DIAGNOSING PERICARDITIS DUE TO COVID-19 VACCINATION

2024 Guidelines of the Polish Society of Laboratory Diagnostics and the Polish Lipid Association on laboratory diagnostics of lipid metabolism disorders

Lipid disorders are the most common (even 70%) and worst monitored cardiovascular risk factor (only 1/4 of patients in Poland and in CEE countries are on the low-density lipoprotein cholesterol (LDL-C) goal). To improve this, clear and simple diagnostic criteria should be introduced for all components of the lipid profile. These are the updated guidelines of the two main scientific societies in Poland in the area - the Polish Society of Laboratory Diagnostics (PSLD) and the Polish Lipid Association (PoLA), which, in comparison to those from 2020, introduce few important changes in recommendations (two main lipid targets, new recommendations on LDL-C measurements, calculations new goals for triglycerides, new recommendations on remnants and small dense LDL) that should help the practitioners to be early with the diagnosis of lipid disorders and in the effective monitoring (after therapy initiation), and in the consequence to avoid the first and recurrent cardiovascular events.

Use of machine learning to identify risk factors for coronary artery disease

Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

Predictive value of triglycerides to high density lipoprotein ratio in patients with first attack of acute coronary syndrome

OBJECTIVES

To identify patients who are at risk for a first cardiovascular event, mitigate the risk, and institute early intervention. The triglyceride to high-density lipoprotein-C (TG/HDL-C) ratio has been found to be a very useful biomarker for directing treatment and prevention therapy.

METHODS

This retrospective cross-sectional study included adult patients (aged >18 years) experiencing first-time acute coronary syndrome (ACS). We examined all patient databases for a definite diagnosis of angina, non-ST segment elevation myocardial infarction (NSTEMI), or ST-segment elevation myocardial infarction (STEMI). Lipid profiles were obtained prior to or at the time of admission.

RESULTS

A total of 265 patients were included in the study (mean age 57.83 ± 11.4 years) and 79.2% were men. Male gender, presence of diabetes, raised total cholesterol, raised low-density lipoprotein (LDL), and raised troponin level on admission were significantly positively correlated with STEMI (p=0.004, p=0.001, p<0.001, and p<0.001), whereas TG/HDL-C ratio was significantly negatively correlated with STEMI (p=0.048), while there was no significant results with NSTEMI (p=0.264) and angina (p=0.326). Total cholesterol and raised low-density lipoprotein (LDL) were significantly positively correlated with NSTEMI (p=0.013 and p=0.024).

CONCLUSION

Patients with first-time ACS may not have an increased TG/HDL-C ratio. High LDL levels had the most significant association with an ACS event, along with total cholesterol and diabetes. Further research is needed on a larger scale to determine the association of TG/HDL-C ratio with ACS and differentiate the different types of ACS events according to their clinical and laboratory characteristics.