A Novel ABC Score Predicts Mortality in Non-ST-Segment Elevation Acute Coronary Syndrome Patients Who underwent Percutaneous Coronary Intervention

The Simplified Thrombo-Inflammatory Score as a Novel Predictor of All-Cause Mortality in Patients with Heart Failure: A Retrospective Cohort Study

Background

The simplified thrombo-inflammatory score (sTIPS) has recently emerged as a novel prognostic score. Hence, we investigated the prognostic value of sTIPS for predicting long-term mortality in patients with heart failure (HF).

Methods

A total of 3741 patients were analyzed in this study. The sTIPS was calculated based on the white blood cell count (WBC) and the mean platelet volume to platelet count (MPV/PC) ratio at admission. The mean follow-up time was 22.75 months. Multivariable Cox regression analyses were used to investigate the associations between the sTIPS and all-cause mortality (ACM).

Results

In the whole study population, multivariate Cox regression analysis showed that patients in both the sTIPS 2 and sTIPS 1 groups had significantly increased risk of ACM as compared with patients in the sTIPS 0 group (hazard ratio [HR]=1.706, 95% confidence interval [CI]: 1.405-2.072, P<0.001 and HR = 1.431, 95% CI 1.270-1.612, P<0.001). The same significant trend was observed in heart failure with preserved ejection fraction (HFpEF) patients (sTIPS1 vs sTIPS0: HR = 1.366, 95% CI 1.100-1.697, P = 0.005; sTIPS2 vs sTIPS0: HR = 1.995, 95% CI 1.460-2.725, P<0.001). However, only sTIPS 1 group had a significantly increased the risk of ACM compared to the sTIPS 0 group among patients with HFmrEF (sTIPS1 vs sTIPS0: HR = 1.648, 95% CI 1.238-2.194, P = 0.001) and HFrEF (sTIPS1 vs sTIPS0: HR = 1.322, 95% CI 1.021-1.712, P = 0.035).

Conclusion

sTIPS is useful in predicting risk for long-term mortality in patients with HF.

Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services

Purpose

We aimed to establish and authenticate a clinical prognostic nomogram for predicting long-term Major Adverse Cardiovascular Events (MACEs) among high-risk patients who have undergone Percutaneous Coronary Intervention (PCI) in county-level health service.

Patients and methods

This prospective study included Acute Coronary Syndrome (ACS) patients treated with PCI at six county-level hospitals between September 2018 and August 2019, selected from both the original training set and external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques and logistic regression were used to assess potential risk factors and construct a risk predictive nomogram. Additionally, the potential non-linear relationships between continuous variables were tested using Restricted Cubic Splines (RCS). The performance of the nomogram was evaluated based on the Receiver Operating Characteristic (ROC) curve analysis, Calibration Curve, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC).

Results

The original training set and external validation set comprised 520 and 1,061 patients, respectively. The final nomogram was developed using nine clinical variables: Age, Killip functional classification III-IV, Hypertension, Hyperhomocysteinemia, Heart failure, Number of stents, Multivessel disease, Low-density Lipoprotein Cholesterol, and Left Ventricular Ejection Fraction. The AUC of the nomogram was 0.79 and 0.75 in the training set and external validation set, respectively. The DCA and CIC validated the clinical value of the constructed prognostic nomogram.

Conclusion

We developed and validated a prognostic nomogram for predicting the probability of 3-year MACEs in ACS patients who underwent PCI at county-level hospitals. The nomogram could provide a precise risk assessment for secondary prevention in ACS patients receiving PCI.

Serum a-1 antitrypsin as a novel biomarker in chronic heart failure

AIMS

Chronic heart failure (CHF) remains a major health issue worldwide. In the present study, we aimed to identify novel circulating biomarkers for CHF using serum proteomics technology and to validate the biomarker in three independent cohorts.

