Gensini Score and Thrombus Burden Add Predictive Value to the SYNTAX Score in Detecting No-Reflow after Myocardial Infarction

A risk predictive model for determining the severity of coronary artery lesions in older postmenopausal women with coronary heart disease

OBJECTIVE

To determine the risk factors affecting the severity of coronary artery disease (CAD) in older postmenopausal women with coronary heart disease (CHD) and to construct a personalized risk predictive model.

METHODS

In this cohort study, clinical records of 527 female patients aged ≥60 with CHD who were hospitalized in the First Affiliated Hospital of the University of Science and Technology of China from March 2018 to February 2019 were analyzed retrospectively. The severity of CAD was determined using the Gensini scores that are based on coronary angiography findings. Patients with Gensini scores ≥40 and <40 were divided into high-risk (n=277) and non-high-risk groups (n=250), respectively. Logistic regression analysis was used to assess independent predictors of CAD severity. The nomogram prediction model of CAD severity was plotted by the R software. The area under the receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive efficiency of the nomogram model, and the decision curve analysis (DCA) was used to assess the clinical applicability of the nomogram model.

RESULTS

Multivariate analysis showed that high-sensitivity C-reactive protein, RBC count, WBC count, BMI, and diabetes mellitus were independent risk factors associated with CAD severity in older menopausal women (P<0.05); the area under the ROC curve of the nomogram constructed based on the independent risk factors was 0.846 (95% CI: 0.756-0.937). The area under the ROC curve after internal validation of the nomogram by the Bootstrap method after resampling 1000 times was 0.840 (95% CI: 0.741-0.923). The calibration curve suggested that the nomogram had an excellent predictive agreement, and the DCA curve indicated that the net benefit of applying the nomogram was significantly higher than that of the "no intervention" and "all intervention" methods when the risk probability of patients with high-risk CAD severity was 0.30-0.81.

CONCLUSION

A personalized risk assessment model was constructed based on the risk factors of severe CAD in older menopausal women with CHD, which had good prediction efficiency based on discrimination, calibration, and clinical applicability evaluation indicators. This model could assist cardiology medical staff in screening older menopausal women with CHD who are at a high risk of severe CAD to implement targeted interventions.

No-Reflow Prediction in Acute Coronary Syndrome During Percutaneous Coronary Intervention: The NORPACS Risk Score

BACKGROUND

Suboptimal coronary reperfusion (no reflow) is common in acute coronary syndrome percutaneous coronary intervention (PCI) and is associated with poor outcomes. We aimed to develop and externally validate a clinical risk score for angiographic no reflow for use following angiography and before PCI.

METHODS

We developed and externally validated a logistic regression model for prediction of no reflow among adult patients undergoing PCI for acute coronary syndrome using data from the Melbourne Interventional Group PCI registry (2005-2020; development cohort) and the British Cardiovascular Interventional Society PCI registry (2006-2020; external validation cohort).

RESULTS

A total of 30 561 patients (mean age, 64.1 years; 24% women) were included in the Melbourne Interventional Group development cohort and 440 256 patients (mean age, 64.9 years; 27% women) in the British Cardiovascular Interventional Society external validation cohort. The primary outcome (no reflow) occurred in 4.1% (1249 patients) and 9.4% (41 222 patients) of the development and validation cohorts, respectively. From 33 candidate predictor variables, 6 final variables were selected by an adaptive least absolute shrinkage and selection operator regression model for inclusion (cardiogenic shock, ST-segment-elevation myocardial infarction with symptom onset >195 minutes pre-PCI, estimated stent length ≥20 mm, vessel diameter <2.5 mm, pre-PCI Thrombolysis in Myocardial Infarction flow <3, and lesion location). Model discrimination was very good (development C statistic, 0.808; validation C statistic, 0.741) with excellent calibration. Patients with a score of ≥8 points had a 22% and 27% risk of no reflow in the development and validation cohorts, respectively.

CONCLUSIONS

The no-reflow prediction in acute coronary syndrome risk score is a simple count-based scoring system based on 6 parameters available before PCI to predict the risk of no reflow. This score could be useful in guiding preventative treatment and future trials.

Gensini Score's Severity and Its Relationship with Risk Factors for Coronary Artery Disease Among Patients Who Underwent Angiography in Somalia's Largest PCI Centre

Aim

Coronary artery diseases (CAD) are chronic disorders in which atherosclerosis plays a major role in their pathogenesis. Their severity is directly related to cardiovascular outcomes. The assessment of Coronary Artery severity is quite complex, in which different parameters are used, such as the gensini score, syntax score, etc.

Methods

This was a retrospective study of adult (≥18 years) patients who underwent angiography in Mogadishu Somali Türkiye Training and Research Hospital from June 2022 to September 2023. Gensini scores were calculated to determine the extent and severity of atherosclerosis among patients with coronary artery disease.

Results

A total of 278 participants (78% male and 22%% women) were analyzed. Their risk factors were assessed with hypertension being the leading risk factor (48%). While a significant relationship was found between gensini score and dyslipidemia in patients with chronic ischemic heart disease (p 0.035), no significance was found with other risk factors On the other hand, the Gensini score had a significant correlation with the final decision of management (medical, PCI vs CABG).

Conclusion

This study indicated that the Gensini score can be used as a guide for CAD decisions.