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Yang CT, Chong KS, Wang CC, Ou HT, Kuo S. Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach. Cardiovasc Diabetol 2024; 23:244. [PMID: 38987773 PMCID: PMC11238483 DOI: 10.1186/s12933-024-02320-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/16/2024] [Indexed: 07/12/2024] Open
Abstract
OBJECTIVE To adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan. METHODS The EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood-Nam-D'Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients' cardiovascular risks across institutions. RESULTS The CHIME risk equations had acceptable discrimination (AUROC: 0.71-0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/- 0.0008, 1.1003/- 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/- 0.0011, 0.8714/0.0030, and 1.0476/- 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05). CONCLUSIONS We provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.
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Affiliation(s)
- Chun-Ting Yang
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kah Suan Chong
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chi-Chuan Wang
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan
| | - Huang-Tz Ou
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
| | - Shihchen Kuo
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
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Asowata OJ, Okekunle AP, Olaiya MT, Akinyemi J, Owolabi M, Akpa OM. Stroke risk prediction models: A systematic review and meta-analysis. J Neurol Sci 2024; 460:122997. [PMID: 38669758 DOI: 10.1016/j.jns.2024.122997] [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: 02/19/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Prediction algorithms/models are viable methods for identifying individuals at high risk of stroke across diverse populations for timely intervention. However, evidence summarizing the performance of these models is limited. This study examined the performance and weaknesses of existing stroke risk-score-prediction models (SRSMs) and whether performance varied by population and region. METHODS PubMed, EMBASE, and Web of Science were searched for articles on SRSMs from the earliest records until February 2022. The Prediction Model Risk of Bias Assessment Tool was used to assess the quality of eligible articles. The performance of the SRSMs was assessed by meta-analyzing C-statistics (0 and 1) estimates from identified studies to determine the overall pooled C-statistics by fitting a linear restricted maximum likelihood in a random effect model. RESULTS Overall, 17 articles (cohort study = 15, nested case-control study = 2) comprising 739,134 stroke cases from 6,396,594 participants from diverse populations/regions (Asia; n = 8, United States; n = 3, and Europe and the United Kingdom; n = 6) were eligible for inclusion. The overall pooled c-statistics of SRSMs was 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%), with most SRSMs developed using cohort studies; 0.78 (95%CI: 0.75, 0.80; I2 = 99.9%). The subgroup analyses by geographical region: Asia [0.81 (95%CI: 0.79, 0.83; I2 = 99.8%)], Europe and the United Kingdom [0.76 (95%CI: 0.69, 0.83; I2 = 99.9%)] and the United States only [0.75 (95%CI: 0.72, 0.78; I2 = 73.5%)] revealed relatively indifferent performances of SRSMs. CONCLUSION SRSM performance varied widely, and the pooled c-statistics of SRSMs suggested a fair predictive performance, with very few SRSMs validated in independent population group(s) from diverse world regions.
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Affiliation(s)
- Osahon Jeffery Asowata
- Department of Epidemiology and Medical Statistics, University of Ibadan, 200284, Nigeria
| | - Akinkunmi Paul Okekunle
- Department of Epidemiology and Medical Statistics, University of Ibadan, 200284, Nigeria; Department of Medicine, College of Medicine, University of Ibadan, 200284, Nigeria; Research Institute of Human Ecology, Seoul National University, 08826, Republic of Korea.
| | - Muideen Tunbosun Olaiya
- Stroke and Ageing Research, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Joshua Akinyemi
- Department of Epidemiology and Medical Statistics, University of Ibadan, 200284, Nigeria
| | - Mayowa Owolabi
- Department of Medicine, College of Medicine, University of Ibadan, 200284, Nigeria; Lebanese American University, 1102 2801 Beirut, Lebanon; Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, 200284, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, University of Ibadan, 200284, Nigeria; Preventive Cardiology Research Unit, Institute of Cardiovascular Diseases, College of Medicine, University of Ibadan, 200284, Nigeria; Division of Epidemiology, Biostatistics and Environmental Health, School of Public Health, University of Memphis, Memphis, USA.
