.A.; Voors, A.A.; Gale, C.P.; Metra, M.; Ravindra, N.; Van Spall, H.G.C. Applications of artificial intelligence and machine learning in heart failure. Eur. Heart J. Digit. Health 2022, 3, 311–322. [Google Scholar] [CrossRef] [PubMed]
- Frontera, J.A.; Starling, R.; Cho, S.M.; Nowacki, A.S.; Uchino, K.; Hussain, M.S.; Mountis, M.; Moazami, N. Risk factors, mortality, and timing of ischemic and hemorrhagic stroke with left ventricular assist devices. J. Heart Lung Transplant. 2017, 36, 673–683. [Google Scholar] [CrossRef]
- Starling, R.C.; Estep, J.D.; Horstmanshof, D.A.; Milano, C.A.; Stehlik, J.; Shah, K.B.; Bruckner, B.A.; Lee, S.; Long, J.W.; Selzman, C.H.; et al. ROADMAP Study Investigators. Risk Assessment and Comparative Effectiveness of Left Ventricular Assist Device and Medical Management in Ambulatory Heart Failure Patients: The ROADMAP Study 2-Year Results. JACC Heart Fail. 2017, 5, 518–527. [Google Scholar] [CrossRef]
- Rogers, J.G.; Pagani, F.D.; Tatooles, A.J.; Bhat, G.; Slaughter, M.S.; Birks, E.J.; Boyce, S.W.; Najjar, S.S.; Jeevanandam, V.; Anderson, A.S.; et al. Intrapericardial Left Ventricular Assist Device for Advanced Heart Failure. N. Engl. J. Med. 2017, 376, 451–460. [Google Scholar] [CrossRef] [PubMed]
- Starling, R.C.; Moazami, N.; Silvestry, S.C.; Ewald, G.; Rogers, J.G.; Milano, C.A.; Rame, J.E.; Acker, M.A.; Blackstone, E.H.; Ehrlinger, J.; et al. Unexpected Abrupt Increase in Left Ventricular Assist Device Thrombosis. N. Engl. J. Med. 2014, 370, 33–40. [Google Scholar] [CrossRef] [PubMed]
- Nair, N.; Yang, S.; Gongora, E. Impact of mechanical circulatory support on post-transplant stroke risk. Int. J. Artif. Organs 2021, 44, 675–680. [Google Scholar] [CrossRef] [PubMed]
- Kirklin, J.K.; Naftel, D.C.; Myers, S.L.; Pagani, F.D.; Colombo, P.C. Quantifying the impact from stroke during support with continuous-flow ventricular assist devices: An STS INTERMACS analysis. J. Heart Lung Transplant. 2020, 39, 782–794. [Google Scholar] [CrossRef] [PubMed]
- Pavlovic, N.M.R.; Randell, T.M.R.; Madeira, T. Risk of left ventricular assist device driveline infection: A systematic literature review. Heart Lung 2019, 48, 90–104. [Google Scholar] [CrossRef] [PubMed]
- Argiriou, M.; Kolokotron, S.M.; Sakellaridis, T.; Argiriou, O.; Charitos, C.; Zarogoulidis, P.; Katsikogiannis, N.; Kougioumtzi, I.; Machairiotis, N.; Tsiouda, T.; et al. Right heart failure post left ventricular assist device implantation. J. Thorac. Dis. 2014, 6 (Suppl. S1), S52–S59. [Google Scholar] [CrossRef] [PubMed]
- Kormos, R.L.; Cowger, J.; Pagani, F.D.; Teuteberg, J.J.; Goldstein, D.J.; Jacobs, J.P.; Higgins, R.S.; Stevenson, L.W.; Stehlik, J.; Atluri, P.; et al. The Society of Thoracic Surgeons Intermacs database annual report: Evolving indications, outcomes, and scientific partnerships. J. Heart Lung Transplant. 2019, 38, 114–126. [Google Scholar] [CrossRef] [PubMed]
- Fitzpatrick, J.R., III; Frederick, J.; Hsu, V.; Kozin, E.D.; O’Hara, M.L.; Howell, E.; Dougherty, D.; McCormick, R.C.; Laporte, C.A.; Cohen, J.E.; et al. Risk Score Derived from Pre-operative Data Analysis Predicts the Need for Biventricular Mechanical Circulatory Support. J. Heart Lung Transplant. 2008, 27, 1286–1292. [Google Scholar] [CrossRef]
- Shoskes, A.; Fan, T.H.; Starling, R.C.; Cho, S.M. Neurologic Complications in Patients with Left Ventricular Assist Devices. Can. J. Cardiol. 2023, 39, 210–221. [Google Scholar] [CrossRef]
