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Tlili NE, Robert L, Gerard E, Lemaitre M, Vambergue A, Beuscart JB, Quindroit P. A systematic review of the value of clinical decision support systems in the prescription of antidiabetic drugs. Int J Med Inform 2024; 191:105581. [PMID: 39106772 DOI: 10.1016/j.ijmedinf.2024.105581] [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: 02/22/2024] [Revised: 07/23/2024] [Accepted: 07/28/2024] [Indexed: 08/09/2024]
Abstract
INTRODUCTION The management of chronic diabetes mellitus and its complications demands customized glycaemia control strategies. Polypharmacy is prevalent among people with diabetes and comorbidities, which increases the risk of adverse drug reactions. Clinical decision support systems (CDSSs) may constitute an innovative solution to these problems. The aim of our study was to conduct a systematic review assessing the value of CDSSs for the management of antidiabetic drugs (AD). MATERIALS AND METHODS We systematically searched the scientific literature published between January 2010 and October 2023. The retrieved studies were categorized as non-specific or AD-specific. The studies' quality was assessed using the Mixed Methods Appraisal Tool. The review's results were reported in accordance with the PRISMA guidelines. RESULTS Twenty studies met our inclusion criteria. The majority of AD-specific studies were conducted more recently (2020-2023) compared to non-specific studies (2010-2015). This trend hints at growing interest in more specialized CDSSs tailored for prescriptions of ADs. The nine AD-specific studies focused on metformin and insulin and demonstrated positive impacts of the CDSSs on different outcomes, including the reduction in the proportion of inappropriate prescriptions of ADs and in hypoglycaemia events. The 11 nonspecific studies showed similar trends for metformin and insulin prescriptions, although the CDSSs' impacts were not significant. There was a predominance of metformin and insulin in the studied CDSSs and a lack of studies on ADs such as sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists. CONCLUSION The limited number of studies, especially randomized clinical trials, interested in evaluating the application of CDSS in the management of ADs underscores the need for further investigations. Our findings suggest the potential benefit of applying CDSSs to the prescription of ADs particularly in primary care settings and when targeting clinical pharmacists. Finally, establishing core outcome sets is crucial for ensuring consistent and standardized evaluation of these CDSSs.
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Affiliation(s)
- Nour Elhouda Tlili
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France.
| | - Laurine Robert
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; Institut de Pharmacie, CHU Lille, F-59000 Lille, France
| | - Erwin Gerard
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; Institut de Pharmacie, CHU Lille, F-59000 Lille, France
| | - Madleen Lemaitre
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France; CHU Lille, Department of Diabetology, Endocrinology, Metabolism and Nutrition, Lille University Hospital, F-59000, Lille, France
| | - Anne Vambergue
- CHU Lille, Department of Diabetology, Endocrinology, Metabolism and Nutrition, Lille University Hospital, F-59000, Lille, France; European Genomic Institute for Diabetes, Lille University School of Medicine, F-59000 Lille, France
| | - Jean-Baptiste Beuscart
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France
| | - Paul Quindroit
- Univ. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France
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Huang S, Liang Y, Li J, Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. J Med Internet Res 2023; 25:e51024. [PMID: 38064249 PMCID: PMC10746969 DOI: 10.2196/51024] [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: 07/21/2023] [Revised: 11/10/2023] [Accepted: 11/12/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking. OBJECTIVE This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care. METHODS We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes. RESULTS Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia. CONCLUSIONS CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.37766/inplasy2022.9.0061.
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Affiliation(s)
- Shan Huang
- Endocrinology Department, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuzhen Liang
- Department of Endocrinology, The Second Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Jiarui Li
- Department of Endocrinology, Cangzhou Central Hospital, Cangzhou, China
| | - Xuejun Li
- Department of Endocrinology and Diabetes, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Diabetes Institute, Xiamen, China
- Fujian Provincial Key Laboratory of Translational Medicine for Diabetes, Xiamen, China
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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.
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Chen W, Howard K, Gorham G, O'Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc 2022; 29:1757-1772. [PMID: 35818299 PMCID: PMC9471723 DOI: 10.1093/jamia/ocac110] [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: 04/01/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases. Material and Methods We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted. Results The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY. Conclusion We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies. Registration PROSPERO (CRD42020203716)
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Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Claire Maree O'Bryan
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
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Ndjaboue R, Ngueta G, Rochefort-Brihay C, Delorme S, Guay D, Ivers N, Shah BR, Straus SE, Yu C, Comeau S, Farhat I, Racine C, Drescher O, Witteman HO. Prediction models of diabetes complications: a scoping review. J Epidemiol Community Health 2022; 76:jech-2021-217793. [PMID: 35772935 DOI: 10.1136/jech-2021-217793] [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: 08/11/2021] [Accepted: 06/08/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes. METHODS Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards. RESULTS Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance. CONCLUSION This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.
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Affiliation(s)
- Ruth Ndjaboue
- Faculty of Medicine, Université Laval, Quebec, Quebec, Canada
- School of social work, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- CIUSSS de l'Estrie, Research Centre on Aging, Sherbrooke, Quebec, Canada
| | - Gérard Ngueta
- Université de Sherbrooke Faculté des Sciences, Sherbrooke, Quebec, Canada
| | | | | | - Daniel Guay
- Diabetes Action Canada, Toronto, Ontario, Canada
| | - Noah Ivers
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
- Department of Family Medicine and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Baiju R Shah
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Sharon E Straus
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Catherine Yu
- Knowledge Translation, St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada
| | - Sandrine Comeau
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Imen Farhat
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Charles Racine
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Olivia Drescher
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Holly O Witteman
- Family and Emergency Medicine, Laval University, Quebec City, Quebec, Canada
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