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

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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Performance Assessment Across Different Care Settings of A Novel Heart Failure Hospitalisation Risk Score For Type 2 Diabetes Patients Developed Using Administrative Claims Only

10.21203/rs.3.rs-810678/v1 ◽

2021 ◽

Author(s):

Alessandro Guazzo ◽

Enrico Longato ◽

Giovanni Sparacino ◽

Bruno Franco-Novelletto ◽

Maurizio Cancian ◽

...

Keyword(s):

Heart Failure ◽

Type 2 Diabetes ◽

Risk Score ◽

Cox Regression ◽

Cardiovascular Complications ◽

P Value ◽

Administrative Claims ◽

Administrative Dataset ◽

Heart Failure Hospitalisation

Abstract Predicting the risk of cardiovascular complications, in particular heart failure hospitalisation (HHF), can improve the management of type 2 diabetes (T2D). Most predictive models proposed so far rely on clinical data not available at the higher Institutional level. Therefore, it is of interest to assess the risk of HHF in people with T2D using administrative claims data only, which are more easily obtainable and could allow public health systems to identify high-risk individuals. In this paper, the administrative claims of >175,000 patients with T2D were used to develop a new risk score for HHF based on Cox regression. Internal validation on the administrative data cohort yielded satisfactory results in terms of discrimination (max AUROC=0.792, C-index=0.786) and calibration (Hosmer-Lemeshow test p-value<0.05). The risk score was then tested on data gathered from two independent centers (one diabetes outpatient clinic and one primary care network) to demonstrate its applicability to different care settings in the medium-long term. Thanks to the large size and broad demographics of the administrative dataset used for training, the proposed model was able to predict HHF without significant performance loss concerning bespoke models developed within each setting using more informative, but harder-to-acquire clinical variables.

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Development of a model to predict 5-year risk of severe hypoglycemia in patients with type 2 diabetes

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2018-000527 ◽

2018 ◽

Vol 6 (1) ◽

pp. e000527 ◽

Cited By ~ 6

Author(s):

Lisa S Chow ◽

Rachel Zmora ◽

Sisi Ma ◽

Elizabeth R Seaquist ◽

Pamela J Schreiner

Keyword(s):

Blood Pressure ◽

Type 2 Diabetes ◽

Prediction Model ◽

Cox Regression ◽

Severe Hypoglycemia ◽

Diabetes Diagnosis ◽

Reductase Inhibitors ◽

Accord Study ◽

Glycemic Management

ObjectiveWe constructed a predictive model of long-term risk for severe hypoglycemia (SH: hypoglycemia requiring assistance) in patients with type 2 diabetes (T2DM).Research design and methodsData from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study (original n=10 251, n=5135 used in the current analysis), a randomized, multicenter, double 2×2 factorial design study examining the effect of glycemic, blood pressure, and lipid control on cardiovascular outcomes in patients with diagnosed T2DM, were used. Over the follow-up (3.76±1.12 years), the ACCORD participants experienced 607 incident SH events. Cox regression was used to identify the SH risk prediction model.ResultsWe identified 17 predictors—glycemic management, age, race, education, waist circumference, medications (insulin, antihypertensive, HMG-CoA reductase inhibitors, sulfonylurea, biguanide and meglitinide), years since diabetes diagnosis, history of hypoglycemia in the last week, systolic blood pressure, diastolic blood pressure, serum creatinine, and urinary albumin creatinine ratio—to construct a prediction model for SH (c-statistic=0.782). Using this information, we derived point scores to estimate the 5-year risk for SH in individual patients with T2DM. After adjusting for other variables in the model, the three strongest predictors for SH over 5 years were intensive glycemic management (HR=2.37, 95% CI 1.99 to 2.83), insulin use (HR=2.14, 95% CI 1.77 to 2.59), and antihypertensive medication use (HR=1.90, 95% CI 1.26 to 2.86).ConclusionUsing the ACCORD data, we identified attributes to predict 5-year risk of SH in patients with T2DM, which warrant evaluation in broader populations to determine applicability.

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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

Journal of Clinical Medicine ◽

10.3390/jcm10204779 ◽

2021 ◽

Vol 10 (20) ◽

pp. 4779

Author(s):

Sherry Yueh-Hsia Chiu ◽

Ying Isabel Chen ◽

Juifen Rachel Lu ◽

Soh-Ching Ng ◽

Chih-Hung Chen

Keyword(s):

Type 2 Diabetes ◽

Prediction Model ◽

Prospective Cohort ◽

Prediction Models ◽

Cox Regression ◽

Predictive Performance ◽

Mortality Prediction ◽

All Cause Mortality ◽

History Of

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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Nomogram Prediction Model for Diabetic Retinopathy Development in Type 2 Diabetes Mellitus Patients: A Retrospective Cohort Study

Journal of Diabetes Research ◽

10.1155/2021/3825155 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Xiaomei Chen ◽

Qiying Xie ◽

Xiaoxue Zhang ◽

Qi Lv ◽

Xin Liu ◽

...

