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Martinez-Rodrigo A, Castillo JC, Saz-Lara A, Otero-Luis I, Cavero-Redondo I. Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing. Health Inf Sci Syst 2024; 12:34. [PMID: 38707839 PMCID: PMC11068708 DOI: 10.1007/s13755-024-00292-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/19/2024] [Indexed: 05/07/2024] Open
Abstract
Purpose Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health. Methods This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions. Results The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention. Conclusion This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.
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Affiliation(s)
| | - Jose Carlos Castillo
- Systems Automation and Engineering Department, Carlos III University of Madrid, Madrid, Spain
| | - Alicia Saz-Lara
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
| | - Iris Otero-Luis
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
| | - Iván Cavero-Redondo
- Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain
- Facultad de Ciencias de la Salud, Universidad Autonoma de Chile, Talca, Chile
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2
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Fathima AJ, Fasla MMN. A comprehensive review on heart disease prognostication using different artificial intelligence algorithms. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38424704 DOI: 10.1080/10255842.2024.2319706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis.
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Affiliation(s)
- A Jainul Fathima
- Assistant Professor, IT Francis Xavier Engineering College, Tirunelveli - 627003, India
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3
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Liu Y, Huang W, Yang J, Yuan S, Li C, Wang W, Liang Z, Wu A. Construction of a multi-classified decision tree model for identifying malignant pleural effusion and tuberculous pleural effusion. Clin Biochem 2023; 120:110655. [PMID: 37769933 DOI: 10.1016/j.clinbiochem.2023.110655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVE Pleural effusion (PE) is a common clinical complication associated with various disorders. We aimed to utilize laboratory variables and their corresponding ratios in serum and PE for the differential diagnosis of multiple types of PE based on a decision tree (DT) algorithm. METHODS A total of 1435 untreated patients with PE admitted to The First Affiliated Hospital of Ningbo University were enrolled. The demographic and laboratory variables were collected and compared. The receiver operating characteristic curve was used to select important variables for diagnosing malignant pleural effusion (MPE) or tuberculous pleural effusion (TPE) and included in the DT model. The data were divided into the training set and the test set at a ratio of 7:3. The training data was used to develop the DT model, and the test data was for evaluating the model. Independent data was collected as external validation. RESULTS Three PE indicators (carcinoembryonic antigen, adenosine deaminase [ADA], and total protein), two serum indicators (neuron-specific enolase and cytokeratin 19 fragments), and two ratios [high-sensitivity C-reactive protein (hsCRP)/ PE lymphocyte and hsCRP/PE ADA] were used to construct the DT model. The area under the curve (AUC), sensitivity, and specificity for diagnosing MPE were 0.963, 84.0%, 91.6% in the training set, 0.976, 84.1%, 88.6% in the test set, and 0.955,83.3%, 86.7% in the external validation set. The AUC, sensitivity, and specificity of diagnosing TPE were 0.898, 86.8%, 92.3% in the training set, 0.888, 88.8%, 92.7% in the test set, and 0.778, 84.8%, 94.3% in the external validation set. CONCLUSION The DT model showed good diagnostic efficacy and could be applied for the differential diagnosis of MPE and TPE in clinical settings.