- Jorde, U.P.; Saeed, O.; Koehl, D.; Morris, A.A.; Wood, K.L.; Meyer, D.M.; Cantor, R.; Jacobs, J.P.; Kirklin, J.K.; Pagani, F.D.; et al. The Society of Thoracic Surgeons Intermacs 2023 Annual Report: Focus on Magnetically Levitated Devices. Ann. Thorac. Surg. 2024, 117, 33–44. [Google Scholar] [CrossRef]
- Foroutan, F.M.; Alba, A.C.M.; Guyatt, G.M.; Duero Posada, J.M.; Hing, N.N.F.; Arseneau, E.; Meade, M.; Hanna, S.; Badiwala, M.; Ross, H.M. Predictors of 1-year mortality in heart transplant recipients: A systematic review and meta-analysis. Heart 2018, 104, 151–160. [Google Scholar] [CrossRef] [PubMed]
- Russo, M.J.; Iribarne, A.; Hong, K.N.; Ramlawi, B.; Chen, J.M.; Takayama, H.; Mancini, D.M.; Naka, Y. Factors associated with primary graft failure after heart transplantation. Transplantation 2010, 90, 444–450. [Google Scholar] [CrossRef] [PubMed]
- Marasco, S.F.; Kras, A.; Schulberg, E.; Vale, M.; Lee, G.A. Impact of warm ischemia time on survival after heart transplantation. Transplant. Proc. 2012, 44, 1385–1389. [Google Scholar] [CrossRef] [PubMed]
- Segovia, J.; Coso, M.D.G.; Barcel, J.M.; Bueno, M.G.; Pavía, P.G.; Burgos, R.; Serrano-Fiz, S.; García-Montero, C.; Castedo, E.; Ugarte, J.; et al. RADIAL: A novel primary graft failure risk score in heart transplantation. J. Heart Lung Transplant. 2011, 30, 644–651. [Google Scholar] [CrossRef] [PubMed]
- D’Alessandro, C.; Golmard, J.L.; Barreda, E.; Laali, M.; Makris, R.; Luyt, C.-E.; Leprince, P.; Pavie, A. Predictive risk factors for primary graft failure requiring temporary extra-corporeal membrane oxygenation support after cardiac transplantation in adults. Eur. J. Cardio-Thorac. Surg. 2011, 40, 962–969. [Google Scholar] [CrossRef] [PubMed]
- D’Ancona, G.; Santise, G.; Falletta, C.; Pirone, F.; Sciacca, S.; Turrisi, M.; Biondo, D.; Pilato, M. Primary graft failure after heart transplantation: The importance of donor pharmacological management. Transplant. Proc. 2010, 42, 710–712. [Google Scholar] [CrossRef]
- Nicoara, A.; Ruffin, D.; Cooter, M.; Patel, C.B.; Thompson, A.; Schroder, J.N.; Daneshmand, M.A.; Hernandez, A.F.; Rogers, J.G.; Podgoreanu, M.V.; et al. Primary graft dysfunction after heart transplantation: Incidence, trends, and associated risk factors. Am. J. Transplant. 2018, 18, 1461–1470. [Google Scholar] [CrossRef]
- Sabatino, M.M.; Vitale, G.M.; Manfredini, V.; Masetti, M.; Borgese, L.; Raffa, G.M.; Loforte, A.; Suarez, S.M.; Falletta, C.; Marinelli, G.; et al. Clinical relevance of the International Society for Heart and Lung Transplantation consensus classification of primary graft dysfunction after heart transplantation: Epidemiology, risk factors, and outcomes. J. Heart Lung Transplant. 2017, 36, 1217–1225. [Google Scholar] [CrossRef]
- Sakusic, A.; Rabinstein, A.A. Neurological Complications in Patients with Heart Transplantation. Semin. Neurol. 2021, 41, 447–452. [Google Scholar] [CrossRef]
- Acampa, M.; Lazzerini, P.E.; Guideri, F.; Tassi, R.; Martini, G. Ischemic Stroke after Heart Transplantation. J. Stroke 2016, 18, 157–168. [Google Scholar] [CrossRef]
- Coutance, G.; Kransdorf, E.; Aubert, O.; Bonnet, G.; Yoo, D.; Rouvier, P.; Van Huyen, J.-P.D.; Bruneval, P.; Taupin, J.-L.; Leprince, P.; et al. Clinical Prediction Model for Antibody-Mediated Rejection: A Strategy to Minimize Surveillance Endomyocardial Biopsies after Heart Transplantation. Circ. Heart Fail. 2022, 15, E009923. [Google Scholar] [CrossRef] [PubMed]
