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Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024; 16:e59797. [PMID: 38846182 PMCID: PMC11154024 DOI: 10.7759/cureus.59797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.
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Affiliation(s)
- Vinay Suresh
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Kaushal K Singh
- General Medicine, King George's Medical University, Lucknow, IND
| | - Esha Vaish
- Internal Medicine, Mount Sinai Morningside West, New York, USA
| | - Mohan Gurjar
- Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND
| | | | - Yashita Khulbe
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Syed Muzaffar
- Critical Care Medicine, King George's Medical University, Lucknow, IND
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Lu L, Zhu T, Ribeiro AH, Clifton L, Zhao E, Zhou J, Ribeiro ALP, Zhang YT, Clifton DA. Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:247-259. [PMID: 38774384 PMCID: PMC11104458 DOI: 10.1093/ehjdh/ztae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 05/24/2024]
Abstract
Aims Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information. Methods and results Utilizing a data set of 2 322 513 ECGs collected from 1 558 772 patients with 7 years follow-up, we developed a deep-learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep-learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Conclusion Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis and the advancement in mortality risk stratification. In addition, it demonstrated the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.
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Affiliation(s)
- Lei Lu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- School of Life Course and Population Sciences, King’s College London, London, SE1 1UL, UK
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
| | - Antonio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Lei Clifton
- Nuffield Department of Population Health, University of Oxford Big Data Institute, Oxford, OX3 7LF, UK
| | - Erying Zhao
- Psychological Science and Health Management Center, Harbin Medical University, Harbin, 150076, China
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Jiandong Zhou
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Division of Health Science, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, and Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Yuan-Ting Zhang
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR, China
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
- Oxford Suzhou Centre for Advanced Research, Suzhou, 215123, China
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Kanaji Y, Ozcan I, Tryon DN, Ahmad A, Sara JDS, Lewis B, Friedman P, Noseworthy PA, Lerman LO, Kakuta T, Attia ZI, Lerman A. Predictive Value of Artificial Intelligence-Enabled Electrocardiography in Patients With Takotsubo Cardiomyopathy. J Am Heart Assoc 2024; 13:e031859. [PMID: 38390798 PMCID: PMC10944041 DOI: 10.1161/jaha.123.031859] [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: 07/19/2023] [Accepted: 12/29/2023] [Indexed: 02/24/2024]
Abstract
BACKGROUND Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well-established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence-augmented ECG (AI-ECG) algorithms in patients with TC. METHODS AND RESULTS This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI-ECG algorithms were used for the estimation of ECG- age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI-ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow-up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high-risk AI-ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high-risk findings detected by AI-ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833-10.66], P=0.001). CONCLUSIONS The combined use of AI-ECG algorithms derived from a single 12-lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high-risk patients with TC.
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Affiliation(s)
- Yoshihisa Kanaji
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
- Division of Cardiovascular MedicineTsuchiura Kyodo General HospitalIbarakiJapan
| | - Ilke Ozcan
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
| | - David N. Tryon
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
| | - Ali Ahmad
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
| | | | - Brad Lewis
- Division of Clinical Trials and BiostatisticsMayo ClinicRochesterMNUSA
| | - Paul Friedman
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
| | | | - Lilach O. Lerman
- Division of Nephrology and HypertensionMayo ClinicRochesterMNUSA
| | - Tsunekazu Kakuta
- Division of Cardiovascular MedicineTsuchiura Kyodo General HospitalIbarakiJapan
| | - Zachi I. Attia
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
| | - Amir Lerman
- Department of Cardiovascular MedicineMayo ClinicRochesterMNUSA
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Boussen S, Benard-Tertrais M, Ogéa M, Malet A, Simeone P, Antonini F, Bruder N, Velly L. Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence. Comput Biol Med 2024; 169:107934. [PMID: 38183707 DOI: 10.1016/j.compbiomed.2024.107934] [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: 05/13/2023] [Revised: 12/10/2023] [Accepted: 01/01/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. METHODS We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. MEASUREMENTS AND MAIN RESULTS The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. CONCLUSIONS The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.