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Affiliation(s)
- Yanqing Liu
- Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Weina Huang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Jing Yang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Songbo Yuan
- Department of Laboratory Medicine, the Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Congcong Li
- Hangzhou DIAN Medical Diagnostics Laboratory, Hangzhou, Zhejiang, China
| | - Weiwei Wang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhigang Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
| | - Aihua Wu
- Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
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4
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Vallée A. Arterial stiffness and biological parameters: A decision tree machine learning application in hypertensive participants. PLoS One 2023; 18:e0288298. [PMID: 37418473 DOI: 10.1371/journal.pone.0288298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 06/23/2023] [Indexed: 07/09/2023] Open
Abstract
Arterial stiffness, measured by arterial stiffness index (ASI), could be considered a main denominator in target organ damage among hypertensive subjects. Currently, no reported ASI normal references have been reported. The index of arterial stiffness is evaluated by calculation of a stiffness index. Predicted ASI can be estimated regardless to age, sex, mean blood pressure, and heart rate, to compose an individual stiffness index [(measured ASI-predicted ASI)/predicted ASI]. A stiffness index greater than zero defines arterial stiffness. Thus, the purpose of this study was 1) to determine determinants of stiffness index 2) to perform threshold values to discriminate stiffness index and then 3) to determine hierarchical associations of the determinants by performing a decision tree model among hypertensive participants without CV diseases. A study was conducted from 53,363 healthy participants in the UK Biobank survey to determine predicted ASI. Stiffness index was applied on 49,452 hypertensives without CV diseases to discriminate determinants of positive stiffness index (N = 22,453) from negative index (N = 26,999). The input variables for the models were clinical and biological parameters. The independent classifiers were ranked from the most sensitives: HDL cholesterol≤1.425 mmol/L, smoking pack years≥9.2pack-years, Phosphate≥1.172 mmol/L, to the most specifics: Cystatin c≤0.901 mg/L, Triglycerides≥1.487 mmol/L, Urate≥291.9 μmol/L, ALT≥22.13 U/L, AST≤32.5 U/L, Albumin≤45.92 g/L, Testosterone≥5.181 nmol/L. A decision tree model was performed to determine rules to highlight the different hierarchization and interactions between these classifiers with a higher performance than multiple logistic regression (p<0.001). The stiffness index could be an integrator of CV risk factors and participate in future CV risk management evaluations for preventive strategies. Decision trees can provide accurate and useful classification for clinicians.
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Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Foch hospital, Suresnes, France
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5
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Rousseau-Portalis M, Cymberknop L, Farro I, Armentano R. Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients. Front Cardiovasc Med 2023; 10:1161914. [PMID: 37260949 PMCID: PMC10228741 DOI: 10.3389/fcvm.2023.1161914] [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: 02/08/2023] [Accepted: 05/02/2023] [Indexed: 06/02/2023] Open
Abstract
Many studies found that increased arterial stiffness is significantly associated with the presence and progression of Coronary Calcium Score (CCS). However, none so far have used machine learning algorithms to improve their value. Therefore, this study aims to evaluate the association between carotid-femoral Pulse Wave Velocity (cfPWV) and CCS score through computational clustering. We conducted a retrospective cross-sectional study using data from a cardiovascular risk screening program that included 377 participants. We used an unsupervised clustering algorithm using age, weight, height, blood pressure, heart rate, and cfPWV as input variables. Differences between cluster groups were analyzed through Chi-square and T-student tests. The association between (i) cfPWV and age groups, (ii) log (CCS) and age groups, and (iii) cfPWV and log(CCS) were addressed through linear regression analysis. Clusters were labeled post hoc based on cardiovascular risk. A "higher-risk group" had significantly higher left (0.76 vs. 0.70 mm, P < 0.001) and right (0.71 vs. 0.66 mm, P = 0.003) intima-media thickness, CCS (42 vs. 4 Agatston units, P = 0.012), and ascending (3.40 vs. 3.20 cm, P < 0.001) and descending (2.60 vs. 2.37 cm, P < 0.001) aorta diameters. Association with age appeared linear for cfPWV and exponential for log (CCS). The progression of the log (CCS) and cfPWV through age groups was steeper in the "higher-risk group" than in the "lower-risk group". cfPWV strongly correlated with CCS, and CCS progression over cfPWV differed among clusters. This finding could improve PWV as a "gate-keeper" of CCS testing and potentially enhance cardiovascular risk stratification.
