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Heerdt PM, Kheyfets VO, Oakland HT, Joseph P, Singh I. Right Ventricular Pressure Waveform Analysis-Clinical Relevance and Future Directions. J Cardiothorac Vasc Anesth 2024:S1053-0770(24)00395-1. [PMID: 39025682 DOI: 10.1053/j.jvca.2024.06.022] [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: 05/11/2024] [Revised: 06/02/2024] [Accepted: 06/15/2024] [Indexed: 07/20/2024]
Abstract
Continuous measurement of pressure in the right atrium and pulmonary artery has commonly been used to monitor right ventricular function in critically ill and surgical patients. This approach is largely based upon the assumption that right atrial and pulmonary arterial pressures provide accurate surrogates for diastolic filling and peak right ventricular pressures, respectively. However, due to both technical and physiologic factors, this assumption is not always true. Accordingly, recent studies have begun to emphasize the potential clinical value of also measuring right ventricular pressure at the bedside. This has highlighted both past and emerging research demonstrating the utility of analyzing not only the amplitude of right ventricular pressure but also the shape of the pressure waveform. This brief review summarizes data demonstrating that combining conventional measurements of right ventricular pressure with variables derived from waveform shape allows for more comprehensive and ideally continuous bedside assessment of right ventricular function, particularly when combined with stroke volume measurement or 3D echocardiography, and discusses the potential use of right ventricular pressure analysis in computational models for evaluating cardiac function.
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Affiliation(s)
- Paul M Heerdt
- Department of Anesthesiology, Applied Hemodynamics, Yale School of Medicine, New Haven, CT.
| | - Vitaly O Kheyfets
- Department of Pediatrics-Critical Care Medicine, University of Colorado - Anschutz Medical Campus, Denver, CO
| | - Hannah T Oakland
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
| | - Phillip Joseph
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
| | - Inderjit Singh
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT
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Liao Z, Liu K, Ding S, Zhao Q, Jiang Y, Wang L, Huang T, Yang L, Luo D, Zhang E, Zhang Y, Zhang C, Xu X, Fei H. Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning. Pulm Circ 2023; 13:e12272. [PMID: 37547487 PMCID: PMC10401077 DOI: 10.1002/pul2.12272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/10/2023] [Accepted: 07/15/2023] [Indexed: 08/08/2023] Open
Abstract
Echocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization. Echocardiographic images on parasternal short axis-papillary muscle level (PSAX-PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver-operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892, p = 0.027 [area under the curve [AUC]]). Similar findings were observed in subgroup analysis and validated in the external validation set (AUC = 0.950 [95% CI: 0.897-1.000]). In summary, ML methods could automatically extract features from traditional PSAX-PML view and automatically assess the probability of PH, which were found to outperform traditional echocardiographic assessments.
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Affiliation(s)
- Zuwei Liao
- Shantou University Medical CollegeShantouGuangdongChina
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
| | - Kaikai Liu
- School of Information EngineeringNorthwest A&F UniversityYanglingShanxiChina
| | - Shangwei Ding
- Department of UltrasoundThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhouGuangdongChina
| | - Qinhua Zhao
- Department of Pulmonary CirculationShanghai Pulmonary Hospital, Tongji University School of MedicineShanghaiChina
| | - Yong Jiang
- State Key Laboratory of Cardiovascular Disease, Department of EchocardiographyNational Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Department of EchocardiographyFuwai Hospital Chinese Academy of Medical SciencesShenzhenChina
| | - Lan Wang
- Department of Pulmonary CirculationShanghai Pulmonary Hospital, Tongji University School of MedicineShanghaiChina
| | - Taoran Huang
- Shantou University Medical CollegeShantouGuangdongChina
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
| | - LiFang Yang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
| | - Dongling Luo
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
| | - Erlei Zhang
- School of Information EngineeringNorthwest A&F UniversityYanglingShanxiChina
| | - Yu Zhang
- School of Information EngineeringNorthwest A&F UniversityYanglingShanxiChina
| | - Caojin Zhang
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of South China Structural Heart DiseaseGuangzhouGuangdongChina
| | - Xiaowei Xu
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of South China Structural Heart DiseaseGuangzhouGuangdongChina
| | - Hongwen Fei
- Shantou University Medical CollegeShantouGuangdongChina
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdongChina
- Guangdong Provincial Key Laboratory of South China Structural Heart DiseaseGuangzhouGuangdongChina
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Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani DN, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam SP. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. SENSORS (BASEL, SWITZERLAND) 2023; 23:5744. [PMID: 37420919 DOI: 10.3390/s23125744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.
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Affiliation(s)
- Sharanya Manga
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Neha Muthavarapu
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Renisha Redij
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Avneet Kaur
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Sunil Gaddam
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Rutuja Shinde
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Poulami Samaddar
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi N Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Suganti Shivaram
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shuvashis Dey
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA
| | - Dipankar Mitra
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Computer Science, University of Wisconsin-La Crosse, La Crosse, WI 54601, USA
| | - Sayan Roy
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Electrical Engineering and Computer Science, South Dakota Mines, Rapid City, SD 57701, USA
| | - Kanchan Kulkarni
- Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, INSERM, U1045, 33000 Bordeaux, France
- IHU Liryc, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Bordeaux, 33600 Pessac, France
| | - Shivaram P Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Ledziński Ł, Grześk G. Artificial Intelligence Technologies in Cardiology. J Cardiovasc Dev Dis 2023; 10:jcdd10050202. [PMID: 37233169 DOI: 10.3390/jcdd10050202] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
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Affiliation(s)
- Łukasz Ledziński
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | - Grzegorz Grześk
- Department of Cardiology and Clinical Pharmacology, Faculty of Health Sciences, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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