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Piayda K, Heilemann JT, Keranov S, Schulz L, Arsalan M, Liebetrau C, Kim WK, Hofmann FJ, Bauer P, Voss S, Troidl C, Sossalla ST, Hamm CW, Nef HM, Dörr O. The role of Matrix Metalloproteinase-2 and Galectin-3 as predictive biomarkers for all-cause mortality in patients undergoing transfemoral transcatheter aortic valve implantation. Biomarkers 2024; 29:205-210. [PMID: 38588595 DOI: 10.1080/1354750x.2024.2341409] [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: 12/28/2023] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Currently available risk scores fail to accurately predict morbidity and mortality in patients with severe symptomatic aortic stenosis who undergo transcatheter aortic valve implantation (TAVI). In this context, biomarkers like matrix metalloproteinase-2 (MMP-2) and Galectin-3 (Gal-3) may provide additional prognostic information. METHODS Patients with severe aortic stenosis undergoing consecutive, elective, transfemoral TAVI were included. Baseline demographic data, functional status, echocardiographic findings, clinical outcomes and biomarker levels were collected and analysed. RESULTS The study cohort consisted of 89 patients (age 80.4 ± 5.1 years, EuroScore II 7.1 ± 5.8%). During a median follow-up period of 526 d, 28 patients (31.4%) died. Among those who died, median baseline MMP-2 (alive: 221.6 [170.4; 263] pg/mL vs. deceased: 272.1 [225; 308.8] pg/mL, p < 0.001) and Gal-3 levels (alive: 19.1 [13.5; 24.6] pg/mL vs. deceased: 25 [17.6; 29.5] pg/mL, p = 0.006) were higher than in survivors. In ROC analysis, MMP-2 reached an acceptable level of discrimination to predict mortality (AUC 0.733, 95% CI [0.62; 0.83], p < 0.001), but the predictive value of Gal-3 was poor (AUC 0.677, 95% CI [0.56; 0.79], p = 0.002). Kaplan-Meier and Cox regression analyses showed that patients with MMP-2 and Gal-3 concentrations above the median at baseline had significantly impaired long-term survival (p = 0.004 and p = 0.02, respectively). CONCLUSIONS In patients with severe aortic stenosis undergoing transfemoral TAVI, MMP-2 and to a lesser extent Gal-3, seem to have additive value in optimizing risk prediction and streamlining decision-making.
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Affiliation(s)
- Kerstin Piayda
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Julian Tim Heilemann
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Stanislav Keranov
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Luisa Schulz
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Mani Arsalan
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
- Department of Cardiothoracic Surgery, Medical Faculty, Goethe-University Frankfurt, Frankfurt, Germany
| | | | - Won-Keun Kim
- Department of Cardiology, Kerckhoff-Klinik, Bad Nauheim, Germany
| | - Felix J Hofmann
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Pascal Bauer
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Sandra Voss
- Department of Cardiology, Kerckhoff-Klinik, Bad Nauheim, Germany
- Kerckhoff Herzforschungsinstitut, Bad Nauheim, Germany
| | | | - Samuel T Sossalla
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
- Department of Cardiology, Kerckhoff-Klinik, Bad Nauheim, Germany
- Kerckhoff Herzforschungsinstitut, Bad Nauheim, Germany
| | - Christian W Hamm
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
| | - Holger M Nef
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
- Kerckhoff Herzforschungsinstitut, Bad Nauheim, Germany
| | - Oliver Dörr
- Department of Cardiology, Justus-Liebig-University Giessen, Medical Clinic I, Giessen, Germany
- Cardioangiologisches Centrum Bethanien, Frankfurt am Main, Germany
- Kerckhoff Herzforschungsinstitut, Bad Nauheim, Germany
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2
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Sazzad F, Ler AAL, Furqan MS, Tan LKZ, Leo HL, Kuntjoro I, Tay E, Kofidis T. Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis. Front Cardiovasc Med 2024; 11:1343210. [PMID: 38883982 PMCID: PMC11176615 DOI: 10.3389/fcvm.2024.1343210] [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: 11/23/2023] [Accepted: 05/16/2024] [Indexed: 06/18/2024] Open
Abstract
Objectives In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out. We searched four databases in total-PubMed, Medline, Embase, and Cochrane-from 19 June 2023-24 June, 2023. Results From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9,398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, p < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, p = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, p < 0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22,933 patients was 0.79 [0.73, 0.85]. Conclusion AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients. Registration and protocol This systematic review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name "All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence" and registration number CRD42023437705. A review protocol was not prepared. There were no amendments to the information provided at registration. Systematic Review Registration https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705).
