1
|
Alizadeh N, Vahdat K, Shashaani S, Swann JL, Özaltιn OY. Risk score models for urinary tract infection hospitalization. PLoS One 2024; 19:e0290215. [PMID: 38875172 PMCID: PMC11178184 DOI: 10.1371/journal.pone.0290215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 05/09/2024] [Indexed: 06/16/2024] Open
Abstract
Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.
Collapse
Affiliation(s)
- Nasrin Alizadeh
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Kimia Vahdat
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Sara Shashaani
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Julie L Swann
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Osman Y Özaltιn
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| |
Collapse
|
2
|
Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [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: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
Collapse
Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| |
Collapse
|
3
|
Pinto-de-Sá R, Sousa-Pinto B, Costa-de-Oliveira S. Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review. Antibiotics (Basel) 2024; 13:307. [PMID: 38666983 PMCID: PMC11047419 DOI: 10.3390/antibiotics13040307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/29/2024] Open
Abstract
Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP.
Collapse
Affiliation(s)
- Rafaela Pinto-de-Sá
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
| | - Bernardo Sousa-Pinto
- Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Sofia Costa-de-Oliveira
- Division of Microbiology, Department of Pathology, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal;
- Center for Health Technology and Services Research—CINTESIS@RISE, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| |
Collapse
|
4
|
Liu L, He Y, Kao C, Fan Y, Yang F, Wang F, Yu L, Zhou F, Xiang Y, Huang S, Zheng C, Cai H, Bao H, Fang L, Wang L, Chen Z, Yu Z. An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study. Chin Med J (Engl) 2024:00029330-990000000-00965. [PMID: 38403898 DOI: 10.1097/cm9.0000000000002891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. METHODS The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. RESULTS The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. CONCLUSIONS We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.
Collapse
Affiliation(s)
- Liyuan Liu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Yong He
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Chunyu Kao
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Yeye Fan
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Fu Yang
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Fei Wang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Lixiang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Fei Zhou
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Yujuan Xiang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Shuya Huang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Chao Zheng
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Han Cai
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Heling Bao
- Department of Maternal and Child Health, School of Public Health, Peking University, Haidian District, Beijing 100191, China
| | - Liwen Fang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Linhong Wang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Zengjing Chen
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Zhigang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| |
Collapse
|
5
|
Naik N, Talyshinskii A, Shetty DK, Hameed BMZ, Zhankina R, Somani BK. Smart Diagnosis of Urinary Tract Infections: is Artificial Intelligence the Fast-Lane Solution? Curr Urol Rep 2024; 25:37-47. [PMID: 38112900 PMCID: PMC10787904 DOI: 10.1007/s11934-023-01192-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) can significantly improve physicians' workflow when examining patients with UTI. However, most contemporary reviews are focused on examining the usage of AI with a restricted quantity of data, analyzing only a subset of AI algorithms, or performing narrative work without analyzing all dedicated studies. Given the preceding, the goal of this work was to conduct a mini-review to determine the current state of AI-based systems as a support in UTI diagnosis. RECENT FINDINGS There are sufficient publications to comprehend the potential applications of artificial intelligence in the diagnosis of UTIs. Existing research in this field, in general, publishes performance metrics that are exemplary. However, upon closer inspection, many of the available publications are burdened with flaws associated with the improper use of artificial intelligence, such as the use of a small number of samples, their lack of heterogeneity, and the absence of external validation. AI-based models cannot be classified as full-fledged physician assistants in diagnosing UTIs due to the fact that these limitations and flaws represent only a portion of all potential obstacles. Instead, such studies should be evaluated as exploratory, with a focus on the importance of future work that complies with all rules governing the use of AI. AI algorithms have demonstrated their potential for UTI diagnosis. However, further studies utilizing large, heterogeneous, prospectively collected datasets, as well as external validations, are required to define the actual clinical workflow value of artificial intelligence.
