Dataset Information

Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data.

ABSTRACT: BACKGROUND:There exist several predictive risk models for cardiovascular disease (CVD), including some developed specifically for patients with type 2 diabetes mellitus (T2DM). However, the models developed for a diabetic population are based on information derived from medical records or laboratory results, which are not typically available to entities like payers or quality of care organizations. The objective of this study is to develop and validate models predicting the risk of cardiovascular events in patients with T2DM based on medical insurance claims data. METHODS:Patients with T2DM aged 50 years or older were identified from the Optum™ Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006-09/30/2016). Risk factors were assessed over a 12-month baseline period and cardiovascular events were monitored from the end of the baseline period until end of data availability, continuous enrollment, or death. Risk models were developed using logistic regressions separately for patients with and without prior CVD, and for each outcome: (1) major adverse cardiovascular events (MACE; i.e., non-fatal myocardial infarction, non-fatal stroke, CVD-related death); (2) any MACE, hospitalization for unstable angina, or hospitalization for congestive heart failure; (3) CVD-related death. Models were developed and validated on 70% and 30% of the sample, respectively. Model performance was assessed using C-statistics. RESULTS:A total of 181,619 patients were identified, including 136,544 (75.2%) without prior CVD and 45,075 (24.8%) with a history of CVD. Age, diabetes-related hospitalizations, prior CVD diagnoses and chronic pulmonary disease were the most important predictors across all models. C-statistics ranged from 0.70 to 0.81, indicating that the models performed well. The additional inclusion of risk factors derived from pharmacy claims (e.g., use of antihypertensive, and use of antihyperglycemic) or from medical records and laboratory measures (e.g., hemoglobin A1c, urine albumin to creatinine ratio) only marginally improved the performance of the models. CONCLUSION:The claims-based models developed could reliably predict the risk of cardiovascular events in T2DM patients, without requiring pharmacy claims or laboratory measures. These models could be relevant for providers and payers and help implement approaches to prevent cardiovascular events in high-risk diabetic patients.

SUBMITTER: Young JB

PROVIDER: S-EPMC6109303 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data.

Young James B JB Gauthier-Loiselle Marjolaine M Bailey Robert A RA Manceur Ameur M AM Lefebvre Patrick P Greenberg Morris M Lafeuille Marie-Hélène MH Duh Mei Sheng MS Bookhart Brahim B Wysham Carol H CH

Cardiovascular diabetology 20180824 1

<h4>Background</h4>There exist several predictive risk models for cardiovascular disease (CVD), including some developed specifically for patients with type 2 diabetes mellitus (T2DM). However, the models developed for a diabetic population are based on information derived from medical records or laboratory results, which are not typically available to entities like payers or quality of care organizations. The objective of this study is to develop and validate models predicting the risk of cardi ...[more]

PMID: 30143045

Similar Datasets

Project description:BackgroundIn sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.MethodsA total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.ResultsIn sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).ConclusionThe nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

Project description:BackgroundPrevious studies reported the prognostic value of the atherogenic index of plasma (AIP) in the course of atherosclerosis and other cardiovascular diseases (CVDs). Still, the predictive utility of the AIP is unknown among patients with type 2 diabetes mellitus (T2DM).MethodsThis was a secondary analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study, which randomized 10,251 patients with long-lasting T2DM. ROC curve analysis was used to determine an optimal threshold for AIP, and the study population was divided into high and low AIP groups. Univariable and multivariable Cox proportional hazards regression analyses were used to determine the association between AIP and primary (major adverse cardiovascular events [MACEs], including nonfatal myocardial infarction, nonfatal stroke, and/or death from cardiovascular causes) and secondary outcomes (all-cause mortality). Stratified analyses were performed to control for the confounding factors.ResultsAIP was an independent risk factor for the prognosis of T2DM (HR = 1.309; 95% CI 1.084-1.581; P = 0.005). The threshold for AIP was determined to be 0.34 in the study population. After adjustments for confounding factors, multivariable analysis showed that AIP was associated with the risk of MACEs (Model 1: HR = 1.333, 95% CI 1.205-1.474, P < 0.001; Model 2: HR = 1.171, 95% CI 1.030-1.333, P = 0.016; Model 3: HR = 1.194, 95% CI 1.049-1.360, P = 0.007), all-cause mortality (Model 1: HR = 1.184, 95% CI 1.077-1.303, P < 0.001), cardiovascular death (Model 1: HR = 1.422, 95% CI 1.201-1.683, P < 0.001; Model 3: HR = 1.264, 95% CI 1.015-1.573, P = 0.036), and nonfatal myocardial infarction (Model 1: HR = 1.447, 95% CI 1.255-1.669, P < 0.001; Model 2: HR = 1.252, 95% CI 1.045-1.499, P = 0.015; Model 3: HR = 1.284, 95% CI 1.071-1.539, P = 0.007). Subgroup stratified analyses showed that AIP might interact with sex, a classical risk factor of cardiovascular events.ConclusionsThis study showed that AIP might be a strong biomarker that could be used to predict the risk of cardiovascular events in patients with T2DM.Trial registrationURL: http://www.clinicaltrials.gov . Unique identifier: NCT00000620.