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Diabetic Retinopathy ◽

Cohort Study ◽

Prediction Model ◽

Retrospective Cohort ◽

Cox Regression

Background. This study is aimed at investigating the systemic risk factors of diabetic retinopathy and further establishing a risk prediction model for DR development in T2DM patients. Methods. This is a retrospective cohort study including 330 type 2 diabetes mellitus (T2DM) patients who were followed up from December 2012 to November 2020. Multivariable cox regression analysis identifying factors associated with the hazard of developing diabetic retinopathy (DR) was used to construct the DR risk prediction model in the form of nomogram. Results. 50.6% of participants (mean age: 58.60 ± 10.55 ) were female, and mean duration of diabetes was 7.09 ± 5.36 years . After multivariate cox regression, the risk factors for developing DR were age (HR 1.068, 95%Cl 1.021-1.118, P = 0.005 ), diabetes duration (HR 1.094, 95%Cl 1.018-1.177, P = 0.015 ), HbA1c (HR 1.411, 95%Cl 1.113-1.788, P = 0.004 ), albuminuria (HR 6.908, 95%Cl 1.794-26.599, P = 0.005 ), and triglyceride (HR 1.554, 95%Cl 1.037-2.330, P = 0.033 ). The AUC values of the nomogram for predicting developing DR at 3-, 4-, and 5-year were 0.854, 0.845, and 0.798. Conclusion. Combining age, diabetes duration, HbA1c, albuminuria, and triglyceride, the nomogram model is effective for early recognition and intervention of individuals at high risk of DR development.

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Fermented pork fat (Sa-um) and lifestyle risk factors as potential indicators for type 2 diabetes among the Mizo population, Northeast India

Journal of Health Population and Nutrition ◽

10.1186/s41043-021-00257-8 ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Freda Lalrohlui ◽

Souvik Ghatak ◽

John Zohmingthanga ◽

Vanlal Hruaii ◽

Nachimuthu Senthil Kumar

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Cox Regression ◽

Control Group ◽

Diabetic Patients ◽

Lifestyle Habits ◽

Pork Fat

AbstractOver the last few decades, Mizoram has shown an increase in cases of type 2 diabetes mellitus; however, no in-depth scientific records are available to understand the occurrence of the disease. In this study, 500 patients and 500 healthy controls were recruited to understand the possible influence of their dietary and lifestyle habits in relation with type 2 diabetes mellitus. A multivariate analysis using Cox regression was carried out to find the influence of dietary and lifestyle factors, and an unpaired t test was performed to find the difference in the levels of biochemical tests. Out of 500 diabetic patients, 261 (52.3%) were males and 239 (47.7%) were females, and among the control group, 238 (47.7%) were males and 262 (52.3%) were females. Fermented pork fat, Sa-um (odds ratio (OR) 18.98), was observed to be a potential risk factor along with tuibur (OR 0.1243) for both males and females. Creatinine level was found to be differentially regulated between the male and female diabetic patients. This is the first report of fermented pork fat and tobacco (in a water form) to be the risk factors for diabetes. The unique traditional foods like Sa-um and local lifestyle habits like tuibur of the Mizo population may trigger the risk for the prevalence of the disease, and this may serve as a model to study other populations with similar traditional practices.

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Cardiovascular disease prevalence in type 2 diabetes – an analysis of a large German statutory health insurance database

BMC Public Health ◽

10.1186/s12889-021-10381-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Maximilian Gabler ◽

Silke Geier ◽

Lukas Mayerhoff ◽

Wolfgang Rathmann

Keyword(s):

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Health System ◽

Cardiovascular Complications ◽

Population Based ◽

Stratified Sample ◽

Icd 10 ◽

Definition Of ◽

Observation Period

Abstract Background The aim of this study was to determine the prevalence of cardiovascular disease in persons with type 2 diabetes mellitus (T2D) in Germany. Methods A claims database with an age- and sex-stratified sample of nearly 4 million individuals insured within the German statutory health system was used. All patients aged ≥18 years with T2D documented between 1 January 2015 and 31 December 2015 and complete retrospective documentation of ≥5 years (continuous enrollment in the German statutory health system) before 2015 were selected based on a validated algorithm. Cardiovascular disease (CVD) events were identified based on ICD-10 and OPS codes according to a previous clinical study (EMPA-REG OUTCOME trial). Results The prevalence of T2D in Germany in 2015 was 9.9% (n = 324,708). Using a narrow definition of CVD, the 6-year observation period prevalence of CVD was estimated as 46.7% [95% CI: 46.52%;46.86%]. Applying a wider CVD definition, the proportion of T2D patients who showed a history of CVD was 57.1% [95% CI: 56.9%;57.24%]. The prevalence of CVD in patients with T2D ranged from 36.3 to 57.1%, depending on the observation period and definition of CVD. Conclusions The results underline the need for a population-based registration of cardiovascular complications in T2D.

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Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

Value in Health Regional Issues ◽

10.1016/j.vhri.2020.10.006 ◽

2021 ◽

Vol 24 ◽

pp. 157-166

Author(s):

Wilailuck Tuntayothin ◽

Stephen John Kerr ◽

Chanchana Boonyakrai ◽

Suwasin Udomkarnjananun ◽

Sumitra Chukaew ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Chronic Kidney Disease ◽

Type 2 Diabetes Mellitus ◽

Kidney Disease ◽

Prediction Model ◽

Disease Prediction ◽

Development And Validation

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Glycemic variability and cardiovascular disease in patients with type 2 diabetes

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2020-002032 ◽

2021 ◽

Vol 9 (1) ◽

pp. e002032

Author(s):

Marcela Martinez ◽

Jimena Santamarina ◽

Adrian Pavesi ◽

Carla Musso ◽

Guillermo E Umpierrez

Keyword(s):

Type 2 Diabetes ◽

Blood Glucose ◽

Glycated Hemoglobin ◽

Glucose Monitoring ◽

Glycemic Variability ◽

Cardiovascular Complications ◽

Glucose Levels ◽

Increased Risk ◽

Patients With Diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

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