Resting Heart Rate and Long-Term Outcomes in Patients with Percutaneous Coronary Intervention: Results from a 10-Year Follow-Up of the CORFCHD-PCI Study

Zheng, Ying‐Ying; Wang, Tingting; You, Chun‐Xiang; Hou, Xian‐Geng; Yang, Yi; Ma, Xiaochi; Ma, Yun; Zhang, Jinying; Xie, Xiang

doi:10.1155/2019/5432076

Cited by 13 publications

(15 citation statements)

References 18 publications

(18 reference statements)

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“…More recently, a single-center retrospective cohort study indicated that PHR is an independent predictor of adverse outcomes at 10-year follow-up in 6049 CAD patients who underwent PCI. High PHR was associated with an increased risk of all-cause mortality, but had no relationship with bleeding events [19]. Our analysis confirmed these findings in a multicenter, much larger size CAD population.…”

Section: Discussionsupporting

confidence: 85%

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The impact of pre-procedure heart rate on adverse clinical outcomes in patients undergoing percutaneous coronary intervention: Results from a 2-year follow-up of the GLOBAL LEADERS trial

et al. 2020

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Section: Discussionsupporting

confidence: 85%

“…To our knowledge, this is the first time that the relationship between heart rate and adverse outcomes has been investigated in a larger, multicenter, prospective, contemporary, all-comers PCI trial. Supplementary Table S4 shows the characteristics and results of the present study and previous ones [10,18,19].…”

Section: Discussionmentioning

confidence: 98%

The impact of pre-procedure heart rate on adverse clinical outcomes in patients undergoing percutaneous coronary intervention: Results from a 2-year follow-up of the GLOBAL LEADERS trial

et al. 2020

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“…Glucose levels were ranked by all ML models, supporting the relationship between hyperglycemia and increased risk in mortality for patients with STEMI in the Asian population [ 43 ]. STEMI patients with higher heart rates were associated with an increased risk of mortality, even after primary PCI [ 44 ]. This may be a reflection of worse presentation (higher Killip class) or even higher pain intensity from a larger infarct.…”

Section: Discussionmentioning

confidence: 99%

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

et al. 2021

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Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

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“…The details of the design have been published previously. 17,18 In the present study, all the participants were aged 18 years and above and hospitalized with NSTE-ACS, including NSTEMI or unstable angina. The flow chart of the study is shown in ►Fig.…”

Section: Study Design and Populationmentioning

confidence: 99%

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

et al. 2020

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Objective In the present study, we aimed to establish a novel score to predict long-term mortality of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI). Methods A total of 2,174 NSTE-ACS patients from the CORFCHD-ZZ study were enrolled as the derivation cohort. The validation cohort including 1,808 NSTE-ACS patients were from the CORFCHD-PCI study. Receiver operating characteristic analysis and area under the curve (AUC) evaluation were used to select the candidate variables. The model performance was validated internally and externally. The primary outcome was cardiac mortality (CM). We also explored the model performance for all-cause mortality (ACM). Results Initially, 28 risk factors were selected and ranked according to their AUC values. Finally, we selected age, N-terminal pro-B-type natriuretic peptide, and creatinine to develop a novel prediction model named “ABC” model. The ABC model had a high discriminatory ability for both CM (C-index: 0.774, p < 0.001) and ACM (C-index: 0.758, p < 0.001) in the derivation cohort. In the validation cohort, the C-index of CM was 0.802 (p < 0.001) and that of ACM was 0.797 (p < 0.001), which suggested good discrimination. In addition, this model had adequate calibration in both the derivation and validation cohorts. Furthermore, the ABC score outperformed the GRACE score to predict mortality in NSTE-ACS patients who underwent PCI. Conclusion In the present study, we developed and validated a novel model to predict mortality in patients with NSTE-ACS who underwent PCI. This model can be used as a credible tool for risk assessment and management of NSTE-ACS after PCI.

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Resting Heart Rate and Long-Term Outcomes in Patients with Percutaneous Coronary Intervention: Results from a 10-Year Follow-Up of the CORFCHD-PCI Study

Cited by 13 publications

References 18 publications

The impact of pre-procedure heart rate on adverse clinical outcomes in patients undergoing percutaneous coronary intervention: Results from a 2-year follow-up of the GLOBAL LEADERS trial

The impact of pre-procedure heart rate on adverse clinical outcomes in patients undergoing percutaneous coronary intervention: Results from a 2-year follow-up of the GLOBAL LEADERS trial

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

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

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