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Shi M, Yang A, Lau ESH, Luk AOY, Ma RCW, Kong APS, Wong RSM, Chan JCM, Chan JCN, Chow E. A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study. PLoS Med 2024; 21:e1004369. [PMID: 38607977 PMCID: PMC11014435 DOI: 10.1371/journal.pmed.1004369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/29/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.
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Affiliation(s)
- Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Aimin Yang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Eric S. H. Lau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Andrea O. Y. Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Alice P. S. Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Raymond S. M. Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Jones C. M. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Juliana C. N. Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
| | - Elaine Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
- Phase 1 Clinical Trial Centre, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China
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Ratzki-Leewing AA, Black JE, Ryan BL, Zou G, Klar N, Webster-Bogaert S, Timcevska K, Harris SB. Development and validation of a real-world model to predict 1-year Level 3 (severe) hypoglycaemia risk in adults with diabetes (the iNPHORM study, United States). Diabetes Obes Metab 2023; 25:2910-2927. [PMID: 37409569 DOI: 10.1111/dom.15186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/21/2023] [Accepted: 06/01/2023] [Indexed: 07/07/2023]
Abstract
AIMS We aimed to develop and internally validate a real-world prognostic model for Level 3 hypoglycaemia risk compatible with outpatient care in the United States. MATERIALS AND METHODS iNPHORM is a 12-month, US-based panel survey. Adults (18-90 years old) with type 1 diabetes mellitus or insulin- and/or secretagogue-treated type 2 diabetes mellitus were recruited from a nationwide, probability-based internet panel. Among participants completing ≥ 1 follow-up questionnaire(s), we modelled 1-year Level 3 hypoglycaemia risk using Andersen and Gill's Cox survival and penalized regression with multiple imputation. Candidate variables were selected for their clinical relevance and ease of capture at point-of-care. RESULTS In total, 986 participants [type 1 diabetes mellitus: 17%; men: 49.6%; mean age: 51 (SD: 14.3) years] were analysed. Across follow-up, 035.1 (95% CI: 32.2-38.1)% reported ≥1 Level 3 event(s), and the rate was 5.0 (95% CI: 4.1-6.0) events per person-year. Our final model showed strong discriminative validity and parsimony (optimism corrected c-statistic: 0.77). Numerous variables were selected: age; sex; body mass index; marital status; level of education; insurance coverage; race; ethnicity; food insecurity; diabetes type; glycated haemoglobin value; glycated haemoglobin variability; number, type and dose of various medications; number of SH events requiring hospital care (past year and over follow-up); type and number of comorbidities and complications; number of diabetes-related health care visits (past year); use of continuous/flash glucose monitoring; and general health status. CONCLUSIONS iNPHORM is the first US-based primary prognostic study on Level 3 hypoglycaemia. Future model implementation could potentiate risk-tailored strategies that reduce real-world event occurrence and overall diabetes burden.
