1
|
Monteiro Lopes S, Maia A, Melo P, Abreu S, Paiva I, Barros L. [Non-Insulin Antidiabetic Agents in the Management of Hyperglycaemia of Non-Critical Hospitalized Patients]. ACTA MEDICA PORT 2024; 37:207-214. [PMID: 38316163 DOI: 10.20344/amp.20858] [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: 10/24/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
Hyperglycaemia affects more than 30% of adults hospitalized for non-critical illness and is associated with an increased risk of adverse clinical outcomes. Insulin therapy is widely used for its safety and efficacy. However, given the growing availability of new drugs and new classes of antidiabetic agents with benefits beyond glycaemic control, challenges arise regarding their use in the hospital setting. This article aims to review and summarize the most recently available evidence and recommendations on the role of non-insulin antidiabetic agents in the management of hyperglycaemia in hospitalized patients. Insulin therapy remains the method of choice. Dipeptidyl peptidase 4 inhibitors can be considered in mild to moderate hyperglycaemia. Glucagon-like peptide 1 receptor agonists have recently shown promising results, with high efficacy in glycaemic control and low risk of hypoglycaemia. There are concerns regarding the increased risk of acidosis with metformin use, especially in cases of acute illness, although there is no evidence to support its suspension in selected patients with relative clinical stability. Sodium-glucose cotransporter-2 inhibitors should be discontinued in clinical situations that may predispose to ketoacidosis, including episodes of acute illness. The hospital use of sulfonylureas and thiazolidinediones is not advised.
Collapse
Affiliation(s)
- Sofia Monteiro Lopes
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço de Endocrinologia, Diabetes e Metabolismo. Centro Hospitalar e Universitário de Coimbra. Coimbra. Portugal
| | - Ariana Maia
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço de Endocrinologia. Centro Hospitalar Universitário do Porto. Porto. Portugal
| | - Pedro Melo
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço de Endocrinologia. Unidade Local de Saúde de Matosinhos. Portugal
| | - Silvestre Abreu
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço Regional de Saúde da Região Autónoma da Madeira. Funchal. Portugal
| | - Isabel Paiva
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço de Endocrinologia, Diabetes e Metabolismo. Centro Hospitalar e Universitário de Coimbra. Coimbra. Portugal
| | - Luísa Barros
- Grupo de Estudos de Diabetes. Sociedade Portuguesa de Endocrinologia, Diabetes e Metabolismo. Lisboa; Serviço de Endocrinologia, Diabetes e Metabolismo. Centro Hospitalar e Universitário de Coimbra. Coimbra. Portugal
| |
Collapse
|
2
|
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
|
3
|
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
|
4
|
Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
Collapse
Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
| |
Collapse
|
5
|
Mantena S, Arévalo AR, Maley JH, da Silva Vieira SM, Mateo-Collado R, da Costa Sousa JM, Celi LA. Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records. J Clin Monit Comput 2022; 36:1297-1303. [PMID: 34606005 PMCID: PMC9152921 DOI: 10.1007/s10877-021-00760-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 09/23/2021] [Indexed: 11/29/2022]
Abstract
Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.
Collapse
Affiliation(s)
| | | | - Jason H Maley
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | | | | | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- , 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
| |
Collapse
|
6
|
Abstract
PURPOSE OF REVIEW Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory. RECENT FINDINGS The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.
Collapse
Affiliation(s)
- Andrew Zale
- Division of Endocrinology, Diabetes & Metabolism, Division of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287 USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes & Metabolism, Division of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287 USA
| |
Collapse
|
7
|
Idrees T, Castro-Revoredo IA, Migdal AL, Moreno EM, Umpierrez GE. Update on the management of diabetes in long-term care facilities. BMJ Open Diabetes Res Care 2022; 10:10/4/e002705. [PMID: 35858714 PMCID: PMC9305812 DOI: 10.1136/bmjdrc-2021-002705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 05/29/2022] [Indexed: 11/10/2022] Open
Abstract
The number of patients with diabetes is increasing among older adults in the USA, and it is expected to reach 26.7 million by 2050. In parallel, the percentage of older patients with diabetes in long-term care facilities (LTCFs) will also rise. Currently, the majority of LTCF residents are older adults and one-third of them have diabetes. Management of diabetes in LTCF is challenging due to multiple comorbidities and altered nutrition. Few randomized clinical trials have been conducted to determine optimal treatment for diabetes management in older adults in LTCF. The geriatric populations are at risk of hypoglycemia since the majority are treated with insulin and have different levels of functionality and nutritional needs. Effective approaches to avoid hypoglycemia should be implemented in these settings to improve outcome and reduce the economic burden. Newer medication classes might carry less risk of developing hypoglycemia along with the appropriate use of technology, such as the use of continuous glucose monitoring. Practical clinical guidelines for diabetes management including recommendations for prevention and treatment of hypoglycemia are needed to appropriately implement resources in the transition of care plans in this vulnerable population.
