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Wu J, Ma K, Ma J, Li Y, Ren Y. Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome. Clin Chim Acta 2024; 563:119904. [PMID: 39117035 DOI: 10.1016/j.cca.2024.119904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/29/2024] [Accepted: 08/05/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS patients. METHODS A derivation cohort of 110 patients with ACS who underwent pre-intervention optical coherence tomography (OCT) were matched 1:1 to the PR and intact fibrous cap (IFC) groups according to traditional risk factors. Candidate PR proteins were identified via mass spectrometry (MS)-based proteomics using unbiased machine learning methods and were further validated by enzyme-linked immunosorbent assay (ELISA) in an external validation cohort of 85 patients with ACS. The performance of candidate biomakers was assessed using the receiver operating characteristic curve analysis. RESULTS 1121 proteins were identified and 535 filtered proteins were used for analysis. Nine candidate proteins were screened by five machine learning algorithms. Three proteins (APOC3, RAB39A, and KNG1) were significantly different between the PR and IFC in validation cohort. The performance of plasm APOC3, RAB39A, and KNG1 for differentiating PR and IFC was superior to that of the conventional biomarkers and risk factors. CONCLUSION The proteins (APOC3, RAB39A, and KNG1) serve as a potential novel diagnostic tool to identify PR in ACS patients.
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Affiliation(s)
- Jianing Wu
- Beijing Anzhen Hospital of Capital Medical University, Beijing, China; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
| | - Ke Ma
- Beijing Anzhen Hospital of Capital Medical University, Beijing, China; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
| | - Jie Ma
- Beijing Anzhen Hospital of Capital Medical University, Beijing, China; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
| | - Yulin Li
- Beijing Anzhen Hospital of Capital Medical University, Beijing, China; Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China.
| | - Yongkui Ren
- Department of Cardiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
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2
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Kuo C, Chen Z, Liu P, Pilling LC, Atkins JL, Fortinsky RH, Kuchel GA, Diniz BS. Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults. Aging Cell 2024; 23:e14195. [PMID: 38747160 PMCID: PMC11320350 DOI: 10.1111/acel.14195] [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: 01/20/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/28/2024] Open
Abstract
Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
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Affiliation(s)
- Chia‐Ling Kuo
- Department of Public Health SciencesUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
- The Cato T. Laurencin Institute for Regenerative EngineeringUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
- UConn Center on AgingUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
| | - Zhiduo Chen
- UConn Center on AgingUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
| | - Peiran Liu
- The Cato T. Laurencin Institute for Regenerative EngineeringUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
| | - Luke C. Pilling
- Epidemiology and Public Health Group, Department of Clinical and Biomedical SciencesUniversity of ExeterExeterUK
| | - Janice L. Atkins
- Epidemiology and Public Health Group, Department of Clinical and Biomedical SciencesUniversity of ExeterExeterUK
| | - Richard H. Fortinsky
- UConn Center on AgingUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
| | - George A. Kuchel
- UConn Center on AgingUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
| | - Breno S. Diniz
- Department of Public Health SciencesUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
- UConn Center on AgingUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
- Department of PsychiatryUniversity of Connecticut Health CenterFarmingtonConnecticutUSA
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3
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Abbasi AF, Asim MN, Ahmed S, Vollmer S, Dengel A. Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases. Front Artif Intell 2024; 7:1428501. [PMID: 39021434 PMCID: PMC11252047 DOI: 10.3389/frai.2024.1428501] [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: 05/07/2024] [Accepted: 06/12/2024] [Indexed: 07/20/2024] Open
Abstract
Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.