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Affiliation(s)
- Faizus Sazzad
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ashlynn Ai Li Ler
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mohammad Shaheryar Furqan
- Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Linus Kai Zhe Tan
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hwa Liang Leo
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Ivandito Kuntjoro
- Department of Cardiology, National University Heart Centre, Singapore, National University Hospital, Singapore, Singapore
| | - Edgar Tay
- Department of Cardiology, National University Heart Centre, Singapore, National University Hospital, Singapore, Singapore
- Asian Heart & Vascular Centre (AHVC), Mount Elizabeth Medical Centre, Singapore, Singapore
| | - Theo Kofidis
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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3
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Brüggemann D, Kuzo N, Anwer S, Kebernik J, Eberhard M, Alkadhi H, Tanner FC, Konukoglu E. Predicting mortality after transcatheter aortic valve replacement using preprocedural CT. Sci Rep 2024; 14:12526. [PMID: 38822074 PMCID: PMC11143216 DOI: 10.1038/s41598-024-63022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/23/2024] [Indexed: 06/02/2024] Open
Abstract
Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. Currently, this involves manual clinical assessment and time-consuming radiological assessment of preprocedural computed tomography (CT) images by an expert radiologist. In this study, we introduce a probabilistic model that predicts post-TAVR mortality automatically using unprocessed, preprocedural CT and 25 baseline patient characteristics. The model utilizes CT volumes by automatically localizing and extracting a region of interest around the aortic root and ascending aorta. It then extracts task-specific features with a 3D deep neural network and integrates them with patient characteristics to perform outcome prediction. As missing measurements or even missing CT images are common in TAVR planning, the proposed model is designed with a probabilistic structure to allow for marginalization over such missing information. Our model demonstrates an AUROC of 0.725 for predicting all-cause mortality during postprocedure follow-up on a cohort of 1449 TAVR patients. This performance is on par with what can be achieved with lengthy radiological assessments performed by experts. Thus, these findings underscore the potential of the proposed model in automatically analyzing CT volumes and integrating them with patient characteristics for predicting mortality after TAVR.
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Affiliation(s)
- David Brüggemann
- Computer Vision Laboratory, ETH Zurich, 8092, Zurich, Switzerland
| | - Nazar Kuzo
- Department of Cardiology, University Heart Center, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Shehab Anwer
- Department of Cardiology, University Heart Center, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Julia Kebernik
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Matthias Eberhard
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Felix C Tanner
- Department of Cardiology, University Heart Center, University Hospital Zurich, 8091, Zurich, Switzerland
| | - Ender Konukoglu
- Computer Vision Laboratory, ETH Zurich, 8092, Zurich, Switzerland.
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Ahmed H, Ismayl M, Mangat M, Palicherla A, Dufani J, Aboeata A, Anavekar N, Goldsweig AM. The role of machine learning models for predicting in-hospital mortality after transcatheter aortic valve replacement. CARDIOVASCULAR REVASCULARIZATION MEDICINE 2024:S1553-8389(24)00516-5. [PMID: 38906749 DOI: 10.1016/j.carrev.2024.05.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Affiliation(s)
- Hasaan Ahmed
- Department of Medicine, Division of Internal Medicine, Creighton University School of Medicine, Omaha, NE, USA.
