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Yanagawa R, Iwadoh K, Akabane M, Imaoka Y, Bozhilov KK, Melcher ML, Sasaki K. LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: Creation of a predictive model through large-scale analysis. Clin Transplant 2024; 38:e15316. [PMID: 38607291 DOI: 10.1111/ctr.15316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 03/18/2024] [Accepted: 03/24/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND The incidence of graft failure following liver transplantation (LTx) is consistent. While traditional risk scores for LTx have limited accuracy, the potential of machine learning (ML) in this area remains uncertain, despite its promise in other transplant domains. This study aims to determine ML's predictive limitations in LTx by replicating methods used in previous heart transplant research. METHODS This study utilized the UNOS STAR database, selecting 64,384 adult patients who underwent LTx between 2010 and 2020. Gradient boosting models (XGBoost and LightGBM) were used to predict 14, 30, and 90-day graft failure compared to conventional logistic regression model. Models were evaluated using both shuffled and rolling cross-validation (CV) methodologies. Model performance was assessed using the AUC across validation iterations. RESULTS In a study comparing predictive models for 14-day, 30-day and 90-day graft survival, LightGBM consistently outperformed other models, achieving the highest AUC of.740,.722, and.700 in shuffled CV methods. However, in rolling CV the accuracy of the model declined across every ML algorithm. The analysis revealed influential factors for graft survival prediction across all models, including total bilirubin, medical condition, recipient age, and donor AST, among others. Several features like donor age and recipient diabetes history were important in two out of three models. CONCLUSIONS LightGBM enhances short-term graft survival predictions post-LTx. However, due to changing medical practices and selection criteria, continuous model evaluation is essential. Future studies should focus on temporal variations, clinical implications, and ensure model transparency for broader medical utility.
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Affiliation(s)
| | - Kazuhiro Iwadoh
- Department of Transplant Surgery, Mita Hospital, International University of Health and Welfare, Tokyo, Japan
| | - Miho Akabane
- Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California, USA
| | - Yuki Imaoka
- Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California, USA
- Department of Gastroenterological and Transplant Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kliment Krassimirov Bozhilov
- Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California, USA
| | - Marc L Melcher
- Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California, USA
| | - Kazunari Sasaki
- Division of Abdominal Transplant, Department of Surgery, Stanford University Medical Center, Stanford, California, USA
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Michelson AP, Oh I, Gupta A, Puri V, Kreisel D, Gelman AE, Nava R, Witt CA, Byers DE, Halverson L, Vazquez-Guillamet R, Payne PRO, Hachem RR. Developing machine learning models to predict primary graft dysfunction after lung transplantation. Am J Transplant 2024; 24:458-467. [PMID: 37468109 DOI: 10.1016/j.ajt.2023.07.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/21/2023] [Accepted: 07/04/2023] [Indexed: 07/21/2023]
Abstract
Primary graft dysfunction (PGD) is the leading cause of morbidity and mortality in the first 30 days after lung transplantation. Risk factors for the development of PGD include donor and recipient characteristics, but how multiple variables interact to impact the development of PGD and how clinicians should consider these in making decisions about donor acceptance remain unclear. This was a single-center retrospective cohort study to develop and evaluate machine learning pipelines to predict the development of PGD grade 3 within the first 72 hours of transplantation using donor and recipient variables that are known at the time of donor offer acceptance. Among 576 bilateral lung recipients, 173 (30%) developed PGD grade 3. The cohort underwent a 75% to 25% train-test split, and lasso regression was used to identify 11 variables for model development. A K-nearest neighbor's model showing the best calibration and performance with relatively small confidence intervals was selected as the final predictive model with an area under the receiver operating characteristics curve of 0.65. Machine learning models can predict the risk for development of PGD grade 3 based on data available at the time of donor offer acceptance. This may improve donor-recipient matching and donor utilization in the future.
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Affiliation(s)
- Andrew P Michelson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA; Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA; Division of Biostatistics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Varun Puri
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Daniel Kreisel
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Andrew E Gelman
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Ruben Nava
- Division of Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Chad A Witt
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Derek E Byers
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Laura Halverson
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Rodrigo Vazquez-Guillamet
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Ramsey R Hachem
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA.
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Zhang Y, Aaronson KD, Gryak J, Wittrup E, Minoccheri C, Golbus JR, Najarian K. Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network. PLoS One 2023; 18:e0295016. [PMID: 38015947 PMCID: PMC10684094 DOI: 10.1371/journal.pone.0295016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/13/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization. METHODS Michigan Medicine heart failure patients from 2013-2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance. RESULTS The model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction. CONCLUSION These results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.