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Affiliation(s)
- Maximo Rousseau-Portalis
- Bioengineering Research and Development Group, National Technological University, Buenos Aires, Argentina
- Department of Internal Medicine, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Leandro Cymberknop
- Bioengineering Research and Development Group, National Technological University, Buenos Aires, Argentina
| | - Ignacio Farro
- Departamento de Ingeniería Biológica, CENUR Litoral Norte, Universidad de la República, Paysandú, Uruguay
| | - Ricardo Armentano
- Bioengineering Research and Development Group, National Technological University, Buenos Aires, Argentina
- Departamento de Ingeniería Biológica, CENUR Litoral Norte, Universidad de la República, Paysandú, Uruguay
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6
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Zhang Y, Chen C, Huang L, Liu G, Lian T, Yin M, Zhao Z, Xu J, Chen R, Fu Y, Liang D, Zeng J, Ni J. Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records. JMIR Public Health Surveill 2022; 8:e38182. [PMID: 36422885 PMCID: PMC9732753 DOI: 10.2196/38182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/13/2022] [Accepted: 09/10/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults. OBJECTIVE This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults. METHODS Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions. RESULTS In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women. CONCLUSIONS Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.
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Affiliation(s)
- Yan Zhang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Chao Chen
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Lingfeng Huang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Gang Liu
- Department of Primary Public Health Promotion, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Tingyu Lian
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Mingjuan Yin
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Zhiguang Zhao
- Administration Office, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Jian Xu
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Ruoling Chen
- Faculty of Education, Health and Wellbeing, University of Wolverhampton, Wolverhampton, United Kingdom
| | - Yingbin Fu
- Department of Primary Public Health Promotion, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Dongmei Liang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Jinmei Zeng
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Jindong Ni
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
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7
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Mehrpour O, Hoyte C, Goss F, Shirazi FM, Nakhaee S. Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases. Drug Chem Toxicol 2022:1-7. [DOI: 10.1080/01480545.2022.2083149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, TX, USA
- Denver Health and Hospital Authority, Denver, CO, USA
| | - Christopher Hoyte
- Department of Emergency Medicine, University of Colorado Hospital, Aurora, Colorado
| | - Foster Goss
- Department of Emergency Medicine, University of Colorado Hospital, Aurora, Colorado
| | - Farshad M. Shirazi
- Arizona Poison & Drug Information Center, University of Arizona, College of Pharmacy and University of Arizona, College of Medicine, Tucson, AZ, USA
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
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8
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Bikia V, Fong T, Climie RE, Bruno RM, Hametner B, Mayer C, Terentes-Printzios D, Charlton PH. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:676-690. [PMID: 35316972 PMCID: PMC7612526 DOI: 10.1093/ehjdh/ztab089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology (LHTC), Swiss Federal Institute of Technology, CH-1015 Lausanne, Vaud, Switzerland
| | - Terence Fong
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Grattan Street, Parkville, Victoria, 3010 Australia
| | - Rachel E Climie
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Rosa-Maria Bruno
- Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Bernhard Hametner
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Christopher Mayer
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 114 Vasilissis Sofias Avenue, 11527, Athens, Greece
| | - Peter H Charlton
- Department of Public Health and Primary Care, Strangeways Research Laboratory, 2 Worts' Causeway, Cambridge, CB1 8RN, UK,Research Centre for Biomedical Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK,Corresponding author.