Collapse
Affiliation(s)
- Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
| | - Ali Talyshinskii
- Department of Urology, Astana Medical University, Astana, 010000, Kazakhstan
| | - Dasharathraj K Shetty
- Department of Data Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India
| | - B M Zeeshan Hameed
- Department of Urology, Father Muller Medical College, Mangalore, 575002, Karnataka, India
- iTRUE-International Training and Research in Urology and Endourology, Manipal, 576104, Karnataka, India
| | - Rano Zhankina
- Department of Urology, Astana Medical University, Astana, 010000, Kazakhstan
| | - Bhaskar K Somani
- iTRUE-International Training and Research in Urology and Endourology, Manipal, 576104, Karnataka, India
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, SO16 6YD, UK
| |
Collapse
|
6
|
Kim J, Kim YK, Kim H, Jung H, Koh S, Kim Y, Yoon D, Yi H, Kim HJ. Machine Learning Algorithms Predict Successful Weaning From Mechanical Ventilation Before Intubation: Retrospective Analysis From the Medical Information Mart for Intensive Care IV Database. JMIR Form Res 2023; 7:e44763. [PMID: 37962939 PMCID: PMC10685278 DOI: 10.2196/44763] [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: 12/02/2022] [Revised: 02/23/2023] [Accepted: 10/08/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The prediction of successful weaning from mechanical ventilation (MV) in advance of intubation can facilitate discussions regarding end-of-life care before unnecessary intubation. OBJECTIVE We aimed to develop a machine learning-based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation. METHODS We used the Medical Information Mart for Intensive Care IV database, which is an open-access database covering 524,740 admissions of 382,278 patients in Beth Israel Deaconess Medical Center, United States, from 2008 to 2019. We selected adult patients who underwent MV in the intensive care unit (ICU). Clinical and laboratory variables that are considered relevant to the prognosis of the patient in the ICU were selected. Data collected before or within 24 hours of intubation were used to develop machine learning models that predict the probability of successful weaning within 14 days of ventilator support. Developed models were integrated into an ensemble model. Performance metrics were calculated by 5-fold cross-validation for each model, and a permutation feature importance and Shapley additive explanations analysis was conducted to better understand the impacts of individual variables on outcome prediction. RESULTS Of the 23,242 patients, 19,025 (81.9%) patients were successfully weaned from MV within 14 days. Using the preselected 46 clinical and laboratory variables, the area under the receiver operating characteristic curve of CatBoost classifier, random forest classifier, and regularized logistic regression classifier models were 0.860 (95% CI 0.852-0.868), 0.855 (95% CI 0.848-0.863), and 0.823 (95% CI 0.813-0.832), respectively. Using the ensemble voting classifier using the 3 models above, the final model revealed the area under the receiver operating characteristic curve of 0.861 (95% CI 0.853-0.869), which was significantly better than that of Simplified Acute Physiology Score II (0.749, 95% CI 0.742-0.756) and Sequential Organ Failure Assessment (0.588, 95% CI 0.566-0.609). The top features included lactate and anion gap. The model's performance achieved a plateau with approximately the top 21 variables. CONCLUSIONS We developed machine learning algorithms that can predict successful weaning from MV in advance to intubation in the ICU. Our models can aid the appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.
Collapse
Affiliation(s)
- Jinchul Kim
- Division of Hematology-Oncology, Department of Internal Medicine, Inha University College of Medicine and Hospital, Incheon, Republic of Korea
| | - Yun Kwan Kim
- Department of the Technology Development, Seers Technology Co, Ltd, Seongnam, Republic of Korea
| | - Hyeyeon Kim
- Crowdworks Co, Ltd, Seoul, Republic of Korea
| | - Hyojung Jung
- Healthcare Artificial Intelligence Team, National Cancer Center, Goyang, Republic of Korea
| | - Soonjeong Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Yujeong Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Hahn Yi
- Asan Medical Center, Asan Institute for Life Sciences, Seoul, Republic of Korea
| | - Hyung-Jun Kim
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
7
|
Gupta A, Singh A. Prediction Framework on Early Urine Infection in IoT-Fog Environment Using XGBoost Ensemble Model. WIRELESS PERSONAL COMMUNICATIONS 2023; 131:1-19. [PMID: 37360131 PMCID: PMC10123571 DOI: 10.1007/s11277-023-10466-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/07/2023] [Indexed: 06/28/2023]
Abstract
Urine infections are one of the most prevalent concerns for the healthcare industry that may impair the functioning of the kidney and other renal organs. As a result, early diagnosis and treatment of such infections are essential to avert any future complications. Conspicuously, in the current work, an intelligent system for the early prediction of urine infections has been presented. The proposed framework uses IoT-based sensors for data collection, followed by data encoding and infectious risk factor computation using the XGBoost algorithm over the fog computing platform. Finally, the analysis results along with the health-related information of users are stored in the cloud repository for future analysis. For performance validation, extensive experiments have been carried out, and results are calculated based on real-time patient data. The statistical findings of accuracy (91.45%), specificity (95.96%), sensitivity (84.79%), precision (95.49%), and f-score(90.12%) reveal the significantly improved performance of the proposed strategy over other baseline techniques.