Project description:BackgroundAspirin has been recommended for the prevention of major adverse cardiovascular events (MACE, composite of non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death) in diabetic patients without previous cardiovascular disease. However, recent meta-analyses have prompted re-evaluation of this practice. The study objective was to evaluate the relative and absolute benefits and harms of aspirin for the prevention of incident MACE in patients with diabetes.MethodsWe performed a systematic review and meta-analysis on seven studies (N=11,618) reporting on the use of aspirin for the primary prevention of MACE in patients with diabetes. Two reviewers conducted a systematic search of electronic databases (MEDLINE, EMBASE, the Cochrane Library, and BIOSIS) and hand searched bibliographies and clinical trial registries. Reviewers extracted data in duplicate, evaluated the quality of the trials, and calculated pooled estimates.ResultsA total of 11,618 participants were included in the analysis. The overall risk ratio (RR) for MACE was 0.91 (95% confidence intervals, CI, 0.82-1.00) with little heterogeneity among trials (I2 0.0%). Secondary outcomes of interest included myocardial infarction (RR, 0.85; 95% CI, 0.66-1.10), stroke (RR, 0.84; 95% CI, 0.64-1.11), cardiovascular death (RR, 0.95; 95% CI, 0.71-1.27), and all-cause mortality (RR, 0.95; 95% CI, 0.85-1.06). There were higher rates of hemorrhagic and gastrointestinal events. In absolute terms, these relative risks indicate that for every 10,000 diabetic patients treated with aspirin, 109 MACE may be prevented at the expense of 19 major bleeding events (with the caveat that the relative risk for the latter is not statistically significant).ConclusionsThe studies reviewed suggest that aspirin reduces the risk of MACE in patients with diabetes without cardiovascular disease, while also causing a trend toward higher rates of bleeding and gastrointestinal complications. These findings and our absolute benefit and risk calculations suggest that those with diabetes but without cardiovascular disease lie somewhere between primary and secondary prevention patients on the spectrum of benefit and risk. This underscores the importance of considering individual risk in clinical decision making regarding aspirin in those with diabetes.

