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Pan D, Wang H, Wu S, Wang J, Ning Y, Guo J, Wang C, Gu Y. Unveiling the Hidden Burden: Estimating All-Cause Mortality Risk in Older Individuals with Type 2 Diabetes. J Diabetes Res 2024; 2024:1741878. [PMID: 38282658 PMCID: PMC10821805 DOI: 10.1155/2024/1741878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 01/30/2024] Open
Abstract
Background The mortality rate among older persons with diabetes has been steadily increasing, resulting in significant health and economic burdens on both society and individuals. The objective of this study is to develop and validate a predictive nomogram for estimating the 5-year all-cause mortality risk in older persons with T2D (T2D). Methods We obtained data from the National Health and Nutrition Survey (NHANES). A random 7 : 3 split was made between the training and validation sets. By linking the national mortality index up until December 31, 2019, we ensured a minimum of 5 years of follow-up to assess all-cause mortality. A nomogram was developed in the training cohort using a logistic regression model as well as a least absolute shrinkage and selection operator (LASSO) regression model for predicting the 5-year risk of all-cause mortality. Finally, the prediction performance of the nomogram is evaluated using several validation methods. Results We constructed a comprehensive prediction model based on the results of multivariate analysis and LASSO binomial regression. These models were then validated using data from the validation cohort. The final model includes four independent predictors: age, gender, estimated glomerular filtration rate, and white blood cell count. The C-index values for the training and validation cohorts were 0.748 and 0.762, respectively. The calibration curve demonstrates satisfactory consistency between the two cohorts. Conclusions The newly developed nomogram proves to be a valuable tool in accurately predicting the 5-year all-cause mortality risk among older persons with diabetes, providing crucial information for tailored interventions.
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Affiliation(s)
- Dikang Pan
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hui Wang
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Sensen Wu
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jingyu Wang
- Renal Division, Peking University First Hospital, Beijing, China
| | - Yachan Ning
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jianming Guo
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Cong Wang
- Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yongquan Gu
- Xuanwu Hospital, Capital Medical University, Beijing, China
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Markovič R, Grubelnik V, Završnik T, Blažun Vošner H, Kokol P, Perc M, Marhl M, Završnik M, Završnik J. Profiling of patients with type 2 diabetes based on medication adherence data. Front Public Health 2023; 11:1209809. [PMID: 37483941 PMCID: PMC10358769 DOI: 10.3389/fpubh.2023.1209809] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases. Methods We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering. Results Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75. Conclusion Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.
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Affiliation(s)
- Rene Markovič
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Vladimir Grubelnik
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Tadej Završnik
- University Clinical Medical Centre Maribor, Maribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Helena Blažun Vošner
- Community Healthcare Center Dr. Adolf Drolc Maribor, Maribor, Slovenia
- Faculty of Health and Social Sciences, Slovenj Gradec, Slovenia
- Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Peter Kokol
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Alma Mater Europaea - ECM, Maribor, Slovenia
- Complexity Science Hub Vienna, Vienna, Austria
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Department of Physics, Kyung Hee University, Seoul, Republic of Korea
| | - Marko Marhl
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Faculty of Education, University of Maribor, Maribor, Slovenia
| | - Matej Završnik
- Department of Endocrinology and Diabetology, University Medical Center Maribor, Maribor, Slovenia
| | - Jernej Završnik
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Community Healthcare Center Dr. Adolf Drolc Maribor, Maribor, Slovenia
- Alma Mater Europaea - ECM, Maribor, Slovenia
- Science and Research Center Koper, Koper, Slovenia
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Qi J, He P, Yao H, Xue Y, Sun W, Lu P, Qi X, Zhang Z, Jing R, Cui B, Ning G. Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China. J Diabetes 2023; 15:27-35. [PMID: 36526273 PMCID: PMC9870741 DOI: 10.1111/1753-0407.13343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 10/23/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap. METHODS Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots). RESULTS A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots. CONCLUSIONS This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.