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Affiliation(s)
- Alexandria A Ratzki-Leewing
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Jason E Black
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Bridget L Ryan
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Guangyong Zou
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Robarts Research Institute, Western University, London, Canada
| | - Neil Klar
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Susan Webster-Bogaert
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Kristina Timcevska
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Stewart B Harris
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada
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van Olst N, Reiber BMM, Vink MRA, Gerdes VEA, Galenkamp H, van der Peet DL, van Rijswijk AS, Bruin SC. Are male patients undergoing bariatric surgery less healthy than female patients? Surg Obes Relat Dis 2023; 19:1013-1022. [PMID: 36967264 DOI: 10.1016/j.soard.2023.02.015] [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: 10/25/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Male patients are underrepresented in bariatric surgery (BS) despite a relatively equal proportion of men and women experiencing obesity. OBJECTIVES Differences in frequency and severity of obesity-associated medical problems (OAMPs) between men and women undergoing BS or in a control group (HELIUS [HEalthy Life In an Urban Setting]) were evaluated. The hypothesis was that men undergoing BS are less healthy than women. SETTING A cross-sectional study of 2 cohorts undergoing BS in 2013 (BS2013) and 2019 (BS2019) and a control group of patients with severe obesity from a general population (HELIUS). METHODS Characteristics concerning weight and OAMPs, medication usage, intoxications, postoperative complications (for BS2019) were compared between men and women. Members of the HELIUS cohort were tested for eligibility for BS. RESULTS Of 3244 patients included, the majority were female (>78.4%). Median (interquartile range) age and body mass index (kg/m2) in male versus female patients were 47.0 (41.0-53.8) versus 43.0 (36.0-51.0) years and 41.5 (38.4-45.2) versus 42.3 (40.2-45.9), respectively, in BS2013, and 52.0 (39.8-57.0) versus 45.0 (35.0-53.0) years and 40.4 (37.4-43.8) versus 41.3 (39.0-44.1) in BS2019 (P < .05). The rates of men with OAMPs were 71.4% and 82.0% compared with 50.2% and 56.9% of women in BS2013 and BS2019, respectively. Overall medication usage was higher in male patients (P = .014). In BS2019, male patients exhibited a higher median HbA1C (P < .001) and blood pressure (P = .003) and used more antihypertensives and antidiabetics (P = .004). Postoperative complications did not differ between men and women. In the control cohort, 66.5% of men and 66.6% of women were eligible for BS. CONCLUSION Men undergoing BS more often experience OAMPs than women, and OAMPs are more advanced in men.
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Affiliation(s)
- Nienke van Olst
- Department of Bariatric Surgery, Spaarne Gasthuis, Hoofddorp, the Netherlands; Department of Surgery, Amsterdam University Medical Centers, Amsterdam, the Netherlands.
| | - Beata M M Reiber
- Department of Surgery, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Marjolein R A Vink
- Department of Bariatric Surgery, Spaarne Gasthuis, Hoofddorp, the Netherlands
| | - Victor E A Gerdes
- Department of Bariatric Surgery, Spaarne Gasthuis, Hoofddorp, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Henrike Galenkamp
- Department of Public and Occupational Health, Amsterdam University Medical Centers, Amsterdam, the Netherlands; Health Behaviors and Chronic Diseases, Amsterdam Public Health, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Department of Surgery, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | | | - Sojoerd C Bruin
- Department of Bariatric Surgery, Spaarne Gasthuis, Hoofddorp, the Netherlands
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Wu Y, Li R, Zhang Y, Long T, Zhang Q, Li M. Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis. Biol Res Nurs 2023; 25:41-50. [PMID: 35839096 DOI: 10.1177/10998004221115856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To systematically summarize the reported prediction models for hypoglycemia in patients with diabetes, compare their performance, and evaluate their applicability in clinical practice. METHODS We selected studies according to the PRISMA, appraised studies according to the Prediction model Risk of Bias Assessment Tool (PROBAST), and extracted and synthesized the data according to the CHARMS. The databases of PubMed, Web of Science, Embase, and Cochrane Library were searched from inception to 31 October 2021 using a systematic review approach to capture all eligible studies developing and/or validating a prognostic prediction model for hypoglycemia in patients with diabetes. The risk bias and clinical applicability were assessed using the PROBAST. The meta-analysis of the performance of the prediction models were also conducted. The protocol of this study was recorded in PROSPERO (CRD42022309852). RESULTS Sixteen studies with 22 models met the eligible criteria. The predictors with the high frequency of occurrence among all models were age, HbA1c, history of hypoglycemia, and insulin use. A meta-analysis of C-statistic was performed for 21 prediction models, and the summary C-statistic and its 95% confidence interval and prediction interval were 0.7699 (0.7299-0.8098), 0.7699 (0.5862-0.9536), respectively. Heterogeneity exists between different hypoglycemia prediction models (τ2 was 0.00734≠0). CONCLUSIONS The existing predictive models are not recommended for widespread clinical use. A high-quality hypoglycemia screening tool should be developed in future studies.