| | - Mahmoud Ismayl
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Manvir Mangat
- Department of Family and Community Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Anirudh Palicherla
- Department of Medicine, Division of Internal Medicine, Creighton University School of Medicine, Omaha, NE, USA
| | - Jalal Dufani
- Department of Medicine, Division of Internal Medicine, Creighton University School of Medicine, Omaha, NE, USA
| | - Ahmed Aboeata
- Department of Medicine, Division of Cardiovascular Disease, Creighton University School of Medicine, Omaha, NE, USA
| | - Nandan Anavekar
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Andrew M Goldsweig
- Department of Cardiovascular Medicine, Baystate Medical Center, Springfield, MA, USA
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Jacquemyn X, Van Onsem E, Dufendach K, Brown JA, Kliner D, Toma C, Serna-Gallegos D, Sá MP, Sultan I. Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00448-3. [PMID: 38815806 DOI: 10.1016/j.jtcvs.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
OBJECTIVES With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency. METHODS We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool. C-statistics were pooled using a random-effects model. RESULTS Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81; 95% confidence interval [CI], 0.65-0.91), acceptable performance for 1-year mortality (C-statistic: 0.76; 95% CI, 0.67-0.84), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75; 95% CI, 0.51-0.90). CONCLUSIONS ML models for TAVI outcomes exhibit adequate-to-excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.
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Affiliation(s)
- Xander Jacquemyn
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
| | | | - Keith Dufendach
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - James A Brown
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Dustin Kliner
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Catalin Toma
- UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa; Department of Interventional Cardiology, University of Pittsburgh, Pittsburgh, Pa
| | - Derek Serna-Gallegos
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Michel Pompeu Sá
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
| | - Ibrahim Sultan
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, Pa; UPMC Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pa
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6
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Goldschmied A, Sigle M, Faller W, Heurich D, Gawaz M, Müller KAL. Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms. Sci Rep 2024; 14:9796. [PMID: 38684774 PMCID: PMC11058266 DOI: 10.1038/s41598-024-60249-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 04/20/2024] [Indexed: 05/02/2024] Open
Abstract
Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled 2760 patients (average age 70 years, 58.6% male) with chest pain and suspected ACS, who were admitted to the Emergency Department of the University Hospital Tübingen, Germany, between August 2016 and October 2020. Using 26 features, eight Machine learning models (non-deep learning models) were trained with data from the preclinical rescue protocol and compared to the "TropOut" score (a modified version of the "preHEART" score which consists of history, ECG, age and cardiac risk but without troponin analysis) to predict major adverse cardiac event (MACE) and acute coronary artery occlusion (ACAO). In our study population MACE occurred in 823 (29.8%) patients and ACAO occurred in 480 patients (17.4%). Interestingly, we found that all machine learning models outperformed the "TropOut" score. The VC and the LR models showed the highest area under the receiver operating characteristic (AUROC) for predicting MACE (AUROC = 0.78) and the VC showed the highest AUROC for predicting ACAO (AUROC = 0.81). A SHapley Additive exPlanations (SHAP) analyses based on the XGB model showed that presence of ST-elevations in the electrocardiogram (ECG) were the most important features to predict both endpoints.
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Affiliation(s)
- Andreas Goldschmied
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany
| | - Manuel Sigle
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany
| | - Wenke Faller
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany
| | - Diana Heurich
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany
| | - Meinrad Gawaz
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany
| | - Karin Anne Lydia Müller
- Department of Cardiology and Angiology, University Hospital of the Eberhard Karls University Tuebingen, Otfried-Mueller-Str.10, 72076, Tübingen, Germany.
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Heston TF, Lewis LM. ChatGPT provides inconsistent risk-stratification of patients with atraumatic chest pain. PLoS One 2024; 19:e0301854. [PMID: 38626142 PMCID: PMC11020975 DOI: 10.1371/journal.pone.0301854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND ChatGPT-4 is a large language model with promising healthcare applications. However, its ability to analyze complex clinical data and provide consistent results is poorly known. Compared to validated tools, this study evaluated ChatGPT-4's risk stratification of simulated patients with acute nontraumatic chest pain. METHODS Three datasets of simulated case studies were created: one based on the TIMI score variables, another on HEART score variables, and a third comprising 44 randomized variables related to non-traumatic chest pain presentations. ChatGPT-4 independently scored each dataset five times. Its risk scores were compared to calculated TIMI and HEART scores. A model trained on 44 clinical variables was evaluated for consistency. RESULTS ChatGPT-4 showed a high correlation with TIMI and HEART scores (r = 0.898 and 0.928, respectively), but the distribution of individual risk assessments was broad. ChatGPT-4 gave a different risk 45-48% of the time for a fixed TIMI or HEART score. On the 44-variable model, a majority of the five ChatGPT-4 models agreed on a diagnosis category only 56% of the time, and risk scores were poorly correlated (r = 0.605). CONCLUSION While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability. The findings suggest that while large language models like ChatGPT-4 hold promise for healthcare applications, further refinement and customization are necessary, particularly in the clinical risk assessment of atraumatic chest pain patients.