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Zhang Y, Jiong OX, Tang S, Tang YC, Wong CT, Ng CS, Quan J. Comparison of prediction models for cardiovascular and mortality risk in people with type 2 diabetes: An external validation in 23 685 adults included in the UK Biobank. Diabetes Obes Metab 2024; 26:1697-1705. [PMID: 38297974 DOI: 10.1111/dom.15474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/11/2024] [Accepted: 01/15/2024] [Indexed: 02/02/2024]
Abstract
AIMS To validate cardiovascular risk prediction models for individuals with diabetes using the UK Biobank in order to assess their applicability. METHODS We externally validated 19 cardiovascular risk scores from seven risk prediction models (Chang et al., Framingham, University of Hong Kong-Singapore [HKU-SG], Li et al, RECODe [risk equations for complications of type 2 diabetes], SCORE [Systematic Coronary Risk Evaluation] and the UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2]), identified from systematic reviews, using UK Biobank data from 2006 to 2021 (n = 23 685; participant age 40-71 years, 63.5% male). We evaluated performance by assessing the discrimination and calibration of the models for the endpoints of mortality, cardiovascular mortality, congestive heart failure, myocardial infarction, stroke, and ischaemic heart disease. RESULTS Over a total of 269 430 person-years of follow-up (median 11.89 years), the models showed low-to-moderate discrimination performance on external validation (concordance indices [c-indices] 0.50-0.71). Most models had low calibration with overprediction of the observed risk. RECODe outperformed other models across four comparable endpoints for discrimination: all-cause mortality (c-index 0.67, 95% confidence interval [CI] 0.65-0.69), congestive heart failure (c-index 0.71, 95% CI 0.69-0.72), myocardial infarction (c-index 0.67, 95% CI 0.65-0.68); and stroke (c-index 0.65, 95% CI 0.62-0.68), and for calibration (except for all-cause mortality). The UKPDS OM2 had comparable performance to RECODe for all-cause mortality (c-index 0.67, 95% CI 0.66-0.69) and cardiovascular mortality (c-index 0.71, 95% CI 0.70-0.73), but worse performance for other outcomes. The models performed better for younger participants and somewhat better for non-White ethnicities. Models developed from non-Western datasets showed worse performance in our UK-based validation set. CONCLUSIONS The RECODe model led to better risk estimations in this predominantly White European population. Further validation is needed in non-Western populations to assess generalizability to other populations.
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Affiliation(s)
- Yikun Zhang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ong Xin Jiong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shiqi Tang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yui Chit Tang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Cheuk Tung Wong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Carmen S Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jianchao Quan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU Business School, The University of Hong Kong, Hong Kong SAR, China
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Yang K, Chen M, Wang Y, Jiang G, Hou N, Wang L, Wen K, Li W. Development of a predictive risk stratification tool to identify the population over age 45 at risk for new-onset stroke within 7 years. Front Aging Neurosci 2023; 15:1101867. [PMID: 37388187 PMCID: PMC10301757 DOI: 10.3389/fnagi.2023.1101867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 05/09/2023] [Indexed: 07/01/2023] Open
Abstract
Background and purpose With the acceleration of the aging process of society, stroke has become a major health problem in the middle-aged and elderly population. A number of new stroke risk factors have been recently found. It is necessary to develop a predictive risk stratification tool using multidimensional risk factors to identify people at high risk for stroke. Methods The study included 5,844 people (age ≥ 45 years) who participated in the China Health and Retirement Longitudinal Study in 2011 and its follow-up up to 2018. The population samples were divided into training set and validation set according to 1:1. A LASSO Cox screening was performed to identify the predictors of new-onset stroke. A nomogram was developed, and the population was stratified according to the score calculated through the X-tile program. Internal and external verifications of the nomogram were performed by ROC and calibration curves, and the Kaplan-Meier method was applied to identify the performance of the risk stratification system. Results The LASSO Cox regression screened out 13 candidate predictors from 50 risk factors. Finally, nine predictors, including low physical performance and the triglyceride-glucose index, were included in the nomogram. The nomogram's overall performance was good in both internal and external validations (AUCs at 3-, 5-, and 7-year periods were 0.71, 0.71, and 0.71 in the training set and 0.67, 0.65, and 0.66 in the validation set, respectively). The nomogram was proven to excellently discriminate between the low-, moderate-, and high-risk groups, with a prevalence of 7-year new-onset stroke of 3.36, 8.32, and 20.13%, respectively (P < 0.001). Conclusion This research developed a clinical predictive risk stratification tool that can effectively identify the different risks of new-onset stroke in 7 years in the middle-aged and elderly Chinese population.