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Affiliation(s)
- Salah Boussen
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Laboratoire de Biomécanique Appliquée-Université Gustave-Eiffel, Aix-Marseille Université, UMR T24, 51 boulevard Pierre Dramard, 13015, Marseille, France.
| | - Manuela Benard-Tertrais
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Mathilde Ogéa
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Arthur Malet
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Pierre Simeone
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France
| | - François Antonini
- Intensive Care and Anesthesiology Department, Hôpital Nord Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Nicolas Bruder
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Lionel Velly
- Intensive Care and Anesthesiology Department, La Timone Teaching Hospital, Aix-Marseille Université Assistance Publique Hôpitaux de Marseille, Marseille, France; Aix Marseille University, CNRS, Inst Neurosci Timone, UMR7289, Marseille, France
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Bouchlarhem A, Bazid Z, Ismaili N, El Ouafi N. Cardiac intensive care unit: where we are in 2023. Front Cardiovasc Med 2023; 10:1201414. [PMID: 38075954 PMCID: PMC10704904 DOI: 10.3389/fcvm.2023.1201414] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/03/2023] [Indexed: 01/19/2024] Open
Abstract
Cardiac intensive care has been a constantly evolving area of research and innovation since the beginning of the 21st century. The story began in 1961 with Desmond Julian's pioneering creation of a coronary intensive care unit to improve the prognosis of patients with myocardial infarction, considered the major cause of death in the world. These units have continued to progress over time, with the introduction of new therapeutic means such as fibrinolysis, invasive hemodynamic monitoring using the Swan-Ganz catheter, and mechanical circulatory assistance, with significant advances in percutaneous interventional coronary and structural procedures. Since acute cardiovascular disease is not limited to the management of acute coronary syndromes and includes other emergencies such as severe arrhythmias, acute heart failure, cardiogenic shock, high-risk pulmonary embolism, severe conduction disorders, and post-implantation monitoring of percutaneous valves, as well as other non-cardiac emergencies, such as septic shock, severe respiratory failure, severe renal failure and the management of cardiac arrest after resuscitation, the conversion of coronary intensive care units into cardiac intensive care units represented an important priority. Today, the cardiac intensive care units (CICU) concept is widely adopted by most healthcare systems, whatever the country's level of development. The main aim of these units remains to improve the overall morbidity and mortality of acute cardiovascular diseases, but also to manage other non-cardiac disorders, such as sepsis and respiratory failure. This diversity of tasks and responsibilities has enabled us to classify these CICUs according to several levels, depending on a variety of parameters, principally the level of care delivered, the staff assigned, the equipment and technologies available, the type of research projects carried out, and the type of connections and networking developed. The European Society of Cardiology (ESC) and the American College of Cardiology (ACC) have detailed this organization in guidelines published initially in 2005 and updated in 2018, with the aim of harmonizing the structure, organization, and care offered by the various CICUs. In this state-of-the-art report, we review the history of the CICUs from the creation of the very first unit in 1968 to the discussion of their current perspectives, with the main objective of knowing what the CICUs will have become by 2023.
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Affiliation(s)
- Amine Bouchlarhem
- Faculty of Medicine and Pharmacy, Mohammed First University, Oujda, Morocco
- Department of Cardiology, Mohammed VI University Hospital, Mohammed First University, Oujda, Morocco
| | - Zakaria Bazid
- Faculty of Medicine and Pharmacy, Mohammed First University, Oujda, Morocco
- Department of Cardiology, Mohammed VI University Hospital, Mohammed First University, Oujda, Morocco
| | - Nabila Ismaili
- Faculty of Medicine and Pharmacy, Mohammed First University, Oujda, Morocco
- Department of Cardiology, Mohammed VI University Hospital, Mohammed First University, Oujda, Morocco
- Faculty of Medicine and Pharmacy, LAMCESM, Mohammed First University, Oujda, Morocco
| | - Noha El Ouafi
- Faculty of Medicine and Pharmacy, Mohammed First University, Oujda, Morocco
- Department of Cardiology, Mohammed VI University Hospital, Mohammed First University, Oujda, Morocco
- Faculty of Medicine and Pharmacy, LAMCESM, Mohammed First University, Oujda, Morocco
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Strange G, Stewart S, Watts A, Playford D. Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study. Open Heart 2023; 10:e002265. [PMID: 37491129 PMCID: PMC10373677 DOI: 10.1136/openhrt-2023-002265] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/30/2023] [Indexed: 07/27/2023] Open
Abstract
OBJECTIVE We developed an artificial intelligence decision support algorithm (AI-DSA) that uses routine echocardiographic measurements to identify severe aortic stenosis (AS) phenotypes associated with high mortality. METHODS 631 824 individuals with 1.08 million echocardiograms were randomly spilt into two groups. Data from 442 276 individuals (70%) entered a Mixture Density Network (MDN) model to train an AI-DSA to predict an aortic valve area <1 cm2, excluding all left ventricular outflow tract velocity or dimension measurements and then using the remainder of echocardiographic measurement data. The optimal probability threshold for severe AS detection was identified at the f1 score probability of 0.235. An automated feature also ensured detection of guideline-defined severe AS. The AI-DSA's performance was independently evaluated in 184 301 (30%) individuals. RESULTS The area under receiver operating characteristic curve for the AI-DSA to detect severe AS was 0.986 (95% CI 0.985 to 0.987) with 4622/88 199 (5.2%) individuals (79.0±11.9 years, 52.4% women) categorised as 'high-probability' severe AS. Of these, 3566 (77.2%) met guideline-defined severe AS. Compared with the AI-derived low-probability AS group (19.2% mortality), the age-adjusted and sex-adjusted OR for actual 5-year mortality was 2.41 (95% CI 2.13 to 2.73) in the high probability AS group (67.9% mortality)-5-year mortality being slightly higher in those with guideline-defined severe AS (69.1% vs 64.4%; age-adjusted and sex-adjusted OR 1.26 (95% CI 1.04 to 1.53), p=0.021). CONCLUSIONS An AI-DSA can identify the echocardiographic measurement characteristics of AS associated with poor survival (with not all cases guideline defined). Deployment of this tool in routine clinical practice could improve expedited identification of severe AS cases and more timely referral for therapy.
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Affiliation(s)
- Geoff Strange
- Cardiology, Heart Research Institute Ltd, Newtown, New South Wales, Australia
- The University of Notre Dame Australia, School of Medicine, Fremantle, Western Australia, Australia
| | - Simon Stewart
- Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia
- School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK
| | - Andrew Watts
- Echo IQ Pty Ltd, Sydney, New South Wales, Australia
| | - David Playford
- School of Medicine, The University of Notre Dame Australia, Fremantle, Western Australia, Australia
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Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, Bibo R, Sabouniaghdam P, Tootooni MS, Bundschuh RA, Lichtenberg A, Aubin H, Schmid F. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Front Med (Lausanne) 2023; 10:1109411. [PMID: 37064042 PMCID: PMC10102653 DOI: 10.3389/fmed.2023.1109411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/10/2023] [Indexed: 04/03/2023] Open
Abstract
BackgroundArtificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.MethodsStudies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.ResultsMore than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.DiscussionClinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.
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Affiliation(s)
- Sobhan Moazemi
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
- *Correspondence: Sobhan Moazemi,
| | - Sahar Vahdati
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Jason Li
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Sebastian Kalkhoff
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Luis J. V. Castano
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Bastian Dewitz
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Roman Bibo
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | | | - Mohammad S. Tootooni
- Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL, United States
| | - Ralph A. Bundschuh
- Nuclear Medicine, Medical Faculty, University Augsburg, Augsburg, Germany
| | - Artur Lichtenberg
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Hug Aubin
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Falko Schmid
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
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Monfredi OJ, Moore CC, Sullivan BA, Keim-Malpass J, Fairchild KD, Loftus TJ, Bihorac A, Krahn KN, Dubrawski A, Lake DE, Moorman JR, Clermont G. Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration. J Electrocardiol 2023; 76:35-38. [PMID: 36434848 PMCID: PMC10061545 DOI: 10.1016/j.jelectrocard.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
Abstract
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Affiliation(s)
- Oliver J Monfredi
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Christopher C Moore
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Brynne A Sullivan
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America
| | - Karen D Fairchild
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Tyler J Loftus
- Department of Surgery, University of Florida, United States of America
| | - Azra Bihorac
- Department of Medicine, University of Florida, United States of America
| | - Katherine N Krahn
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Artur Dubrawski
- Robotics Institute, Carnegie Mellon University, United States of America
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
| | - Gilles Clermont
- Department of Critical Care, University of Pittsburgh, United States of America
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Barrios JP, Tison GH. Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective. Cell Rep Med 2022; 3:100869. [PMID: 36543095 PMCID: PMC9798021 DOI: 10.1016/j.xcrm.2022.100869] [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: 08/03/2022] [Revised: 10/26/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests. Here, we present examples of some impactful advances in cardiovascular medicine using ML across a variety of modalities, with a focus on deep learning applications.
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Affiliation(s)
- Joshua P. Barrios
- Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA
| | - Geoffrey H. Tison
- Department of Medicine, Division of Cardiology, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA,Bakar Computational Health Sciences Institute, University of California, San Francisco, 555 Mission Bay Blvd South Box 3120, San Francisco, CA 94158, USA,Corresponding author
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Jentzer JC, Rayfield C, Soussi S, Berg DD, Kennedy JN, Sinha SS, Baran DA, Brant E, Mebazaa A, Billia F, Kapur NK, Henry TD, Lawler PR. Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2. JACC. ADVANCES 2022; 1:100126. [PMID: 38939698 PMCID: PMC11198618 DOI: 10.1016/j.jacadv.2022.100126] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/30/2022] [Accepted: 08/11/2022] [Indexed: 06/29/2024]
Abstract
Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.
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Affiliation(s)
- Jacob C. Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Corbin Rayfield
- Department of Cardiovascular Medicine, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Sabri Soussi
- Department of Anesthesiology and Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, AP–HP Nord, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), University of Paris, Paris, France
- Interdepartmental Division of Critical Care, Faculty of Medicine, Keenan Research Centre for Biomedical Science and Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - David D. Berg
- TIMI Study Group, Department of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jason N. Kennedy
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, Pennsylvania, USA
| | - Shashank S. Sinha
- INOVA Heart and Vascular Institute, Inova Fairfax Medical Campus, Falls Church, Virginia, USA
| | - David A. Baran
- Cleveland Clinic Heart Vascular and Thoracic Institute, Weston, Florida, USA
| | - Emily Brant
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexandre Mebazaa
- Department of Anesthesiology and Critical Care, Lariboisière - Saint-Louis Hospitals, DMU Parabol, AP–HP Nord, Inserm UMR-S 942, Cardiovascular Markers in Stress Conditions (MASCOT), University of Paris, Paris, France
| | - Filio Billia
- Peter Munk Cardiac Center and Ted Roger’s Center for Heart Research, Toronto, Ontario, Canada
| | - Navin K. Kapur
- The Cardiovascular Center, Tufts Medical Center, Boston, Massachusetts, USA
| | - Timothy D. Henry
- The Carl and Edyth Lindner Center for Research and Education at the Christ Hospital Health Network, Cincinnati, Ohio, USA
| | - Patrick R. Lawler
- Peter Munk Cardiac Center and Ted Roger’s Center for Heart Research, Toronto, Ontario, Canada
- Division of Cardiology and Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
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11
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Rafie N, Jentzer JC, Noseworthy PA, Kashou AH. Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches. Front Artif Intell 2022; 5:876007. [PMID: 35711617 PMCID: PMC9193583 DOI: 10.3389/frai.2022.876007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.
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Affiliation(s)
- Nikita Rafie
- Department of Medicine, Mayo Clinic, Rochester, MN, United States
- *Correspondence: Nikita Rafie
| | - Jacob C. Jentzer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, United States
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
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