- Michaels, P.M.; Espejo, M.B.; Kobashigawa, J.M.; Alejos, J.C.; Burch, C.; Takemoto, S.; Reed, E.F.; Fishbein, M.M. Humoral rejection in cardiac transplantation: Risk factors, hemodynamic consequences and relationship to transplant coronary artery disease. J. Heart Lung Transplant. 2003, 22, 58–69. [Google Scholar] [CrossRef]
- Moayedi, Y.; Fan, C.P.S.; Cherikh, W.S.; Stehlik, J.; Teuteberg, J.J.; Ross, H.J.; Khush, K.K. Survival Outcomes After Heart Transplantation: Does Recipient Sex Matter? Circ. Heart Fail. 2019, 12, e006218. [Google Scholar] [CrossRef] [PubMed]
- Mehra, M.R. Contemporary Concepts in Prevention and Treatment of Cardiac Allograft Vasculopathy. Am. J. Transplant. 2006, 6, 1248–1256. [Google Scholar] [CrossRef] [PubMed]
- Atluri, P.; Goldstone, A.B.; Fairman, A.S.; MacArthur, J.W.; Shudo, Y.; Cohen, J.E.; Acker, A.L.; Hiesinger, W.; Howard, J.L.; Acker, M.A.; et al. Predicting right ventricular failure in the modern, continuous flow left ventricular assist device era. Ann. Thorac. Surg. 2013, 96, 857–863. [Google Scholar] [CrossRef] [PubMed]
- Teuteberg, J.J.; Ewald, G.A.; Adamson, R.M.; Lietz, K.; Miller, L.W.; Tatooles, A.J.; Kormos, R.L.; Sundareswaran, K.S.; Farrar, D.J.; Rogers, J.G. Risk assessment for continuous flow left ventricular assist devices: Does the destination therapy risk score work? An analysis of over 1000 patients. J. Am. Coll. Cardiol. 2012, 60, 44–51. [Google Scholar] [CrossRef] [PubMed]
- Lietz, K.; Long, J.W.; Kfoury, A.G.; Slaughter, M.S.; Silver, M.A.; Milano, C.A.; Rogers, J.G.; Naka, Y.; Mancini, D.; Miller, L.W. Outcomes of left ventricular assist device implantation as destination therapy in the post-REMATCH era: Implications for patient selection. Circulation 2007, 116, 497–505. [Google Scholar] [CrossRef] [PubMed]
- Cowger, J.; Sundareswaran, K.; Rogers, J.G.; Park, S.J.; Pagani, F.D.; Bhat, G.; Jaski, B.; Farrar, D.J.; Slaughter, M.S. Predicting survival in patients receiving continuous flow left ventricular assist devices: The Heartmate II risk score. J. Am. Coll. Cardiol. 2013, 61, 313–321. [Google Scholar] [CrossRef] [PubMed]
- Adamo, L.; Nassif, M.; Tibrewala, A.; Novak, E.; Vader, J.; Silvestry, S.C.; Itoh, A.; Ewald, G.A.; Mann, D.L.; LaRue, S.J. The Heartmate Risk Score predicts morbidity and mortality in unselected left ventricular assist device recipients and risk stratifies INTERMACS class 1 patients. JACC Heart Fail. 2015, 3, 283–290. [Google Scholar] [CrossRef]
- Soliman, O.I.I.; Akin, S.; Muslem, R.; Boersma, E.; Manintveld, O.C.; Krabatsch, T.; Gummert, J.F.; de By, T.M.M.H.; Bogers, A.J.J.C.; Zijlstra, F.; et al. Derivation and validation of a novel right-sided heart failure model after implantation of continuous flow left ventricular assist devices. Circulation 2018, 137, 891–906. [Google Scholar] [CrossRef]
- Loghmanpour, N.A.; Kanwar, M.K.; Druzdzel, M.J.; Benza, R.L.; Murali, S.; Antaki, J.F. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO J. 2015, 61, 313–323. [Google Scholar] [CrossRef] [PubMed]
- Kanwar, M.K.; Lohmueller, L.C.; Kormos, R.L.; Teuteberg, J.J.; Rogers, J.G.; Lindenfeld, J.A.; Bailey, S.H.; McIlvennan, C.K.; Benza, R.; Murali, S.; et al. A Bayesian Model to Predict Survival After Left Ventricular Assist Device Implantation. JACC Heart Fail. 2018, 6, 771–779. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.; Simon, M.A.; Bonde, P.; Harris, B.U.; Teuteberg, J.J.; Kormos, R.L.; Antaki, J.F. Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device. J. Heart Lung Transplant. 2012, 31, 140–149. [Google Scholar] [CrossRef] [PubMed]
- Matthews, J.C.; Koelling, T.M.; Pagani, F.D.; Aaronson, K.D. The Right Ventricular Failure Risk Score. A Pre-Operative Tool for Assessing the Risk of Right Ventricular Failure in Left Ventricular Assist Device Candidates. J. Am. Coll. Cardiol. 2008, 51, 2163–2172. [Google Scholar] [CrossRef]
- Kormos, R.L.; Teuteberg, J.J.; Pagani, F.D.; Russell, S.D.; John, R.; Miller, L.W.; Massey, T.; Milano, C.A.; Moazami, N.; Sundareswaran, K.S.; et al. Right ventricular failure in patients with the HeartMate II continuous-flow left ventricular assist device: Incidence, risk factors, and effect on outcomes. J. Thorac. Cardiovasc. Surg. 2010, 139, 1316–1324. [Google Scholar] [CrossRef] [PubMed]
- Frankfurter, C.; Molinero, M.; Vishram-Nielsen, J.K.K.; Foroutan, F.; Mak, S.; Rao, V.; Billia, F.; Orchanian-Cheff, A.; Alba, A.C. Predicting the Risk of Right Ventricular Failure in Patients Undergoing Left Ventricular Assist Device Implantation: A Systematic Review. Circ. Heart Fail. 2020, 13, E006994. [Google Scholar] [CrossRef] [PubMed]
- Misumi, Y.; Asanoi, H.; Sawa, Y. Abstract 10867: Predicting Cerebrovascular Accident in Patients with Implantable Ventricular Assist Device Using Artificial Intelligence Trained on Acoustic Spectra. Circulation 2019, 140, A10867. [Google Scholar]
- Misumi, Y.; Miyagawa, S.; Yoshioka, D.; Kainuma, S.; Kawamura, T.; Kawamura, A.; Maruyama, Y.; Ueno, T.; Toda, K.; Asanoi, H.; et al. Prediction of aortic valve regurgitation after continuous-flow left ventricular assist device implantation using artificial intelligence trained on acoustic spectra. J. Artif. Organs 2021, 24, 164–172. [Google Scholar] [CrossRef] [PubMed]
- Luneberg, N.; Reiss, N.; Feldmann, C.; Van Der Meulen, P.; Van De Steeg, M.; Schmidt, T.; Wendl, R.; Jansen, S. Photographic LVAD Driveline Wound Infection Recognition Using Deep Learning. Stud. Health Technol. Inform. 2019, 260, 192–199. [Google Scholar]
- Taleb, I.; Kyriakopoulos, C.P.; Fong, R.; Ijaz, N.; Demertzis, Z.; Sideris, K.; Wever-Pinzon, O.; Koliopoulou, A.G.; Bonios, M.J.; Shad, R.; et al. Machine Learning Multicenter Risk Model to Predict Right Ventricular Failure After Mechanical Circulatory Support: The STOP-RVF Score. JAMA Cardiol. 2024, 9, 272–282. [Google Scholar] [CrossRef]
- Aaronson, K.D.; Schwartz, J.S.; Chen, T.M.; Wong, K.L.; Goin, J.E.; Mancini, D.M. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation 1997, 95, 2660–2667. [Google Scholar] [CrossRef] [PubMed]
- Weiss, E.S.; Allen, J.G.; Arnaoutakis, G.J.; George, T.J.; Russell, S.D.; Shah, A.S.; Conte, J.V. Creation of a quantitative recipient risk index for mortality prediction after cardiac transplantation (IMPACT). Ann. Thorac. Surg. 2011, 92, 914–922. [Google Scholar] [CrossRef] [PubMed]
- Hong, K.N.; Iribarne, A.; Worku, B.; Takayama, H.; Gelijns, A.C.; Naka, Y.; Jeevanandam, V.; Russo, M.J. Who is the high-risk recipient? Predicting mortality after heart transplant using pretransplant donor and recipient risk factors. Ann. Thorac. Surg. 2011, 92, 520–527. [Google Scholar] [CrossRef] [PubMed]
- Weiss, E.; Allen, J.; Kilic, A.; Russell, S.D.; Baumgartner, W.; Conte, J.; Shah, A. Development of a quantitative donor risk index to predict short-term mortality in orthotopic heart transplantation. J. Heart Lung Transplant. 2012, 31, 266–273. [Google Scholar] [CrossRef] [PubMed]