Project description:BackgroundManaging hypertension (HT) and diabetes mellitus (DM) is crucial to preventing cardiovascular diseases. Few studies have investigated the incidence and risk of cardiovascular diseases or mortality in uncontrolled HT or DM in the Asian population. Epidemiological studies of cardiovascular disease should be conducted with continuous consideration of the changing disease risk profiles, lifestyles, and socioeconomic status over time.ObjectiveWe aimed to examine the association of uncontrolled HT or DM with the incidence of cardiovascular events or deaths from any cause.MethodsThis population-based retrospective study was conducted using data from the Korean National Health Insurance Service-National Health Screening Cohort, including patients aged 40-79 years who participated in national screening from 2002 to 2003 and were followed up until 2015. The health screening period from 2002 to 2013 was stratified into 6 index periods in 2-year cycles, and the follow-up period from 2004 to 2015 was stratified accordingly into 6 subsequent 2-year periods. The incidence rates and hazard ratio (HR) for major adverse cardiovascular events (MACE) and death from any cause were estimated according to HT or DM control status. Extended Cox models with time-dependent variables updated every 2 years, including sociodemographic characteristics, blood pressure (BP), fasting blood glucose (FBG), medication prescription, and adherence, were used.ResultsAmong the total cohort of 440,249 patients, 155,765 (35.38%) were in the uncontrolled HT or DM group. More than 60% of the patients with HT or DM who were prescribed medications did not achieve the target BP or FBG. The incidence of MACE was 10.8-15.5 and 9.6-13.3 per 1000 person-years in the uncontrolled DM and uncontrolled HT groups, respectively, and increased with age. In the uncontrolled HT and DM group, the incidence of MACE was high (15.2-17.5 per 1000 person-years) at a relatively young age and showed no age-related trend. Adjusted HR for MACE were 1.28 (95% CI 1.23-1.32) for the uncontrolled DM group, 1.32 (95% CI 1.29-1.35) for the uncontrolled HT group, and 1.54 (95% CI 1.47-1.60) for the uncontrolled HT and DM group. Adjusted HR for death from any cause were 1.05 (95% CI 1.01-1.10) for the uncontrolled DM group, 1.13 (95% CI 1.10-1.16) for the uncontrolled HT group, and 1.17 (95% CI 1.12-1.23) for the uncontrolled HT and DM group.ConclusionsThis up-to-date evidence of cardiovascular epidemiology in South Korea serves as the basis for planning public health policies to prevent cardiovascular diseases. The high uncontrolled rates of HT or DM, regardless of medication prescription, have led us to suggest the need for a novel system for effective BP or glycemic control, such as a community-wide management program using mobile health technology.

Project description:BACKGROUND:Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. METHODS:Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity - the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. RESULTS:The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. CONCLUSIONS:The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

Project description:Soluble TNF-like weak inducer of apoptosis (sTWEAK) is a proinflammatory cytokine belonging to the TNF superfamily. sTWEAK concentrations have been associated with the presence of CKD and cardiovascular disease (CVD). We hypothesized that sTWEAK levels may relate to a higher prevalence of atherosclerotic plaques, vascular calcification, and cardiovascular outcomes observed in patients with CKD.A 4-year prospective, multicenter, longitudinal study was conducted in 1058 patients with CKD stages 3-5D (mean age =58±13 years old; 665 men) but without any history of CVD from the NEFRONA Study (a study design on the prevalence of surrogate markers of CVD). Ankle-brachial index and B-mode ultrasound were performed to detect the presence of carotid and/or femoral atherosclerotic plaques together with biochemical measurements and sTWEAK assessment. Patients were followed for cardiovascular outcomes (follow-up of 3.13±1.15 years).Patients with more advanced CKD had lower sTWEAK levels. sTWEAK concentrations were independently and negatively associated with carotid intima-media thickness. sTWEAK levels were lower in patients with carotid atherosclerotic plaques but not in those with femoral plaques. After adjustment by confounders, the odds ratio (OR) for presenting carotid atherosclerotic plaques in patients in the lowest versus highest tertile of sTWEAK was 4.18 (95% confidence interval [95% CI], 2.89 to 6.08; P<0.001). Furthermore, sTWEAK levels were lower in patients with calcified carotid atherosclerotic plaques. The OR for presenting calcified carotid plaques was 1.77 (95% CI, 1.06 to 2.93; P=0.02) after multivariable adjustment. After the follow-up, 41 fatal and 68 nonfatal cardiovascular events occurred. In a Cox model, after controlling for potential confounding factors, patients in the lowest tertile of sTWEAK concentrations had a higher risk of fatal and nonfatal cardiovascular events (hazard ratio [HR], 2.40; 95% CI, 1.33 to 4.33; P=0.004) and cardiovascular mortality (HR, 2.67; 95% CI, 1.05 to 6.76; P=0.04).Low sTWEAK levels were associated with the presence of carotid atherosclerotic plaques in patients with CKD. Additionally, lower sTWEAK levels were associated with a higher risk of cardiovascular morbidity and mortality.

Dataset Information

Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data.

Publications

Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets