1
|
Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artif Intell Med 2024; 151:102868. [PMID: 38632030 DOI: 10.1016/j.artmed.2024.102868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/03/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024]
Abstract
Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.
Collapse
Affiliation(s)
- Maryam Eghbali-Zarch
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA
| | - Sara Masoud
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA.
| |
Collapse
|
2
|
Alhalafi A, Alqahtani SM, Alqarni NA, Aljuaid AT, Aljaber GT, Alshahrani LM, Mushait H, Nandi PA. Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis. Cureus 2024; 16:e58713. [PMID: 38779284 PMCID: PMC11110080 DOI: 10.7759/cureus.58713] [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: 03/27/2024] [Accepted: 04/21/2024] [Indexed: 05/25/2024] Open
Abstract
Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.
Collapse
Affiliation(s)
- Abdullah Alhalafi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
| | | | | | | | - Ghade T Aljaber
- Department of Medicine, Batterjee Medical College, Aseer, SAU
| | | | | | - Partha A Nandi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
| |
Collapse
|
3
|
Shi M, Yang A, Lau ESH, Luk AOY, Ma RCW, Kong APS, Wong RSM, Chan JCM, Chan JCN, Chow E. A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study. PLoS Med 2024; 21:e1004369. [PMID: 38607977 PMCID: PMC11014435 DOI: 10.1371/journal.pmed.1004369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/29/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.
Collapse
Affiliation(s)
- Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Aimin Yang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Eric S. H. Lau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Andrea O. Y. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Alice P. S. Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Raymond S. M. Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Jones C. M. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Elaine Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| |
Collapse
|
4
|
Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Med Inform 2023; 11:e47833. [PMID: 37983072 PMCID: PMC10696506 DOI: 10.2196/47833] [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: 04/03/2023] [Revised: 08/21/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.
Collapse
Affiliation(s)
- Kui Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Linyi Li
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yifei Ma
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Jun Jiang
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zhenhua Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Zichen Ye
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Shuang Liu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Chen Pu
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| | - Changsheng Chen
- Department of Health Statistics, Air Force Medical University, Xi'an, Shaanxi, China
| | - Yi Wan
- Department of Health Service, Air Force Medical University, Xi'an, Shaanxi, China
| |
Collapse
|
5
|
Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
Collapse
Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| |
Collapse
|
6
|
Chen J, Yin D, Dou K. Intensified glycemic control by HbA1c for patients with coronary heart disease and Type 2 diabetes: a review of findings and conclusions. Cardiovasc Diabetol 2023; 22:146. [PMID: 37349787 PMCID: PMC10288803 DOI: 10.1186/s12933-023-01875-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/02/2023] [Indexed: 06/24/2023] Open
Abstract
The occurrence and development of coronary heart disease (CHD) are closely linked to fluctuations in blood glucose levels. While the efficacy of intensified treatment guided by HbA1c levels remains uncertain for individuals with diabetes and CHD, this review summarizes the findings and conclusions regarding HbA1c in the context of CHD. Our review showed a curvilinear correlation between regulated level of HbA1c and therapeutic effectiveness of intensified glycemic control among patients with type 2 diabetes and coronary heart disease. It is necessary to optimize the dynamic monitoring indicators of HbA1c, combine genetic profiles, haptoglobin phenotypes for example and select more suitable hypoglycemic drugs to establish more appropriate glucose-controlling guideline for patients with CHD at different stage of diabetes.
