scholarly journals Development and validation of the DIabetes Severity SCOre (DISSCO) in 139 626 individuals with type 2 diabetes: a retrospective cohort study

2020 ◽  
Vol 8 (1) ◽  
pp. e000962 ◽  
Author(s):  
Salwa S Zghebi ◽  
Mamas A Mamas ◽  
Darren M Ashcroft ◽  
Chris Salisbury ◽  
Christian D Mallen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Predictive Value ◽  
Severity Score ◽  
Validation Cohort ◽  
Operating Characteristics ◽  
Training Cohort ◽  
Severity Scores ◽  
General Practices ◽  
Diabetes Severity

ObjectiveClinically applicable diabetes severity measures are lacking, with no previous studies comparing their predictive value with glycated hemoglobin (HbA1c). We developed and validated a type 2 diabetes severity score (the DIabetes Severity SCOre, DISSCO) and evaluated its association with risks of hospitalization and mortality, assessing its additional risk information to sociodemographic factors and HbA1c.Research design and methodsWe used UK primary and secondary care data for 139 626 individuals with type 2 diabetes between 2007 and 2017, aged ≥35 years, and registered in general practices in England. The study cohort was randomly divided into a training cohort (n=111 748, 80%) to develop the severity tool and a validation cohort (n=27 878). We developed baseline and longitudinal severity scores using 34 diabetes-related domains. Cox regression models (adjusted for age, gender, ethnicity, deprivation, and HbA1c) were used for primary (all-cause mortality) and secondary (hospitalization due to any cause, diabetes, hypoglycemia, or cardiovascular disease or procedures) outcomes. Likelihood ratio (LR) tests were fitted to assess the significance of adding DISSCO to the sociodemographics and HbA1c models.ResultsA total of 139 626 patients registered in 400 general practices, aged 63±12 years were included, 45% of whom were women, 83% were White, and 18% were from deprived areas. The mean baseline severity score was 1.3±2.0. Overall, 27 362 (20%) people died and 99 951 (72%) had ≥1 hospitalization. In the training cohort, a one-unit increase in baseline DISSCO was associated with higher hazard of mortality (HR: 1.14, 95% CI 1.13 to 1.15, area under the receiver operating characteristics curve (AUROC)=0.76) and cardiovascular hospitalization (HR: 1.45, 95% CI 1.43 to 1.46, AUROC=0.73). The LR tests showed that adding DISSCO to sociodemographic variables significantly improved the predictive value of survival models, outperforming the added value of HbA1c for all outcomes. Findings were consistent in the validation cohort.ConclusionsHigher levels of DISSCO are associated with higher risks for hospital admissions and mortality. The new severity score had higher predictive value than the proxy used in clinical practice, HbA1c. This reproducible algorithm can help practitioners stratify clinical care of patients with type 2 diabetes.

scholarly journals Validity of algorithms for identifying five chronic conditions in MedicineInsight, an Australian national general practice database

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alys Havard ◽  
Jo-Anne Manski-Nankervis ◽  
Jill Thistlethwaite ◽  
Benjamin Daniels ◽  
Rimma Myton ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Care ◽  
Primary Health Care ◽  
Predictive Value ◽  
Chronic Conditions ◽  
Care Quality ◽  
Primary Health ◽  
General Practices ◽  
Anxiety Depression

Abstract Background MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes. Methods Patients’ disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice. Results A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively. Conclusions The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning.

scholarly journals A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xintian Cai ◽  
Qing Zhu ◽  
Yuanyuan Cao ◽  
Shasha Liu ◽  
Mengru Wang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Fatty Liver Disease ◽  
Validation Cohort ◽  
Japanese Patients ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Training Cohort ◽  
Nonalcoholic Fatty Liver

Background. The prevention of type 2 diabetes (T2D) and its associated complications has become a major priority of global public health. In addition, there is growing evidence that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of diabetes. Therefore, the purpose of this study was to develop and validate a nomogram based on independent predictors to better assess the 8-year risk of T2D in Japanese patients with NAFLD. Methods. This is a historical cohort study from a collection of databases that included 2741 Japanese participants with NAFLD without T2D at baseline. All participants were randomized to a training cohort ( n = 2058 ) and a validation cohort ( n = 683 ). The data of the training cohort were analyzed using the least absolute shrinkage and selection operator method to screen the suitable and effective risk factors for Japanese patients with NAFLD. A cox regression analysis was applied to build a nomogram incorporating the selected features. The C-index, receiver operating characteristic curve (ROC), calibration plot, decision curve analysis, and Kaplan-Meier analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. The results were reevaluated by internal validation in the validation cohort. Results. We developed a simple nomogram that predicts the risk of T2D for Japanese patients with NAFLD by using the parameters of smoking status, waist circumference, hemoglobin A1c, and fasting blood glucose. For the prediction model, the C-index of training cohort and validation cohort was 0.839 (95% confidence interval (CI), 0.804-0.874) and 0.822 (95% CI, 0.777-0.868), respectively. The pooled area under the ROC of 8-year T2D risk in the training cohort and validation cohort was 0.811 and 0.805, respectively. The calibration curve indicated a good agreement between the probability predicted by the nomogram and the actual probability. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions. We developed and validated a nomogram for the 8-year risk of incident T2D among Japanese patients with NAFLD. Our nomogram can effectively predict the 8-year incidence of T2D in Japanese patients with NAFLD and helps to identify people at high risk of T2D early, thus contributing to effective prevention programs for T2D.