Collapse
Affiliation(s)
- Thaer Idrees
- Department of Medicine, Division of Endocrinology, Emory University, Atlanta, Georgia, USA
| | - Iris A Castro-Revoredo
- Department of Medicine, Division of Endocrinology, Emory University, Atlanta, Georgia, USA
| | - Alexandra L Migdal
- Department of Medicine, Division of Endocrinology, Emory University, Atlanta, Georgia, USA
| | - Emmelin Marie Moreno
- Department of Medicine, Division of Endocrinology, Emory University, Atlanta, Georgia, USA
| | - Guillermo E Umpierrez
- Department of Medicine, Division of Endocrinology, Emory University, Atlanta, Georgia, USA
| |
Collapse
|
8
|
Galindo RJ, Dhatariya K, Gomez-Peralta F, Umpierrez GE. Safety and Efficacy of Inpatient Diabetes Management with Non-insulin Agents: an Overview of International Practices. Curr Diab Rep 2022; 22:237-246. [PMID: 35507117 PMCID: PMC9065239 DOI: 10.1007/s11892-022-01464-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/14/2022] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW The field of inpatient diabetes has advanced significantly over the last 20 years, leading to the development of personalized treatment approaches. However, outdated guidelines still recommend the use of basal-bolus insulin therapy as the preferred treatment approach, and against the use of non-insulin anti-hyperglycemic agents. RECENT FINDINGS Several observational and prospective randomized controlled studies have demonstrated that oral anti-hyperglycemic agents are widely used in the hospital, including studies of DPP-4 agents and GLP-1 agonists. With advances in the field of inpatient diabetes management, a paradigm shift has occurred, from an approach of recommending "basal-bolus regimens" for all patients to a more precision medicine option for hospitalized non-critically ill patients with type 2 diabetes.
Collapse
Affiliation(s)
- Rodolfo J Galindo
- Associate Professor of Medicine, Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, USA.
| | - Ketan Dhatariya
- Consultant Diabetes & Endocrinology / Honorary Professor, Norwich Medical School, Elsie Bertram Diabetes Centre, Norfolk and Norwich University Hospitals, NHS Foundation Trust, Norwich, UK
| | | | - Guillermo E Umpierrez
- Professor of Medicine, Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, USA
| |
Collapse
|
9
|
Kamel Rahimi A, Canfell OJ, Chan W, Sly B, Pole JD, Sullivan C, Shrapnel S. Machine learning models for diabetes management in acute care using electronic medical records: A systematic review. Int J Med Inform 2022; 162:104758. [PMID: 35398812 DOI: 10.1016/j.ijmedinf.2022.104758] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. OBJECTIVE The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.
Collapse
Affiliation(s)
- Amir Kamel Rahimi
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Digital Health Cooperative Research Centre, Australian Government, Sydney, New South Wales, Australia.
| | - Oliver J Canfell
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Digital Health Cooperative Research Centre, Australian Government, Sydney, New South Wales, Australia; UQ Business School, The University of Queensland, St Lucia 4072, Brisbane, Australia
| | - Wilkin Chan
- The School of Clinical Medicine, The University of Queensland, Herston 4006, Brisbane, Australia
| | - Benjamin Sly
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Princess Alexandra Hospital, 199 Ipswich Road, Woolloongabba 4102, Brisbane, Australia
| | - Jason D Pole
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Dalla Lana School of Public Health, The University of Toronto, Toronto, Canada; ICES, Toronto, Canada
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; Metro North Hospital and Health Service, Department of Health, Queensland Government, Herston 4006, Brisbane, Australia
| | - Sally Shrapnel
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Herston 4006, Brisbane, Australia; The School of Mathematics and Physics, The University of Queensland, St Lucia 4072, Brisbane, Australia
| |
Collapse
|
10
|
Gracia-Ramos AE, Carretero-Gómez J, Mendez CE, Carrasco-Sánchez FJ. Evidence-based therapeutics for hyperglycemia in hospitalized noncritically ill patients. Curr Med Res Opin 2022; 38:43-53. [PMID: 34694181 DOI: 10.1080/03007995.2021.1997288] [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] [Indexed: 10/20/2022]
Abstract
Hyperglycemia in hospitalized patients, either with or without diabetes, is a common, serious, and costly healthcare problem. Evidence accumulated over 20 years has associated hyperglycemia with a significant increase in morbidity and mortality, both in surgical and medical patients. Based on this documented link between hyperglycemia and poor outcomes, clinical guidelines from professional organizations recommend the treatment of hospital hyperglycemia with a therapeutic goal of maintaining blood glucose (BG) levels less than 180 mg/dL. Insulin therapy remains a mainstay of glycemic management in the inpatient setting. The use of non-insulin antidiabetic drugs in the hospital setting is limited because little data are available regarding their safety and efficacy. However, information about the use of incretin-based therapy in inpatients has increased in the past 15 years. This review aims to summarize the different treatment strategies for hyperglycemia in hospitalized noncritical patients that are supported by observational studies or clinical trials with insulin and non-insulin drugs. In addition, we propose a protocol to help with the management of this important clinical problem.
