Prognostic implication of serum glycated albumin for patients with non-ST-segment elevation acute coronary syndrome undergoing percutaneous coronary intervention

Second-order scattering sensor based on the Zn0.97La0.03O compound for selective and stable detection of glycated albumin

Here, we presented a second-order scattering sensor based on the Zn0.97La0.03O compound (LaZnO) for selective and stable detection of glycated albumin (GA, glycemic long-term biomarker). The LaZnO sample was obtained through the co-precipitation method and then characterized using microscopic and spectroscopic techniques. Furthermore, the selectivity, molecular interference, temporal stability, and pH effects of the LaZnO SOS signal in the absence and presence of GA were investigated. The results indicate the stability of the SOS signal over more than 60 days. Assays conducted within the pH range of 5 to 8 indicate that the detection of GA remains unaffected under the given conditions. Selectivity studies show that the SOS signal of LaZnO is reduced only upon contact with GA, while interference studies show that detection is not affected by other chemical species. Additionally, the calibration curve test showed high sensitivity of the material, with a detection limit of 0.55 µg/ml. All the results suggest that LaZnO can deliver efficiency, selectivity, accuracy, and fast response as a GA biosensor, emphasizing LaZnO's usefulness in detecting protein biomarkers.

A nomogram for predicting the readmission within 6 months after treatment in patients with acute coronary syndrome

Purpose

To explore predictors for readmission within 6 months of ACS patients, and to build a prediction model, and generate a nomogram.

Methods

The retrospective cohort study included 498 patients with ACS in the Second Medical Center of the Chinese People’s Liberation Army General Hospital between January 2016 and March 2019. Univariate and multivariate logistic regression with odds ratios (OR) and two-sided 95% confidence interval (CI) analysis were used to investigate predictors for readmission within 6 months. The cohort was randomly divided into training cohort to develop a prediction model, and the validation cohort to validate the model. The receiver operating characteristic curve (ROC) and the calibration curve was used to assess discriminative power and calibration.

Results

Eighty-three ACS patients were readmitted within six months, with a readmission rate of 16.67%. Predictors included ACS type, treatment, hypertension, SUA, length of stay, statins, and adverse events occurred during hospitalization were used to form a six-month readmission prediction model for readmission within 6 months in ACS patients. The area under the curve (AUC) of the model was 0.788 (95%CI: 0.735–0.878) and 0.775 (95%CI: 0.686–0.865) in the training cohort and the validation cohort, respectively. Calibration curves showed the good calibration of the prediction model. Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable.

Conclusion

We used seven readily available predictors to develop a prediction model for readmission within six months after treatment in ACS patients, which could be used to identify high-risk patients for ACS readmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02873-6.

Serum glycated albumin is associated with in-stent restenosis in patients with acute coronary syndrome after percutaneous coronary intervention with drug-eluting stents: An observational study

Background

Previous studies have confirmed the predicted value of serum glycated albumin (GA) in atherosclerotic cardiovascular disease. However, the relationship between GA and the development of in-stent restenosis (ISR) after drug-eluting stent (DES) implantation has not been verified in patients with acute coronary syndrome (ACS).

Materials and methods

In this study, 797 patients diagnosed with ACS who underwent re-coronary angiography more than 6 months after the first successful DES-based percutaneous coronary intervention (PCI) were eventually included. Patients were categorized into two groups based on the median GA levels of 14.94%. Moreover, multivariate logistic regression analysis models and the net reclassification improvement and integrated differentiation improvement risk models were constructed to assess the relationship between the GA and DES-ISR in patients with ACS.

Results

The GA was significantly associated with an increased risk of DES-ISR, upon adjusting for confounding factors (as nominal variate: OR 1.868, 95% CI 1.191-2.932, P = 0.007; as continuous variate: OR 1.109, 95% CI 1.040-1.183, P = 0.002). The addition of GA to a baseline risk model had an incremental effect on the predictive value for DES-ISR (AUC: GA vs. baseline model, 0.714 vs. 0.692, comparison P = 0.017; category-free net reclassification improvement (NRI) 0.080, P = 0.035; integrated discrimination improvement (IDI) 0.023, P < 0.001).

Conclusion

GA level was significantly associated with a high risk of DES-ISR in patients with ACS treated with PCI. Moreover, the addition of the GA to a baseline risk model has an incremental effect on the predictive potential for DES-ISR.