The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined.

All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI.

Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32–10.70).

Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

To evaluate the predictive value of fasting plasma glucose (FPG) for in-hospital mortality in patients with acute myocardial infarction (AMI) with different glucose metabolism status.

We selected 5,308 participants with AMI from the prospective, nationwide, multicenter CAMI registry, of which 2,081 were diabetic and 3,227 were nondiabetic. Patients were divided into high FPG and low FPG groups according to the optimal cutoff values of FPG to predict in-hospital mortality for diabetic and nondiabetic cohorts, respectively. The primary endpoint was in-hospital mortality.

Overall, 94 diabetic patients (4.5%) and 131 nondiabetic patients (4.1%) died during hospitalization, and the optimal FPG thresholds for predicting in-hospital death of the two cohorts were 13.2 mmol/L and 6.4 mmol/L, respectively. Compared with individuals who had low FPG, those with high FPG were significantly associated with higher in-hospital mortality in diabetic cohort (10.1% vs. 2.8%; odds ratio [OR] = 3.862, 95% confidence interval [CI]: 2.542–5.869) and nondiabetic cohort (7.4% vs. 1.7%; HR = 4.542, 95%CI: 3.041–6.782). After adjusting the potential confounders, this significant association was not changed. Furthermore, FPG as a continuous variable was positively associated with in-hospital mortality in single-variable and multivariable models regardless of diabetic status. Adding FPG to the original model showed a significant improvement in C-statistic and net reclassification in diabetic and nondiabetic cohorts.

This large-scale registry indicated that there is a strong positive association between FPG and in-hospital mortality in AMI patients with and without diabetes. FPG might be useful to stratify patients with AMI.

Stress-related glycemic indicators, including admission blood glucose (ABG), stress-hyperglycemia ratio (SHR), and glycemic gap (GG), have been associated with worse outcomes after acute myocardial infarction (AMI). However, data regarding their prognostic value in the oldest old with AMI are unavailable. Therefore, this study aimed to investigate the association of stress-related glycemic indicators with short- and long-term cardiovascular mortality (CVM) in the oldest old (≥ 80 years) with AMI.

In this prospective study, a total of 933 consecutive old patients with AMI admitted to FuWai hospital (Beijing, China) were enrolled. On admission, ABG, SHR, and GG were assessed and all participants were classified according to their quartiles. Kaplan-Meier, restricted cubic splines (RCS), and multivariate Cox regression analyses were performed to evaluate the association between these glycemic indicators and CVM within 30 days and long-term follow-up.

During an average of 1954 patient-years of follow-up, a total of 250 cardiovascular deaths were recorded. Kaplan-Meier analyses showed the lowest CVM in quartile 1 of ABG and in quartile 2 of SHR and GG. After adjusting for potential covariates, patients in quartile 4 of ABG, SHR, and GG had a respective 1.67-fold (95% CI: 1.03-2.69; P = 0.036), 1.80-fold (95% CI: 1.16-2.79; P = 0.009), and 1.78-fold (95% CI: 1.14-2.79; P = 0.011) higher risk of long-term CVM risk compared to those in the reference groups (quartile 1 of ABG and quartile 2 of SHR and GG). Furthermore, RCS suggested a J-shaped relationship of ABG and a U-shaped association of SHR and GG with long-term CVM. Additionally, we observed similar associations of these acute glycemic parameters with 30-day CVM.

Our data first indicated that SHR and GG consistently had a U-shaped association with both 30-day and long-term CVM among the oldest old with AMI, suggesting that they may be useful for risk stratification in this special population.

To establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI).

In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.

In both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer–Lemeshow χ² = 7.070; P = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis (adjusted HR = 1.89; 95% CI: 1.18–3.04; log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.

An improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.

To assess the correlation between triglyceride glucose (TyG) index and in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI).

A total of 2190 patients with STEMI who underwent primary angiography within 12 h from symptom onset were selected from the prospective, nationwide, multicenter CAMI registry. TyG index was calculated with the formula: Ln [fasting triglycerides (mmol/L) × fasting glucose (mmol/L)/2]. Patients were divided into three groups according to the tertiles of TyG index. The primary endpoint was in-hospital mortality.

Overall, 46 patients died during hospitalization, in-hospital mortality was 1.5%, 2.2%, 2.6% for tertile 1, tertile 2, and tertile 3, respectively. However, TyG index was not significantly correlated with in-hospital mortality in single-variable logistic regression analysis. Nonetheless, after adjusting for age and sex, TyG index was significantly associated with higher mortality when regarded as a continuous variable (adjusted OR = 1.75, 95% CI: 1.16-2.63) or categorical variable (tertile 3 vs. tertile 1: adjusted OR = 2.50, 95% CI: 1.14-5.49). Furthermore, TyG index, either as a continuous variable (adjusted OR = 2.54, 95% CI: 1.42-4.54) or categorical variable (tertile 3 vs. tertile 1: adjusted OR = 3.57, 95% CI: 1.24-10.29), was an independent predictor of in-hospital mortality after adjusting for multiple confounders in multivariable logistic regression analysis. In subgroup analysis, the prognostic effect of high TyG index was more significant in patients with body mass index < 18.5 kg/m² (P_interaction = 0.006).

This study showed that TyG index was positively correlated with in-hospital mortality in STEMI patients who underwent primary angiography, especially in underweight patients.