Collapse
Affiliation(s)
- Abraham Edgar Gracia-Ramos
- Department of Internal Medicine, General Hospital, National Medicinal Center "La Raza," Instituto Mexicano del Seguro Social, Mexico City, Mexico
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City, Mexico
| | | | - Carlos E Mendez
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
- Division of Diabetes and Endocrinology, Milwaukee VA Medical Center, Milwaukee, WI, USA
| | - Francisco Javier Carrasco-Sánchez
- Department of Internal Medicine, Diabetes and Cardiovascular Risk Factor Unit, University Hospital Juan Ramón Jimenez, Huelva, Spain
| |
Collapse
|
11
|
Crutzen S, Belur Nagaraj S, Taxis K, Denig P. Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning-based screening tool. Diabetes Metab Res Rev 2021; 37:e3426. [PMID: 33289318 PMCID: PMC8518928 DOI: 10.1002/dmrr.3426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/05/2020] [Accepted: 11/23/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION In primary care, identifying patients with type 2 diabetes (T2D) who are at increased risk of hypoglycaemia is important for the prevention of hypoglycaemic events. We aimed to develop a screening tool based on machine learning to identify such patients using routinely available demographic and medication data. METHODS We used a cohort study design and the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) medical record database to develop models for hypoglycaemia risk. The first hypoglycaemic event in the observation period (2007-2013) was the outcome. Demographic and medication data were used as predictor variables to train machine learning models. The performance of the models was compared with a model using additional clinical data using fivefold cross validation with the area under the receiver operator characteristic curve (AUC) as a metric. RESULTS We included 13,876 T2D patients. The best performing model including only demographic and medication data was logistic regression with least absolute shrinkage and selection operator, with an AUC of 0.71. Ten variables were included (odds ratio): male gender (0.997), age (0.990), total drug count (1.012), glucose-lowering drug count (1.039), sulfonylurea use (1.62), insulin use (1.769), pre-mixed insulin use (1.109), insulin count (1.827), insulin duration (1.193), and antidepressant use (1.05). The proposed model obtained a similar performance to the model using additional clinical data. CONCLUSION Using demographic and medication data, a model for identifying patients at increased risk of hypoglycaemia was developed using machine learning. This model can be used as a tool in primary care to screen for patients with T2D who may need additional attention to prevent or reduce hypoglycaemic events.
Collapse
Affiliation(s)
- Stijn Crutzen
- Department of Clinical Pharmacy and PharmacologyUniversity Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
| | - Sunil Belur Nagaraj
- Department of Clinical Pharmacy and PharmacologyUniversity Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
| | - Katja Taxis
- Unit of Pharmaco Therapy, Epidemiology and EconomicsGroningen Research Institute of PharmacyUniversity of GroningenGroningenThe Netherlands
| | - Petra Denig
- Department of Clinical Pharmacy and PharmacologyUniversity Medical Center GroningenUniversity of GroningenGroningenThe Netherlands
| |
Collapse
|
12
|
Pasquel FJ, Lansang MC, Dhatariya K, Umpierrez GE. Management of diabetes and hyperglycaemia in the hospital. Lancet Diabetes Endocrinol 2021; 9:174-188. [PMID: 33515493 PMCID: PMC10423081 DOI: 10.1016/s2213-8587(20)30381-8] [Citation(s) in RCA: 120] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 10/25/2020] [Accepted: 11/02/2020] [Indexed: 01/08/2023]
Abstract
Hyperglycaemia in people with and without diabetes admitted to the hospital is associated with a substantial increase in morbidity, mortality, and health-care costs. Professional societies have recommended insulin therapy as the cornerstone of inpatient pharmacological management. Intravenous insulin therapy is the treatment of choice in the critical care setting. In non-intensive care settings, several insulin protocols have been proposed to manage patients with hyperglycaemia; however, meta-analyses comparing different treatment regimens have not clearly endorsed the benefits of any particular strategy. Clinical guidelines recommend stopping oral antidiabetes drugs during hospitalisation; however, in some countries continuation of oral antidiabetes drugs is commonplace in some patients with type 2 diabetes admitted to hospital, and findings from clinical trials have suggested that non-insulin drugs, alone or in combination with basal insulin, can be used to achieve appropriate glycaemic control in selected populations. Advances in diabetes technology are revolutionising day-to-day diabetes care and work is ongoing to implement these technologies (ie, continuous glucose monitoring, automated insulin delivery) for inpatient care. Additionally, transformations in care have occurred during the COVID-19 pandemic, including the use of remote inpatient diabetes management-research is needed to assess the effects of such adaptations.