METHODS AND RESULTS

The isobaric tags for relative and absolute quantitation technology was utilized to identify the potential biomarkers of CHF. The validation was conducted in three independent cohort. Cohort A included 223 patients with ischaemic heart disease (IHD) and 321 patients with ischaemic heart failure (IHF) from the CORFCHD-PCI study. Cohort B recruited 817 patients with IHD and 1139 patients with IHF from the PRACTICE study. Cohort C enrolled 559 non-ischaemic heart disease patients with CHF (n = 316) or without CHF (n = 243). We found the expression of a-1 antitrypsin (AAT) was elevated significantly in patients with CHF compared with that in the patients with stable IHD using statistical and bioinformatics analyses. In a validation study, there was a significant difference between patients with stable IHD and patients with IHF in AAT concentration either in cohort A (1.35 ± 0.40 vs. 1.64 ± 0.56, P < 0.001) or in cohort B (1.37 ± 0.42 vs. 1.70 ± 0.48, P < 0.001). The area under the receiver operating characteristic curve was 0.70 [95% confidence interval (CI): 0.66 to 0.74, P < 0.001] in cohort A and 0.74 (95% CI: 0.72 to 0.76, P < 0.001) in cohort B. Furthermore, AAT was negative correlated with left ventricular ejection fraction (r = -0.261, P < 0.001). After adjusting for confounders using a multivariate logistic regression analysis, AAT remained an independent association with CHF in both cohort A (OR = 3.14, 95% CI: 1.667 to 5.90, P < 0.001) and cohort B (OR = 4.10, 95% CI: 2.97 to 5.65, P < 0.001). This association was also validated in cohort C (OR = 1.86, 95% CI: 1.02 to 3.38, P = 0.043).

CONCLUSIONS

The present study suggests that serum AAT is a reliable biomarker for CHF in a Chinese population.

The higher the serum albumin, the better? Findings from the PRACTICE study

AIMS

The relation between hypoalbuminemia and coronary artery disease (CAD) has been established. However, the association of increased albumin level and outcomes of CAD has not been investigated.

METHODS

There were 14 994 CAD patients from the PRACTICE study, which is a large, single center prospective cohort study based on case records and follow-up registry performed in the First Affiliated Hospital of Xinjiang Medical University from Dec. 2016 to Oct. 2021 in the present study. All the 14 994 patients were divided into five categories according albumin levels: <35 g/L group (n = 1 478), 35-40 g/L group (n = 5 007), 40-45 g/L group (n = 6 076), 45-50 g/L group (n = 1 835), and ≥50 g/L group (n = 598).

RESULTS

A total of 448 all-cause deaths(ACD), 333 cardiac deaths (CD), 1 162 MACEs and 1 276 MACCEs were recorded during up to 60-months follow-up period. After adjusting for confounders, we observed a non-linear relation for either MACE or MACCE with the lowest risk at 45 g/L of albumin levels. A threshold value of albumin ≥50 g/L was associated with an increased risk for either MACE (adjusted HR=1.617, 95%CI:1.130-2.315, P = 0.009) or MACCE (adjusted HR= 1.439, 95%CI: 1.007-2.056, P = 0.045) in multivariable Cox regression model. For mortality, we only found decreased (<35 g/L) but not increased albumin level was associated with either ACD (HR=2.744, 95%CI: 1.631-4.617, P<0.001) or CD (HR=2.736, 95%CI: 1.484-5.045, P = 0.001).

CONCLUSIONS

In the present study, a U-shaped curve relation was identified between albumin levels and MACE and MACCE in CAD patients, with the lowest risk at 45 g/L levels.

Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention

OBJECTIVES

We developed a nomogram model derived from inflammatory indices, clinical data, and imaging data to predict in-hospital major adverse cardiac and cerebrovascular events (MACCEs) following emergency percutaneous coronary intervention (PCI) in patients with new-onset ST-elevation myocardial infarction (STEMI).

METHODS

Patients with new-onset STEMI admitted between June 2020 and November 2022 were retrospectively reviewed. Data pertaining to coronary angiograms, clinical data, biochemical indices, and in-hospital clinical outcomes were derived from electronic medical records. Lasso regression model was employed to screen risk factors and construct a prediction model.

RESULTS

Overall, 547 patients with new-onset STEMI who underwent PCI were included and assigned to the training cohort (n = 384) and independent verification cohort (n = 163). Six clinical features (age, diabetes mellitus, current smoking, hyperuricemia, neutrophil-to-lymphocyte ratio, and Gensini score) were selected by LASSO regression to construct a nomogram to predict the risk of in-hospital MACCEs. The area-under-the-curve (AUC) values for in-hospital MACCEs risk in the training and independent verification cohorts were 0.921 (95% CI 0.881-0.961) and 0.898 (95% CI 0.821-0.976), respectively. It was adequately calibrated in both training cohort and independent verification cohorts, and predictions were correlated with actual outcomes. Decision curve analysis demonstrated that the nomogram was capable of predicting in-hospital MACCEs with good clinical benefit.