453-P: Metabolic Syndrome Severity Score, All-Cause Mortality, and Incident Vascular Events in Type 2 Diabetes: A 13-Year, Single-Centre, Follow-Up Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 453-P
Author(s):  
MONIA GAROFOLO ◽  
ELISA GUALDANI ◽  
DANIELA LUCCHESI ◽  
LAURA GIUSTI ◽  
VERONICA SANCHO-BORNEZ ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Severity Score ◽  
Single Centre ◽  
Vascular Events ◽  
Follow Up Study ◽  
All Cause Mortality

scholarly journals Treatment with a DPP-4 inhibitor at time of hospital admission for COVID-19 is not associated with improved clinical outcomes: data from the COVID-PREDICT cohort study in The Netherlands

2021 ◽  
Author(s):  
Rick I. Meijer ◽  
Trynke Hoekstra ◽  
Niels C. Gritters van den Oever ◽  
Suat Simsek ◽  
Joop P. van den Bergh ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Hospital Admission ◽  
Clinical Outcomes ◽  
Primary Outcome ◽  
Infectious Complications ◽  
Glucose Lowering ◽  
Diabetes Severity ◽  
Outcome Mortality

Abstract Purpose Inhibition of dipeptidyl peptidase (DPP-)4 could reduce coronavirus disease 2019 (COVID-19) severity by reducing inflammation and enhancing tissue repair beyond glucose lowering. We aimed to assess this in a prospective cohort study. Methods We studied in 565 patients with type 2 diabetes in the CovidPredict Clinical Course Cohort whether use of a DPP-4 inhibitor prior to hospital admission due to COVID-19 was associated with improved clinical outcomes. Using crude analyses and propensity score matching (on age, sex and BMI), 28 patients using a DPP-4 inhibitor were identified and compared to non-users. Results No differences were found in the primary outcome mortality (matched-analysis = odds-ratio: 0,94 [95% confidence interval: 0,69 – 1,28], p-value: 0,689) or any of the secondary outcomes (ICU admission, invasive ventilation, thrombotic events or infectious complications). Additional analyses comparing users of DPP-4 inhibitors with subgroups of non-users (subgroup 1: users of metformin and sulphonylurea; subgroup 2: users of any insulin combination), allowing to correct for diabetes severity, did not yield different results. Conclusions We conclude that outpatient use of a DPP-4 inhibitor does not affect the clinical outcomes of patients with type 2 diabetes who are hospitalized because of COVID-19 infection.

scholarly journals Validity of the Finnish Diabetes Risk Score for Detecting Undiagnosed Type 2 Diabetes among General Medical Outpatients in Botswana

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bernard Omech ◽  
Julius Chacha Mwita ◽  
Jose-Gaby Tshikuka ◽  
Billy Tsima ◽  
Oathokwa Nkomazna ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Value ◽  
Total Population ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Haemoglobin A1c ◽  
Diabetes Risk Score ◽  
Medical Outpatients

This was a cross-sectional study designed to assess the validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. Participants aged ≥20 years without previously diagnosed diabetes were screened by (1) an 8-item Finnish diabetes risk assessment questionnaire and (2) Haemoglobin A1c test. Data from 291 participants were analyzed (74.2% were females). The mean age of the participants was 50.1 (SD = ±11) years, and the prevalence of undiagnosed diabetes was 42 (14.4%) with no significant differences between the gender (20% versus 12.5%,P=0.26). The area under curve for detecting undiagnosed diabetes was 0.63 (95% CI 0.55–0.72) for the total population, 0.65 (95% CI: 0.56–0.75) for women, and 0.67 (95% CI: 0.52–0.83) for men. The optimal cut-off point for detecting undiagnosed diabetes was 17 (sensitivity = 48% and specificity = 73%) for the total population, 17 (sensitivity = 56% and specificity = 66%) for females, and 13 (sensitivity = 53% and specificity = 77%) for males. The positive predictive value and negative predictive value were 20% and 89.5%, respectively. The findings indicate that the Finnish questionnaire was only modestly effective in predicting undiagnosed diabetes among outpatients in Botswana.