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Affiliation(s)
- Ahtisham Fazeel Abbasi
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Muhammad Nabeel Asim
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Sheraz Ahmed
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Sebastian Vollmer
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
- Smart Data & Knowledge Services, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Kaiserslautern, Germany
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Baragetti A, Grigore L, Olmastroni E, Mattavelli E, Catapano AL. Plasma proteins associate with carotid plaques and predict incident atherosclerotic cardiovascular events. Vascul Pharmacol 2024; 156:107394. [PMID: 38866119 DOI: 10.1016/j.vph.2024.107394] [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: 02/17/2024] [Revised: 05/10/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE Performing non-invasive carotid imaging is challenging, owing inter-operator variability and organizational barriers, but plasma proteomics can offer an alternative. We sought plasma proteins that associate with the presence of carotid plaques, their number and predict the incidence of clinically overt atherosclerotic cardiovascular events (ASCVD) above currently recognized risk factors in "apparently healthy" subjects. METHODS We studied the plasma levels of 368 proteins in 664 subjects from the PLIC study, who underwent an ultrasound imaging screening of the carotids to check for the presence of plaques. We clustered, by artificial intelligence (A.I.), the proteins that associate with the presence, the number of plaques and that predict incident ASCVDs over 22 years (198 events were registered). FINDINGS 299/664 subjects had at least 1 carotid plaque (1+) (77 with only one plaque, 101 with 2 plaques, 121 with ≥3 plaques (3+)). The remaining 365 subjects with no plaques acted as controls. 106 proteins were associated with 1+ plaques, but 97 proteins significantly predicted 3+ plaques only (AUC = 0.683 (0.601-0.785), p < 0.001), when considered alone. A.I. underscored 87 proteins that improved the performance of the classical risk factors both in detecting 3+ plaques (AUC = 0.918 (0.887-0.943) versus risk factors alone, AUC = 0.760 (0.716-0.801), p < 0.001) and in predicting the incident ASCVD (AUC = 0.739 (0.704-0.773) vs risk factors alone AUC = 0.559 (0.521-0.598), p < 0.001). The chemotaxis/migration of leukocytes and interleukins/cytokines signaling were biological pathways mostly represented by these proteins. DISCUSSION AND CONCLUSIONS Plasma proteomics marks the number of carotid plaques and improve the prediction of incidence ASCVDs in apparently healthy subjects.
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Affiliation(s)
- Andrea Baragetti
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", University of Milan, Milan, Italy
| | | | - Elena Olmastroni
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", University of Milan, Milan, Italy
| | - Elisa Mattavelli
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", University of Milan, Milan, Italy; Bassini Hospital, Cinisello Balsamo, Milan, Italy
| | - Alberico Luigi Catapano
- Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", University of Milan, Milan, Italy; IRCCS Multimedica Hospital, Milan, Italy
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Yi X, He Y, Gao S, Li M. A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance. Diabetes Metab Syndr 2024; 18:103000. [PMID: 38604060 DOI: 10.1016/j.dsx.2024.103000] [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: 05/08/2022] [Revised: 01/23/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND AIMS Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research. METHODS An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies. RESULTS Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11). CONCLUSIONS This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).
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Affiliation(s)
- Xinghao Yi
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yangzhige He
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
| | - Shan Gao
- Department of Endocrinology, Xuan Wu Hospital, Capital Medical University, Beijing 10053, China
| | - Ming Li
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China.
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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8
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Li J, Lu X. Cardiovascular risk assessment: The key path toward precision prevention. Chronic Dis Transl Med 2023; 9:273-276. [PMID: 37915392 PMCID: PMC10617312 DOI: 10.1002/cdt3.90] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 11/03/2023] Open
Affiliation(s)
- Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Key Laboratory of Cardiovascular EpidemiologyChinese Academy of Medical SciencesBeijingChina
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Liu Y, Sun S, Zhang Y, Huang X, Wang K, Qu Y, Chen X, Wu R, Zhang J, Luo J, Li Y, Wang J, Yi J. Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma. JOURNAL OF THE NATIONAL CANCER CENTER 2023; 3:295-305. [PMID: 39036668 PMCID: PMC11256522 DOI: 10.1016/j.jncc.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/09/2023] [Accepted: 10/09/2023] [Indexed: 07/23/2024] Open
Abstract
Objective Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasopharyngeal carcinoma (LA-NPC) remain challenging. This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival (OS) and their value in guiding treatment in patients with LA-NPC. Methods Individuals with LA-NPC were reviewed retrospectively. Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods, the interpretable eXtreme Gradient Boosting (XGBoost) risk prediction model, and DeepHit time-to-event neural network. The models' prediction performances were compared using the concordance index (C-index) and the area under the curve (AUC). The patients were divided into two cohorts based on the risk predictions of the most successful model. The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone. Results The 1 221 eligible individuals, assigned to the training (n = 813) or validation (n = 408) set, showed significant respective differences in the C-indices of the XGBoost, DeepHit, and nomogram models (0.849 and 0.768, 0.811 and 0.767, 0.730 and 0.705). The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS (0.881 and 0.760, 0.845 and 0.776, and 0.764 and 0.729, P < 0.001). IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group. Conclusion This research used machine-learning algorithms to create and verify a comprehensive model integrating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC. This model could be valuable for delivering patient counseling and conducting clinical evaluations.