Collapse
Affiliation(s)
- Jingyang Chen
- Cardiometabolic Medicine Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China
| | - Dong Yin
- Cardiometabolic Medicine Center, Department of Cardiology, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China
| | - Kefei Dou
- Cardiometabolic Medicine Center, Department of Cardiology, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037 China
| |
Collapse
|
7
|
Jaques-Albuquerque LT, Dos Anjos-Martins E, Torres-Nunes L, Valério-Penha AG, Coelho-Oliveira AC, da Silva Sarandy VL, Reis-Silva A, Seixas A, Bernardo-Filho M, Taiar R, de Sá-Caputo DC. Effectiveness of Using the FreeStyle Libre ® System for Monitoring Blood Glucose during the COVID-19 Pandemic in Diabetic Individuals: Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13081499. [PMID: 37189600 DOI: 10.3390/diagnostics13081499] [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: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI) is an area of computer science/engineering that is aiming to spread technological systems. The COVID-19 pandemic caused economic and public health turbulence around the world. Among the many possibilities for using AI in the medical field is FreeStyle Libre® (FSL), which uses a disposable sensor inserted into the user's arm, and a touchscreen device/reader is used to scan and retrieve other continuous monitoring of glucose (CMG) readings. The aim of this systematic review is to summarize the effectiveness of FSL blood glucose monitoring during the COVID-19 pandemic. METHODS This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and was registered in the international prospective register of systematic reviews (PROSPERO: CRD42022340562). The inclusion criteria considered studies involving the use of the FSL device during the COVID-19 pandemic and published in English. No publication date restrictions were set. The exclusion criteria were abstracts, systematic reviews, studies with patients with other diseases, monitoring with other equipment, patients with COVID-19, and bariatrics patients. Seven databases were searched (PubMed, Scopus, Embase, Web of Science, Scielo, PEDro and Cochrane Library). The ACROBAT-NRSI tool (A Cochrane Risk of Bias Assessment Tool for Non-Randomized Studies) was used to evaluate the risk of bias in the selected articles. RESULTS A total of 113 articles were found. Sixty-four were excluded because they were duplicates, 39 were excluded after reading the titles and abstracts, and twenty articles were considered for full reading. Of the 10 articles analyzed, four articles were excluded because they did not meet the inclusion criteria. Thus, six articles were included in the current systematic review. It was observed that among the selected articles, only two were classified as having serious risk of bias. It was shown that FSL had a positive impact on glycemic control and on reducing the number of individuals with hypoglycemia. CONCLUSION The findings suggest that the implementation of FSL during COVID-19 confinement in this population can be confidently stated to have been effective in diabetes mellitus patients.
Collapse
Affiliation(s)
- Luelia Teles Jaques-Albuquerque
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Elzi Dos Anjos-Martins
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Luiza Torres-Nunes
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Programa de Pós-Graduação em Fisiopatologia Clínica e Experimental, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Ana Gabriellie Valério-Penha
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Ana Carolina Coelho-Oliveira
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Viviani Lopes da Silva Sarandy
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Aline Reis-Silva
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Programa de Pós-Graduação em Ciências Médicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Adérito Seixas
- Escola Superior de Saúde Fernando Pessoa, 4200-256 Porto, Portugal
| | - Mario Bernardo-Filho
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| | - Redha Taiar
- MATériaux et Ingénierie Mécanique (MATIM), Université de Reims Champagne Ardenne, 51100 Reims, France
| | - Danúbia Cunha de Sá-Caputo
- Laboratório de Vibrações Mecânicas e Práticas Integrativas, Departamento de Biofísica e Biometria, Instituto de Biologia Roberto Alcântara Gomes, Policlínica Universitária Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
- Mestrado Profissional em Saúde, Medicina Laboratorial e Tecnologia Forense, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil
| |
Collapse
|
8
|
Zhang L, Yang L, Zhou Z. Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice. Front Public Health 2023; 11:1044059. [PMID: 36778566 PMCID: PMC9910805 DOI: 10.3389/fpubh.2023.1044059] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Background and objective Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention. Materials and methods PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related). Results From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction. Conclusion In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.