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Affiliation(s)
- Kang Yang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Minfang Chen
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaoling Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gege Jiang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Niuniu Hou
- Department of Thyroid, Breast, and Vascular Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liping Wang
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wen
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Wei Li
- Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Chen F, Wang J, Chen X, Yu L, An Y, Gong Q, Chen B, Xie S, Zhang L, Shuai Y, Zhao F, Chen Y, Li G, Zhang B. Development of models to predict 10-30-year cardiovascular disease risk using the Da Qing IGT and diabetes study. Diabetol Metab Syndr 2023; 15:62. [PMID: 36998090 PMCID: PMC10061839 DOI: 10.1186/s13098-023-01039-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/23/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND This study aimed to develop cardiovascular disease (CVD) risk equations for Chinese patients with newly diagnosed type 2 diabetes (T2D) to predict 10-, 20-, and 30-year of risk. METHODS Risk equations for forecasting the occurrence of CVD were developed using data from 601 patients with newly diagnosed T2D from the Da Qing IGT and Diabetes Study with a 30-year follow-up. The data were randomly assigned to a training and test data set. In the training data set, Cox proportional hazard regression was used to develop risk equations to predict CVD. Calibration was assessed by the slope and intercept of the line between predicted and observed probabilities of outcomes by quintile of risk, and discrimination was examined using Harrell's C statistic in the test data set. Using the Sankey flow diagram to describe the change of CVD risk over time. RESULTS Over the 30-year follow-up, corresponding to a 10,395 person-year follow-up time, 355 of 601 (59%) patients developed incident CVD; the incidence of CVD in the participants was 34.2 per 1,000 person-years. Age, sex, smoking status, 2-h plasma glucose level of oral glucose tolerance test, and systolic blood pressure were independent predictors. The C statistics of discrimination for the risk equations were 0.748 (95%CI, 0.710-0.782), 0.696 (95%CI, 0.655-0.704), and 0.687 (95%CI, 0.651-0.694) for 10-, 20-, and 30- year CVDs, respectively. The calibration statistics for the CVD risk equations of slope were 0.88 (P = 0.002), 0.89 (P = 0.027), and 0.94 (P = 0.039) for 10-, 20-, and 30-year CVDs, respectively. CONCLUSIONS The risk equations forecast the long-term risk of CVD in patients with newly diagnosed T2D using variables readily available in routine clinical practice. By identifying patients at high risk for long-term CVD, clinicians were able to take the required primary prevention measures.
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Affiliation(s)
- Fei Chen
- Department of Endocrinology, Friendship Hospital, Beijing, China
| | - Jinping Wang
- Department of Cardiology, Da Qing First Hospital, Da Qing, China
| | - Xiaoping Chen
- Department of Endocrinology, Friendship Hospital, Beijing, China
| | - Liping Yu
- Department of Endocrinology, Friendship Hospital, Beijing, China
| | - Yali An
- Endocrinology Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiuhong Gong
- Endocrinology Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Chen
- Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shuo Xie
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lihong Zhang
- Endocrinology Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Shuai
- Department of Endocrinology, Friendship Hospital, Beijing, China
| | - Fang Zhao
- Department of Endocrinology, Friendship Hospital, Beijing, China
| | - Yanyan Chen
- Endocrinology Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangwei Li
- Department of Endocrinology, Friendship Hospital, Beijing, China
- Endocrinology Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Endocrinology, Friendship Hospital, Beijing, China.