- Naruka, V.; Arjomandi Rad, A.; Subbiah Ponniah, H.; Francis, J.; Vardanyan, R.; Tasoudis, P.; Magouliotis, D.E.; Lazopoulos, G.L.; Salmasi, M.Y.; Athanasiou, T. Machine learning and artificial intelligence in cardiac transplantation: A systematic review. Artif. Organs 2022, 46, 1741–1753. [Google Scholar] [CrossRef] [PubMed]
- Yoon, J.; Zame, W.R.; Banerjee, A.; Cadeiras, M.; Alaa, A.M.; van der Schaar, M. Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation. PLoS ONE 2018, 13, e0194985. [Google Scholar] [CrossRef] [PubMed]
- Miller, R.; Tumin, D.; Cooper, J.; Hayes, D.; Tobias, J.D. Prediction of mortality following pediatric heart transplant using machine learning algorithms. Pediatr. Transplant. 2019, 23, e13360. [Google Scholar] [CrossRef]
- Miller, P.E.; Pawar, S.; Vaccaro, B.; McCullough, M.; Rao, P.; Ghosh, R.; Warier, P.; Desai, N.; Ahmad, T. Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights from the UNOS Database. J. Card. Fail. 2019, 25, 479–483. [Google Scholar] [CrossRef]
- Kampaktsis, P.; Tzani, A.; Doulamis, I.; Moustakidis, S.; Drosou, A.; Diakos, N.; Drakos, S.G.; Briasoulis, A. State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database. Clin. Transplant. 2021, 35, e14388. [Google Scholar] [CrossRef]
- Ahady Dolatsara, H.; Chen, Y.J.; Evans, C.; Gupta, A.; Megahed, F.M. A two-stage machine learning framework to predict heart transplantation survival probabilities over time with a monotonic probability constraint. Decis. Support Syst. 2020, 137, 113363. [Google Scholar] [CrossRef]
- Ayers, B.; Sandholm, T.; Gosev, I.; Prasad, S.; Kilic, A. Using machine learning to improve survival prediction after heart transplantation. J. Card. Surg. 2021, 36, 4113–4120. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Chen, S.; Rao, Z.; Yang, D.; Liu, X.; Dont, N.; Li, F. Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. Int. J. Cardiol. 2021, 339, 21–27. [Google Scholar] [CrossRef] [PubMed]
- Nilsson, J.; Ohlsson, M.; Höglund, P.; Ekmehag, B.; Koul, B.; Andersson, B. The international heart transplant survival algorithm (IHTSA): A new model to improve organ sharing and survival. PLoS ONE 2015, 10, e0118644. [Google Scholar] [CrossRef] [PubMed]
- Medved, D.M.; Nugues, P.M.; Nilsson, J.M. Predicting the outcome for patients in a heart transplantation queue using deep learning. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea, 11–15 July 2017; pp. 74–77. [Google Scholar] [CrossRef]
- Medved, D.; Gugues, P.; Nilsson, J. Simulating the Outcome of Heart Allocation Policies Using Deep Neural Networks. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; pp. 6141–6144. [Google Scholar] [CrossRef]
- Hsich, E.M.; Thuita, L.; McNamara, D.M.; Rogers, J.G.; Valapour, M.; Goldberg, L.R.; Yancy, C.W.; Blackstone, E.H.; Ishwaran, H.; Transplantation of HEarts to MaxImize Survival (THEMIS) Investigators. Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality. Am. J. Transplant. 2019, 19, 2067–2076. [Google Scholar] [CrossRef] [PubMed]
- Foroutan, F.; Alba, A.C.; Stein, M.; Krakovsky, J.; Chien, K.G.W.; Chih, S.; Guyatt, G.; Ross, H. Validation of the International Society for Heart and Lung Transplantation primary graft dysfunction instrument in heart transplantation. J. Heart Lung Transplant. 2019, 38, 260–266. [Google Scholar] [CrossRef] [PubMed]