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Affiliation(s)
- Thomas F. Heston
- Department of Family Medicine, University of Washington School of Medicine, Seattle, Washington, United States of America
- Department of Medical Education and Clinical Sciences, Washington State University, Spokane, Washington, United States of America
| | - Lawrence M. Lewis
- Department of Emergency Medicine, Washington University, Saint Louis, Missouri, United States of America
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8
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Zisiopoulou M, Berkowitsch A, Redlich L, Walther T, Fichtlscherer S, Leistner DM. Personalised preinterventional risk stratification of mortality, length of stay and hospitalisation costs in transcatheter aortic valve implantation using a machine learning algorithm: a pilot trial. Open Heart 2024; 11:e002540. [PMID: 38388188 PMCID: PMC10884198 DOI: 10.1136/openhrt-2023-002540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/20/2024] [Indexed: 02/24/2024] Open
Abstract
INTRODUCTION Risk stratification based on Euroscore II (ESII) is used in some centres to assist decisions to perform transcatheter aortic valve implant (TAVI) procedures. ESII is a generic, non-TAVI-specific metric, and its performance fades for mortality at follow-up longer than 30 days. We investigated if a TAVI-specific predictive model could achieve improved predictive preinterventional accuracy of 1-year mortality compared with ESII. PATIENTS AND METHODS In this prospective pilot study, 284 participants with severe symptomatic aortic valve stenosis who underwent TAVI were enrolled. Standard clinical metrics (American Society of Anesthesiology (ASA), New York Heart Association and ESII) and patient-reported outcome measures (EuroQol-5 Dimension-Visual Analogue Scale, Kansas City Cardiomyopathy Questionnaire and Clinical Frailty Scale (CFS)) were assessed 1 day before TAVI. Using these data, we tested predictive models (logistic regression and decision tree algorithm (DTA)) with 1-year mortality as the dependent variable. RESULTS Logistic regression yielded the best prediction, with ASA and CFS as the strongest predictors of 1-year mortality. Our logistic regression model score showed significantly better prediction accuracy than ESII (area under the curve=0.659 vs 0.800; p=0.002). By translating our results to a DTA, cut-off score values regarding 1-year mortality risk emerged for low, intermediate and high risk. Treatment costs and length of stay (LoS) significantly increased in high-risk patients. CONCLUSIONS AND SIGNIFICANCE A novel TAVI-specific model predicts 1-year mortality, LoS and costs after TAVI using simple, established, transparent and inexpensive metrics before implantation. Based on this preliminary evidence, TAVI team members and patients can make informed decisions based on a few key metrics. Validation of this score in larger patient cohorts is needed.
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Affiliation(s)
- Maria Zisiopoulou
- Cardiology and Vascular Medicine Department, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
- German Center of Cardiovascular Research (DZHK), Partner Site Rhine/Main, Frankfurt am Main, Germany
| | - Alexander Berkowitsch
- Cardiology and Vascular Medicine Department, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
| | - Leonard Redlich
- Cardiology and Vascular Medicine Department, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
| | - Thomas Walther
- German Center of Cardiovascular Research (DZHK), Partner Site Rhine/Main, Frankfurt am Main, Germany
- Department of Cardiothoracic Surgery, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
| | - Stephan Fichtlscherer
- Cardiology and Vascular Medicine Department, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
| | - David M Leistner
- Cardiology and Vascular Medicine Department, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany
- German Center of Cardiovascular Research (DZHK), Partner Site Rhine/Main, Frankfurt am Main, Germany
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9
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Benjamin MM, Rabbat MG. Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges. Diagnostics (Basel) 2024; 14:261. [PMID: 38337777 PMCID: PMC10855497 DOI: 10.3390/diagnostics14030261] [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: 12/15/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024] Open
Abstract
Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.
