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Yao PF, Diao YD, McMullen EP, Manka M, Murphy J, Lin C. Predicting amputation using machine learning: A systematic review. PLoS One 2023; 18:e0293684. [PMID: 37934767 PMCID: PMC10629636 DOI: 10.1371/journal.pone.0293684] [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: 08/19/2023] [Accepted: 10/17/2023] [Indexed: 11/09/2023] Open
Abstract
Amputation is an irreversible, last-line treatment indicated for a multitude of medical problems. Delaying amputation in favor of limb-sparing treatment may lead to increased risk of morbidity and mortality. This systematic review aims to synthesize the literature on how ML is being applied to predict amputation as an outcome. OVID Embase, OVID Medline, ACM Digital Library, Scopus, Web of Science, and IEEE Xplore were searched from inception to March 5, 2023. 1376 studies were screened; 15 articles were included. In the diabetic population, models ranged from sub-optimal to excellent performance (AUC: 0.6-0.94). In trauma patients, models had strong to excellent performance (AUC: 0.88-0.95). In patients who received amputation secondary to other etiologies (e.g.: burns and peripheral vascular disease), models had similar performance (AUC: 0.81-1.0). Many studies were found to have a high PROBAST risk of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to overcome the limitations of current studies and to bring applicability to a clinical setting.
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Affiliation(s)
- Patrick Fangping Yao
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Yi David Diao
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Eric P. McMullen
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Marlin Manka
- Department of Computer Science, University of Western Ontario, London, ON, Canada
| | - Jessica Murphy
- Division of Physical Medicine and Rehabilitation, McMaster University, Hamilton, ON, Canada
| | - Celina Lin
- Division of Physical Medicine and Rehabilitation, McMaster University, Hamilton, ON, Canada
- Division of Physical Medicine and Rehabilitation, Hamilton Health Sciences, Hamilton, ON, Canada
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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: 3] [Impact Index Per Article: 3.0] [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.
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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.
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Abegaz TM, Ahmed M, Sherbeny F, Diaby V, Chi H, Ali AA. Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data. Healthcare (Basel) 2023; 11:1138. [PMID: 37107973 PMCID: PMC10137945 DOI: 10.3390/healthcare11081138] [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: 02/24/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.
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Affiliation(s)
- Tadesse M. Abegaz
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
| | - Muktar Ahmed
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Fatimah Sherbeny
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
| | - Vakaramoko Diaby
- College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
| | - Hongmei Chi
- The Department of Computer and Information Sciences, Florid A&M University, Tallahassee, FL 32307, USA
| | - Askal Ayalew Ali
- Economic, Social and Administrative Pharmacy (ESAP), College of Pharmacy and Pharmaceutical Sciences, Institute of Public Heath, Florida A&M University, Tallahassee, FL 32307, USA
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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.
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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.
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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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.
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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
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Rodionov RN, Peters F, Marschall U, L'Hoest H, Jarzebska N, Behrendt CA. Initiation of SGLT2 Inhibitors and the Risk of Lower Extremity Minor and Major Amputation in Patients with Type 2 Diabetes and Peripheral Arterial Disease: A Health Claims Data Analysis. Eur J Vasc Endovasc Surg 2021; 62:981-990. [PMID: 34782230 DOI: 10.1016/j.ejvs.2021.09.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/01/2021] [Accepted: 09/18/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To assess the association between long term risk of hospitalisation for heart failure (HHF) and lower extremity minor and major amputation (LEA) in patients initiating sodium glucose cotransporter 2 inhibitors (SGLT2i) suffering from type 2 diabetes and peripheral arterial disease (PAD). Outcomes were compared with patients without PAD and evaluated separately for the time periods before and after the official warning of the European Medicines Agency (EMA) in early 2017. METHODS This study used BARMER German health claims data including all patients suffering from type 2 diabetes initiating SGLT2i therapy between 1 January 2013 and 31 December 2019 with follow up until the end of 2020. New users of glucagon like peptide 1 receptor agonists (GLP1-RAs) were used as active comparators. Inverse probability weighting with truncated stabilised weights was used to adjust for confounding, and five year risks of HHF and LEA were estimated using Cox regression. Periods before and after the EMA warning were analysed separately and stratified by presence of concomitant PAD. RESULTS In total, 44 284 (13.6% PAD) and 56 878 (16.3% PAD) patients initiated SGLT2i or GLP1-RA, respectively. Before the EMA warning, initiation of SGLT2i was associated with a lower risk of HHF in patients with PAD (hazard ratio, HR, 0.85, 95% confidence interval, CI, 0.73 - 0.99) and a higher risk of LEA in patients without PAD (HR 1.79, 95% CI 1.04 - 2.92). After the EMA warning, the efficacy and safety endpoints were no longer statistically different between groups. CONCLUSION The results from this large nationwide real world study highlight that PAD patients exhibit generally high amputation risks. This study refutes the idea that the presence of PAD explains the excess LEA risk associated with initiation of SGLT2i. The fact that differentials among study groups diminished after the EMA warning in early 2017 emphasises that regulatory surveillance measures worked in everyday clinical practice.
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Affiliation(s)
- Roman N Rodionov
- University Centre for Vascular Medicine, University Clinic Carl Gustav Carus, Technische Universität Dresden, Germany; College of Medicine and Public Health, Flinders University and Flinders Medical Centre, Adelaide, Australia
| | - Frederik Peters
- Research Group GermanVasc, Department of Vascular Medicine, University Heart and Vascular Centre UKE Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | | | | | - Natalia Jarzebska
- University Centre for Vascular Medicine, University Clinic Carl Gustav Carus, Technische Universität Dresden, Germany; Clinical Sensoring and Monitoring, Department of Anaesthetics and Intensive Care Medicine, University Clinic Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Christian-Alexander Behrendt
- Research Group GermanVasc, Department of Vascular Medicine, University Heart and Vascular Centre UKE Hamburg, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
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8
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Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: A review. Metabolism 2021; 124:154872. [PMID: 34480920 DOI: 10.1016/j.metabol.2021.154872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/27/2021] [Accepted: 08/28/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
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Affiliation(s)
- Thibault Gautier
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America.
| | - Leah B Ziegler
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Matthew S Gerber
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Enrique Campos-Náñez
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
| | - Stephen D Patek
- Dexcom/TypeZero, 946 Grady Avenue, Suite 203, Charlottesville, VA 22903, United States of America
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