- Squiers, J.J.; Saracino, G.; Chamogeorgakis, T.; MacHannaford, J.C.; Rafael, A.E.; Gonzalez-Stawinski, G.V.; Hall, S.A.; DiMaio, J.M.; Lima, B. Application of the International Society for Heart and Lung Transplantation (ISHLT) criteria for primary graft dysfunction after cardiac transplantation: Outcomes from a high-volume centre. Eur. J. Cardio-Thorac. Surg. 2017, 51, 263–270. [Google Scholar] [CrossRef]
- Palmieri, V.; Montisci, A.; Vietri, M.T.; Colombo, P.C.; Sala, S.; Maiello, C.; Coscioni, E.; Donatelli, F.; Napoli, C. Artificial intelligence, big data and heart transplantation: Actualities. Int. J. Med. Inform. 2023, 176, 105110. [Google Scholar] [CrossRef]
(A) | |||||
Study (Authors and Date) | Study Type | Study Method | Subjects | Duration (Months) | Conclusion of Study |
Kampaktsis et al., 2021 [59] | Multicenter, retrospective | LR, decision tree, K-nearest neighbor, logistic regression, Adaboost | 18,625 | 12-month mortality post-transplant | Reasonable predictive accuracy of mortality after transplant; highest predictive performance with Adaboost model; AUCs for the prediction of 1-year survival were 0.689, 0.642, 0.649, 0.637, and 0.526 for the Adaboost, logistic regression, decision tree, support vector machine, and K-nearest neighbor models, respectively. |
Zhou et al., 2021 [62] | Single center, retrospective | Artificial neural network, gradient boost machines, Adaboost, random forest, support vector machine, logistic regression | 381 | 12-month mortality post-transplant | Random forest plot performed highest discrimination with largest AUG (0.801) when validated; albumin level, recipient age, and left atrium diameter were the most important prognostic variables. |
Ayers et al., 2021 [61] | Multicenter (UNOS), retrospective | Deep neural network, logistic regression, Adaboost | 33,657 | 12-month mortality post-transplant | Model derived from preoperative variables; final ensemble ML model outperformed traditional models (p < 0.001); AUROC of logistic regression (0.649) vs. random forest (0.691), deep neural network (0.691), Adaboost (0.653), and final ensemble ML (0.764). |
Ahady Dolatsara et al., 2020 [60] | Multicenter (UNOS), retrospective | Logistic regression, XG Boost, linear discriminant analysis, random forest, artificial neural network, classification and regression tree | 103,570 | First, ML was used to predict transplant outcomes for time periods; second, survival probabilities were calibrated over time using isotonic regression. | First stage showed AUC (0.60 and 0.71) for years 1–10; the 10-year AUC of 0.70 is higher than most results; isotonic regression can calibrate survival probabilities for each patient over a 10-year period. |
Agasthi et al., 2020 [1] | Multicenter (ISHLT registry), retrospective | Gradient boost machines | 15,236 | 5-year mortality and graft failure | Length of stay, recipient and donor age, recipient and donor BMI, and ischemic time had the highest prediction of mortality; model used 87 variables to predict mortality and graft failure; AUC for 5-year mortality was 0.717 and 0.716. |
Hsich et al., 2019 [66] | Multicenter (Scientific Registry of Transplant Recipients), retrospective | Random survival forest | 33,069 | NA | Identified strong and weak predictive variables from registry between 1985 and 2015; most predictive variables are currently in the tiered allocation system; new variables identified were eGFR and serum albumin. |