Collapse
Affiliation(s)
- Aditya Gupta
- Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India
- Manipal University Jaipur, Jaipur, India
| | - Amritpal Singh
- Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India
| |
Collapse
|
8
|
Weng S, Hu D, Chen J, Yang Y, Peng D. Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms. Diagnostics (Basel) 2023; 13:diagnostics13061168. [PMID: 36980476 PMCID: PMC10047083 DOI: 10.3390/diagnostics13061168] [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: 02/19/2023] [Revised: 03/13/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Fatty liver disease (FLD) is an important risk factor for liver cancer and cardiovascular disease and can lead to significant social and economic burden. However, there is currently no nationwide epidemiological survey for FLD in China, making early FLD screening crucial for the Chinese population. Unfortunately, liver biopsy and abdominal ultrasound, the preferred methods for FLD diagnosis, are not practical for primary medical institutions. Therefore, the aim of this study was to develop machine learning (ML) models for screening individuals at high risk of FLD, and to provide a new perspective on early FLD diagnosis. METHODS This study included a total of 30,574 individuals between the ages of 18 and 70 who completed abdominal ultrasound and the related clinical examinations. Among them, 3474 individuals were diagnosed with FLD by abdominal ultrasound. We used 11 indicators to build eight classification models to predict FLD. The model prediction ability was evaluated by the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and kappa value. Feature importance analysis was assessed by Shapley value or root mean square error loss after permutations. RESULTS Among the eight ML models, the prediction accuracy of the extreme gradient boosting (XGBoost) model was highest at 89.77%. By feature importance analysis, we found that the body mass index, triglyceride, and alanine aminotransferase play important roles in FLD prediction. CONCLUSION XGBoost improves the efficiency and cost of large-scale FLD screening.
Collapse
Affiliation(s)
- Shuwei Weng
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Die Hu
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Jin Chen
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| | - Yanyi Yang
- Health Management Center, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Daoquan Peng
- Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, China
- Research Institute of Blood Lipid and Atherosclerosis, Central South University, Changsha 410011, China
| |
Collapse
|
9
|
Jha NG, Dkhar DS, Singh SK, Malode SJ, Shetti NP, Chandra P. Engineered Biosensors for Diagnosing Multidrug Resistance in Microbial and Malignant Cells. BIOSENSORS 2023; 13:235. [PMID: 36832001 PMCID: PMC9954051 DOI: 10.3390/bios13020235] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/17/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
To curtail pathogens or tumors, antimicrobial or antineoplastic drugs have been developed. These drugs target microbial/cancer growth and survival, thereby improving the host's health. In attempts to evade the detrimental effects of such drugs, these cells have evolved several mechanisms over time. Some variants of the cells have developed resistances against multiple drugs or antimicrobial agents. Such microorganisms or cancer cells are said to exhibit multidrug resistance (MDR). The drug resistance status of a cell can be determined by analyzing several genotypic and phenotypic changes, which are brought about by significant physiological and biochemical alterations. Owing to their resilient nature, treatment and management of MDR cases in clinics is arduous and requires a meticulous approach. Currently, techniques such as plating and culturing, biopsy, gene sequencing, and magnetic resonance imaging are prevalent in clinical practices for determining drug resistance status. However, the major drawbacks of using these methods lie in their time-consuming nature and the problem of translating them into point-of-care or mass-detection tools. To overcome the shortcomings of conventional techniques, biosensors with a low detection limit have been engineered to provide quick and reliable results conveniently. These devices are highly versatile in terms of analyte range and quantities that can be detected to report drug resistance in a given sample. A brief introduction to MDR, along with a detailed insight into recent biosensor design trends and use for identifying multidrug-resistant microorganisms and tumors, is presented in this review.
Collapse
Affiliation(s)
- Niharika G. Jha
- School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi 221005, Uttar Pradesh, India
| | - Daphika S. Dkhar
- School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi 221005, Uttar Pradesh, India
| | - Sumit K. Singh
- School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi 221005, Uttar Pradesh, India
| | - Shweta J. Malode
- Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Hubballi 580031, Karnataka, India
| | - Nagaraj P. Shetti
- Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Hubballi 580031, Karnataka, India
- University Center for Research & Development (UCRD), Chandigarh University, Mohali 140413, Panjab, India
| | - Pranjal Chandra
- School of Biochemical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi 221005, Uttar Pradesh, India
| |
Collapse
|
10
|
Texture Feature-Based Machine Learning Classification on MRI Image for Sepsis-Associated Encephalopathy Detection: A Pilot Study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:6403556. [PMID: 36778786 PMCID: PMC9911249 DOI: 10.1155/2023/6403556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/21/2022] [Accepted: 12/26/2022] [Indexed: 02/05/2023]
Abstract
Objective The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone. Method Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model. Results The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set. Conclusion The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone.
Collapse
|
11
|
Li Y, Liu Y, Huang Y, Zhang J, Ma Q, Liu X, Chen Q, Yu H, Dong L, Lu G. Development and validation of a user-friendly risk nomogram for the prediction of catheter-associated urinary tract infection in neuro-intensive care patients. Intensive Crit Care Nurs 2023; 74:103329. [PMID: 36192313 DOI: 10.1016/j.iccn.2022.103329] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study aimed to develop a user-friendly nomogram model to evaluate the risk of catheter-associated urinary tract infections in neuro-critically ill patients. METHODS A retrospective cohort analysis was conducted on 537 patients with indwelling catheters admitted to the neuro-intensive care unit. Patients' general information, laboratory examination findings, and clinical characteristics were collected. Multivariate regression analysis was applied to develop the nomogram for the prediction of catheter-associated urinary tract infections in this group of patients. The discriminative capacity, calibration ability, and clinical effectiveness of the nomogram were evaluated. RESULTS The occurrence of catheter-associated urinary tract infections was 3.91 % and Escherichia coli was the major causative pathogen. Multivariate regression analysis showed that age ≥ 60 years (odds ratio: 35.2, 95 % confidence interval: 2.3-550.8), epilepsy (39.3, 5.1-301.4), a length of neuro-intensive care stay > 30 days (272.2, 8.3-8963.5), and low albumin levels (<35 g/L) (12.1, 2.1-69.9) were independent risk factors associated with catheter-associated urinary tract infection in neuro-intensive care patients. The nomogram demonstrated good calibration and discrimination in both the training and the validation sets. The model exhibited good clinical use since the decision curve analysis covered a large threshold probability. CONCLUSIONS We developed a user-friendly nomogram to predict catheter-associated urinary tract ibfection in neuro-intensive care patients. The nomogram incorporated clinical variables collected on admission (age, admission diagnosis, and albumin levels) and the length of stay and enabled the effective prediction of the likelihood of catheter-associated urinary tract infections.
Collapse
Affiliation(s)
- Yuping Li
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China; Clinical Medical College of Yangzhou University, Yangzhou 225009, China
| | - Yuting Liu
- School of Nursing, Yangzhou University, Yangzhou 225009, China
| | - Yujia Huang
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China; Clinical Medical College of Yangzhou University, Yangzhou 225009, China
| | - Jingyue Zhang
- School of Nursing, Yangzhou University, Yangzhou 225009, China
| | - Qiang Ma
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China
| | - Xiaoguang Liu
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China
| | - Qi Chen
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou 225009, China
| | - Hailong Yu
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China
| | - Lun Dong
- Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou 225001, China
| | - Guangyu Lu
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou 225009, China.