CONCLUSIONS

Our prediction nomogram based on multi-modal data (inflammatory indices, clinical and imaging data) reliably predicted in-hospital MACCEs in new-onset STEMI patients with emergency PCI. This prediction nomogram can enable individualized treatment strategies.

Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model

BACKGROUND

The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI) .

METHODS

This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell's C-index and decision curve analysis (DCA).

RESULTS

In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell's C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721-0.823) in the development cohort and 0.736 (95%CI: 0.656-0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram.

CONCLUSIONS

Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI.

Daily exercise improves the long-term prognosis of patients with acute coronary syndrome

Objective

To demonstrate the effect of daily exercise on the incidence of major adverse cardiovascular events (MACE) for patients with acute coronary syndrome (ACS).

Methods

A cohort of 9,636 patients with ACS were consecutively enrolled in our retrospective study between November 2015 and September 2017, which were used for model development. 6,745 patients were assigned as the derivation cohort and 2,891 patients were assigned as the validation cohort. The least absolute shrinkage and selection operator (LASSO) regression and COX regression were used to screen out significant variables for the construction of the nomogram. Multivariable COX regression analysis was employed for the development of a model represented by a nomogram. The nomogram was then evaluated for performance traits such as discrimination, calibration, and clinical efficacy.

Results

Among 9,636 patients with ACS (mean [SD] age, 60.3 [10.4] years; 7,235 men [75.1%]), the 5-year incidence for MACE was 0.19 at a median follow-up of 1,747 (1,160–1,825) days. Derived from the LASSO regression and COX regression, the nomogram has included 15 factors in total including age, previous myocardial infarction (MI), previous percutaneous coronary intervention (PCI), systolic pressure, N-terminal Pro-B-type natriuretic peptide (NT-proBNP), high-density lipoprotein cholesterol (HDL), serum creatinine, left ventricular end-diastolic diameter (LVEDD), Killip class, the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score, left anterior descending (LAD) stenosis (≥50%), circumflex (LCX) stenosis (≥50%), right coronary artery (RCA) stenosis (≥50%), exercise intensity, cumulative time. The 5-year area under the ROC curve (AUC) of derivation and validation cohorts were 0.659 (0.643–0.676) and 0.653 (0.629–0.677), respectively. The calibration plots showed the strong concordance performance of the nomogram model in both two cohorts. Moreover, decision curve analysis (DCA) also showed the usefulness of nomogram in clinical practice.

Conclusion

The present work provided a prediction nomogram predicting MACE for patients with ACS after incorporating the already known factors and the daily exercise, which demonstrated the effectiveness of daily exercise on the improvement of prognosis for patients with ACS.

Development and validation of a nomogram for predicting significant coronary artery stenosis in suspected non-ST-segment elevation acute coronary artery syndrome with low-to-intermediate risk stratification

Background

Patients with non-ST-segment coronary artery syndrome (NSTE-ACS) have significant heterogeneity in their coronary arteries. A better assessment of significant coronary artery stenosis (SCAS) in low-to-intermediate risk NSTE-ACS patients would help identify who might benefit from invasive coronary angiography (ICA). Our study aimed to develop a multivariable-based model for pretesting SCAS in suspected NSTE-ACS with low-to-intermediate risk.

Methods

This prediction nomogram was constructed retrospectively in 469 suspected NSTE-ACS patients with low-to-intermediate risk. Patients were divided into a development group (n = 331, patients admitted to hospital before 1 May 2021) and a temporal validation group (n = 138, patients admitted to hospital since 1 May 2021). The outcome was existing SCAS, including left main artery stenosis ≥50% or any subepicardial coronary artery stenosis ≥70%, all confirmed by invasive coronary angiography. Pretest predictors were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise logistic regression.

Results

Derivation analyses from the development group (n = 331, admitted before 1 May 2021) generated the 7 strongest predictors out of 25 candidate variables comprising smoker, diabetes, heart rate, cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-density lipoprotein cholesterol, and left atrial diameter. This nomogram model showed excellent discrimination ability with an area under the receiver operating characteristic curve (AUC) of 0.83 in the development set and 0.79 in the validation dataset. Good calibration was generally displayed, although it slightly overestimated patients' SCAS risk in the validation group. Decision curve analysis demonstrated the clinical benefit of this model, indicating its value in clinical practice. Furthermore, an optimal cut-off of prediction probability was assigned as 0.61 according to the Youden index.