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Affiliation(s)
- Yufeng Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Keith D. Aaronson
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jonathan Gryak
- Department of Computer Science, Queens College, City University of New York, New York, New York, United States of America
| | - Emily Wittrup
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Cristian Minoccheri
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jessica R. Golbus
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
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Killian MO, Tian S, Xing A, Hughes D, Gupta D, Wang X, He Z. Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches. JMIR Cardio 2023; 7:e45352. [PMID: 37338974 DOI: 10.2196/45352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/17/2023] [Accepted: 05/10/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND The prediction of posttransplant health outcomes for pediatric heart transplantation is critical for risk stratification and high-quality posttransplant care. OBJECTIVE The purpose of this study was to examine the use of machine learning (ML) models to predict rejection and mortality for pediatric heart transplant recipients. METHODS Various ML models were used to predict rejection and mortality at 1, 3, and 5 years after transplantation in pediatric heart transplant recipients using United Network for Organ Sharing data from 1987 to 2019. The variables used for predicting posttransplant outcomes included donor and recipient as well as medical and social factors. We evaluated 7 ML models-extreme gradient boosting (XGBoost), logistic regression, support vector machine, random forest (RF), stochastic gradient descent, multilayer perceptron, and adaptive boosting (AdaBoost)-as well as a deep learning model with 2 hidden layers with 100 neurons and a rectified linear unit (ReLU) activation function followed by batch normalization for each and a classification head with a softmax activation function. We used 10-fold cross-validation to evaluate model performance. Shapley additive explanations (SHAP) values were calculated to estimate the importance of each variable for prediction. RESULTS RF and AdaBoost models were the best-performing algorithms for different prediction windows across outcomes. RF outperformed other ML algorithms in predicting 5 of the 6 outcomes (area under the receiver operating characteristic curve [AUROC] 0.664 and 0.706 for 1-year and 3-year rejection, respectively, and AUROC 0.697, 0.758, and 0.763 for 1-year, 3-year, and 5-year mortality, respectively). AdaBoost achieved the best performance for prediction of 5-year rejection (AUROC 0.705). CONCLUSIONS This study demonstrates the comparative utility of ML approaches for modeling posttransplant health outcomes using registry data. ML approaches can identify unique risk factors and their complex relationship with outcomes, thereby identifying patients considered to be at risk and informing the transplant community about the potential of these innovative approaches to improve pediatric care after heart transplantation. Future studies are required to translate the information derived from prediction models to optimize counseling, clinical care, and decision-making within pediatric organ transplant centers.
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Affiliation(s)
- Michael O Killian
- College of Social Work, Florida State University, Tallahassee, FL, United States
| | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Aiwen Xing
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Dana Hughes
- College of Social Work, Florida State University, Tallahassee, FL, United States
| | - Dipankar Gupta
- Congenital Heart Center, Shands Children's Hospital, University of Florida, Gainesville, FL, United States
| | - Xiaoyu Wang
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Zhe He
- School of Information, Florida State University, Tallahassee, FL, United States
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Linse B, Ohlsson M, Stehlik J, Lund LH, Andersson B, Nilsson J. A machine learning model for prediction of 30-day primary graft failure after heart transplantation. Heliyon 2023; 9:e14282. [PMID: 36938431 PMCID: PMC10015245 DOI: 10.1016/j.heliyon.2023.e14282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Background Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry. Methods We developed a non-linear artificial neural networks (ANN) model to evaluate the association between recipient-donor variables and post-transplant PGF. Patients in the ISHLT registry were randomly divided into derivation and an independent internal validation cohort. The primary endpoint was PGF defined as death within 30 days due to Graft failure or Cardiovascular causes or retransplant within 30 days for causes other than rejection. Results Among 64,964 adult recipients transplanted between 1994 and 2013, mean age was 51 years and 22% were female. The incidence of PGF up to 30 days was 3.7%. The ANN model selected 33 of 77 risk variables as relevant for PGF prediction. The C-index in the test cohort was 0.70 (95% CI: 0.68-0.71). The risk variables which most influenced the PGF were underlying HF diagnosis, ischemia time and sex, while renal function had a lower influence. Conclusion An ANN model to predict primary graft dysfunction was derived and independently validated. The good discrimination of the ANN model likely results from its flexibility to model potentially non-linear relationships and interactions. Whether this model with improved discrimination can assist in clinical decisions at the time of transplant should be tested.