Collapse
|
9
|
Worth C, Hoskyns L, Salomon-Estebanez M, Nutter PW, Harper S, Derks TG, Beardsall K, Banerjee I. Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023. Front Endocrinol (Lausanne) 2023; 14:1116864. [PMID: 36755920 PMCID: PMC9900115 DOI: 10.3389/fendo.2023.1116864] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/02/2023] [Indexed: 01/24/2023] Open
Abstract
In 2023, childhood hypoglycaemia remains a major public health problem and significant risk factor for consequent adverse neurodevelopment. Irrespective of the underlying cause, key elements of clinical management include the detection, prediction and prevention of episodes of hypoglycaemia. These tasks are increasingly served by Continuous Glucose Monitoring (CGM) devices that measure subcutaneous glucose at near-continuous frequency. While the use of CGM in type 1 diabetes is well established, the evidence for widespread use in rare hypoglycaemia disorders is less than convincing. However, in the few years since our last review there have been multiple developments and increased user feedback, requiring a review of clinical application. Despite advances in device technology, point accuracy of CGM remains low for children with non-diabetes hypoglycaemia. Simple provision of CGM devices has not replicated the efficacy seen in those with diabetes and is yet to show benefit. Machine learning techniques for hypoglycaemia prevention have so far failed to demonstrate sufficient prediction accuracy for real world use even in those with diabetes. Furthermore, access to CGM globally is restricted by costs kept high by the commercially-driven speed of technical innovation. Nonetheless, the ability of CGM to digitally phenotype disease groups has led to a better understanding of natural history of disease, facilitated diagnoses and informed changes in clinical management. Large CGM datasets have prompted re-evaluation of hypoglycaemia incidence and facilitated improved trial design. Importantly, an individualised approach and focus on the behavioural determinants of hypoglycaemia has led to real world reduction in hypoglycaemia. In this state of the art review, we critically analyse the updated evidence for use of CGM in non-diabetic childhood hypoglycaemia disorders since 2020 and provide suggestions for qualified use.
Collapse
Affiliation(s)
- Chris Worth
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
- *Correspondence: Chris Worth,
| | - Lucy Hoskyns
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | - Maria Salomon-Estebanez
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
| | - Paul W. Nutter
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Simon Harper
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Terry G.J Derks
- Section of Metabolic Diseases, Beatrix Children’s Hospital, University of Groningen, Groningen, Netherlands
| | - Kathy Beardsall
- Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
| | - Indraneel Banerjee
- Department of Paediatric Endocrinology, Royal Manchester Children’s Hospital, Manchester, United Kingdom
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
10
|
Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope? Digit Health 2023; 9:20552076231203879. [PMID: 37786401 PMCID: PMC10541760 DOI: 10.1177/20552076231203879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
Collapse
Affiliation(s)
- Xiantong Zou
- Xiantong Zou, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
| | | | - Linong Ji
- Linong Ji, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
| |
Collapse
|
11
|
Shahid S, Geetha M, Sadasivuni KK, Remani D, Muthusamy S, Muthalif AGA, Al-maadeed S. Highly sensitive and selective colorimetric sensing of CO 2 for biomedical applications. 3 Biotech 2022; 12:334. [PMID: 36330379 PMCID: PMC9622963 DOI: 10.1007/s13205-022-03396-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/12/2022] [Indexed: 12/02/2022] Open
Abstract
The concentration of carbon dioxide (CO2) in unhealthy people differs greatly from healthy people. High-precision CO2 detection with a quick response time is essential for many biomedical applications. A major focus of this research is on the detection of CO2, one of the most important health biomarkers. We investigated a low-cost, flexible, and reliable strategy by using dyes for colorimetric CO2 sensing in this study. The impacts of temperature, pH, reaction time, reusability, concentration, and dye selectivity were studied thoroughly. This study described real-time CO2 analysis. Using this multi-dye method, we got an average detection limit of 1.98 ppm for CO2, in the range of 50-120 ppm. A portable colorimetric instrument with a smartphone-assisted unit was constructed to determine the relative red/green/blue values for real-time and practical applications within 15 s of interaction and the readings are very similar to those of an optical fiber probe. Environmental and biological chemistry applications are likely to benefit greatly from this unique approach.