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Affiliation(s)
- Yang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shiran Sun
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Ye Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xiaodong Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kai Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuan Qu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xuesong Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Runye Wu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jianghu Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jingwei Luo
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jingbo Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Junlin Yi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences (CAMS), Langfang 065001, China
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10
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Jin D, Trichia E, Islam N, Lewington S, Lacey B. Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank. J Epidemiol Community Health 2023; 77:802-808. [PMID: 37699667 DOI: 10.1136/jech-2023-220801] [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: 05/03/2023] [Accepted: 08/25/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND Metabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as compared with established atherosclerotic cardiovascular disease (CVD) risk scores. METHODS UK Biobank is a prospective study of 0.5 million participants, aged 40-69 at recruitment. Analyses were restricted to 74 780 participants with metabolic profiling (measured using nuclear magnetic resonance) and without CVD at baseline. Cox regression was used to compare model performance before and after addition of metabolites to QRISK3 (an established CVD risk score used in primary care in England); analyses derived three models, with metabolites selected by association significance or by employing two different machine learning approaches. RESULTS We identified 5097 incident CVD events within the 10-year follow-up. Harrell's C-index of QRISK3 was 0.750 (95% CI 0.739 to 0.763) for women and 0.706 (95% CI 0.696 to 0.716) for men. Adding selected metabolites did not significantly improve measures of discrimination in women (Harrell's C-index of three models are 0.759 (0.747 to 0.772), 0.759 (0.746 to 0.770) and 0.759 (0.748 to 0.771), respectively) or men (0.710 (0.701 to 0.720), 0.710 (0.700 to 0.719) and 0.710 (0.701 to 0.719), respectively), and neither did it improve reclassification or calibration. CONCLUSION This large-scale study applied both conventional and machine learning approaches to assess the potential benefit of metabolic profiling to well-established CVD risk scores. However, there was no evidence that metabolic profiling improved CVD risk prediction in this population.
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Affiliation(s)
- Danyao Jin
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Eirini Trichia
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | - Nazrul Islam
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - Sarah Lewington
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- MRC Population Health Research Unit, NDPH, University of Oxford, Oxford, UK
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
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11
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Nedadur R, Bhatt N, Chung J, Chu MWA, Ouzounian M, Wang B. Machine learning and decision making in aortic arch repair. J Thorac Cardiovasc Surg 2023:S0022-5223(23)01108-X. [PMID: 38016622 DOI: 10.1016/j.jtcvs.2023.11.032] [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: 05/08/2023] [Revised: 11/16/2023] [Accepted: 11/19/2023] [Indexed: 11/30/2023]
Abstract
BACKGROUND Decision making during aortic arch surgery regarding cannulation strategy and nadir temperature are important in reducing risk, and there is a need to determine the best individualized strategy in a data-driven fashion. Using machine learning (ML), we modeled the risk of death or stroke in elective aortic arch surgery based on patient characteristics and intraoperative decisions. METHODS The study cohort comprised 1323 patients from 9 institutions who underwent an elective aortic arch procedure between 2002 and 2021. A total of 69 variables were used in developing a logistic regression and XGBoost ML model trained for binary classification of mortality and stroke. Shapely additive explanations (SHAP) values were studied to determine the importance of intraoperative decisions. RESULTS During the study period, 3.9% of patients died and 5.4% experienced stroke. XGBoost (area under the curve [AUC], 0.77 for death, 0.87 for stroke) demonstrated better discrimination than logistic regression (AUC, 0.65 for death, 0.75 for stroke). From SHAP analysis, intraoperative decisions are 3 of the top 20 predictors of death and 6 of the top 20 predictors of stroke. Predictor weights are patient-specific and reflect the patient's preoperative characteristics and other intraoperative decisions. Patient-level simulation also demonstrates the variable contribution of each decision in the context of the other choices that are made. CONCLUSIONS Using ML, we can more accurately identify patients at risk of death and stroke, as well as the strategy that better reduces the risk of adverse events compared to traditional prediction models. Operative decisions made may be tailored based on a patient's specific characteristics, allowing for maximized, personalized benefit.