Collapse
Affiliation(s)
- Francisco J Pasquel
- Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
| | - M Cecilia Lansang
- Department of Endocrinology, Diabetes and Metabolism, Cleveland Clinic, Cleveland, OH, USA
| | - Ketan Dhatariya
- Elsie Bertram Diabetes Centre, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | - Guillermo E Umpierrez
- Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| |
Collapse
|
13
|
Mathioudakis NN, Abusamaan MS, Shakarchi AF, Sokolinsky S, Fayzullin S, McGready J, Zilbermint M, Saria S, Golden SH. Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients. JAMA Netw Open 2021; 4:e2030913. [PMID: 33416883 PMCID: PMC7794667 DOI: 10.1001/jamanetworkopen.2020.30913] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/01/2020] [Indexed: 12/19/2022] Open
Abstract
Importance Accurate clinical decision support tools are needed to identify patients at risk for iatrogenic hypoglycemia, a potentially serious adverse event, throughout hospitalization. Objective To predict the risk of iatrogenic hypoglycemia within 24 hours after each blood glucose (BG) measurement during hospitalization using a machine learning model. Design, Setting, and Participants This retrospective cohort study, conducted at 5 hospitals within the Johns Hopkins Health System, included 54 978 admissions of 35 147 inpatients who had at least 4 BG measurements and received at least 1 U of insulin during hospitalization between December 1, 2014, and July 31, 2018. Data from the largest hospital were split into a 70% training set and 30% test set. A stochastic gradient boosting machine learning model was developed using the training set and validated on internal and external validation. Exposures A total of 43 clinical predictors of iatrogenic hypoglycemia were extracted from the electronic medical record, including demographic characteristics, diagnoses, procedures, laboratory data, medications, orders, anthropomorphometric data, and vital signs. Main Outcomes and Measures Iatrogenic hypoglycemia was defined as a BG measurement less than or equal to 70 mg/dL occurring within the pharmacologic duration of action of administered insulin, sulfonylurea, or meglitinide. Results This cohort study included 54 978 admissions (35 147 inpatients; median [interquartile range] age, 66.0 [56.0-75.0] years; 27 781 [50.5%] male; 30 429 [55.3%] White) from 5 hospitals. Of 1 612 425 index BG measurements, 50 354 (3.1%) were followed by iatrogenic hypoglycemia in the subsequent 24 hours. On internal validation, the model achieved a C statistic of 0.90 (95% CI, 0.89-0.90), a positive predictive value of 0.09 (95% CI, 0.08-0.09), a positive likelihood ratio of 4.67 (95% CI, 4.59-4.74), a negative predictive value of 1.00 (95% CI, 1.00-1.00), and a negative likelihood ratio of 0.22 (95% CI, 0.21-0.23). On external validation, the model achieved C statistics ranging from 0.86 to 0.88, positive predictive values ranging from 0.12 to 0.13, negative predictive values of 0.99, positive likelihood ratios ranging from 3.09 to 3.89, and negative likelihood ratios ranging from 0.23 to 0.25. Basal insulin dose, coefficient of variation of BG, and previous hypoglycemic episodes were the strongest predictors. Conclusions and Relevance These findings suggest that iatrogenic hypoglycemia can be predicted in a short-term prediction horizon after each BG measurement during hospitalization. Further studies are needed to translate this model into a real-time informatics alert and evaluate its effectiveness in reducing the incidence of inpatient iatrogenic hypoglycemia.