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Zheng M, Wu S, Chen S, Zhang X, Zuo Y, Tong C, Li H, Li C, Yang X, Wu L, Wang A, Zheng D. Development and validation of risk prediction models for new-onset type 2 diabetes in adults with impaired fasting glucose. Diabetes Res Clin Pract 2023; 197:110571. [PMID: 36758640 DOI: 10.1016/j.diabres.2023.110571] [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: 08/30/2022] [Revised: 01/14/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023]
Abstract
AIMS To develop and validate sex-specific risk prediction models based on easily obtainable clinical data for predicting 5-year risk of type 2 diabetes (T2D) among individuals with impaired fasting glucose (IFG), and generate practical tools for public use. METHODS The data used for model training and internal validation came from a large prospective cohort (N = 18,384). Two independent cohorts were used for external validation. A two-step approach was applied to screen variables. Coefficient-based models were constructed by multivariate Cox regression analyses, and score-based models were subsequently generated. The predictive power was evaluated by the area under the curve (AUC). RESULTS During a median follow-up of 7.55 years, 5697 new-onset T2D cases were identified. Predictor variables included age, body mass index, waist circumference, diastolic blood pressure, triglycerides, fasting plasma glucose, and fatty liver. The proposed models outperformed five existing models. In internal validation, the AUCs of the coefficient-based models were 0.741 (95% CI 0.723-0.760) for men and 0.762 (95% CI 0.720-0.802) for women. External validation yielded comparable prediction performance. We finally constructed a risk scoring system and a web calculator. CONCLUSIONS The risk prediction models and derived tools had well-validated performance to predict the 5-year risk of T2D in IFG adults.
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Affiliation(s)
- Manqi Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, Tangshan, Hebei, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, Tangshan, Hebei, China
| | - Xiaoyu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Department of Anesthesiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Yingting Zuo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Chao Tong
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Haibin Li
- Heart Center & Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Xinghua Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Lijuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Deqiang Zheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Department of Clinical Sciences Malmö, Center for Primary Health Care Research, Lund University, Lund, Sweden.
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Shao X, Liu H, Hou F, Bai Y, Cui Z, Lin Y, Jiang X, Bai P, Wang Y, Zhang Y, Lu C, Liu H, Zhou S, Yu P. Development and validation of risk prediction models for stroke and mortality among patients with type 2 diabetes in northern China. J Endocrinol Invest 2023; 46:271-283. [PMID: 35972686 DOI: 10.1007/s40618-022-01898-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/01/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Stroke is one of the leading causes of disability and mortality in patients with type 2 diabetes mellitus (T2DM). Risk models have been developed for predicting stroke and stroke-associated mortality among patients with T2DM. Here, we evaluated risk factors of stroke for individualized prevention measures in patients with T2DM in northern China. METHODS In the community-based Tianjin Chronic Disease Cohort study, 58,042 patients were enrolled between January 2014 and December 2019. We used multiple imputation (MI) to impute missing variables and univariate and multivariate Cox's proportional hazard regression to screen risk factors of stroke. Furthermore, we established and validated first-ever prediction models for stroke (Model 1 and Model 2) and death from stroke (Model 3) and evaluated their performance. RESULTS In the derivation and validation groups, the area under the curves (AUCs) of Models 1-3 was better at 5 years than at 8 years. The Harrell's C-index for all models was above 0.7. All models had good calibration, discrimination, and clinical net benefit. Sensitivity analysis using the MI dataset indicated that all models had good and stable prediction performance. CONCLUSION In this study, we developed and validated first-ever risk prediction models for stroke and death from stroke in patients with T2DM, with good discrimination and calibration observed in all models. Based on lifestyle, demographic characteristics, and laboratory examination, these models could provide multidimensional management and individualized risk assessment. However, the models developed here may only be applicable to Han Chinese.
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Affiliation(s)
- X Shao
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - H Liu
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - F Hou
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - Y Bai
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Z Cui
- Department of Epidemiology and Health Statistics, Tianjin Medical University, Heping District, Tianjin, China
| | - Y Lin
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - X Jiang
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - P Bai
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Y Wang
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - Y Zhang
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - C Lu
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - H Liu
- Community Health Service Center, Jiefang Road, Tanggu Street, Binhai New District, Tianjin, China
| | - S Zhou
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China
| | - P Yu
- NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China.
- Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China.
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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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9
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Deischinger C, Dervic E, Nopp S, Kaleta M, Klimek P, Kautzky-Willer A. Diabetes mellitus is associated with a higher relative risk for venous thromboembolism in females than in males. Diabetes Res Clin Pract 2022; 194:110190. [PMID: 36471550 DOI: 10.1016/j.diabres.2022.110190] [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: 08/25/2022] [Revised: 11/12/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022]
Abstract
AIMS The risk for developing venous thromboembolism (VTE) is about equal in both sexes. Research suggests diabetes mellitus (DM) is a risk factor for pulmonary embolism and deep vein thrombosis, both forms of VTE. We aimed at investigating the sex-specific impact of DM on VTE risk. MATERIALS AND METHODS Medical claims data were analyzed in a retrospective, population-level cohort study in Austria between 1997 and 2014. 180,034 patients with DM were extracted and compared to 540,102 sex and age-matched controls without DM in terms of VTE risk and whether specific DM medications might modulate VTE risk. RESULTS The risk to develop VTE was 1.4 times higher amongst patients with DM than controls (95% CI 1.36-1.43, p < 0.001). The association of DM with newly diagnosed VTE was significantly greater in females (OR = 1.52, 95% CI 1.46-1.58, p < 0.001) resulting in a relative risk increase of 1.17 (95% CI 1.11-1.23) across all age groups with a peak of 1.65 (95% CI 1.43-1.89) between 50 and 59 years. Dipeptidyl peptidase 4 inhibitors were associated with a higher risk for VTE amongst female DM patients (OR = 2.3, 95% CI 1.3-4.3, p = 0.0096). CONCLUSION Amongst DM patients, females appear to be associated with a higher relative risk increase in VTE than males, especially during perimenopause.
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Affiliation(s)
- Carola Deischinger
- Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Gender Medicine Unit, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Elma Dervic
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, Austria; Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria
| | - Stephan Nopp
- Department of Internal Medicine I, Clinical Division of Hematology and Hemostaseology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Michaela Kaleta
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, Austria; Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria
| | - Peter Klimek
- Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, Vienna, Austria; Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria
| | - Alexandra Kautzky-Willer
- Department of Internal Medicine III, Clinical Division of Endocrinology and Metabolism, Gender Medicine Unit, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; Gender Institute, Gars am Kamp, Austria.
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10
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Ndjaboue R, Ngueta G, Rochefort-Brihay C, Delorme S, Guay D, Ivers N, Shah BR, Straus SE, Yu C, Comeau S, Farhat I, Racine C, Drescher O, Witteman HO. Prediction models of diabetes complications: a scoping review. J Epidemiol Community Health 2022; 76:jech-2021-217793. [PMID: 35772935 DOI: 10.1136/jech-2021-217793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 06/08/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes. METHODS Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards. RESULTS Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance. CONCLUSION This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.
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Affiliation(s)
- Ruth Ndjaboue
- Faculty of Medicine, Université Laval, Quebec, Quebec, Canada
- School of social work, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- CIUSSS de l'Estrie, Research Centre on Aging, Sherbrooke, Quebec, Canada
| | - Gérard Ngueta
- Université de Sherbrooke Faculté des Sciences, Sherbrooke, Quebec, Canada
| | | | | | - Daniel Guay
- Diabetes Action Canada, Toronto, Ontario, Canada
| | - Noah Ivers
- Women's College Research Institute, Women's College Hospital, Toronto, Ontario, Canada
- Department of Family Medicine and Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Baiju R Shah
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Sharon E Straus
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Catherine Yu
- Knowledge Translation, St. Michael's Hospital, Li Ka Shing Knowledge Institute, Toronto, Ontario, Canada
| | - Sandrine Comeau
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Imen Farhat
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Charles Racine
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Olivia Drescher
- Université Laval Faculté de médecine, Quebec, Quebec, Canada
| | - Holly O Witteman
- Family and Emergency Medicine, Laval University, Quebec City, Quebec, Canada
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11
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Galbete A, Tamayo I, Librero J, Enguita-Germán M, Cambra K, Ibáñez-Beroiz B. Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models. Diabetes Res Clin Pract 2022; 184:109089. [PMID: 34648890 DOI: 10.1016/j.diabres.2021.109089] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 12/23/2022]
Abstract
AIMS To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations. RESULTS The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated. CONCLUSIONS There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.