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Affiliation(s)
- Yi Wu
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Ruxue Li
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Yating Zhang
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Tianxue Long
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Qi Zhang
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
| | - Mingzi Li
- Peking University Health Science Center, Beijing, China.,School of Nursing, 540405Peking University, Beijing, China.,Peking University Health Science Centre for Evidence-Based Nursing, A Joanna Briggs Institute Affiliated Group, Beijing, China
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5
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Chen Y, Zhong Q, Luo J, Tang Y, Li M, Lin Q, Willey JA, Chen JL, Whittemore R, Guo J. The 6-Month Efficacy of an Intensive Lifestyle Modification Program on Type 2 Diabetes Risk Among Rural Women with Prior Gestational Diabetes Mellitus: a Cluster Randomized Controlled Trial. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2022; 23:1156-1168. [PMID: 35773443 PMCID: PMC9489585 DOI: 10.1007/s11121-022-01392-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2022] [Indexed: 12/08/2022]
Abstract
This study aimed to evaluate the efficacy of an intensive lifestyle modification program tailored to rural Chinese women with prior gestational diabetes mellitus compared with usual care. In a cluster randomized controlled trial, 16 towns (clusters) in two distinct rural areas in China were randomly selected (8 towns per district); and 320 women with prior gestational diabetes mellitus were recruited from these towns. With stratification for the two study districts, eight towns (160 women) were randomly assigned to the intervention group of a tailored intensive lifestyle modification program and 8 towns (160 women) to the control group. Process measures were collected on attendance, engagement, fidelity, and satisfaction. Primary efficacy outcomes included glycemic and weight-related outcomes, while secondary efficacy outcomes were behavioral outcomes and type 2 diabetes risk score, which were collected at baseline, 3-month, and 6-month follow-up. Generalized estimation equations were used to analyze the data. High attendance (72% of sessions), engagement (67% of interactive activities and group discussions), fidelity (98%), and satisfaction (92%) with the tailored intensive lifestyle modification program were achieved. There were significant reductions in fasting blood glucose, oral glucose tolerance test 2 h, waist circumference, and type 2 diabetes risk score of participants in the intervention group compared to the control group (p < .05). There was no significant intervention effect on body mass index or behavioral outcomes (p > .05). In this study, we demonstrate the successful efficacy of an Intensive Lifestyle Modification Program in reducing type 2 diabetes risk among younger women with prior gestational diabetes mellitus. This tailored program delivered by local healthcare providers is a promising approach for diabetes prevention in rural China, reducing health disparities in rural communities about diabetes prevention. Registered in the Chinese Clinical Trial Registry (ChiCTR2000037956) on 3rd Jan 2018.
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Affiliation(s)
- Yao Chen
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Qinyi Zhong
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Jiaxin Luo
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Yujia Tang
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Mingshu Li
- Xiangya School of Public Health, Central South University, Changsha, 410078, Hinan, China
| | - Qian Lin
- Xiangya School of Public Health, Central South University, Changsha, 410078, Hinan, China
| | - James Allen Willey
- Philip R. Lee Institute for Health Policy Research, University of California, San Francisco, San Francisco, CA, 94118, USA
| | - Jyu-Lin Chen
- School of Nursing, University of California, San Francisco, San Francisco, CA, 94118, USA
| | | | - Jia Guo
- Xiangya School of Nursing, Central South University, 172 Tongzipo Road, Changsha, 410013, Hunan, China.
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Bektaş M, Reiber BMM, Pereira JC, Burchell GL, van der Peet DL. Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives. Obes Surg 2022; 32:2772-2783. [PMID: 35713855 PMCID: PMC9273535 DOI: 10.1007/s11695-022-06146-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/03/2022] [Accepted: 06/03/2022] [Indexed: 11/25/2022]
Abstract
Background Machine learning (ML) has been successful in several fields of healthcare, however the use of ML within bariatric surgery seems to be limited. In this systematic review, an overview of ML applications within bariatric surgery is provided. Methods The databases PubMed, EMBASE, Cochrane, and Web of Science were searched for articles describing ML in bariatric surgery. The Cochrane risk of bias tool and the PROBAST tool were used to evaluate the methodological quality of included studies. Results The majority of applied ML algorithms predicted postoperative complications and weight loss with accuracies up to 98%. Conclusions In conclusion, ML algorithms have shown promising capabilities in the prediction of surgical outcomes after bariatric surgery. Nevertheless, the clinical introduction of ML is dependent upon the external validation of ML.