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Affiliation(s)
- Björn Linse
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
| | - Mattias Ohlsson
- Computational Biology and Biological Physics, Lund University, Lund, Sweden
- Center for Applied Intelligent Systems Research, Halmstad University, Sweden
| | - Joseph Stehlik
- Department of Cardiovascular Medicine, University of Utah School of Medicine, Utah, USA
- The ISHLT Transplant Registry, USA
| | - Lars H. Lund
- Department of Medicine, Unit of Cardiology, Karolinska Institute, Stockholm, Sweden
- Heart and Vascular Theme, Karolinska University Hospital, Stockholm, Sweden
| | - Bodil Andersson
- Department of Clinical Sciences, Surgery, Lund University, Sweden
- Department of Surgery, Skane University Hospital, Lund, Sweden
| | - Johan Nilsson
- Department of Translational Medicine, Cardiothoracic Surgery and Bioinformatics, Lund University, Sweden
- Department of Cardiothoracic and Vascular Surgery, Skane University Hospital, Lund, Sweden
- Corresponding author.
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6
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Miller RJ. Artificial Intelligence in Nuclear Cardiology. Cardiol Clin 2023; 41:151-161. [PMID: 37003673 DOI: 10.1016/j.ccl.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.
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Al-Ani MA, Bai C, Hashky A, Parker AM, Vilaro JR, Aranda JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Front Cardiovasc Med 2023; 10:1127716. [PMID: 36910520 PMCID: PMC9999024 DOI: 10.3389/fcvm.2023.1127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
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Affiliation(s)
- Mohammad A Al-Ani
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Chen Bai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Amal Hashky
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Alex M Parker
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan R Vilaro
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan M Aranda
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Mamoun T Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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Enhanced survival prediction using explainable artificial intelligence in heart transplantation. Sci Rep 2022; 12:19525. [PMID: 36376402 PMCID: PMC9663731 DOI: 10.1038/s41598-022-23817-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 11/07/2022] [Indexed: 11/16/2022] Open
Abstract
The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.
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Sabovčik F, Ntalianis E, Cauwenberghs N, Kuznetsova T. Improving predictive performance in incident heart failure using machine learning and multi-center data. Front Cardiovasc Med 2022; 9:1011071. [PMID: 36330000 PMCID: PMC9623026 DOI: 10.3389/fcvm.2022.1011071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/03/2022] [Indexed: 12/05/2022] Open
Abstract
Objective To mitigate the burden associated with heart failure (HF), primary prevention is of the utmost importance. To improve early risk stratification, advanced computational methods such as machine learning (ML) capturing complex individual patterns in large data might be necessary. Therefore, we compared the predictive performance of incident HF risk models in terms of (a) flexible ML models and linear models and (b) models trained on a single cohort (single-center) and on multiple heterogeneous cohorts (multi-center). Design and methods In our analysis, we used the meta-data consisting of 30,354 individuals from 6 cohorts. During a median follow-up of 5.40 years, 1,068 individuals experienced a non-fatal HF event. We evaluated the predictive performance of survival gradient boosting (SGB), CoxNet, the PCP-HF risk score, and a stacking method. Predictions were obtained iteratively, in each iteration one cohort serving as an external test set and either one or all remaining cohorts as a training set (single- or multi-center, respectively). Results Overall, multi-center models systematically outperformed single-center models. Further, c-index in the pooled population was higher in SGB (0.735) than in CoxNet (0.694). In the precision-recall (PR) analysis for predicting 10-year HF risk, the stacking method, combining the SGB, CoxNet, Gaussian mixture and PCP-HF models, outperformed other models with PR/AUC 0.804, while PCP-HF achieved only 0.551. Conclusion With a greater number and variety of training cohorts, the model learns a wider range of specific individual health characteristics. Flexible ML algorithms can be used to capture these diverse distributions and produce more precise prediction models.
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Miller RJ, Chew DS, Howlett JG. Can Machines Find the Sweet Spot in End-Stage Heart Failure? JACC. ADVANCES 2022; 1:100122. [PMID: 38939701 PMCID: PMC11198332 DOI: 10.1016/j.jacadv.2022.100122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Robert J.H. Miller
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Derek S. Chew
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
| | - Jonathan G. Howlett
- Department of Cardiac Sciences, University of Calgary and Libin Cardiovascular Institute, Calgary, Alberta, Canada
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11
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Fine NM, Miller RJ. Heart Transplantation for Cardiac Amyloidosis: The Need for High Quality Data to Improve Patient Selection. Can J Cardiol 2022; 38:1144-1146. [DOI: 10.1016/j.cjca.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 11/02/2022] Open
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