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Kuang Z, Kong M, Yan N, Ma X, Wu M, Li J. Precision Cardio-oncology: Update on Omics-Based Diagnostic Methods. Curr Treat Options Oncol 2024; 25:679-701. [PMID: 38676836 PMCID: PMC11082000 DOI: 10.1007/s11864-024-01203-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2024] [Indexed: 04/29/2024]
Abstract
OPINION STATEMENT Cardio-oncology is an emerging interdisciplinary field dedicated to the early detection and treatment of adverse cardiovascular events associated with anticancer treatment, and current clinical management of anticancer-treatment-related cardiovascular toxicity (CTR-CVT) remains limited by a lack of detailed phenotypic data. However, the promise of diagnosing CTR-CVT using deep phenotyping has emerged with the development of precision medicine, particularly the use of omics-based methodologies to discover sensitive biomarkers of the disease. In the future, combining information produced by a variety of omics methodologies could expand the clinical practice of cardio-oncology. In this review, we demonstrate how omics approaches can improve our comprehension of CTR-CVT deep phenotyping, discuss the positive and negative aspects of available omics approaches for CTR-CVT diagnosis, and outline how to integrate multiple sets of omics data into individualized monitoring and treatment. This will offer a reliable technical route for lowering cardiovascular morbidity and mortality in cancer patients and survivors.
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Affiliation(s)
- Ziyu Kuang
- Oncology Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Miao Kong
- Oncology Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ningzhe Yan
- Oncology Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Xinyi Ma
- Oncology Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Min Wu
- Cardiovascular Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
| | - Jie Li
- Oncology Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
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Padte S, Samala Venkata V, Mehta P, Tawfeeq S, Kashyap R, Surani S. 21st century critical care medicine: An overview. World J Crit Care Med 2024; 13:90176. [PMID: 38633477 PMCID: PMC11019625 DOI: 10.5492/wjccm.v13.i1.90176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/28/2023] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.
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Affiliation(s)
- Smitesh Padte
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | | | - Priyal Mehta
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Sawsan Tawfeeq
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
| | - Rahul Kashyap
- Department of Research, Global Remote Research Scholars Program, St. Paul, MN 55104, United States
- Department of Research, WellSpan Health, York, PA 17403, United States
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Salim Surani
- Department of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
- Department of Medicine & Pharmacology, Texas A&M University, College Station, TX 77843, United States
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Zhang K, Fan Y, Long K, Lan Y, Gao P. Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study. J Multidiscip Healthc 2023; 16:2155-2166. [PMID: 37539364 PMCID: PMC10395519 DOI: 10.2147/jmdh.s420709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/25/2023] [Indexed: 08/05/2023] Open
Abstract
Background Interest in the application of deep learning (DL) in critical care medicine (CCM) is growing rapidly. However, comprehensive bibliometric research that analyze and measure the global literature is still lacking. Objective The present study aimed to systematically evaluate the research hotspots and trends of DL in CCM worldwide based on the output of publications, cooperative relationships of research, citations, and the co-occurrence of keywords. Methods A total of 1708 articles in all were obtained from Web of Science. Bibliometric analysis was performed by Bibliometrix package in R software (4.2.2), Microsoft Excel 2019, VOSviewer (1.6.18), and CiteSpace (5.8.R3). Results The annual publications increased steeply in the past five years, accounting for 95.67% (1634/1708) of all the included literature. China and USA contributed to approximately 71.66% (1244/1708) of all publications. Seven of the top ten most productive organizations rank in the top 100 universities globally. Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms were all commonly used DL technologies. Conclusion Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Extensive collaborative research to improve the maturity and robustness of the model remains necessary to make DL-based model applications sufficiently compelling for conventional CCM practice.
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Affiliation(s)
- Kaichen Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Yihua Fan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Kunlan Long
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Ying Lan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Peiyang Gao
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
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Gary PJ, Lal A, Simonetto DA, Gajic O, Gallo de Moraes A. Acute on chronic liver failure: prognostic models and artificial intelligence applications. Hepatol Commun 2023; 7:02009842-202304010-00015. [PMID: 36972378 PMCID: PMC10043584 DOI: 10.1097/hc9.0000000000000095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023] Open
Abstract
Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.
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Affiliation(s)
- Phillip J Gary
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Amos Lal
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Ognjen Gajic
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
| | - Alice Gallo de Moraes
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, Minnesota, USA
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Rostam Niakan Kalhori S, Deserno TM, Haghi M, Ganapathy N. A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms. PLoS One 2023; 18:e0283010. [PMID: 36920960 PMCID: PMC10016632 DOI: 10.1371/journal.pone.0283010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 02/28/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). METHODOLOGY This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. DISCUSSION This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. TRIAL REGISTRATION Systematic review registration: PROSPERO CRD42022334988.
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Affiliation(s)
- Sharareh Rostam Niakan Kalhori
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- * E-mail:
| | - Thomas M. Deserno
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
| | - Mostafa Haghi
- Ubiquitous Computing Laboratory, Department of Computer Science, Konstanz University of Applied Sciences, Konstanz, Germany
| | - Nagarajan Ganapathy
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
- Biomedical Informatics Laboratory, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
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MONTOMOLI J, HILTY MP, INCE C. Artificial intelligence in intensive care: moving towards clinical decision support systems. Minerva Anestesiol 2022; 88:1066-1072. [DOI: 10.23736/s0375-9393.22.16739-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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