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9
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Mehrpour O, Saeedi F, Hoyte C. Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System. Basic Clin Pharmacol Toxicol 2021; 130:191-199. [PMID: 34649297 DOI: 10.1111/bcpt.13674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/25/2022]
Abstract
Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a 6-year retrospective cohort study using National Poison Data System (NPDS) data. A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86 and an F1_score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1_score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, Texas, USA.,Rocky Mountain Poison and Drug Safety, Denver Health and Hospital Authority, Denver, Colorado, USA
| | - Farhad Saeedi
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.,Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Christopher Hoyte
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,University of Colorado Hospital, Aurora, Colorado, USA
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10
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Ben Ahmed H, Allouche E, Chetoui A, Beji M, Boudiche F, Ouechtati W, Bazdeh L. [Relationship between arterial stiffness and the severity of coronary artery disease in acute coronary syndrome]. Ann Cardiol Angeiol (Paris) 2020; 70:33-40. [PMID: 33256951 DOI: 10.1016/j.ancard.2020.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 11/04/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND The association between arterial stiffness (AS) and stable coronary artery disease (CAD) has been previously demonstrated. Whether increased arterial stiffness is associated with severe CAD in patients with acute coronary syndrome (ACS) is less explored. AIM We aim to investigate the relationship between AS parameters and the extent and severity of CAD in patients with ACS. METHODS The study population consisted of 275 patients with ACS. We measured various AS parameters including pulse wave velocity (PWV), augmentation index (AIx), and central pulse pressure (cPP). CAD extent and severity was evaluated by the number of vessels with greater than 70% stenosis. RESULTS The study population was predominantly men (77, 5%) with an average age of 56, 4±10, 6 years. One hundred and fifteen patients were diabetic and 97 were hypertensive. One hundred fifty patients were admitted for ST elevation myocardial infarction (54, 5%) and 37, 5% for non ST elevation myocardial infarction. Thirty six percent of patients had single vessel disease and 47, 6% of the study population had multivessel disease. At the multivariate analysis, a positive correlation was observed between the number of coronary vessels disease and PWV. PWV (OR=1,272; IC95% [1,090; 1,483]; p=0,002) and cPP (OR=1,071; IC95% [1,024; 1,121]; p=0,003) were also independent predictors of multivessel disease. CONCLUSION In patient with ACS, PWV is correlated with the extent of coronary artery disease, as measured by the number of vessels disease. PWV and cPP were also independent predictors of multivessel disease.
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Affiliation(s)
- H Ben Ahmed
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie.
| | - E Allouche
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
| | - A Chetoui
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
| | - M Beji
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
| | - F Boudiche
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
| | - W Ouechtati
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
| | - L Bazdeh
- Department of cardiology, Charles Nicolle Hospital, Tunis, Tunisie; Faculty of Medicine, University of Tunis El Manar, 2092 Tunis, Tunisie
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11
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Liu H, He YD, Liu JB, Huang W, Zhao N, Zhao HW, Zhou XH, Wang HY. [Predictive value of vascular health indicators on newly cardiovascular events: Preliminary validation of Beijing vascular health stratification system]. JOURNAL OF PEKING UNIVERSITY. HEALTH SCIENCES 2020; 52:514-520. [PMID: 32541986 DOI: 10.19723/j.issn.1671-167x.2020.03.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To explore the predictive value of carotid femoral artery pulse wave velocity (CF-PWV), carotid radial artery pulse wave velocity (CR-PWV), cardio-ankle vascular index (CAVI), and ankle brachial index (ABI) on coronary heart disease (CHD) and cerebral infarction (CI), and the preliminary validation of Beijing vascular health stratification (BVHS). METHODS Subjects with at least 2 in-patient records were included into the study between 2010 and 2017 from Vascular Medicine Center of Peking University Shougang Hospital. Subjects with CHD or CI, and without data of vascular function at baseline were excluded. Eventually, 467 subjects free of CHD [cohort 1, mean age: (63.4±12.3) years, female 42.2%] and 658 subjects free of CI [cohort 2, mean age: (64.3±12.2) years, female 48.7%] at baseline were included. The first in-patient records were as the baseline data, the second in-patient records were as a following-up data. Cox proportional hazard regression was used to establish the predictive models of CHD or CI derived from BVHS by multivariable-adjusted analysis. RESULTS The median follow-up time of cohort 1 and cohort 2 was 1.9 years and 2.1 years, respectively. During the follow-up, 164 first CHD events occurred in cohort 1 and 117 first CI events occurred in cohort 2. Four indicators were assessed as continuous variables simultaneously by multivariable-adjusted analysis. In cohort 1, CF-PWV, CR-PWV, ABI, and CAVI reached statistical significance in the multivariable-adjusted models (P<0.05). In cohort 2, only CAVI (P<0.05) was of statistical significance. In addition, the higher CF-PWV became a protector of CHD or CI (P<0.05). The prediction value of BVHS reached the statistical significance for CHD and CI in the unadjusted models (all P<0.05), however, BVHS could only predict the incidence of CHD (P<0.05), but not the incidence of CI (P>0.05) in the multivariable-adjusted models. CF-PWV, CR-PWV, ABI, and CAVI were associated factors of CHD independent of each other (P<0.05), only CAVI (P<0.05) was the risk factor of CI independent of the other three. CONCLUSION The different vascular indicators might have different effect on CHD or CI. CAVI might be a stable predictor of both CHD and CI. Higher baseline CF-PWV was not necessarily a risk factor of CHD or CI because of proper vascular health management. BVHS was a potential factor for the prediction of CHD, and further research is needed to explore the prediction value for CI.