Collapse
Affiliation(s)
- Shahina Shahid
- grid.412603.20000 0004 0634 1084Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Mithra Geetha
- grid.412603.20000 0004 0634 1084Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Kishor Kumar Sadasivuni
- grid.412603.20000 0004 0634 1084Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Divya Remani
- grid.412603.20000 0004 0634 1084Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Suresh Muthusamy
- grid.252262.30000 0001 0613 6919Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu India
| | - Asan G. A. Muthalif
- grid.412603.20000 0004 0634 1084Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Somaya Al-maadeed
- grid.412603.20000 0004 0634 1084Department of Computer Science and Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| |
Collapse
|
12
|
Obaideen K, Abu Shihab KH, Madkour MI, Faris ME. Seven decades of Ramadan intermittent fasting research: Bibliometrics analysis, global trends, and future directions. Diabetes Metab Syndr 2022; 16:102566. [PMID: 35872466 DOI: 10.1016/j.dsx.2022.102566] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND AND AIMS There is a large body of research focused on various aspects related to Ramadan intermittent fasting (RIF) and human health and disease. This study aimed to quantify the bibliometric data of RIF medical research over the past seven decades and explore these variables qualitatively via text mining analysis. METHODS We used the Scopus search engine to identify published articles related to RIF from inception to December 31, 2021. All types of research articles were included. Scientometric and bibliometric measures were determined using Excel, Biblioshiny, and VOSviewer. This study proposed a bibliometric and text mining method to qualitatively and quantitatively recognize the RIF research trend. RESULTS The Scopus search returned 1915 relevant articles. Most citations pertained to publications from the last two decades, and most publications were original research articles. These publications had received around 27,000 citations, and the 20 most prolific publishing journals had an average h-index of 112.25. More than one-third of all medical publications were in open-access journals. There was a 13-fold increase in medical research on RIF over the past few decades. We identified the 10 most prolific publishing countries, institutes, journals, and authors. We also identified five scientific hotspots of RIF scientific literature, which were: diabetes, metabolic health, public health, physiology, and maternity. CONCLUSION This is the first comprehensive bibliometric analysis of medical research related to RIF. The research gaps identified will shape future research directions and foster collaborative research activities toward enhanced medical nutrition research revolving around RIF.
Collapse
Affiliation(s)
- Khaled Obaideen
- Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates
| | - Katia H Abu Shihab
- Department of Clinical Nutrition and Dietetics, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohamed I Madkour
- Department of Medical Laboratory Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - MoezAlIslam E Faris
- Department of Clinical Nutrition and Dietetics, University of Sharjah, Sharjah, United Arab Emirates.
| |
Collapse
|
13
|
Motaib I, Aitlahbib F, Fadil A, Z Rhmari Tlemcani F, Elamari S, Laidi S, Chadli A. Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models. Diabetes Res Clin Pract 2022; 190:109982. [PMID: 35803316 DOI: 10.1016/j.diabres.2022.109982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/17/2022] [Accepted: 07/04/2022] [Indexed: 11/30/2022]
Abstract
AIMS This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models. METHODS First, we conducted three consultations, before, during, and after Ramadan to assess demographics, diabetes history, caloric intake, anthropometric and metabolic parameters. Second, machine learning techniques (Logistic Regression, Support Vector Machine, Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, Extra Trees Classifier and Catboost) were trained using the data to predict poor glycemic control among patients. Then, we conducted several simulations with the best performing machine learning model using variables that were found as main predictors of poor glycemic control. RESULTS The prevalence of poor glycemic control among patients was 52.6%. Extra tree Classifier was the best performing model for glycemic deterioration (accuracy = 0.87, AUC = 0,87). Caloric intake evolution, gender, baseline caloric intake, baseline weight, BMI variation, waist circumference evolution and Total Cholesterol serum level after Ramadan were selected as the most significant for the prediction of poor glycemic control. We determined thresholds for each predicting factor among which this risk is present. CONCLUSIONS The clinical use of our findings may help to improve glycemic control during Ramadan among patients who do not fast by targeting risk factors of poor glycemic control.
Collapse
Affiliation(s)
- Imane Motaib
- Department of Endocrinology Diabetology Metabolic Disease and Nutrition, Cheikh Khalifa International University Hospital, Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco.