Collapse
Affiliation(s)
- Nestoras N. Mathioudakis
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Mohammed S. Abusamaan
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ahmed F. Shakarchi
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sam Sokolinsky
- Department of Quality Improvement and Clinical Analytics, Johns Hopkins Health System, Baltimore, Maryland
| | - Shamil Fayzullin
- Department of Quality Improvement and Clinical Analytics, Johns Hopkins Health System, Baltimore, Maryland
| | - John McGready
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Mihail Zilbermint
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Johns Hopkins Community Physicians at Suburban Hospital, Suburban Hospital, Bethesda, Maryland
| | - Suchi Saria
- Departments of Computer Science, Applied Math and Statistics, and Health Policy, Johns Hopkins University, Baltimore, Maryland
| | - Sherita Hill Golden
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| |
Collapse
|
14
|
Elbaz M, Nashashibi J, Kushnir S, Leibovici L. Predicting hypoglycemia in hospitalized patients with diabetes: A derivation and validation study. Diabetes Res Clin Pract 2021; 171:108611. [PMID: 33290718 DOI: 10.1016/j.diabres.2020.108611] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/24/2020] [Accepted: 12/03/2020] [Indexed: 10/22/2022]
Abstract
AIMS Develop and validate a model for predicting hypoglycemia in inpatients. METHODS Derivation cohort: patients treated with hypoglycemic drugs and admitted to the departments of medicine of a university hospital during 2016. VALIDATION patients admitted to a community hospital, and patients admitted to a university hospital in the north of Israel, 2017-2018. Data available in the electronic patient record (EPR) during the first hours of hospital stay were used to develop a logistic model to predict the probability of hypoglycemia. The performance of the model was measured in the validation cohorts. RESULTS In the derivation cohort, hypoglycemia was measured in 474 out of 3605 patients, 13.1%. The logistic model to predict hypoglycemia included age, nasogastric or percutaneous gastrostomy tube, Charlson score, vomiting, chest pain, acute renal failure, insulin, hemoglobin and diastolic blood pressure. The area under the ROC curve (AUROC) was 0.71 (95% CI 0.69-0.73). In the highest probability group the percentage of hypoglycemia was 24.3% (258/1061). In the two validation groups hypoglycemia was measured in 269/2592 patients (11.1%); and 393/3635 (10.8%). AUROC was 0.72 (95% CI 0.68-0.76); and 0.71 (95% CI 0.68-0.74). In the highest probability groups hypoglycemia was measured in 28.1% (111/395); and 23.0% (211/909) of patients. CONCLUSIONS The derived model performed well in the validation cohorts. Assuming that most of the hypoglycemia episodes could be prevented we would need to invest efforts to avoid hypoglycemia in 4-5 patients to prevent one episode of hypoglycemia.
Collapse
Affiliation(s)
- Michal Elbaz
- Department of Medicine E, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva, Israel
| | | | - Shiri Kushnir
- Research Authority, Rabin Medical Center, Petah-Tiqva, Israel
| | - Leonard Leibovici
- Department of Medicine E, Beilinson Hospital, Rabin Medical Center, Petah-Tiqva, Israel; Sackler Faculty of Medicine, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, Israel.
| |
Collapse
|
15
|
Alwafi H, Alsharif AA, Wei L, Langan D, Naser AY, Mongkhon P, Bell JS, Ilomaki J, Al Metwazi MS, Man KKC, Fang G, Wong ICK. Incidence and prevalence of hypoglycaemia in type 1 and type 2 diabetes individuals: A systematic review and meta-analysis. Diabetes Res Clin Pract 2020; 170:108522. [PMID: 33096187 DOI: 10.1016/j.diabres.2020.108522] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/30/2020] [Accepted: 10/13/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Previous meta-analysis investigating the incidence and prevalence of hypoglycaemia in both types of diabetes is limited. The purpose of this review is to conduct a systematic review and meta-analysis of the existing literature which investigates the incidence and prevalence of hypoglycaemia in individuals with diabetes. METHODS PubMed, Embase and Cochrane library databases were searched up to October 2018. Observational studies including individuals with diabetes of all ages and reporting incidence and/or prevalence of hypoglycaemia were included. Two reviewers independently screened articles, extracted data and assessed the quality of included studies. Meta-analysis was performed using a random effects model with 95% confidence interval (CI) to estimate the pooled incidence and prevalence of hypoglycaemia in individuals with diabetes. RESULTS Our search strategy generated 35,007 articles, of which 72 studies matched the inclusion criteria and were included in the meta-analysis. The prevalence of hypoglycaemia ranged from 0.074% to 73.0%, comprising a total of 2,462,810 individuals with diabetes. The incidence rate of hypoglycaemia ranged from 0.072 to 42,890 episodes per 1,000 person-years: stratified by type of diabetes, it ranged from 14.5 to 42,890 episodes per 1,000 person-years and from 0.072 to 16,360 episodes per 1,000-person years in type 1 and type 2 diabetes, respectively. CONCLUSION Hypoglycaemia is very common among individuals with diabetes. Further studies are needed to investigate hypoglycaemia-associated risk factors.