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Affiliation(s)
- Mina M. Benjamin
- Division of Cardiovascular Medicine, SSM—Saint Louis University Hospital, Saint Louis University, Saint Louis, MO 63104, USA
| | - Mark G. Rabbat
- Department of Cardiovascular Medicine, Loyola University Medical Center, Maywood, IL 60153, USA;
- Department of Cardiology, Edward Hines Jr. VA Hospital, Hines, IL 60141, USA
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10
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Olender RT, Roy S, Nishtala PS. Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis. BMC Geriatr 2023; 23:561. [PMID: 37710210 PMCID: PMC10503191 DOI: 10.1186/s12877-023-04246-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 08/19/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. DESIGN Systematic review and meta-analyses. PARTICIPANTS Older adults (≥ 65 years) in any setting. INTERVENTION Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. OUTCOME MEASURES Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. RESULTS Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power. CONCLUSION The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.
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Affiliation(s)
- Robert T Olender
- Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK.
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK
| | - Prasad S Nishtala
- Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath, BA2 7AY, UK
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Pollari F, Hitzl W, Rottmann M, Vogt F, Ledwon M, Langhammer C, Eckner D, Jessl J, Bertsch T, Pauschinger M, Fischlein T. A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study. J Clin Med 2023; 12:5481. [PMID: 37685547 PMCID: PMC10488486 DOI: 10.3390/jcm12175481] [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: 07/11/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization. METHODS Routinely acquired data (134 variables) were used from 629 patients, who underwent transfemoral TAVI from 2012 up to 2018. Support vector machines, neuronal networks, random forests, nearest neighbour and Bayes models were used with new, previously unseen patients to predict 1-year mortality in TAVI patients. A genetic variable selection algorithm identified a set of predictor variables with high predictive power. RESULTS Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 1-year survival. Before applying the reject option, the model performances in terms of negative predictive value (NPV) and positive predictive value (PPV) were similar between all models. After applying the reject option, the random forest model identified a subcohort showing a negative predictive value of 96% (positive predictive value = 92%, accuracy = 96%). CONCLUSIONS Our model can predict the 1-year survival with very high negative and sufficiently high positive predictive value, with very high accuracy. The "reject option" allows a high performance and harmonic integration of machine learning in the clinical decision process.
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Affiliation(s)
- Francesco Pollari
- Cardiac Surgery, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90471 Nuremberg, Germany; (M.R.); (F.V.); (M.L.); (T.F.)
| | - Wolfgang Hitzl
- Research and Innovation Management (RIM), Team Biostatistics and Publication of Clinical Trial Studies, Paracelsus Medical University, 5020 Salzburg, Austria;
- Department of Ophthalmology and Optometry, Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
- Research Program Experimental Ophthalmology and Glaucoma Research, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Magnus Rottmann
- Cardiac Surgery, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90471 Nuremberg, Germany; (M.R.); (F.V.); (M.L.); (T.F.)
| | - Ferdinand Vogt
- Cardiac Surgery, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90471 Nuremberg, Germany; (M.R.); (F.V.); (M.L.); (T.F.)
| | - Miroslaw Ledwon
- Cardiac Surgery, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90471 Nuremberg, Germany; (M.R.); (F.V.); (M.L.); (T.F.)
| | - Christian Langhammer
- Institute of Clinical Chemistry, Laboratory Medicine and Transfusion Medicine, Paracelsus Medical University, 90471 Nuremberg, Germany; (C.L.); (T.B.)
| | - Dennis Eckner
- Cardiology, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90419 Nuremberg, Germany; (D.E.); (J.J.); (M.P.)
| | - Jürgen Jessl
- Cardiology, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90419 Nuremberg, Germany; (D.E.); (J.J.); (M.P.)
| | - Thomas Bertsch
- Institute of Clinical Chemistry, Laboratory Medicine and Transfusion Medicine, Paracelsus Medical University, 90471 Nuremberg, Germany; (C.L.); (T.B.)
| | - Matthias Pauschinger
- Cardiology, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90419 Nuremberg, Germany; (D.E.); (J.J.); (M.P.)
| | - Theodor Fischlein
- Cardiac Surgery, Cardiovascular Center, Paracelsus Medical University-Klinikum Nuremberg, 90471 Nuremberg, Germany; (M.R.); (F.V.); (M.L.); (T.F.)