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Affiliation(s)
- H Liu
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China.,Vascular Health Research Center of Peking University Health Science Center, Beijing 100191, China
| | - Y D He
- Department of Biostatistics, Peking University, Beijing International Center for Mathematical Research, Beijing 100871, China
| | - J B Liu
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China.,Vascular Health Research Center of Peking University Health Science Center, Beijing 100191, China
| | - W Huang
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China
| | - N Zhao
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China
| | - H W Zhao
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China
| | - X H Zhou
- Vascular Health Research Center of Peking University Health Science Center, Beijing 100191, China.,Department of Biostatistics, Peking University, Beijing International Center for Mathematical Research, Beijing 100871, China
| | - H Y Wang
- Vascular Medicine Center, Peking University Shougang Hospital, Beijing 100144, China.,Vascular Health Research Center of Peking University Health Science Center, Beijing 100191, China
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Vallée A, Cinaud A, Protogerou A, Zhang Y, Topouchian J, Safar ME, Blacher J. Arterial Stiffness and Coronary Ischemia: New Aspects and Paradigms. Curr Hypertens Rep 2020; 22:5. [PMID: 31925555 DOI: 10.1007/s11906-019-1006-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW Aortic stiffness (AS) is widely associated with hypertension and considered as a major predictor of coronary heart disease (CHD). AS is measured using carotid-femoral pulse wave velocity (PWV), particularly when this parameter is associated with an index involving age, gender, heart rate, and mean blood pressure. The present review focuses on the interest of measurement of PWV and the calculation of individual PWV index for the prediction of CHD, in addition with the use of new statistical nonlinear models enabling results with very high levels of accuracy. RECENT FINDINGS PWV index may so constitute a substantial marker of large arteries prediction and damage in CHD and may be also used in cerebrovascular and renal circulations models. PWV index determinations are particularly relevant to consider in angiographic CHD decisions and in the presence of vulnerable plaques with high cardiovascular risk. Due to the variability in symptoms and clinical characteristics of patients, together with some imperfections in results, there is no very simple adequate diagnosis approach enabling to improve the so defined CHD prediction in usual clinical practice. In recent works in relation to "artificial intelligence" and involving "decision tree" models and "artificial neural networks," it has been possible to determine consistent pathways introducing predictive medicine and enabling to obtain efficient algorithm classification models of coronary prediction.
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Affiliation(s)
- Alexandre Vallée
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France.
| | - Alexandre Cinaud
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France
| | - Athanase Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National and Kapodistrian University of Athens, Athens, Greece
| | - Yi Zhang
- Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jirar Topouchian
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France
| | - Michel E Safar
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France
| | - Jacques Blacher
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, Paris-Descartes University, AP-HP, Paris, France
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Vallée A, Safar ME, Blacher J. Application of a decision tree to establish factors associated with a nomogram of aortic stiffness. J Clin Hypertens (Greenwich) 2019; 21:1484-1492. [PMID: 31479194 DOI: 10.1111/jch.13662] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 05/20/2019] [Accepted: 05/28/2019] [Indexed: 11/29/2022]
Abstract
Aortic stiffness is a marker of vascular aging and may reflect occurrence of cardiovascular (CV) diseases. Aortic pulse wave velocity (PWV), a marker of aortic stiffness, can be measured by applanation tonometry. A nomogram of aortic stiffness was evaluated by the calculation of PWV index. Theoretical PWV can be calculated according to age, gender, mean blood pressure, and heart rate, allowing to form an individual PWV index [(measured PWV - theoretical PWV)/theoretical PWV]. The purpose of the present cross-sectional study was to investigate the determinants of the PWV index, by applying a decision tree. A cross-sectional study was conducted from 2012 to 2017, and 597 individuals were included. A training decision tree was constructed based on seventy percent of these subjects (N = 428). The remaining 30% (N = 169) were used as the testing dataset to evaluate the performance of the decision trees. The input variables for the models were clinical and biochemical parameters. The different input variables remained in the model were diabetes, tobacco status, carotid plaque, albuminuria, C-reactive protein, total cholesterol, BMI, and previous CV diseases. For the validation decision model, the sensitivity, specificity, and accuracy values for identifying the related risk factors of PWV index were 70%, 78%, and 0.73. Since determinants of PWV index were all well-accepted CV risk factors, a nomogram of aortic stiffness could be considered as an integrator of CV risk factors on their duration of exposure and could be utilized to develop future programs for CV risk assessment and reduction strategies.