| | - Faiçal Aitlahbib
- Hassania School of Public Works, Casablanca, Morocco; Office Chérifien des Phosphates (OCP), Casablanca, Morocco
| | | | - Fatima Z Rhmari Tlemcani
- Department of Endocrinology Diabetology Metabolic Disease and Nutrition, Cheikh Khalifa International University Hospital, Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
| | - Saloua Elamari
- Department of Endocrinology Diabetology Metabolic Disease and Nutrition, Cheikh Khalifa International University Hospital, Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
| | - Soukaina Laidi
- Department of Endocrinology Diabetology Metabolic Disease and Nutrition, Cheikh Khalifa International University Hospital, Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
| | - Asma Chadli
- Department of Endocrinology Diabetology Metabolic Disease and Nutrition, Cheikh Khalifa International University Hospital, Faculty of Medicine, Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
| |
Collapse
|
14
|
Spataru A, van Dommelen P, Arnaud L, Le Masne Q, Quarteroni S, Koledova E. Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device. BMC Med Inform Decis Mak 2022; 22:179. [PMID: 35794586 PMCID: PMC9261072 DOI: 10.1186/s12911-022-01918-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/31/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders. METHODS Adherence to r-hGH treatment was assessed in children (aged < 18 years) who started using a connected auto-injector device (easypod™), and transmitted injection data for ≥ 12 months. Adherence in the following 3, 6, or 9 months after treatment start was categorized as optimal (≥ 85%) versus sub-optimal (< 85%). Logistic regression and tree-based models were applied. RESULTS Data from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort settings, and starting treatment at an older age was important in predicting the risk of sub-optimal adherence in the following 3, 6, or 9 months. Sensitivities ranged between 0.72 and 0.77, and specificities between 0.80 and 0.81. CONCLUSIONS To the authors' knowledge, this is the first attempt to integrate a machine learning model into a digital health ecosystem to help healthcare providers to identify patients at risk of sub-optimal adherence to r-hGH in the following 3, 6, or 9 months. This information, together with patient-specific indicators of sub-optimal adherence, can be used to provide support to at-risk patients and their caregivers to achieve optimal adherence and, subsequently, improve clinical outcomes.
Collapse
Affiliation(s)
- Amalia Spataru
- Swiss Data Science Center, ETH Zürich and EPFL, Zürich, Switzerland
| | - Paula van Dommelen
- The Netherlands Organization for Applied Scientific Research TNO, P.O. Box 2215, 2301 CE, Leiden, The Netherlands.
| | - Lilian Arnaud
- Connected Health and Devices, Global Healthcare Operations, Ares Trading S.A., An Affiliate of Merck KGaA, Eysins, Switzerland
| | - Quentin Le Masne
- Connected Health and Devices, Global Healthcare Operations, Ares Trading S.A., An Affiliate of Merck KGaA, Eysins, Switzerland
| | | | - Ekaterina Koledova
- Global Medical Affairs Cardiometabolic & Endocrinology, Merck Healthcare KGaA, Darmstadt, Germany
| |
Collapse
|
15
|
Barreto J, Wolf V, Bonilha I, Luchiari B, Lima M, Oliveira A, Vitte S, Machado G, Cunha J, Borges C, Munhoz D, Fernandes V, Kimura-Medorima ST, Breder I, Fernandez MD, Quinaglia T, Oliveira RB, Chaves F, Arieta C, Guerra-Júnior G, Avila S, Nadruz W, Carvalho LSF, Sposito AC. Rationale and design of the Brazilian diabetes study: a prospective cohort of type 2 diabetes. Curr Med Res Opin 2022; 38:523-529. [PMID: 35174749 DOI: 10.1080/03007995.2022.2043658] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Optimal control of traditional risk factors only partially attenuates the exceeding cardiovascular mortality of individuals with diabetes. Employment of machine learning (ML) techniques aimed at the identification of novel features of risk prediction is a compelling target to tackle residual cardiovascular risk. The objective of this study is to identify clinical phenotypes of T2D which are more prone to developing cardiovascular disease. METHODS The Brazilian Diabetes Study is a single-center, ongoing, prospective registry of T2D individuals. Eligible patients are 30 years old or older, with a confirmed T2D diagnosis. After an initial visit for the signature of the informed consent form and medical history registration, all volunteers undergo biochemical analysis, echocardiography, carotid ultrasound, ophthalmologist visit, dual x-ray absorptiometry, coronary artery calcium score, polyneuropathy assessment, advanced glycation end-products reader, and ambulatory blood pressure monitoring. A 5-year follow-up will be conducted by yearly phone interviews for endpoints disclosure. The primary endpoint is the difference between ML-based clinical phenotypes in the incidence of a composite of death, myocardial infarction, revascularization, and stroke. Since June/2016, 1030 patients (mean age: 57 years, diabetes duration of 9.7 years, 58% male) were enrolled in our study. The mean follow-up time was 3.7 years in October/2021. CONCLUSION The BDS will be the first large population-based cohort dedicated to the identification of clinical phenotypes of T2D at higher risk of cardiovascular events. Data derived from this study will provide valuable information on risk estimation and prevention of cardiovascular and other diabetes-related events. CLINICALTRIALS.GOV IDENTIFIER NCT04949152.