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12
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Mohsen F, Al-Saadi B, Abdi N, Khan S, Shah Z. Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine. J Pers Med 2023; 13:1268. [PMID: 37623518 PMCID: PMC10455092 DOI: 10.3390/jpm13081268] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/27/2023] [Accepted: 03/04/2023] [Indexed: 08/26/2023] Open
Abstract
Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.
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Affiliation(s)
| | | | | | | | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
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13
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Dietrich M, Antonovici A, Hölle T, Nusshag C, Kapp AC, Studier-Fischer A, Arif R, Nickel F, Weigand MA, Frey N, Lichtenstern C, Leuschner F, Fischer D. Microcirculatory tissue oxygenation correlates with kidney function after transcatheter aortic valve implantation-Results from a prospective observational study. Front Cardiovasc Med 2023; 10:1108256. [PMID: 36865886 PMCID: PMC9971913 DOI: 10.3389/fcvm.2023.1108256] [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: 11/25/2022] [Accepted: 01/20/2023] [Indexed: 02/16/2023] Open
Abstract
Introduction Kidney dysfunction is common in patients with aortic stenosis (AS) and correction of the aortic valve by transcatheter aortic valve implantation (TAVI) often affects kidney function. This may be due to microcirculatory changes. Methods We evaluated skin microcirculation with a hyperspectral imaging (HSI) system, and compared tissue oxygenation (StO2), near-infrared perfusion index (NIR), tissue hemoglobin index (THI) and tissue water index (TWI) in 40 patients undergoing TAVI versus 20 control patients. HSI parameters were measured before TAVI (t1), directly after TAVI (t2), and on postinterventional day 3 (t3). The primary outcome was the correlation of tissue oxygenation (StO2) to the creatinine level after TAVI. Results We performed 116 HSI image recordings in patients undergoing TAVI for the treatment of severe aortic stenosis and 20 HSI image recordings in control patients. Patients with AS had a lower THI at the palm (p = 0.034) and a higher TWI at the fingertips (p = 0.003) in comparison to control patients. TAVI led to an increase of TWI, but had no uniform enduring effect on StO2 and THI. Tissue oxygenation StO2 at both measurement sites correlated negatively with creatinine levels after TAVI at t2 (palm: ρ = -0.415; p = 0.009; fingertip: ρ = -0.519; p < 0.001) and t3 (palm: ρ = -0.427; p = 0.008; fingertip: ρ = -0.398; p = 0.013). Patients with higher THI at t3 reported higher physical capacity and general health scores 120 days after TAVI. Conclusion HSI is a promising technique for periinterventional monitoring of tissue oxygenation and microcirculatory perfusion quality, which are related to kidney function, physical capacity, and clinical outcomes after TAVI. Clinical trial registration https://drks.de/search/de/trial, identifier DRKS00024765.
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Affiliation(s)
- Maximilian Dietrich
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany,*Correspondence: Maximilian Dietrich, ; orcid.org/0000-0003-0960-038X
| | - Ana Antonovici
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Hölle
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Nusshag
- Department of Nephrology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne-Christine Kapp
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Studier-Fischer
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Rawa Arif
- Institute of Cardiac Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix Nickel
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Norbert Frey
- Department of Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Florian Leuschner
- Department of Internal Medicine III, Heidelberg University Hospital, Heidelberg, Germany
| | - Dania Fischer
- Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
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14
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On the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review. PROSTHESIS 2022. [DOI: 10.3390/prosthesis4010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Transcatheter aortic valve replacement (TAVR) has become a milestone for the management of aortic stenosis in a growing number of patients who are unfavorable candidates for surgery. With the new generation of transcatheter heart valves (THV), the feasibility of transcatheter mitral valve replacement (TMVR) for degenerated mitral bioprostheses and failed annuloplasty rings has been demonstrated. In this setting, computational simulations are modernizing the preoperative planning of transcatheter heart valve interventions by predicting the outcome of the bioprosthesis interaction with the human host in a patient-specific fashion. However, computational modeling needs to carry out increasingly challenging levels including the verification and validation to obtain accurate and realistic predictions. This review aims to provide an overall assessment of the recent advances in computational modeling for TAVR and TMVR as well as gaps in the knowledge limiting model credibility and reliability.
