scholarly journals All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease

2021 ◽  
Author(s):  
Massimiliano Copetti ◽  
Edoardo Biancalana ◽  
Andrea Fontana ◽  
Federico Parolini ◽  
Monia Garofolo ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Early Stage ◽  
Mortality Prediction ◽  
Stage Of Disease ◽  
All Cause Mortality ◽  
Mortality Prediction Models

scholarly journals 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

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.

Abstract P122: P-wave Axis is Associated With All-Cause Mortality in Diabetes: The ACCORD Trial

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Lauren Ehrhardt-Humbert ◽  
Matthew J Singleton ◽  
Bharathi Upadhya ◽  
Muhammad Imtiaz-Ahmad ◽  
Elsayed Z SOLIMAN ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
P Wave ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Increased Risk ◽  
All Cause Mortality

Introduction: Abnormal P-wave axis (PWA) has emerged as a novel marker of risk for both cardiovascular disease and all-cause mortality in the general population, though this relationship has not been adequately explored among those with type 2 diabetes. Hypothesis: We hypothesized that abnormal PWA is associated with all-cause mortality in a large, well-phenotyped group of participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Methods: This analysis included 8,899 ACCORD participants with available PWA data on baseline electrocardiogram. Cox proportional hazards models were used to examine the association between PWA and ACM in models adjusted for demographics, ACCORD trial treatment assignment, and potential confounders. PWA was modeled as either normal (0° - 75°) or abnormal (<0° or >75°). We evaluated the predictive value of PWA by comparing area under the receiver operating characteristic curves in models with and without PWA. Results: Over 44,000 person-years, there were 609 deaths. Participants with abnormal PWA had increased risk of all-cause mortality (HR 1.61, 95% CI 1.25 – 2.08). After multivariable adjustment, the association remained significant (HR 1.32, 95% CI 1.02 – 1.71; see TABLE). Inclusion of abnormal PWA in prediction models afforded a small increase in area under the receiver operating characteristic curves (AUC 0.653 vs. 0.643, p-value for difference of 0.002). Conclusions: In conclusion, among ACCORD trial participants, abnormal PWA was associated with an increased risk of mortality. Abnormal PWA may have added value beyond traditional risk factors in prediction models.

scholarly journals Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003–2006

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 4
Author(s):  
Lucia Tabacu ◽  
Mark Ledbetter ◽  
Andrew Leroux ◽  
Ciprian Crainiceanu ◽  
Ekaterina Smirnova
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Time Horizons ◽  
All Cause Mortality ◽  
Mortality Prediction Models ◽  
Activity Measures ◽  
Traditional Risk Factors ◽  
Wearable Accelerometers

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

scholarly journals A modified mayfly-SVM approach for early detection of type 2 diabetes mellitus

2022 ◽  
Vol 12 (1) ◽  
pp. 524
Author(s):  
Ratna Patil ◽  
Sharvari Tamane ◽  
Shitalkumar Adhar Rawandale ◽  
Kanishk Patil
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Models ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Test Accuracy ◽  
Proposed Model

<p>Diabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA.</p>

A serum resistin and multi-cytokine inflammatory pathway is linked with and helps predict all-cause death in diabetes

2021 ◽  
Author(s):  
Maria Giovanna Scarale ◽  
Alessandra Antonucci ◽  
Marina Cardellini ◽  
Massimiliano Copetti ◽  
Lucia Salvemini ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
High Risk ◽  
Prediction Models ◽  
Total Mortality ◽  
Heart Association ◽  
Plaque Instability ◽  
Prediction Of Mortality ◽  
All Cause Mortality ◽  
Risk Patients

Abstract Context Type 2 diabetes shows high mortality rate, partly mediated by atherosclerotic plaque instability. Discovering novel biomarkers may help identify high-risk patients to expose to more aggressive and specific managements. We recently described a serum REsistin and multiMulti-cytokine inflammAatory Pathway (REMAP), including resistin, IL-1β, IL-6, IL-8 and TNF-α) which associates with cardiovascular disease. Objective We investigated whether REMAP associates with and improves the prediction of mortality in type 2 diabetes. Design A REMAP score was investigated in three cohorts comprising 1,528 patients with T2D (409 incident deaths) and in 59 patients who underwent carotid endoarterectomy (CEA; 24 deaths). Plaques were classified as unstable/stable according to the modified American Heart Association atherosclerosis classification. Results REMAP was associated to all-cause mortality in each cohort and in all 1,528 individuals (fully-adjusted HR for one SD increase =1.34, p&lt;0.001). In CEA patients, REMAP was associated with mortality (HR =1.64, p = 0.04) and a modest change was observed when plaque stability was taken into account [HR =1.58; P = 0.07]. REMAP improved discrimination and reclassification measures of both ENFORCE and RECODe, well-established prediction models of mortality in type 2 diabetes (P&lt;0.05-&lt;0.001). Conclusions REMAP is independently associated with and improves predict all-cause mortality in type 2 diabetes; it can therefore be used to identify high-risk individuals to be targeted with more aggressive managements. Whether REMAP can also identify those patients who are more responsive to IL-6 and IL-1β monoclonal antibodies which reduce cardiovascular burden and total mortality is an intriguing possibility to be tested.

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 Association between risk of type 2 diabetes and changes in energy intake at breakfast and dinner over 14 years: a latent class trajectory analysis from the China health and nutrition Survey, 1997–2011

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e046183
Author(s):  
Xiyun Ren ◽  
Jian Gao ◽  
Tianshu Han ◽  
Changhao Sun
Keyword(s):  
Type 2 Diabetes ◽  
Energy Consumption ◽  
Energy Intake ◽  
Latent Class ◽  
Early Stage ◽  
Harmful Effect ◽  
Nutrition Survey ◽  
Health And Nutrition ◽  
Diabetes Status

ObjectiveThis study aimed to investigate the association between the trajectories of energy consumption at dinner versus breakfast and the risk of type 2 diabetes (T2D).DesignCohort study.SettingThe study was conducted in China.ParticipantsA total of 10 727 adults, including 5239 men and 5488 women, with a mean age of 42.7±11.2 years and a mean follow-up time of 9.1 years, met the study criteria and completed a questionnaire about energy intake and diabetes status from the China Health and Nutrition Survey in 1997–2011.Primary outcome measuresParticipants were divided into subgroups based on the trajectories of the ratio of energy consumption at dinner versus breakfast. Cox multivariate regression models were used to explore the associations between different trajectories and the risk of T2D after adjustment for confounders and their risk factors. Mediation analysis was performed to explore the intermediary effect of triacylglycerol (TG), total cholesterol (TC), uric acid (UA) and apolipoprotein B (ApoB) between the trajectories and the risk of T2D.ResultsFor energy consumption at dinner versus breakfast, compared with a low-stable trajectory, the adjusted HR of T2D in low-increasing from early-stage trajectory was 1.29 (95% CI 1.04 to 1.60). TG, TC, UA and ApoB were significantly higher in low-increasing from early-stage trajectory than other trajectories and play partial regulation roles between trajectories and T2D.ConclusionsThis study emphasised the harmful effect of a gradual increase in the ratio of energy consumption at dinner versus breakfast from early stage on the development of T2D and partially mediated by TG, TC, UA and ApoB, highlighting that it is necessary to intake more energy at breakfast compared with dinner to prevent T2D in adults.

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