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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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He W, Xi Q, Cui H, Zhang P, Huang R, Wang T, Wang D. Liang-Ge Decoction Ameliorates Coagulation Dysfunction in Cecal Ligation and Puncture-Induced Sepsis Model Rats through Inhibiting PAD4-Dependent Neutrophil Extracellular Trap Formation. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2023; 2023:5042953. [PMID: 37159591 PMCID: PMC10163969 DOI: 10.1155/2023/5042953] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 01/30/2023] [Accepted: 02/14/2023] [Indexed: 05/11/2023]
Abstract
Liang-Ge (LG) decoction could ameliorate coagulation dysfunction in septic model rats. However, the mechanism of LG in treating sepsis still needs to be clarified. Our current study established a septic rat model to evaluate the effect of LG on coagulation dysfunction in septic rats first. Second, we investigated the effect of LG on NET formation in septic rats. Finally, NETs and PAD4 inhibitors were further used to clarify if LG could improve the mechanism of sepsis coagulation dysfunction by inhibiting NET formation. Our findings indicated that treatment with LG improved the survival rate, reduced inflammatory factor levels, enhanced hepatic and renal function, and reduced pathological changes in rats with sepsis. LG could also alleviate coagulation dysfunction in septic model rats. Besides, LG treatment reduced NETs formation and decreased PAD4 expression in neutrophiles. In addition, LG treatment showed a similar result in comparison to the treatment with either NET inhibitors or PAD4 inhibitors alone. In conclusion, this study confirmed that LG has therapeutic effects on septic rats. Furthermore, the improvement of coagulation dysfunction in septic rats by LG was achieved through inhibiting PAD4-mediated NET formation.
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Affiliation(s)
- Wenju He
- Department of Integration of Traditional Chinese and Western Medicine, First Central Hospital Affiliated to Nankai University, Tianjin First Central Hospital, Tianjin, China
| | - Qiang Xi
- Department of Practice and Education, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Huantian Cui
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Shandong University, Qingdao, China
| | - Pingping Zhang
- Department of Integration of Traditional Chinese and Western Medicine, First Central Hospital Affiliated to Nankai University, Tianjin First Central Hospital, Tianjin, China
| | - Rui Huang
- Department of Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Taihuan Wang
- Department of Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dongqiang Wang
- Department of Integration of Traditional Chinese and Western Medicine, First Central Hospital Affiliated to Nankai University, Tianjin First Central Hospital, Tianjin, China
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Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. [PMID: 36465940 PMCID: PMC9715756 DOI: 10.3389/fmed.2022.935366] [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: 05/03/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.
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Affiliation(s)
- Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Ming Chao
- Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying, Taiwan
- Department of Dental Laboratory Technology, Min-Hwei College of Health Care Management, Liouying, Taiwan
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung, Taiwan
- Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan
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Villagrana-Bañuelos KE, Galván-Tejada CE, Galván-Tejada JI, Gamboa-Rosales H, Celaya-Padilla JM, Soto-Murillo MA, Solís-Robles R. Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome. Healthcare (Basel) 2022; 10:healthcare10071303. [PMID: 35885829 PMCID: PMC9317003 DOI: 10.3390/healthcare10071303] [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: 05/12/2022] [Revised: 07/03/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022] Open
Abstract
Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.
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Ren K, Wang L, Wang Y, An G, Du Q, Cao J, Jin Q, Yun K, Guo Z, Wang Y, Liang Q, Sun J. Wound age estimation based on next-generation sequencing: Fitting the optimal index system using machine learning. Forensic Sci Int Genet 2022; 59:102722. [DOI: 10.1016/j.fsigen.2022.102722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/30/2022] [Accepted: 05/10/2022] [Indexed: 11/04/2022]
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Hong X, Wang J, Li S, Zhao Z, Feng Z. RETRACTED: MicroRNA-375-3p in endothelial progenitor cells-derived extracellular vesicles relieves myocardial injury in septic rats via BRD4-mediated PI3K/AKT signaling pathway. Int Immunopharmacol 2021; 96:107740. [PMID: 34020393 DOI: 10.1016/j.intimp.2021.107740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/08/2021] [Accepted: 04/27/2021] [Indexed: 02/04/2023]
Abstract
This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Concern was raised about the reliability of the Western blot results in Figs. 1E, 4A+F, 5A+B and Supplementary Fig. 1O+P, which appear to have the same eyebrow shaped phenotype as many other publications tabulated here (https://docs.google.com/spreadsheets/d/149EjFXVxpwkBXYJOnOHb6RhAqT4a2llhj9LM60MBffM/edit#gid=0 [docs.google.com]). The journal requested the corresponding author comment on these concerns and provide the raw data. However, the authors were not responsive to the request for comment. Since original data could not be provided, the overall validity of the results could not be confirmed. Therefore, the Editor-in-Chief decided to retract the article.
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Affiliation(s)
- Xiaoyang Hong
- PICU, The Seventh Medical Center, PLA General Hospital, Beijing 100700, China
| | - Jie Wang
- Surgical Pediatric Intensive Care Unit, Children's Hospital Affiliated of Zhengzhou University, Zhengzhou, China
| | - Shuanglei Li
- Cardiovascular Surgery Department, PLA General Hospital, Beijing 100853, China
| | - Zhe Zhao
- PICU, The Seventh Medical Center, PLA General Hospital, Beijing 100700, China
| | - Zhichun Feng
- PICU, The Seventh Medical Center, PLA General Hospital, Beijing 100700, China.
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O'Connor KM, Ashoori M, Dias ML, Dempsey EM, O'Halloran KD, McDonald FB. Influence of innate immune activation on endocrine and metabolic pathways in infancy. Am J Physiol Endocrinol Metab 2021; 321:E24-E46. [PMID: 33900849 DOI: 10.1152/ajpendo.00542.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.
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Affiliation(s)
- K M O'Connor
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, Cork, Ireland
| | - M Ashoori
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, Cork, Ireland
- Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
| | - M L Dias
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, Cork, Ireland
| | - E M Dempsey
- Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
- Department of Paediatrics and Child Health, School of Medicine, College of Medicine and Health, Cork University Hospital, Wilton, Cork, Ireland
| | - K D O'Halloran
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, Cork, Ireland
- Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
| | - F B McDonald
- Department of Physiology, School of Medicine, College of Medicine and Health, University College Cork, Cork, Ireland
- Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland
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