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15
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Juan-Salvadores P, Veiga C, Jiménez Díaz VA, Guitián González A, Iglesia Carreño C, Martínez Reglero C, Baz Alonso JA, Caamaño Isorna F, Romo AI. Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients. Diagnostics (Basel) 2022; 12:422. [PMID: 35204511 PMCID: PMC8870965 DOI: 10.3390/diagnostics12020422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 12/03/2022] Open
Abstract
Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71-0.89) vs. LR 0.50 (95%CI 0.33-0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.
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Affiliation(s)
- Pablo Juan-Salvadores
- Cardiovascular Research Unit, Cardiology Department, Hospital Alvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain; (P.J.-S.); (V.A.J.D.)
- Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain; (J.A.B.A.); (A.I.R.)
| | - Cesar Veiga
- Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain; (J.A.B.A.); (A.I.R.)
| | - Víctor Alfonso Jiménez Díaz
- Cardiovascular Research Unit, Cardiology Department, Hospital Alvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain; (P.J.-S.); (V.A.J.D.)
- Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain; (J.A.B.A.); (A.I.R.)
- Interventional Cardiology Unit, Cardiology Department, Hospital Álvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain
| | - Alba Guitián González
- Cardiology Department, Hospital Álvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain; (A.G.G.); (C.I.C.)
| | - Cristina Iglesia Carreño
- Cardiology Department, Hospital Álvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain; (A.G.G.); (C.I.C.)
| | - Cristina Martínez Reglero
- Methodology and Statistics Unit, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain;
| | - José Antonio Baz Alonso
- Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain; (J.A.B.A.); (A.I.R.)
- Interventional Cardiology Unit, Cardiology Department, Hospital Álvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain
| | - Francisco Caamaño Isorna
- Department of Preventive Medicine, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain;
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública-CIBERESP), 15782 Santiago de Compostela, Spain
| | - Andrés Iñiguez Romo
- Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain; (J.A.B.A.); (A.I.R.)
- Cardiology Department, Hospital Álvaro Cunqueiro, University Hospital of Vigo, 36213 Vigo, Spain; (A.G.G.); (C.I.C.)
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Jia Y, Luosang G, Li Y, Wang J, Li P, Xiong T, Li Y, Liao Y, Zhao Z, Peng Y, Feng Y, Jiang W, Li W, Zhang X, Yi Z, Chen M. Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement. Clin Epidemiol 2022; 14:9-20. [PMID: 35046728 PMCID: PMC8763202 DOI: 10.2147/clep.s333147] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/29/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Patients and Methods Results Conclusion
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Affiliation(s)
- Yuheng Jia
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Gaden Luosang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
- Department of Information Science and Technology, Tibet University, Lhasa City, People’s Republic of China
| | - Yiming Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Pengyu Li
- West China Medical School, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Tianyuan Xiong
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Yijian Li
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Yanbiao Liao
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Zhengang Zhao
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Yuan Feng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Weili Jiang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Wenjian Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Xinpei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China
- Zhang Yi Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of ChinaTel +86-13882217717Fax +86-28-85466062 Email
| | - Mao Chen
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China
- Correspondence: Mao Chen Department of Cardiology, West China Hospital, Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, People’s Republic of ChinaTel +86-18980602046Fax +86-28-85423169 Email
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Kilic A, Dochtermann D, Padman R, Miller JK, Dubrawski A. Using machine learning to improve risk prediction in durable left ventricular assist devices. PLoS One 2021; 16:e0247866. [PMID: 33690687 PMCID: PMC7946192 DOI: 10.1371/journal.pone.0247866] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 02/15/2021] [Indexed: 11/24/2022] Open
Abstract
Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683–0.730] versus 0.740 [0.717–0.762] and 1-year: 0.691 [0.673–0.710] versus 0.714 [0.695–0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.
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Affiliation(s)
- Arman Kilic
- Division of Cardiac Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America
- * E-mail:
| | | | - Rema Padman
- Carnegie Mellon University, Pittsburgh, PA, United States of America
| | - James K. Miller
- Carnegie Mellon University, Pittsburgh, PA, United States of America
| | - Artur Dubrawski
- Carnegie Mellon University, Pittsburgh, PA, United States of America
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