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Affiliation(s)
- Rashmi Nedadur
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
| | - Nitish Bhatt
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
| | - Jennifer Chung
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
| | - Michael W A Chu
- Department of Cardiac Surgery, London Health Sciences Center, London, Ontario, Canada
| | - Maral Ouzounian
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada.
| | - Bo Wang
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
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12
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Xu D, Cunningham J, Marti-Castellote PM, Zhang L, Patel-Murray NL, Prescott MF, Chutkow W, Mendelson MM, Solomon SD, Claggett BL. Machine Learning for Proteomic Risk Scores in Heart Failure. J Card Fail 2023; 29:1583-1585. [PMID: 37722615 DOI: 10.1016/j.cardfail.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/20/2023]
Affiliation(s)
- Dongchu Xu
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA
| | | | | | - Luqing Zhang
- Novartis Institutes for Biomedical Research, Cambridge, MA
| | | | | | | | | | - Scott D Solomon
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA.
| | - Brian L Claggett
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA
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13
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Kresoja KP, Unterhuber M, Wachter R, Rommel KP, Besler C, Shah S, Thiele H, Edelmann F, Lurz P. Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials. EBioMedicine 2023; 96:104795. [PMID: 37689023 PMCID: PMC10498181 DOI: 10.1016/j.ebiom.2023.104795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/11/2023] Open
Abstract
BACKGROUND Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large randomized clinical trials. METHODS Using a reiterative cluster allocating permutation approach, patients from the derivation cohort (Aldo-DHF) were identified according to their treatment response to spironolactone with respect to improvement in E/e'. Heterogenous features of response ('responders' and 'non-responders') were characterized by an extreme gradient boosting (XGBoost) algorithm. XGBoost was used to predict treatment response in the validation cohort (TOPCAT). The primary endpoint of the validation cohort was a combined endpoint of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. Patients with missing variables for the XGboost model were excluded from the validation analysis. FINDINGS Out of 422 patients from the derivation cohort, reiterative cluster allocating permutation identified 159 patients (38%) as spironolactone responders, in whom E/e' significantly improved (p = 0.005). Within the validation cohort (n = 525) spironolactone treatment significantly reduced the occurrence of the primary outcome among responders (n = 185, p log rank = 0.008), but not among patients in the non-responder group (n = 340, p log rank = 0.52). INTERPRETATION Machine learning approaches might aid in identifying HFpEF patients who are likely to show a favorable therapeutic response to spironolactone. FUNDING See Acknowledgements section at the end of the manuscript.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig and Clinic for Cardiology and Pneumology, University Medicine Göttingen, Germany; German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Germany
| | - Karl-Philipp Rommel
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany
| | - Christian Besler
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany
| | - Sanjiv Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, USA
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany
| | - Frank Edelmann
- Department of Internal Medicine and Cardiology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum and German Cardiovascular Research Center (DZHK), Partner Site Berlin, Germany
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany; Leipzig Heart Institute at Heart Center Leipzig, Leipzig, Germany.