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Affiliation(s)
- Jiying Qi
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Ping He
- Link Healthcare Engineering and Information Department, Shanghai Hospital Development CenterShanghaiChina
| | - Huayan Yao
- Computer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Yanbin Xue
- Computer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Wen Sun
- Wonders Information Co. Ltd.ShanghaiChina
| | - Ping Lu
- Wonders Information Co. Ltd.ShanghaiChina
| | - Xiaohui Qi
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zizheng Zhang
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Renjie Jing
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Bin Cui
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Guang Ning
- Department of Endocrine and Metabolic DiseasesShanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical GenomicsRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
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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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Profiling risk factors of patients diagnosed with type 2 diabetes awaiting outpatient diabetes specialist consultant appointment, a narrative review. Collegian 2022. [DOI: 10.1016/j.colegn.2021.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study. J Clin Med 2021; 10:jcm10204779. [PMID: 34682901 PMCID: PMC8537078 DOI: 10.3390/jcm10204779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/26/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022] Open
Abstract
Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007-2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models' selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell's C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time.
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Rosa LDS, Mistro S, Oliveira MG, Kochergin CN, Cortes ML, de Medeiros DS, Soares DA, Louzado JA, Silva KO, Bezerra VM, Amorim WW, Barone M, Passos LC. Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting. Front Pharmacol 2021; 11:588309. [PMID: 33542687 PMCID: PMC7851089 DOI: 10.3389/fphar.2020.588309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 11/23/2020] [Indexed: 12/18/2022] Open
Abstract
Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes. Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram. Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20-$10,683.53 and 0.33-0.37 for POC-A1c test group, and $9,288.28-$10,413.99 and 0.08-0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model's result. Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.
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Affiliation(s)
- Lorena de Sousa Rosa
- Program of Post-Graduation in Medicine and Health, Federal University of Bahia, Salvador, Brazil
| | - Sóstenes Mistro
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Marcio Galvão Oliveira
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | | | - Mateus Lopes Cortes
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Danielle Souto de Medeiros
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Daniela Arruda Soares
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - José Andrade Louzado
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Kelle Oliveira Silva
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Vanessa Moraes Bezerra
- Program of Post-Graduation in Collective Health, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista, Brazil
| | - Welma Wildes Amorim
- Departament of Natural Sciences, State University of Southwest Bahia, Vitória da Conquista, Brazil
| | - Mark Barone
- Intersectoral Forum to Fight NCDs in Brazil, São Paulo, Brazil
| | - Luiz Carlos Passos
- Program of Post-Graduation in Medicine and Health, Federal University of Bahia, Salvador, Brazil
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Lin SY, Lin CL, Hsu WH, Lin CC, Lo SF, Kao CH. Risk of idiopathic peripheral neuropathy in end-stage renal disease: A population-based cohort study. Int J Clin Pract 2021; 75:e13641. [PMID: 32750233 DOI: 10.1111/ijcp.13641] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/24/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Whether patients with end-stage renal disease (ESRD) have a higher risk of idiopathic polyneuropathy (IPN) than those without ESRD remains unclear. We hypothesised that carpal tunnel syndrome (CTS) is a prodrome of IPN in patients with ESRD. METHODS Data were collected from the Taiwan National Health Insurance research database (NHIRD) for the 2000-2011 period. Two matching strategies, age- and sex-matching and propensity matching, were used, which yielded 2596 age- and sex-matched patients with ESRD and 2210 propensity-matched patients with ESRD. The comparison cohort was chosen in a 1:4 ratio for the age- and sex-matched method and in a 1:1 ratio for the propensity-matching method. The primary outcome was the incidence of IPN. Cox proportional hazards modelling was used. RESULTS In the age- and sex-matched cohort, the IPN incidence was 7.64 and 2.88 per 1000 person-years for the ESRD and controls cohorts, respectively. After we adjusted for age, sex, comorbidities and medications relative to controls, having ESRD was significantly associated with increased risk of IPN (hazard ratio [HR] = 2.45, 95% confidence interval [CI] = 1.76-3.41). Competing risk of death as sensitivity analysis revealed that having ESRD with CTS was still associated with higher risk of IPN than having CTS without ESRD (HR = 2.85, 95% CI = 1.87-4.34). CONCLUSION Patients with ESRD with CTS had higher incidences of idiopathic peripheral neuropathy than those without ESRD with CTS.