Miller et al., 2019 [57] | Multicenter (UNOS), retrospective | Logistic regression, decision tree, neural networks, random forest, support vector machine | 56,447 | 1-year mortality after transplant | ML did not result in improvements in 1-year prediction compared to traditional models (c-statistic 0.66 for all models); identified predictive variables consistent with prior findings, including age, renal function, liver function tests, hemodynamics, and BMI. |
Miller et al., 2019 [58] | Multicenter (UNOS), retrospective | Artificial neural network, classification and regression tree, random forest | 2802 | 1-, 3-, and 5-year mortality after pediatric transplantation | ML algorithms demonstrated a fair predictive ability but had a poor sensitivity; incomplete and missing registry data limit prediction; AUCs for 1-, 3-, and 5-year mortality were 0.72, 0.61, and 0.60, respectively. |
(B) | |||||
Study Authors and Reference # | Study Type | Study Method | Subjects | Duration (Months) | Conclusions of the Study |
Yoon et al., 2018 [56] | Multicenter (UNOS), retrospective | Trees of predictors | 95,275 | 1-, 3-month, and 10-year mortality | ToP improves survival prediction both post- and pre-transplant and performs better than existing clinical models and other ML methods; AUC for 3 months was 0.660 and best clinical risk score was 0.587; ToPs is practical and adaptable to clinical practice. |
Medved et al., 2018 [65] | Multicenter (UNOS), retrospective | International Heart Transplantation Survival Algorithm (IHTSA), Index for Mortality Prediction After Cardiac Transplantation (IMPACT) | 27,705 | Compares IHTSA and IMPACT models in prediction of short- and long-term mortality after transplant | IHTSA showed better discriminatory power at 1 year and overall survival; IHTSA was more accurate than the IMPACT model; c-index for IHTSA was 0.627 and for IMPACT was 0.608. |
Medved et al., 2018 [3] | Multicenter (UNOS), retrospective | International Heart Transplantation Survival Algorithm (IHTSA) and Lund deep learning transplant algorithm (LuDeLTA) | 49,566 | Predicts status of patients on list and post-transplant survival | The predicted mean survival for allocation according to the wait time was 4300 days, with clinical rules was 4300 days, and using neural networks was 4700 days. |
Medved et al., 2017 [64] | Multicenter (UNOS), retrospective | Artificial neural network, Keras framework | 27,444 | 180, 365, and 720 days after entering heart transplant list (outcome: waiting, transplanted, or dead) | Identified top ten weighted parameters affecting the outcome. |
Nilsson et al., 2015 [63] | Multicenter (ISHLT registry), retrospective | Flexible nonlinear artificial neural network model (IHTSA) | 56,625 | 1-, 5-, and 10-year survival | The IHTSA model can predict short- and long-term morality with a high accuracy (ROC 0.650); recipients matched to donors under 38 years had an additional survival of 2.8 years; model accuracy was excellent (0.6) at 1-, 5-, and 10-year survival. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Grzyb, C.; Du, D.; Nair, N. Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation. J. Clin. Med. 2024, 13, 2076. https://doi.org/10.3390/jcm13072076
Grzyb C, Du D, Nair N. Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation. Journal of Clinical Medicine. 2024; 13(7):2076. https://doi.org/10.3390/jcm13072076
Chicago/Turabian StyleGrzyb, Chloe, Dongping Du, and Nandini Nair. 2024. "Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation" Journal of Clinical Medicine 13, no. 7: 2076. https://doi.org/10.3390/jcm13072076