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14
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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15
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Ren H, Sun Y, Xu C, Fang M, Xu Z, Jing F, Wang W, Tse G, Zhang Q, Cheng W, Jin W. Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach. Curr Probl Cardiol 2023; 48:101480. [PMID: 36336116 DOI: 10.1016/j.cpcardiol.2022.101480] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.
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Affiliation(s)
- Hao Ren
- Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Yu Sun
- Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chenyu Xu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Ming Fang
- Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Zhongzhi Xu
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Fengshi Jing
- Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China; UNC Project-China, UNC Global, School of Medicine, University of North Carolina at Chapel Hill, NC
| | - Weilan Wang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China; Kent and Medway Medical School, Canterbury, Kent, UK
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Weibin Cheng
- Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
| | - Wen Jin
- Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China.
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16
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Li H, Shen C, Wang G, Sun Q, Yu K, Li Z, Liang X, Chen R, Wu H, Wang F, Wang Z, Lian C. BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference. Brief Bioinform 2023; 24:6960974. [PMID: 36572655 DOI: 10.1093/bib/bbac557] [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: 06/26/2022] [Revised: 10/28/2022] [Indexed: 12/28/2022] Open
Abstract
The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.
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Affiliation(s)
- Huiyu Li
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chen Shen
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Gongji Wang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Qinru Sun
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Kai Yu
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Zefeng Li
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - XingGong Liang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Run Chen
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Hao Wu
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Zhenyuan Wang
- Key Laboratory of National Ministry of Health for Forensic Sciences, School of Medicine & Forensics, Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chunfeng Lian
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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17
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Moore JS, Nesbit MA, Moore T. Appraisal of Cardiovascular Risk Factors, Biomarkers, and Ocular Imaging in Cardiovascular Risk Prediction. Curr Cardiol Rev 2023; 19:72-81. [PMID: 37497700 PMCID: PMC10636798 DOI: 10.2174/1573403x19666230727101926] [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: 09/30/2022] [Revised: 05/12/2023] [Accepted: 06/15/2023] [Indexed: 07/28/2023] Open
Abstract
Cardiovascular disease remains a leading cause of death worldwide despite the use of available cardiovascular disease risk prediction tools. Identification of high-risk individuals via risk stratification and screening at sub-clinical stages, which may be offered by ocular screening, is important to prevent major adverse cardiac events. Retinal microvasculature has been widely researched for potential application in both diabetes and cardiovascular disease risk prediction. However, the conjunctival microvasculature as a tool for cardiovascular disease risk prediction remains largely unexplored. The purpose of this review is to evaluate the current cardiovascular risk assessment methods, identifying gaps in the literature that imaging of the ocular microcirculation may have the potential to fill. This review also explores the themes of machine learning, risk scores, biomarkers, medical imaging, and clinical risk factors. Cardiovascular risk classification varies based on the population assessed, the risk factors included, and the assessment methods. A more tailored, standardised and feasible approach to cardiovascular risk prediction that utilises technological and medical imaging advances, which may be offered by ocular imaging, is required to support cardiovascular disease prevention strategies and clinical guidelines.
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Affiliation(s)
- Julie S. Moore
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
| | - M. Andrew Nesbit
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
| | - Tara Moore
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
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18
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Li JX, Li L, Zhong X, Fan SJ, Cen T, Wang J, He C, Zhang Z, Luo YN, Liu XX, Hu LX, Zhang YD, Qiu HL, Dong GH, Zou XG, Yang BY. Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS). Glob Health Res Policy 2022; 7:48. [PMID: 36474302 PMCID: PMC9724436 DOI: 10.1186/s41256-022-00282-y] [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: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. METHODS A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods-Random Forest, Random Ferns, and Extreme Gradient Boosting-to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. RESULTS The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. CONCLUSIONS CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.