Collapse
Affiliation(s)
- Hassan Alwafi
- Research Department of Practice and Policy, School of Pharmacy, University College London (UCL), London, United Kingdom; Faculty of Medicine, Umm Al Qura University, Mecca, Saudi Arabia
| | - Alaa A Alsharif
- Department of Pharmacy Practice, Faculty of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Li Wei
- Research Department of Practice and Policy, School of Pharmacy, University College London (UCL), London, United Kingdom
| | - Dean Langan
- UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | | | - Pajaree Mongkhon
- Department of Pharmacy Practice School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand; Pharmacoepidemiology and Statistics Research Center (PESRC), Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand
| | - J Simon Bell
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Jenni Ilomaki
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
| | - Mansour S Al Metwazi
- Clinical Pharmacy Department, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Kenneth K C Man
- Research Department of Practice and Policy, School of Pharmacy, University College London (UCL), London, United Kingdom; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Gang Fang
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ian C K Wong
- Research Department of Practice and Policy, School of Pharmacy, University College London (UCL), London, United Kingdom; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong; The University of Hong Kong - Shenzhen Hospital, 1, Haiyuan 1st Road, Futian District, Shenzhen, Guangdong, China.
| |
Collapse
|
16
|
Ruan Y, Bellot A, Moysova Z, Tan GD, Lumb A, Davies J, van der Schaar M, Rea R. Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records. Diabetes Care 2020; 43:1504-1511. [PMID: 32350021 DOI: 10.2337/dc19-1743] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 04/04/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. RESEARCH DESIGN AND METHODS Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation. RESULTS We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia. CONCLUSIONS Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.
Collapse
Affiliation(s)
- Yue Ruan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals National Health Service Foundation Trust, Oxford, U.K.,Oxford National Institute for Health Research Biomedical Research Centre, Oxford, U.K.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K
| | - Alexis Bellot
- Department of Mathematics, University of Cambridge, Cambridge, U.K.,Alan Turing Institute, London, U.K
| | - Zuzana Moysova
- Big Data Institute, University of Oxford Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, U.K
| | - Garry D Tan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals National Health Service Foundation Trust, Oxford, U.K.,Oxford National Institute for Health Research Biomedical Research Centre, Oxford, U.K
| | - Alistair Lumb
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals National Health Service Foundation Trust, Oxford, U.K.,Oxford National Institute for Health Research Biomedical Research Centre, Oxford, U.K
| | - Jim Davies
- Big Data Institute, University of Oxford Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, U.K
| | - Mihaela van der Schaar
- Department of Mathematics, University of Cambridge, Cambridge, U.K.,Alan Turing Institute, London, U.K
| | - Rustam Rea
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals National Health Service Foundation Trust, Oxford, U.K. .,Oxford National Institute for Health Research Biomedical Research Centre, Oxford, U.K
| |
Collapse
|
17
|
Avanzini F, Marelli G, Amodeo R, Chiappa L, Colombo EL, Di Rocco E, Grioni M, Moro C, Roncaglioni MC, Saltafossi D, Vandoni P, Vannini T, Vilei V, Riva E. The 'brick diet' and postprandial insulin: a practical method to balance carbohydrates ingested and prandial insulin to prevent hypoglycaemia in hospitalized persons with diabetes. Diabet Med 2020; 37:1125-1133. [PMID: 32144811 DOI: 10.1111/dme.14293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/03/2020] [Indexed: 12/13/2022]
Abstract
AIM Insulin is the preferred treatment for the control of diabetes in hospital, but it raises the risk of hypoglycaemia, often because oral intake of carbohydrates in hospitalized persons is lower than planned. Our aim was to assess the effect on the incidence of hypoglycaemia of giving prandial insulin immediately after a meal depending on the amount of carbohydrate ingested. METHODS A prospective pre-post intervention study in hospitalized persons with diabetes eating meals with stable doses of carbohydrates present in a few fixed foods. Foods were easily identifiable on the tray and contained fixed doses of carbohydrates that were easily quantifiable by nurses as multiples of 10 g (a 'brick'). Prandial insulin was given immediately after meals in proportion to the amount of carbohydrates eaten. RESULTS In 83 of the first 100 people treated with the 'brick diet', the oral carbohydrate intake was lower than planned on at least one occasion (median: 3 times; Q1-Q3: 2-6 times) over a median of 5 days. Compared with the last 100 people treated with standard procedures, postprandial insulin given on the basis of ingested carbohydrate significantly reduced the incidence of hypoglycaemic events per day, from 0.11 ± 0.03 to 0.04 ± 0.02 (P < 0.001) with an adjusted incidence rate ratio of 0.70 (95% confidence interval 0.54-0.92; P = 0.011). CONCLUSIONS In hospitalized persons with diabetes treated with subcutaneous insulin, the 'brick diet' offers a practical method to count the amount of carbohydrates ingested, which is often less than planned. Prandial insulin given immediately after a meal, in doses balanced with actual carbohydrate intake reduces the risk of hypoglycaemia.
