Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning

OBJECTIVE

To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy.

METHODS

Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited. Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort.

RESULTS

A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets.

CONCLUSION

Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients. Both nomogram and ML models showed good discrimination and generalizability.

Evaluation of Cardiovascular Disease Risk in Patients with Type 2 Diabetes Mellitus Using Clinical Laboratory Markers

Background: Cardiovascular diseases (CVD) are the main cause of death in the population with diabetes mellitus. This study purposed to determine clinical laboratory markers that might be correlated with the risk of CVD in individuals with type 2 diabetes mellitus (T2DM). Methods: Using data from the Clinical Center of the University of Debrecen from 2016 to 2020, we assessed cardiovascular risk in 5593 individuals with T2DM over a five-year follow-up period. There were 347 new cases of acute myocardial infarction (AMI) and stroke during the period. Following the stratification of these individuals into two groups according to the diagnosis of these CVDs until 2020, the risk of these CVDs was assessed through the utilization of the Chi-square test and Cox proportional hazards regression. Results: The findings of the Cox proportional hazards regression model showed that the number of HbA1C measurements per year (HR = 0.46, 95% CI 0.31-0.7), decreased levels of estimated glomerular filtration rate (eGFR) (HR = 1.6, 95% CI 1.04-2.47), and elevated triglyceride levels (HR = 1.56, 95% CI 1.06-2.29) were correlated with CVD in patients with T2DM. The area under the curve (AUC) was increased from 0.557 (95% CI 0.531-0.582) to 0.628 (95% CI 0.584-0.671) after the inclusion of the laboratory variables into the model showing improved discrimination for AMI and stroke. Conclusions: These findings indicated that eGFR, triglyceride, and the number of HbA1C per year are correlated with AMI and stroke in patients with T2DM.

Association between blood urea nitrogen levels and diabetic retinopathy in diabetic adults in the United States (NHANES 2005-2018)

Objective

To investigate the association between blood urea nitrogen (BUN) levels and diabetic retinopathy (DR) in adults with diabetes mellitus (DM).

Methods

Seven cycles of cross-sectional population information acquired from NHANES(national health and nutrition examination surveys) 2005-2018 were collected, from which a sample of diabetic adults was screened and separated into two groups based on whether or not they had DR, followed by weighted multivariate regression analysis. This study collected a complete set of demographic, biological, and sociological risk factor indicators for DR. Demographic risk factors comprised age, gender, and ethnicity, while biological risk factors included blood count, blood pressure, BMI, waist circumference, and glycated hemoglobin. Sociological risk factors included education level, deprivation index, smoking status, and alcohol consumption.

Results

The multiple regression model revealed a significant connection between BUN levels and DR [odds ratio =1.04, 95% confidence interval (1.03-1.05), p-value <0.0001],accounting for numerous variables. After equating BUN levels into four groups, multiple regression modeling showed the highest quartile (BUN>20 mg/dl) was 2.22 times more likely to develop DR than the lowest quartile [odds ratio =2.22, 95% confidence interval (1.69-2.93), p- value <0.0001]. Subgroup analyses revealed that gender, race, diabetes subtype, and duration of diabetes had a regulating effect on the relationship between BUN and DR.

Conclusion

BUN levels were related with an increased prevalence of DR, particularly in individuals with BUN >20 mg/dl. These findings highlight the significance of BUN level in assessing the risk of DR.