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Rubulotta F, Bahrami S, Marshall DC, Komorowski M. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction. Crit Care Med 2024:00003246-990000000-00361. [PMID: 39133071 DOI: 10.1097/ccm.0000000000006390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.
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Affiliation(s)
- Francesca Rubulotta
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Sahar Bahrami
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Dominic C Marshall
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
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Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD. A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 2024; 25:232. [PMID: 38834976 DOI: 10.1186/s12931-024-02834-x] [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: 02/13/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
AIM Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
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Affiliation(s)
- Tu K Tran
- Department of Engineering and Science, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Minh C Tran
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Arun Joseph
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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Ali H, Inayat F, Dhillon R, Patel P, Afzal A, Wilkinson C, Rehman AU, Anwar MS, Nawaz G, Chaudhry A, Awan JR, Afzal MS, Samanta J, Adler DG, Mohan BP. Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning. Proc AMIA Symp 2024; 37:437-447. [PMID: 38628340 PMCID: PMC11018057 DOI: 10.1080/08998280.2024.2326371] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/19/2024] [Indexed: 04/19/2024] Open
Abstract
Background Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. Methods The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. Results A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. Conclusions This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.
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Affiliation(s)
- Hassam Ali
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Faisal Inayat
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | - Rubaid Dhillon
- Department of Gastroenterology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Pratik Patel
- Department of Gastroenterology, Mather Hospital and Hofstra University Zucker School of Medicine, Port Jefferson, New York, USA
| | - Arslan Afzal
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Christin Wilkinson
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Attiq Ur Rehman
- Department of Hepatology, Geisinger Wyoming Valley Medical Center, Wilkes-Barre, Pennsylvania, USA
| | - Muhammad Sajeel Anwar
- Department of Internal Medicine, UHS Wilson Medical Center, Johnson City, New York, USA
| | - Gul Nawaz
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | | | - Junaid Rasul Awan
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | - Muhammad Sohaib Afzal
- Department of Internal Medicine, Louisiana State University Health, Shreveport, Louisiana, USA
| | - Jayanta Samanta
- Department of Gastroenterology, Post Graduate Institute of Medical Research and Education, Chandigarh, Punjab, India
| | - Douglas G. Adler
- Center for Advanced Therapeutic Endoscopy, Porter Adventist Hospital, Centura Health, Denver, Colorado, USA
| | - Babu P. Mohan
- Department of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, Utah, USA
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Hu JX, Zhao CF, Wang SL, Tu XY, Huang WB, Chen JN, Xie Y, Chen CR. Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence. World J Gastroenterol 2023; 29:5268-5291. [PMID: 37899784 PMCID: PMC10600804 DOI: 10.3748/wjg.v29.i37.5268] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/31/2023] [Accepted: 09/14/2023] [Indexed: 09/25/2023] Open
Abstract
Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.
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Affiliation(s)
- Jian-Xiong Hu
- Intensive Care Unit, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
| | - Cheng-Fei Zhao
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, Fujian Province, China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine, Putian University, Putian 351100, Fujian Province, China
| | - Shu-Ling Wang
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Xiao-Yan Tu
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Wei-Bin Huang
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Jun-Nian Chen
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Ying Xie
- School of Mechanical, Electrical and Information Engineering, Putian University, Putian 351100, Fujian Province, China
| | - Cun-Rong Chen
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
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Yehia Kamel M, Zekry Attia J, Mahmoud Ahmed S, Hassan Saeed Z, Welson NN, Yehia Abdelzaher W. Protective effect of rivastigmine against lung injury in acute pancreatitis model in rats via Hsp 70/IL6/ NF-κB signaling cascade. Int J Immunopathol Pharmacol 2023; 37:3946320231222804. [PMID: 38112159 PMCID: PMC10734328 DOI: 10.1177/03946320231222804] [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: 02/11/2023] [Accepted: 12/08/2023] [Indexed: 12/20/2023] Open
Abstract
Acute lung injury (ALI) that develops as a result of AP can progress to acute respiratory distress syndrome. Some hypotheses are proposed to explain the pathophysiology of AP and its related pulmonary hazards. This experiment aimed to evaluate the mitigating action of rivastigmine (Riva) in lung injury that occurs on the top of acute pancreatitis (AP) induced in rats. Thirty-two male Wister rats were randomized to one of four groups: control, Riva-treated, acute pancreatitis (AP), and acute pancreatitis treated by Riva. Serum amylase and lipase levels were assessed. Pulmonary oxidative stress and inflammatory indicators were estimated. A pancreatic and pulmonary histopathological examination, as well as an immunohistochemical study of HSP70, was carried out. Riva significantly attenuated the L-arginine-related lung injury that was characterized by increased pulmonary inflammatory biomarkers (interleukin-6 [IL-6]), nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α), increased pulmonary oxidative markers (total nitrite/nitrate [NOx]), MDA, decreased total antioxidant capacity (TAC), and reduced glutathione level (GSH)) with increased caspase-3 expression. Therefore, Riva retains potent ameliorative effects against lung injury that occur on the top of AP by relieving oxidative stress, inflammation, and apoptosis via HSP70/IL6/NF-κB signaling.
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Affiliation(s)
- Maha Yehia Kamel
- Department of Pharmacology, Minia University, Faculty of Medicine, Minia, Egypt
| | - Josef Zekry Attia
- Department of Anesthesia and I.C.U, Minia University, Faculty of Medicine, Minia, Egypt
| | - Sabreen Mahmoud Ahmed
- Department of Human Anatomy and Embryology, Faculty of Medicine, Minia University, Delegated to Deraya University, New Minia City, Egypt
| | | | - Nermeen N Welson
- Department of Forensic Medicine and Clinical Toxicology, Beni-Suef University, Faculty of Medicine, Beni Suef, Egypt
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