Collapse
Affiliation(s)
- F Avanzini
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - G Marelli
- Endocrine Metabolic and Nutrition Diseases Departmental Unit, ASST Vimercate, Vimercate, Italy
| | - R Amodeo
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - L Chiappa
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - E L Colombo
- Endocrinology and Diabetology Departmental Unit, Ospedale di Desio, Desio, Italy
| | - E Di Rocco
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - M Grioni
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - C Moro
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - M C Roncaglioni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - D Saltafossi
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - P Vandoni
- Division of Clinical Cardiology, Ospedale di Desio, Desio, Italy
| | - T Vannini
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - V Vilei
- Endocrine Metabolic and Nutrition Diseases Departmental Unit, ASST Vimercate, Vimercate, Italy
| | - E Riva
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| |
Collapse
|
18
|
Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital. Can J Diabetes 2020; 45:114-121.e3. [PMID: 33011129 DOI: 10.1016/j.jcjd.2020.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/06/2020] [Accepted: 06/03/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Given the high incidence of hyperglycemia and hypoglycemia in hospital and the lack of prediction tools for this problem, we developed a clinical tool to assist early identification of individuals at risk for persistent adverse glycemia (AG) in hospital. METHODS We analyzed a cohort of 594 consecutive adult inpatients with type 2 diabetes. We identified clinical factors available early in the admission course that were associated with persistent AG (defined as ≥2 days with capillary glucose <4 or >15 mmol/L during admission). A prediction model for persistent AG was constructed using logistic regression and internal validation was performed using a split-sample approach. RESULTS Persistent AG occurred in 153 (26%) of inpatients, and was associated with admission dysglycemia (odds ratio [OR], 3.65), glycated hemoglobin ≥8.1% (OR, 5.08), glucose-lowering treatment regimen containing sulfonylurea (OR, 3.50) or insulin (OR, 4.22), glucocorticoid medication treatment (OR, 2.27), Charlson Comorbidity Index score and the number of observed days. An early-identification prediction tool, based on clinical factors reliably available at admission (admission dysglycemia, glycated hemoglobin, glucose-lowering regimen and glucocorticoid treatment), could accurately predict persistent AG (receiver-operating characteristic area under curve = 0.806), and, at the optimal cutoff, the sensitivity, specificity and positive predictive value were 84%, 66% and 53%, respectively. CONCLUSIONS A clinical prediction tool based on clinical risk factors available at admission to hospital identified patients at increased risk for persistent AG and could assist early targeted management by inpatient diabetes teams.
Collapse
|
19
|
Abstract
Hypoglycemia in inpatients with diabetes remains the most common complication of diabetes therapies. Hypoglycemia is independently associated with increased morbidity and mortality, increased length of stay, increased readmission rate, and increased cost. This review describes the importance of reporting and addressing inpatient hypoglycemia; it further summarizes eight strategies that aid clinicians in the prevention of inpatient hypoglycemia: auditing the electronic medical record, formulary restrictions and dose-limiting strategies, hyperkalemia order sets, electronic glucose management systems, prediction tools, diabetes self-management, remote surveillance, and noninsulin medications.
Collapse
Affiliation(s)
- Paulina Cruz
- Division of Endocrinology, Metabolism and Lipid Research, Washington University in St. Louis, MO, USA
- Paulina Cruz, MD, Division of Endocrinology, Metabolism and Lipid Research, Washington University in St. Louis, Campus Box 8127, 660 S. Euclid Avenue, Saint Louis, MO 63110, USA.
| |
Collapse
|
20
|
Pasquel FJ, Fayfman M, Umpierrez GE. Debate on Insulin vs Non-insulin Use in the Hospital Setting-Is It Time to Revise the Guidelines for the Management of Inpatient Diabetes? Curr Diab Rep 2019; 19:65. [PMID: 31353426 DOI: 10.1007/s11892-019-1184-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW Hyperglycemia contributes to a significant increase in morbidity, mortality, and healthcare costs in the hospital. Professional associations recommend insulin as the mainstay of diabetes therapy in the inpatient setting. The standard of care basal-bolus insulin regimen is a labor-intensive approach associated with a significant risk of iatrogenic hypoglycemia. This review summarizes recent evidence from observational studies and clinical trials suggesting that not all patients require treatment with complex insulin regimens. RECENT FINDINGS Evidence from clinical trials shows that incretin-based agents are effective in appropriately selected hospitalized patients and may be a safe alternative to complicated insulin regimens. Observational studies also show that older agents (i.e., metformin and sulfonylureas) are commonly used in the hospital, but there are few carefully designed studies addressing their efficacy. Therapy with dipeptidyl peptidase-4 (DPP-4) inhibitors, alone or in combination with basal insulin, may effectively control glucose levels in patients with mild to moderate hyperglycemia. Further studies with glucagon-like peptide-1 (GLP-1) receptor analogs and older oral agents are needed to confirm their safety in the hospital.