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Affiliation(s)
- Arkaitz Galbete
- Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Departamento de Estadística, Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain
| | - Ibai Tamayo
- Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain
| | - Julián Librero
- Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain
| | - Mónica Enguita-Germán
- Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain
| | - Koldo Cambra
- Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Dirección de Salud Pública y Adicciones, Departamento de Sanidad, Gobierno Vasco, Vitoria, Spain
| | - Berta Ibáñez-Beroiz
- Navarrabiomed-Hospital Universitario de Navarra (HUN)-Universidad Pública de Navarra (UPNA), Pamplona, Spain; Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Bilbao, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), IdiSNA, Pamplona, Spain; Departamento de Ciencias de la Salud, Universidad Pública de Navarra (UPNA), Pamplona, Spain.
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12
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Sex-Related Disparities in the Incidence and Outcomes of Ischemic Stroke among Type 2 Diabetes Patients. A Matched-Pair Analysis Using the Spanish National Hospital Discharge Database for Years 2016-2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073659. [PMID: 33915785 PMCID: PMC8037293 DOI: 10.3390/ijerph18073659] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 12/11/2022]
Abstract
Background: To analyze the incidence, use of therapeutic procedures, and in-hospital outcomes among patients suffering an ischemic stroke (IS) according to the presence of type 2 diabetes mellitus (T2DM) in Spain (2016–2018) and to assess the existence of sex differences. Methods: Matched-pair analysis using the Spanish National Hospital discharge. Results: IS was coded in 92,524 men and 79,731 women (29.53% with T2DM). The adjusted incidence of IS (IRR 2.02; 95% CI 1.99–2.04) was higher in T2DM than non-T2DM subjects, with higher IRRs in both sexes. Men with T2DM had a higher incidence of IS than T2DM women (IRR 1.54; 95% CI 1.51–1.57). After matching patients with T2DM, those with other comorbid conditions, however, significantly less frequently received endovascular thrombectomy and thrombolytic therapy. In-hospital mortality (IHM) was lower among T2DM men than matched non-T2DM men (8.23% vs. 8.71%; p < 0.001). Women with T2DM had a higher IHM rate than T2DM men (11.5% vs. 10.20%; p = 0.004). After adjusting for confounders, women with T2DM had a 12% higher mortality risk than T2DM men (OR 1.12; 95% CI 1.04–1.21). Conclusions: T2DM is associated with higher incidence of IS in both sexes. Men with T2DM have a higher incidence rates of IS than T2DM women. Women with T2DM have a higher risk of dying in the hospital.
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13
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Yao Q, Zhang J, Yan K, Zheng Q, Li Y, Zhang L, Wu C, Yang Y, Zhou M, Zhu C. Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China. Medicine (Baltimore) 2020; 99:e22680. [PMID: 33031337 PMCID: PMC7544427 DOI: 10.1097/md.0000000000022680] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Multiple factors, including increasing incidence, poor knowledge of stroke and lack of effective, noninvasive and convenient stroke risk prediction tools, make it more difficult for precautions against stroke in China. Effective prediction models for stroke may assist to establish better risk awareness and management, healthier lifestyle, and lower stroke incidence for people.The China Health and Retirement Longitudinal Survey was the development cohort. Logistic regression was applied to model's development, in which the candidate variables with statistically significant coefficient were included in the prediction model. The area under receiver operating characteristic curve (AUC) and 10-times cross-validation were used for internal validation. Cutoff point of high-risk group was measured by Youden index. The China Health and Nutrition Survey was the validation cohort.The development cohort and the validation cohort included 16557 and 5065 participants, and the incidence density was 358.207/100,000 person-year and 350.701/100,000 person-year, respectively. The model for 2-year new-onset stroke risk prediction included age, hypertension, diabetes, heart disease, and smoking. The AUC and cross-validation AUC were 0.707 (95% confidence interval[CI]: 0.664, 0.750) and the 0.710 (95% CI: 0.650, 0.736). The sensitivity, specificity and accuracy of the cutoff point were 0.774, 0.545, and 0.319. The AUC and cross-validation AUC were 0.800 (95% CI: 0.744, 0.856) and 0.811(95% CI:0.714, 0.847), and the sensitivity, specificity and accuracy of cutoff point being 0.857,0.569, and 0.426 in external validation.A simple prediction tool using 5 noninvasive and easily accessible factors can assist in 2-year new-onset stroke risk prediction in Chinese people over 45 years old, which is believed to be applicable in identifying high-risk individuals and health management in China.
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Affiliation(s)
- Qiang Yao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Jing Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Ke Yan
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Qianwen Zheng
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Yawen Li
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Lu Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Chenyao Wu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Yanling Yang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
| | - Muke Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Cairong Zhu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University
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14
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Dong W, Wan EYF, Bedford LE, Wu T, Wong CKH, Tang EHM, Lam CLK. Prediction models for the risk of cardiovascular diseases in Chinese patients with type 2 diabetes mellitus: a systematic review. Public Health 2020; 186:144-156. [PMID: 32836004 DOI: 10.1016/j.puhe.2020.06.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/23/2020] [Accepted: 06/07/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVES Diabetes mellitus (DM) is a serious public health issue worldwide, and DM patients have higher risk of cardiovascular diseases (CVDs), which is the leading cause of DM-related deaths. China has the largest DM population, yet a robust model to predict CVDs in Chinese DM patients is still lacking. This systematic review is carried out to summarize existing models and identify potentially important predictors for CVDs in Chinese DM patients. STUDY DESIGN Systematic review. METHODS Medline and Embase were searched for data from April 1st, 2011 to May 31st, 2018. A study was eligible if it developed CVD (defined as total CVD or any major cardiovascular component) risk prediction models or explored potential predictors of CVD specifically for Chinese people with type 2 DM. Standardized forms were utilized to extract information, appraise applicability, risk of bias, and availabilities. RESULTS Five models and 29 studies focusing on potential predictors were identified. Models for a primary care setting, or to predict total CVD, are rare. A number of common predictors (e.g. age, sex, diabetes duration, smoking status, glycated hemoglobin (HbA1c), blood pressure, lipid profile, and treatment modalities) were observed in existing models, in which urine albumin:creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) are highly recommended for the Chinese population. Variability of blood pressure (BP) and HbA1c should be included in prediction model development as novel factors. Meanwhile, interactions between age, sex, and risk factors should also be considered. CONCLUSIONS A 10-year prediction model for CVD risk in Chinese type 2 DM patients is lacking and urgently needed. There is insufficient evidence to support the inclusion of other novel predictors in CVDs risk prediction functions for routine clinical use.
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Affiliation(s)
- W Dong
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
| | - E Y F Wan
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China; Department of Pharmacology and Pharmacy, The University of Hong Kong, L02-56, 2/F, Laboratory Block, 21 Sassoon Road, Pokfulam, Hong Kong, China.
| | - L E Bedford
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
| | - T Wu
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
| | - C K H Wong
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
| | - E H M Tang
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
| | - C L K Lam
- Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong, China
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15
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Ko MM, Jang S, Jung J. An observational study on diagnosis index of metabolic disease with blood-stasis. Medicine (Baltimore) 2020; 99:e21140. [PMID: 32629750 PMCID: PMC7337439 DOI: 10.1097/md.0000000000021140] [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] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION Treating blood stasis is effective in treating obesity and metabolic diseases in traditional Korean medicine. The aim of this prospective observational study is to determine the effectiveness of the diagnosis index for metabolic diseases with blood stasis by analyzing clinical data and blood samples. METHODS AND ANALYSIS We will perform a prospective observational study. Participants who meet the inclusion criteria will be recruited from the Dongguk university Ilsan Oriental hospital. The outcomes are resistin, serum amyloid P component, C-reactive protein, D-dimer, and blood stasis scores. In addition, the blood pressure, ankle-brachial pressure index, brachial-ankle pulse wave velocity, body mass index, waist circumference, and levels of blood lipid will be assessed. DISCUSSION Through this study, we could collect specific data for diagnosing metabolic diseases with blood stasis. Therefore, the findings of this study will provide a summary of the current state of evidence regarding the effectiveness of the diagnosis index in managing metabolic disease with blood stasis. ETHICS AND DISSEMINATION The study was approved by the Institutional Review Board of the Dongguk University Ilsan Oriental Hospital (DUIOH-2018-09-001-007). The results will be published in a peer-reviewed journal and will be disseminated electronically and in print. TRIAL REGISTRATION NUMBER Clinical Research Information Service: KCT0003548.
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Shi R, Zhang T, Sun H, Hu F. Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus. Front Endocrinol (Lausanne) 2020; 11:559. [PMID: 32982965 PMCID: PMC7479835 DOI: 10.3389/fendo.2020.00559] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 07/09/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose: Stroke has sparked global concern as it seriously threatens people's life, bringing about dramatic health burdens on patients, especially for type 2 diabetes mellitus (T2DM) patients. Therefore, a risk scoring model is urgently valuable for T2DM patients to predict the risk of stroke incidence and for positive health intervention. Methods: We randomly divided 4,335 T2DM patients into two groups, training set (n = 3,252) and validation set (n = 1,083), at the ratio of 3:1. Characteristic variables were then selected based on the data of training set through least absolute shrinkage and selection operator regression. Three models were established to verify predictive ability. Foundation model was composed of basic information and physical indicators. Biochemical model consisted of biochemical indexes. Integrated model combined the above two models. Data of three models were then put into logistic regression analysis to form nomogram prediction models. Tools including C index, calibration plot, and curve analysis were implemented to test discrimination, calibration, and clinical use. To select the best predicting model, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were put into effect. Results: Eleven risk factors were determined, including age, duration of T2DM, estimated glomerular filtration rate, systolic blood pressure, diastolic blood pressure, low-density lipoprotein, high-density lipoprotein, triglyceride, body mass index, uric acid, and glycosylated hemoglobin A1c, all with significant P-values through logistic regression analysis. In the training set, areas under the curve of three models were 0.810, 0.819, and 0.884, whereas in the validation set, they were 0.836, 0.832, and 0.909. Through calibration plot, the S:P values in the training set were 0.836, 0.754, and 0.621 and were 0.918, 0.682, and 0.666 separately in the validation set. In terms of the decision curve analysis, the risk thresholds were, respectively, 8-73%, 8-98%, and 8%~ in the training set and 8-70%, 8-90%, and 8-95% in the validation set. With the aid of NRI and IDI, integrated model is proved to be the best model in training set and validation set. Besides, internal validation was conducted on all the subjects in this study, and the C index was 0.890 (0.873-0.907). Conclusion: This study established a model predicting risk of stroke for T2DM patients through a community-based survey.
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17
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Chowdhury MZI, Yeasmin F, Rabi DM, Ronksley PE, Turin TC. Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics. BMJ Open 2019; 9:e025579. [PMID: 31473609 PMCID: PMC6719765 DOI: 10.1136/bmjopen-2018-025579] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Stroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis. DESIGN Systematic review and meta-analysis. DATA SOURCES A detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models. ELIGIBILITY CRITERIA All studies that developed stroke prediction models in populations with diabetes were included. DATA EXTRACTION AND SYNTHESIS Two reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic. RESULTS Our search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68. CONCLUSIONS Overall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models' predictive ability and further external validation of the existing models in diverse population to improve generalisability.
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Affiliation(s)
| | - Fahmida Yeasmin
- Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada
| | - Doreen M Rabi
- Department of Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Paul E Ronksley
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Tanvir C Turin
- Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada
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