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Affiliation(s)
- Jia-Xin Li
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li Li
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Xuemei Zhong
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Shu-Jun Fan
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Tao Cen
- grid.284723.80000 0000 8877 7471Department of Research and Development, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Jianquan Wang
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Chuanjiang He
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Zhoubin Zhang
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Ya-Na Luo
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Xuan Liu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li-Xin Hu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Yi-Dan Zhang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Hui-Ling Qiu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Guang-Hui Dong
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Guang Zou
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Bo-Yi Yang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
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19
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Unterhuber M, Kresoja KP, Lurz P, Thiele H. Artificial intelligence in proteomics: new frontiers from risk prediction to treatment? Eur Heart J 2022; 43:4525-4527. [PMID: 35869894 DOI: 10.1093/eurheartj/ehac391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Matthias Unterhuber
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig 04289, Germany
| | - Karl-Patrik Kresoja
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig 04289, Germany
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig 04289, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University of Leipzig, Strümpellstraße 39, Leipzig 04289, Germany.,Leipzig Heart Institute at Heart Center Leipzig, Russenstraße 69A, Leipzig 04289, Germany
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Bao J, Liu S, Liang X, Wang C, Cao L, Li Z, Wei F, Fu A, Shi Y, Shen B, Zhu X, Zhao Y, Liu H, Miao L, Wang Y, Liang S, Wu L, Huang J, Guo T, Liu F. A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters. Life Sci Alliance 2022; 6:6/1/e202201576. [PMID: 36261228 PMCID: PMC9585965 DOI: 10.26508/lsa.202201576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 11/24/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.
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Affiliation(s)
- Jianfeng Bao
- Department of Hepatology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shourong Liu
- Department of Hepatology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Liang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Congcong Wang
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lili Cao
- Department of Nursing, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhaoyi Li
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Furong Wei
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ai Fu
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yingqiu Shi
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Bo Shen
- Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Xiaoli Zhu
- Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Yuge Zhao
- Department of Pathology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hong Liu
- Department of Pathology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liangbin Miao
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Wang
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuang Liang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Linyan Wu
- Department of Nursing, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jinsong Huang
- Department of Hepatology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China .,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
| | - Fang Liu
- Insititute of Hepatology and Epidemiology, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China
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21
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Baragetti A, Mattavelli E, Grigore L, Pellegatta F, Magni P, Catapano AL. Targeted Plasma Proteomics to Predict the Development of Carotid Plaques. Stroke 2022; 53:e411-e414. [PMID: 35775410 DOI: 10.1161/strokeaha.122.038887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cardiovascular risk stratification in primary prevention is a clinical challenge. We recently identified a large set of circulating proteins improving the risk prediction for cardiovascular events. We now evaluate which of these proteins predicts the development of subclinical carotid atherosclerosis (SCA) in primary cardiovascular prevention. METHODS Three hundred sixty-eight proteins were quantified, by proximity extension assay, from the plasma collected at basal visit from 586 subjects without previous cardiovascular events and without preclinical atherosclerosis. These subjects were reevaluated 11 years after median follow-up (10-12) in a longitudinal observational analysis, to assess the development of SCA, defined as the formation of focal lesion in any carotid tract and detected by carotid ultrasound at basal visit and after follow-up. Common carotid (intima-media thickness [IMT]) was also measured by ultrasound during the same follow-up to identify subjects with faster common carotid intima-media thickness (IMT) progression (increase IMT)>1.3 mm in the common carotid tract). RESULTS The variation of 68 proteins predicted SCA development and, among them, higher levels of PIgR2 (polymeric immunoglobulin receptor), chemokine (C-C motif) ligand 18, CA1 (carbonic anhydrase 1), Fc gamma receptor IIa and reduced MMP10 (matrix metallopeptidase 10), GT (gastrotropin), IL7R (interleukin 7 receptor) were the most predictive for SCA development. These 7 proteins improved the sensitivity and the specificity for SCA development versus risk factors (age, sex, overweight, hypertension, low HDL-cholesterol, high triglyceride); area under the curve: 0.747 ([0.707-0.784] versus 0.620 [0.577-0.663]; P<0.001). Vice versa, 25 proteins (not in common with the previous 68) predicted faster common carotid IMT progression. Among them, increased IL7D (interleukin 7), chemokine (C-X-C motif) ligand 1, and reduced TNFS13B (TNF superfamily member 13b) significantly increased the sensitivity and the specificity to predict faster common carotid IMT progression as compared with same risk factors (area under the curve: 0.719 [0.680-0.756] versus 0.569 [0.527-0.610]; P<0.001). CONCLUSIONS A new set of circulating proteins have been identified that may be considered as markers of preclinical atherosclerosis development. The difference of the protein identified to predict SCA versus IMT progression may reflect different etiological factors.