Conclusion

A prediction nomogram consisting of seven readily available clinical parameters was established to pretest the probability of SCAS in suspected NSTE-ACS patients with low-to-intermediate risk, which may serve as a cost-effective risk stratification tool and thus assist in initial decision making.

Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment

BACKGROUND

Disruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS.

METHODS

ACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis.

RESULTS

There were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702-0.877) in the development cohort and 0.894 (95% CI: 0.820-0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739-0.866) in the development cohort and 0.798 (95% CI: 0.693-0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs.

CONCLUSION

Our validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.

ADS Score as a Novel Predictor of Outcomes in Patients Who Underwent Percutaneous Coronary Intervention

Objectives: A novel AFR– albumin-derived neutrophil to lymphocyte ratio (dNLR) score (ADS) were reported to associate with clinical outcome in various malignancies, However, the relation between the ADS score and outcomes in coronary artery disease (CAD) patients after percutaneous coronary intervention (PCI) has not been investigated.

Methods: Three thousand five hundred and sixty-one patients were divided into two groups according to ADS score: low group (ADS score <2; n = 2,682) and high group (ADS score ≥ 2; n = 879). Overall, there were 133 all-cause mortality (ACM) during the following up. The incidence of ACM in the low group is 2.7% (72/2,682) and high group is 6.9% (61/879). The ACM incidence was significantly higher in high group compared to that in the low group (P < 0.001). Cardiac mortality (CM) occurred in 82 patients: 44(1.6%) in the low group and 38 (4.3%) in the high group. There was significant difference in the CM incidence between the low group and high group (P < 0.001). Major adverse cardiac and cerebrovascular events (MACCE) occurred in 520 patients: 366 (13.6%) in the low group and 154 (17.5%) in the high group. There was significant difference in the MACCE incidence between the low group and high group (P = 0.005). Major adverse cardiac and events (MACE) occurred in 395 patients: 281(10.5%) in the low group and 114 (13.0%) in the high group. There was significant difference in the MACE incidence between the low group and high group (P = 0.041). The multivariate Cox proportional hazards model showed that ADS score was independently correlated with the ACM [adjusted HR = 2.031 (1.357–3.039), P = 0.001]; CM [adjusted HR = 1.883 (1.127–3.147), P = 0.016]; MACCE [adjusted HR = 1.352 (1.096–1.668), P = 0.005], and MACE [adjusted HR = 1.260 (0.987–1.608), P = 0.063].

Conclusion: The present study indicated that the ADS score was associated with long-term mortality, the MACCE, and the MACE in CAD patients underwent PCI.

A prognostic nomogram for long-term major adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention

BACKGROUND

Accurate prediction of major adverse cardiovascular events (MACEs) is very important for the management of acute coronary syndrome (ACS) patients. We aimed to construct an effective prognostic nomogram for individualized risk estimates of MACEs for patients with ACS after percutaneous coronary intervention (PCI).

METHODS

This was a prospective study of patients with ACS after PCI from January 2013 to July 2019 (n = 2465). After removing patients with incomplete clinical information, a total of 1986 patients were randomly divided into evaluation (n = 1324) and validation (n = 662) groups. Predictors included in the nomogram were determined by a multivariate Cox proportional hazards regression model based on the training set. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the discrimination and predictive accuracy of the nomogram, which were then compared with those of the classic models. The clinical utility of the nomogram was assessed by X-tile analysis and Kaplan-Meier curve analysis.

RESULTS

Independent prognostic factors, including lactate level, age, left anterior descending branch stenosis, right coronary artery stenosis, brain natriuretic peptide level, and left ventricular ejection fraction, were determined and contained in the nomogram. The nomogram achieved good areas under the ROC curve of 0.712-0.762 in the training set and 0.724-0.818 in the validation set and well-fitted calibration curves. In addition, participants could be divided into two risk groups (low and high) according to this model.

CONCLUSIONS

A simple-to-use nomogram incorporating lactate level effectively predicted 6-month, 1-year, and 4-year MACE incidence among patients with ACS after PCI.