| |
Collapse
|
12
|
Machine-learning approaches prevent post-treatment resistance-gaining bacterial recurrences. Trends Microbiol 2022; 30:612-614. [DOI: 10.1016/j.tim.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 11/23/2022]
|
13
|
Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform 2022; 13:161-179. [PMID: 35139564 PMCID: PMC8828453 DOI: 10.1055/s-0041-1742218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
Collapse
Affiliation(s)
- Brian J. Douthit
- Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Rachel L. Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia P. Coviak
- Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Thompson Forbes
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Grace Gao
- Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States
| | - Theresa A. Kapetanovic
- College of Nursing, East Carolina University, Greenville, North California, United States
| | - Mikyoung A. Lee
- College of Nursing, Texas Woman's University, Denton, Texas, United States
| | - Lisiane Pruinelli
- School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States
| | - Mary A. Schultz
- Department of Nursing, California State University, San Bernardino, California, United States
| | - Ann Wieben
- School of Nursing, University of Wisconsin-Madison, Wisconsin, United States
| | - Alvin D. Jeffery
- School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States,Address for correspondence Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC 461 21st Avenue South, Nashville, TN 37240United States
| |
Collapse
|
14
|
Prediction of Poststroke Urinary Tract Infection Risk in Immobile Patients Using Machine Learning: a observational cohort study. J Hosp Infect 2022; 122:96-107. [PMID: 35045341 DOI: 10.1016/j.jhin.2022.01.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/08/2022] [Accepted: 01/08/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it's still challenging to accurately estimate personal UTI risk. OBJECTIVES We aimed to develop predictive models for UTI risk identification for immobile stroke patients. METHODS Research data were collected from our previous multi-centre study. Derivation cohort included 3982 immobile stroke patients collected from November 1, 2015 to June 30, 2016; external validation cohort included 3837 patients collected from November 1, 2016 to July 30, 2017. 6 machine learning models and an ensemble learning model were derived based on 80% of derivation cohort and effectiveness was evaluated with the remaining 20%. We used Shapley additive explanation values to determine feature importance and examine the clinical significance of prediction models. RESULTS 2.59% (103/3982) patients were diagnosed with UTI in derivation cohort, 1.38% (53/3837) in external cohort. The ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation (82.2%); second best in external validation (80.8%). In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). We also identified seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization). CONCLUSIONS Our ensemble learning model demonstrated promising performance. Future work should continue to develop a more concise scoring tool based on machine learning models and prospectively examining the model in practical use, thus improving clinical outcomes.
Collapse
|
15
|
Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. Int J Med Inform 2021; 159:104679. [PMID: 34990939 DOI: 10.1016/j.ijmedinf.2021.104679] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice. METHODS Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered. RESULTS Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice. CONCLUSIONS ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.
Collapse
Affiliation(s)
- Ezekwesiri Michael Nwanosike
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Barbara R Conway
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Hamid A Merchant
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom
| | - Syed Shahzad Hasan
- Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate Huddersfield HD1 3DH, West Yorkshire, United Kingdom; School of Biomedical Sciences & Pharmacy, University of Newcastle, Callaghan, Australia.
| |
Collapse
|
16
|
He S, Leanse LG, Feng Y. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Adv Drug Deliv Rev 2021; 178:113922. [PMID: 34461198 DOI: 10.1016/j.addr.2021.113922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/23/2022]
Abstract
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.
Collapse
Affiliation(s)
- Sheng He
- Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Leon G Leanse
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Yanfang Feng
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
| |
Collapse
|
17
|
Abstract
PURPOSE OF REVIEW Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings. RECENT FINDINGS The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts. SUMMARY As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.