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Affiliation(s)
- Andrea Baragetti
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy (A.B., E.M., P.M., A.L.C.).,IRCCS Multimedica, Milan, Italy (A.B., L.G., F.P., P.M., A.L.C.)
| | - Elisa Mattavelli
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy (A.B., E.M., P.M., A.L.C.).,S.I.S.A. Centre for the Study of Atherosclerosis, Bassini Hospital, Cinisello Balsamo, Milan, Italy (E.M.)
| | - Liliana Grigore
- IRCCS Multimedica, Milan, Italy (A.B., L.G., F.P., P.M., A.L.C.)
| | - Fabio Pellegatta
- IRCCS Multimedica, Milan, Italy (A.B., L.G., F.P., P.M., A.L.C.)
| | - Paolo Magni
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy (A.B., E.M., P.M., A.L.C.).,IRCCS Multimedica, Milan, Italy (A.B., L.G., F.P., P.M., A.L.C.)
| | - Alberico Luigi Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy (A.B., E.M., P.M., A.L.C.).,IRCCS Multimedica, Milan, Italy (A.B., L.G., F.P., P.M., A.L.C.)
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22
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Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement. J Interv Cardiol 2022; 2022:1368878. [PMID: 35539443 PMCID: PMC9046000 DOI: 10.1155/2022/1368878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/20/2021] [Accepted: 03/21/2022] [Indexed: 12/04/2022] Open
Abstract
Background Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS). Methods Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed. Results Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943–0.997) p=0.032; automated: HR 0.967 (95% CI 0.939–0.995) p=0.022) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938–0.999) p=0.043; automated: HR 0.963 [95% CI 0.933–0.995] p=0.024; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920–0.996) p=0.027; automated: HR 0.954 (95% CI 0.920–0.989) p=0.011). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p=0.21). Absolute values of LV volumes differed significantly between automated and manual approaches (p < 0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient. Conclusion Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR.
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23
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Leopold JA. Personalizing treatments for patients based on cardiovascular phenotyping. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2022; 7:4-16. [PMID: 36778892 PMCID: PMC9913616 DOI: 10.1080/23808993.2022.2028548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Introduction Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes. Areas covered With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient's unique phenotype. Expert opinion Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.
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Affiliation(s)
- Jane A. Leopold
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, 77 Ave Louis Pasteur, NRB0630K, Boston, Massachusetts, USA
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24
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Identifying Atrial Fibrillation Mechanisms for Personalized Medicine. J Clin Med 2021; 10:jcm10235679. [PMID: 34884381 PMCID: PMC8658178 DOI: 10.3390/jcm10235679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/27/2021] [Accepted: 11/28/2021] [Indexed: 01/02/2023] Open
Abstract
Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.
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25
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Hulot JS, Clopton P. When Natural Peptides Meet Artificial Intelligence to Improve Risk Prediction. J Am Coll Cardiol 2021; 78:1632-1634. [PMID: 34649701 DOI: 10.1016/j.jacc.2021.08.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Affiliation(s)
- Jean-Sébastien Hulot
- Université de Paris, INSERM, PARCC, Paris, France; CIC1418 and DMU CARTE, AP-HP, Hôpital Européen Georges-Pompidou, Paris, France.
| | - Paul Clopton
- Stanford University School of Medicine, Stanford, California, USA
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