Collapse
Affiliation(s)
- Joaquim Barreto
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Vaneza Wolf
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Growth and Body Composition Lab, Center for Investigation in Pediatrics, Faculty of Medical Sciences, University of Campinas, São Paulo, Brazil
| | - Isabella Bonilha
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Beatriz Luchiari
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Marcus Lima
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Alessandra Oliveira
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Sofia Vitte
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Gabriela Machado
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Jessica Cunha
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Cynthia Borges
- Nephrology Division, Clinics Hospital, University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Daniel Munhoz
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Vicente Fernandes
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Sheila Tatsumi Kimura-Medorima
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Ikaro Breder
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Marta Duran Fernandez
- Clarity Healthcare Intelligence, Sao Paulo, Brazil
- School of Electrical and Computer Engineering, Unicamp, Sao Paulo, Brazil
| | - Thiago Quinaglia
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Rodrigo B Oliveira
- Nephrology Division, Clinics Hospital, University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Fernando Chaves
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Carlos Arieta
- Department of Ophthalmology, Clinics Hospital, University of Campinas, Sao Paulo, Brazil
| | - Gil Guerra-Júnior
- Growth and Body Composition Lab, Center for Investigation in Pediatrics, Faculty of Medical Sciences, University of Campinas, São Paulo, Brazil
| | - Sandra Avila
- School of Electrical and Computer Engineering, Unicamp, Sao Paulo, Brazil
- Institute of Computing, Unicamp, Sao Paulo, Brazil
| | - Wilson Nadruz
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| | - Luiz Sergio F Carvalho
- Clarity Healthcare Intelligence, Sao Paulo, Brazil
- Laboratory of Data for Quality of Care and Outcomes Research, Institute of Strategic Management in Healthcare, Brasılia, Federal District, Brazil
| | - Andrei C Sposito
- Atherosclerosis and Vascular Biology Laboratory (Atherolab), Cardiology Division, State University of Campinas (Unicamp), Sao Paulo, Brazil
- Cardiology Division, Department of Internal Medicine, State University of Campinas (Unicamp), Sao Paulo, Brazil
| |
Collapse
|
16
|
AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|
17
|
Machine learning algorithms for diabetes detection: a comparative evaluation of performance of algorithms. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00685-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
18
|
Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. J Clin Med 2021; 10:jcm10194576. [PMID: 34640594 PMCID: PMC8509372 DOI: 10.3390/jcm10194576] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023] Open
Abstract
Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.
Collapse
|
19
|
Kodama S, Fujihara K, Shiozaki H, Horikawa C, Yamada MH, Sato T, Yaguchi Y, Yamamoto M, Kitazawa M, Iwanaga M, Matsubayashi Y, Sone H. Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis. JMIR Diabetes 2021; 6:e22458. [PMID: 33512324 PMCID: PMC7880810 DOI: 10.2196/22458] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/09/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022] Open
Abstract
Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682
Collapse
Affiliation(s)
- Satoru Kodama
- Department of Prevention of Noncommunicable Diseases and Promotion of Health Checkup, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Haruka Shiozaki
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Chika Horikawa
- Department of Health and Nutrition, Faculty of Human Life Studies, University of Niigata Prefecture, Niigata, Japan
| | - Mayuko Harada Yamada
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takaaki Sato
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yuta Yaguchi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masahiko Yamamoto
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Masaru Kitazawa
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Midori Iwanaga
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yasuhiro Matsubayashi
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| |
Collapse
|
20
|
Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|