Collapse
|
18
|
Bordoni L, Petracci I, Pelikant-Malecka I, Radulska A, Piangerelli M, Samulak JJ, Lewicki L, Kalinowski L, Gabbianelli R, Olek RA. Mitochondrial DNA copy number and trimethylamine levels in the blood: New insights on cardiovascular disease biomarkers. FASEB J 2021; 35:e21694. [PMID: 34165220 DOI: 10.1096/fj.202100056r] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 04/20/2021] [Accepted: 05/11/2021] [Indexed: 12/14/2022]
Abstract
Among cardiovascular disease (CVD) biomarkers, the mitochondrial DNA copy number (mtDNAcn) is a promising candidate. A growing attention has been also dedicated to trimethylamine-N-oxide (TMAO), an oxidative derivative of the gut metabolite trimethylamine (TMA). With the aim to identify biomarkers predictive of CVD, we investigated TMA, TMAO, and mtDNAcn in a population of 389 coronary artery disease (CAD) patients and 151 healthy controls, in association with established risk factors for CVD (sex, age, hypertension, smoking, diabetes, glomerular filtration rate [GFR]) and troponin, an established marker of CAD. MtDNAcn was significantly lower in CAD patients; it correlates with GFR and TMA, but not with TMAO. A biomarker including mtDNAcn, sex, and hypertension (but neither TMA nor TMAO) emerged as a good predictor of CAD. Our findings support the mtDNAcn as a promising plastic biomarker, useful to monitor the exposure to risk factors and the efficacy of preventive interventions for a personalized CAD risk reduction.
Collapse
Affiliation(s)
- Laura Bordoni
- Unit of Molecular Biology and Nutrigenomics, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Irene Petracci
- School of Advanced Studies, University of Camerino, Camerino, Italy
| | - Iwona Pelikant-Malecka
- Division of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland.,Biobanking and Biomolecular Resources Research Infrastructure Poland (BBMRI.PL), Gdansk, Poland
| | - Adriana Radulska
- Division of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland.,Biobanking and Biomolecular Resources Research Infrastructure Poland (BBMRI.PL), Gdansk, Poland
| | - Marco Piangerelli
- Computer Science Division and Mathematics Division, School of Science and Technology, University of Camerino, Camerino, Italy
| | - Joanna J Samulak
- Doctoral School, Gdansk University of Physical Education and Sport, Gdansk, Poland
| | - Lukasz Lewicki
- Department of Cardiology and Angiology, Kashubian Center for Heart and Vascular Diseases, Pomeranian Hospitals, Wejherowo, Poland
| | - Leszek Kalinowski
- Division of Medical Laboratory Diagnostics, Medical University of Gdansk, Gdansk, Poland.,Biobanking and Biomolecular Resources Research Infrastructure Poland (BBMRI.PL), Gdansk, Poland.,Department of Mechanics of Materials and Structures, Gdansk University of Technology, Gdansk, Poland
| | - Rosita Gabbianelli
- Unit of Molecular Biology and Nutrigenomics, School of Pharmacy, University of Camerino, Camerino, Italy
| | - Robert A Olek
- Department of Athletics, Strength and Conditioning, Poznan University of Physical Education, Poznan, Poland
| |
Collapse
|
19
|
Gorshkov O, Ombao H. Multi-Chaotic Analysis of Inter-Beat (R-R) Intervals in Cardiac Signals for Discrimination between Normal and Pathological Classes. ENTROPY 2021; 23:e23010112. [PMID: 33467750 PMCID: PMC7830666 DOI: 10.3390/e23010112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/10/2021] [Accepted: 01/13/2021] [Indexed: 11/16/2022]
Abstract
Cardiac signals have complex structures representing a combination of simpler structures. In this paper, we develop a new data analytic tool that can extract the complex structures of cardiac signals using the framework of multi-chaotic analysis, which is based on the p-norm for calculating the largest Lyapunov exponent (LLE). Appling the p-norm is useful for deriving the spectrum of the generalized largest Lyapunov exponents (GLLE), which is characterized by the width of the spectrum (which we denote by W). This quantity measures the degree of multi-chaos of the process and can potentially be used to discriminate between different classes of cardiac signals. We propose the joint use of the GLLE and spectrum width to investigate the multi-chaotic behavior of inter-beat (R-R) intervals of cardiac signals recorded from 54 healthy subjects (hs), 44 subjects diagnosed with congestive heart failure (chf), and 25 subjects diagnosed with atrial fibrillation (af). With the proposed approach, we build a regression model for the diagnosis of pathology. Multi-chaotic analysis showed a good performance, allowing the underlying dynamics of the system that generates the heart beat to be examined and expert systems to be built for the diagnosis of cardiac pathologies.
Collapse
|