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Affiliation(s)
- Mustafa Bektaş
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands.
| | - Beata M M Reiber
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Jaime Costa Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, the Netherlands
| | - George L Burchell
- Medical Library Department, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Department of Gastrointestinal Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
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Heller S, Lingvay I, Marso SP, Philis‐Tsimikas A, Pieber TR, Poulter NR, Pratley RE, Hachmann‐Nielsen E, Kvist K, Lange M, Moses AC, Andresen MT, Buse JB. Risk of severe hypoglycaemia and its impact in type 2 diabetes in DEVOTE. Diabetes Obes Metab 2020; 22:2241-2247. [PMID: 32250536 PMCID: PMC7754351 DOI: 10.1111/dom.14049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/30/2020] [Indexed: 01/10/2023]
Abstract
AIMS To undertake a post-hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. MATERIALS AND METHODS The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all-cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all-cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all-cause mortality were investigated. RESULTS Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all-cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all-cause mortality). CONCLUSIONS The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all-cause mortality and that, in this high-risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events.
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Affiliation(s)
- Simon Heller
- Department of Oncology and MetabolismUniversity of SheffieldSheffieldUK
| | - Ildiko Lingvay
- Department of Internal Medicine and Department of Population and Data SciencesUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Steven P. Marso
- HCA Midwest Health Heart and Vascular InstituteOverland ParkKansasUSA
| | | | - Thomas R. Pieber
- Department of Internal MedicineMedical University of GrazGrazAustria
| | - Neil R. Poulter
- Imperial Clinical Trials Unit, National Heart and Lung InstituteImperial College LondonLondonUK
| | | | | | | | | | - Alan C. Moses
- Novo Nordisk A/SSøborgDenmark
- Independent consultant, Novo Nordisk A/SPortsmouthNew HampshireUSA
| | | | - John B. Buse
- University of North Carolina School of MedicineChapel HillNorth CarolinaUSA
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Heller S, Lingvay I, Marso SP, Philis‐Tsimikas A, Pieber TR, Poulter NR, Pratley RE, Hachmann‐Nielsen E, Kvist K, Lange M, Moses AC, Trock Andresen M, Buse JB. Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE. Diabetes Obes Metab 2020; 22:2248-2256. [PMID: 32996693 PMCID: PMC7756403 DOI: 10.1111/dom.14208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 09/21/2020] [Accepted: 09/25/2020] [Indexed: 12/21/2022]
Abstract
AIMS The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2-year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. MATERIALS AND METHODS Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data-driven machine-learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data-driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data-driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset. RESULTS Both the data-driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time-dependent area under the curve index (0.63 and 0.66, respectively) over a 2-year time horizon. CONCLUSIONS Both the data-driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care.
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Affiliation(s)
- Simon Heller
- Department of Oncology and MetabolismUniversity of SheffieldSheffieldUK
| | - Ildiko Lingvay
- Department of Internal Medicine and Department of Population and Data SciencesUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Steven P. Marso
- HCA Midwest Health Heart and Vascular InstituteOverland ParkKansasUSA
| | | | - Thomas R. Pieber
- Department of Internal MedicineMedical University of GrazGrazAustria
| | - Neil R. Poulter
- Imperial Clinical Trials Unit, Imperial College LondonLondonUK
| | | | | | | | | | - Alan C. Moses
- Novo Nordisk A/SSøborgDenmark
- Independent ConsultantPortsmouthNew HampshireUSA
| | | | - John B. Buse
- University of North Carolina School of MedicineChapel HillNorth CarolinaUSA
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