Incidence and Predictors of Obstructive Coronary Artery Disease and the Role of Cardiac Troponin Assays in Patients with Unstable Angina

Predictors of Revascularization in Patients with Unstable Angina

BACKGROUND

The factors that determine the necessity of coronary artery revascularization in patients with unstable angina (UA) have been supported by limited data. Therefore, this study aimed to identify the predictors of revascularization in patients with UA.

METHODS

The study included the recorded data of 3668 patients with UA who underwent cardiac catheterization (age 66 ± 9.2, men 70%); 2615 of them (71%) underwent revascularization (percutaneous transluminal coronary angioplasty (PTCA), coronary artery bypass graft (CABG), or hybrid revascularization. The remaining 1053 patients (29%) had no significant coronary stenosis and were regarded as controls. Multivariable logistic regression analysis was performed to separate the predictors of revascularization.

RESULTS

It was found that severe angina (OR 2.7, 95%CI 1.9-3.7), male gender (OR 1.4, 95%CI 1.1-1.7), and hyperlipidemia were the predictors of revascularization. It was also noted that intraventricular conduction disorders including left and right bundle branch blocks and a history of previous revascularization and myocardial infarction were associated with lower odds of revascularization.

CONCLUSION

Overall, however, the predictive value of the studied factors proved to be poor and may still point to the multifactorial nature of significant coronary artery stenosis and the need for revascularization in patients with UA.

Diagnostic Performance of Novel Troponin Algorithms for the Rule-Out of Non-ST-Elevation Acute Coronary Syndrome

BACKGROUND

The European Society of Cardiology (ESC) rule-out algorithms use cutoffs optimized for exclusion of non-ST elevation myocardial infarction (NSTEMI). We investigated these and several novel algorithms for the rule-out of non-ST elevation acute coronary syndrome (NSTE-ACS) including less urgent coronary ischemia.

METHOD

A total of 1504 unselected patients with suspected NSTE-ACS were included and divided into a derivation cohort (n = 988) and validation cohort (n = 516). The primary endpoint was the diagnostic performance to rule-out NSTEMI and unstable angina pectoris during index hospitalization. The secondary endpoint was combined MI, all-cause mortality (within 30 days) and urgent (24 h) revascularization. The ESC algorithms for high-sensitivity cardiac troponin T (hs-cTnT) and I (hs-cTnI) were compared to different novel low-baseline (limit of detection), low-delta (based on the assay analytical and biological variation), and 0-1-h and 0-3-h algorithms.

RESULTS

The prevalence of NSTE-ACS was 24.8%, 60.0% had noncardiac chest pain, and 15.2% other diseases. The 0-1/0-3-h algorithms had superior clinical sensitivity for the primary endpoint compared to the ESC algorithm (validation cohort); hs-cTnT: 95% vs 63%, and hs-cTnI: 87% vs 64%, respectively. Regarding the secondary endpoint, the algorithms had similar clinical sensitivity (100% vs 94%-96%) but lower clinical specificity (41%-19%) compared to the ESC algorithms (77%-74%). The rule-out rates decreased by a factor of 2-4.

CONCLUSION

Low concentration/low-delta troponin algorithms improve the clinical sensitivity for a combined endpoint of NSTEMI and unstable angina pectoris, with the cost of a substantial reduction in total rule-out rate. There was no clear benefit compared to ESC for diagnosing high-risk events.

Predictive nursing helps improve treatment efficacy, treatment compliance, and quality of life in unstable angina pectoris patients

OBJECTIVE

This paper aims to explore the effects of predictive nursing on the treatment efficacy in unstable angina pectoris (UAP) patients and on their treatment compliance and quality of life (QOL).

METHODS

Admitted to our hospital from November 2017 to August 2019, 110 UAP patients were recruited as the study cohort and randomized into a control group (CG) and an observation group (OG). Among them, 53 patients in the CG underwent routine nursing, and the remaining 57 patients in the OG underwent additional predictive nursing in addition to the routine nursing. The patients in both groups were observed with respect to their general data, the therapeutic effects after the nursing, their clinical indicators (length of hospital stays, stable states of angina pectoris, attacks of angina pectoris), their treatment compliance after the nursing, their negative emotions before and after the nursing, their adverse reactions during the nursing, and the changes in their blood pressure (diastolic blood pressure, systolic blood pressure) and QOL indices after the nursing.

RESULTS

There were no differences in the general data between the two groups (P>0.05), but the clinical indicators were better in the OG (P<0.05). After the nursing, the treatment compliance and improvement of the negative emotions were better in the OG (P<0.05), and the efficacy was also better in this group (P<0.05). The incidence of adverse reactions was lower in the OG (P<0.05), and the changes in the blood pressure indices were better in this group (P<0.05). After the nursing, the QOL in the OG was better (P<0.05).

CONCLUSION

Predictive nursing is conducive to improving the efficacy, the treatment compliance, and the QOL of UAP patients.