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Affiliation(s)
- Shih-Yi Lin
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Division of Nephrology and Kidney Institute, China Medical University Hospital, Taichung, Taiwan
| | - Cheng-Li Lin
- Management Office for Health Data, China Medical University Hospital, Taichung, Taiwan
- College of Medicine, China Medical University, Taichung, Taiwan
| | - Wu-Huei Hsu
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Division of Pulmonary and Critical Care Medicine, China Medical University Hospital and China Medical University, Taichung, Taiwan
| | - Cheng-Chieh Lin
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Sui-Foon Lo
- Department of Physical Medicine and Rehabilitation, China Medical University Hospital, Taichung, Taiwan
| | - Chia-Hung Kao
- Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
- Center of Augmented Intelligence in Healthcare, China Medical University Hospital, Taichung, Taiwan
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Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study. BIOMED RESEARCH INTERNATIONAL 2020; 2020:9108216. [PMID: 33029529 PMCID: PMC7537695 DOI: 10.1155/2020/9108216] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 07/15/2020] [Indexed: 12/18/2022]
Abstract
Background Hypertension is now common in China. Patients with hypertension and type 2 diabetes are prone to severe cardiovascular complications and poor prognosis. Therefore, this study is aimed at establishing an effective risk prediction model to provide early prediction of the risk of new-onset diabetes for patients with a history of hypertension. Methods A LASSO regression model was used to select potentially relevant features. Univariate and multivariate Cox regression analyses were used to determine independent predictors. Based on the results of multivariate analysis, a nomogram of the 5-year incidence of T2D in patients with hypertension in mainland China was established. The discriminative capacity was assessed by Harrell's C-index, AUC value, calibration plot, and clinical utility. Results After random sampling, 1273 and 415 patients with hypertension were included in the derivation and validation cohorts, respectively. The prediction model included age, body mass index, FPG, and TC as predictors. In the derivation cohort, the AUC value and C-index of the prediction model are 0.878 (95% CI, 0.861-0.895) and 0.862 (95% CI, 0.830-0.894), respectively. In the validation cohort, the AUC value and C-index of the prediction model were 0.855 (95% CI, 0.836-0.874) and 0.841 (95% CI, 0.817-0.865), respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. Decision curve analysis shows that nomograms are clinically useful. Conclusion Our nomogram can be used as a simple, affordable, reasonable, and widely implemented tool to predict the 5-year T2D risk of hypertension patients in mainland China. This application helps timely intervention to reduce the incidence of T2D in patients with hypertension in mainland China.
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Mukasheva A, Saparkhojayev N, Akanov Z, Apon A, Kalra S. Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models. Diabetes Ther 2019; 10:2079-2093. [PMID: 31520363 PMCID: PMC6848515 DOI: 10.1007/s13300-019-00684-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION The prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies. METHODS A brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software. RESULTS The predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019. CONCLUSION Mathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.
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Affiliation(s)
- Assel Mukasheva
- Department of Cybersecurity, Data Processing and Storage, Satbayev University, Almaty, Kazakhstan.
| | - Nurbek Saparkhojayev
- Dean of Engineering Faculty, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan
| | - Zhanay Akanov
- President of Kazakh Society for Study of Diabetes, Member of AASD, Almaty, Kazakhstan
| | - Amy Apon
- Professor, Chair of the Computer Science Division, Clemson University, Clemson, SC, USA
| | - Sanjay Kalra
- Department of Diabetes and Endocrinology, Bharti Hospital, Karnal, India
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