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Affiliation(s)
- Alexandre Vallée
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, AP-HP, Paris-Descartes University, Paris, France
| | - Michel E Safar
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, AP-HP, Paris-Descartes University, Paris, France
| | - Jacques Blacher
- Diagnosis and Therapeutic Center, Hypertension and Cardiovascular Prevention Unit, Hôtel-Dieu Hospital, AP-HP, Paris-Descartes University, Paris, France
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De Backer T, De Buyzere M. The Seven Deadly Sins in Cardiovascular Medicine: More Than a Question of Stiffness of the Mind! Am J Hypertens 2019; 32:723-724. [PMID: 31067310 DOI: 10.1093/ajh/hpz076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 05/07/2019] [Indexed: 11/12/2022] Open
Affiliation(s)
- Tine De Backer
- Department of Cardiovascular diseases, University Hospital Gent, Gent, Belgium
| | - Marc De Buyzere
- Department of Cardiovascular diseases, University Hospital Gent, Gent, Belgium
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Association between different lipid parameters and aortic stiffness: clinical and therapeutic implication perspectives. J Hypertens 2019; 37:2240-2246. [PMID: 31188165 DOI: 10.1097/hjh.0000000000002161] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Recommendations about lipid parameters varied from different guidelines. Aortic stiffness is a marker of vascular aging and may reflect occurrence of cardiovascular diseases. Aortic pulse wave velocity (PWV), a marker of aortic stiffness, can be measured by applanation tonometry. The purpose of our study was to test the associations between lipid parameters and aortic stiffness. METHODS A cross-sectional study was conducted from 2012 to 2017, 603 participants were included: 517 patients and 86 'healthy' individuals used to calculate the theoretical PWV. Lipid parameters, including total cholesterol, triglycerides, low-density lipoprotein (LDL), high-density lipoprotein (HDL), non-HDL, total cholesterol/HDL ratio, triglycerides/HDL ratio and LDL/HDL ratio were measured. Theoretical PWV can be calculated according to age, sex, mean blood pressure and heart rate, allowing to form an individual PWV index [(measured PWV - theoretical PWV)/theoretical PWV]. PWV index [(measured PWV - theoretical PWV)/theoretical PWV] greater than 0 defined aortic stiffness. RESULTS In multiple linear regression analyses, total cholesterol (P = 0.03), LDL (P = 0.04), non-HDL (P = 0.03), total cholesterol/HDL (P = 0.01) and LDL/HDL (P = 0.03) were significantly correlated with PWV. In multiple logistic regression analyses, non-HDL [OR = 1.12 (1.04-1.20), P = 0.01, R value: 0.224], total cholesterol/HDL [OR = 1.12 (1.02-1.22), P = 0.03, R value: 0.219] and total cholesterol [OR = 1.11 (1.01-1.23), P = 0.03, R value: 0.209] were significantly associated with aortic stiffness. CONCLUSION Non-HDL, total cholesterol and total cholesterol/HDL were significantly associated with aortic stiffness than others and especially individually lipid parameters. This result should be considered in future clinical lipid-lowering trials.
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