scholarly journals A Novel Risk Scoring Tool to Predict Saphenous Vein Graft Occlusion in Patients with Type 2 Diabetes Mellitus After Cardiac Artery Bypass Graft Surgery

2020 ◽  
Author(s):  
Yujing Cheng ◽  
Xiaoteng Ma ◽  
Xiaoli Liu ◽  
Yingxin Zhao ◽  
Yan Sun ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Validation Cohort ◽  
Saphenous Vein Graft ◽  
Artery Bypass ◽  
Graft Occlusion ◽  
Risk Scoring ◽  
Predictive Risk ◽  
Scoring Tool

Abstract Background: Coronary artery bypass grafting (CABG) success is reduced by graft occlusion. Patients with type 2 diabetes mellitus(T2DM) are more likely to develop graft occlusion. Understanding factors associated with graft occlusion may improve T2DM patient outcomes. The aim of this study was to develop a predictive risk score for saphenous vein graft (SVG) occlusion in T2DM patients after CABG.Methods: This retrospective cohort study enrolled 3716 CABG patients with T2DM from January 2012 to March 2013. The development cohort included 2477 patients and the validation cohort included 1239 patients. The baseline clinical data at index CABG was analyzed for their independent impact on graft occlusion in our study using Cox proportional hazards regression. The predictive risk scoring tool was weighted by beta coefficients from the final model. Concordance (c)-statistics and comparison of the predicted and observed probabilities of predicted risk were used for discrimination and calibration.Results: A total of 959 (25.8%) out of 3716 patients developed at least one SVG occlusion. Significant risk factors for occlusion were male sex, estimated glomerular filtration rate<90, smoking (currently), hyperuricemia, dyslipidemia, prior percutaneous coronary intervention (PCI), a rising number of lesion vessels, and SVG. On-pump surgery, and the use of angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB) and calcium channel blockers (CCB) were protective factors. The risk scoring tool with 11 variables was developed from the derivation cohort, which delineated each patient into risk quartiles. The c-statistic for this model was 0.71 in the validation cohort.Conclusions: An easy-to-use risk scoring tool that used common clinical variables was developed and validated. The scoring tool accurately estimated the risk of late SVG occlusion in T2DM patients after CABG.

scholarly journals IDENTIFICATION AND ASSESSMENT OF PREDIABETES-A RURAL INDIAN STUDY (A CORRELATIVE STUDY BETWEEN QUESTIONNAIRE AND BIOCHEMICAL ANALYSIS)

2020 ◽  
pp. 36-40
Author(s):  
VENKATESAN S. ◽  
SUSILA S. ◽  
SUTHANTHIRAN S. ◽  
MADHUSUDHAN S. ◽  
PAARI N.
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Risk Score ◽  
Predictive Value ◽  
Screening Tool ◽  
Diabetic Patients ◽  
Cross Sectional ◽  
Family History Of Diabetes

Objective: To identify and prevent the vulnerable prediabetic population becoming diabetic patients in the future using the Indian Diabetic Risk Score (IDRS) and to evaluate the performance of the IDRS questionnaire for detecting prediabetes and predicting the risk of Type 2 Diabetes Mellitus in Chidambaram rural Indian population. Methods: A cross-sectional descriptive study was carried out among patients attending a master health check-up of RMMCH hospital located at Chidambaram. The IDRS was calculated by using four simple measures of age, family history of diabetes, physical activity, and waist measurement. The relevant blood test, like Fasting plasma glucose (FBS), Glycated hemoglobin (HbA1C) test, were observed for identifying prediabetes. Subjects were classified as Normoglycemic, prediabetics, and diabetics based on the questionnaire and diagnostic criteria of the Indian Council of Medical Research (ICMR) guidelines. Results: In the study, sensitivity and specificity of IDRS score were found to be 84.21% and 63.4% respectively for detecting prediabetes in community with the positive predictive value of 51.6% and negative predictive value of 89.6% and prevalence of prediabetes in the Chidambaram rural population is 31.6% among the 60 participants. Conclusion: The Indian diabetic risk score questionnaire designed by Ma­dras diabetic research federation is a useful screening tool to identify unknown type 2 diabetes mellitus. The question­naire is a reliable, valuable, and easy to use screening tool which can be used in a primary care setup. 

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