Collapse
Affiliation(s)
- Francisco J Pasquel
- Department of Medicine/Endocrinology, Emory University School of Medicine, 69 Jesse Hill Jr Dr, Atlanta, GA, 30303, USA
| | - Maya Fayfman
- Department of Medicine/Endocrinology, Emory University School of Medicine, 69 Jesse Hill Jr Dr, Atlanta, GA, 30303, USA
| | - Guillermo E Umpierrez
- Department of Medicine/Endocrinology, Emory University School of Medicine, 69 Jesse Hill Jr Dr, Atlanta, GA, 30303, USA.
| |
Collapse
|
21
|
Ruan Y, Tan GD, Lumb A, Rea RD. Importance of inpatient hypoglycaemia: impact, prediction and prevention. Diabet Med 2019; 36:434-443. [PMID: 30653706 DOI: 10.1111/dme.13897] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/14/2019] [Indexed: 12/16/2022]
Abstract
Hypoglycaemia is a key barrier to achieving euglycaemic control in people who are hospitalized. Inpatient hypoglycaemia has been linked to adverse clinical outcomes, including mortality and longer stay in hospital. A number of studies have applied mathematical tools and statistical models to predict inpatient hypoglycaemia and identify factors that may result in hypoglycaemic events. Several different approaches have been tested to prevent inpatient hypoglycaemia. These can be categorized as human intervention, computerized methods or application of medical devices. In this review we provide an overview of the epidemiology of inpatient hypoglycaemia and its impact on patients and hospitals. We also discuss the existing methodology used to predict inpatient hypoglycaemia and the limited number of trials performed to prevent inpatient hypoglycaemia. The review highlights the urgent need for evidence-based methods to reduce inpatient hypoglycaemia.
Collapse
Affiliation(s)
- Y Ruan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, UK
| | - G D Tan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - A Lumb
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - R D Rea
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| |
Collapse
|
22
|
Adderley NJ, Mallett S, Marshall T, Ghosh S, Rayman G, Bellary S, Coleman J, Akiboye F, Toulis KA, Nirantharakumar K. Temporal and external validation of a prediction model for adverse outcomes among inpatients with diabetes. Diabet Med 2018; 35:798-806. [PMID: 29485723 DOI: 10.1111/dme.13612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/22/2018] [Indexed: 02/06/2023]
Abstract
AIM To temporally and externally validate our previously developed prediction model, which used data from University Hospitals Birmingham to identify inpatients with diabetes at high risk of adverse outcome (mortality or excessive length of stay), in order to demonstrate its applicability to other hospital populations within the UK. METHODS Temporal validation was performed using data from University Hospitals Birmingham and external validation was performed using data from both the Heart of England NHS Foundation Trust and Ipswich Hospital. All adult inpatients with diabetes were included. Variables included in the model were age, gender, ethnicity, admission type, intensive therapy unit admission, insulin therapy, albumin, sodium, potassium, haemoglobin, C-reactive protein, estimated GFR and neutrophil count. Adverse outcome was defined as excessive length of stay or death. RESULTS Model discrimination in the temporal and external validation datasets was good. In temporal validation using data from University Hospitals Birmingham, the area under the curve was 0.797 (95% CI 0.785-0.810), sensitivity was 70% (95% CI 67-72) and specificity was 75% (95% CI 74-76). In external validation using data from Heart of England NHS Foundation Trust, the area under the curve was 0.758 (95% CI 0.747-0.768), sensitivity was 73% (95% CI 71-74) and specificity was 66% (95% CI 65-67). In external validation using data from Ipswich, the area under the curve was 0.736 (95% CI 0.711-0.761), sensitivity was 63% (95% CI 59-68) and specificity was 69% (95% CI 67-72). These results were similar to those for the internally validated model derived from University Hospitals Birmingham. CONCLUSIONS The prediction model to identify patients with diabetes at high risk of developing an adverse event while in hospital performed well in temporal and external validation. The externally validated prediction model is a novel tool that can be used to improve care pathways for inpatients with diabetes. Further research to assess clinical utility is needed.
Collapse
Affiliation(s)
- N J Adderley
- Institute of Applied Health Research, University of Birmingham, Birmingham
| | - S Mallett
- Institute of Applied Health Research, University of Birmingham, Birmingham
| | - T Marshall
- Institute of Applied Health Research, University of Birmingham, Birmingham
| | - S Ghosh
- Diabetes Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham
| | - G Rayman
- Ipswich Hospital NHS Trust, Ipswich
| | - S Bellary
- Heart of England Foundation Trust, Birmingham, UK
| | - J Coleman
- Institute of Applied Health Research, University of Birmingham, Birmingham
| | | | - K A Toulis
- Institute of Applied Health Research, University of Birmingham, Birmingham
- 424 General Military Hospital, Thessaloniki, Greece
| | - K Nirantharakumar
- Institute of Applied Health Research, University of Birmingham, Birmingham
- Diabetes Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham
| |
Collapse
|