Risk Assessment of Patients After ST-Segment Elevation Myocardial Infarction by Killip Classification: An Institutional Experience

Hashmi, Kashif Ali; Adnan, Fahar; Ahmed, Omer; Yaqeen, Syed Rafay; Ali, Javaria; Irfan, Muhammad; Edhi, Muhammad Muzzammil; Hashmi, Atif Ali

doi:10.7759/cureus.12209

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…As seen from Table 2, there are differences between NDA results and correlation methods. As it can be seen from Table 2 Killip class has the highest effect, which is compositable with other studies [73]. LDH has a high effect on mortality, which is compatible with other studies [74].…”

Section: In-hospital Mortality Patients With St-segment-elevation Myo...supporting

confidence: 90%

Numerical Dependency Analysis (NDA): A new method for estimating the statistical dependence (not correlation) of two variables

Zanghaei,

Doosti,

Ameri

et al. 2024

Preprint

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Dependence and correlation are different statistical concepts. Although there are methods to measure linear or nonlinear correlation between two variables, measuring the statistical dependence between two variables is of great interest. The main contribution of this paper is to present a heuristic algorithmic method to estimate the measure of dependence between two variables. This method first transforms the X-Y scatter plot into functional relation plots. This procedure may have many answers for big data. Then measures the dependence of Y on X by using the new concept of successive triangles. The desirable features of this method are: This method can be applied to both numerical and categorical (nominal) variables. The presented bivariate method is distribution-free, so it can be used for non-Gaussian numerical variables. As an application of this method, it can also be used to measure the correlation. This novel and non-parametric method is validated by both simulated and clinical data. This method has other applications, such as template matching for single-dimensional patterns.

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Section: In-hospital Mortality Patients With St-segment-elevation Myo...supporting

confidence: 90%

Numerical Dependency Analysis (NDA): A new method for estimating the statistical dependence (not correlation) of two variables

Zanghaei,

Doosti,

Ameri

et al. 2024

Preprint

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“…Class I is defined as patients without any clinical sign of heart failure, class II refers to patients with crackles or rales in the lungs, class III is defined as patients with evident acute pulmonary edema, and class IV refers to patients with cardiogenic shock or hypotension. 47 Killip classes were used in nine (40.90%) studies.…”

Section: Resultsmentioning

confidence: 99%

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

et al. 2022

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Background: Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. Objective: The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. Methods: In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. Results: According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. Conclusion: Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome. Keywords: Machine learning, Mortality, Acute coronary syndrome, Myocardial infarction, Prediction, Major adverse cardiac event.

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“…Inclusion criteria were patients aged >18 years of both sexes and undergoing primary PCI for acute STEMI. (9) . GRACE score: It is useful for guiding clinical decisions, such as the urgency of interventions and the intensity of monitoring and medical therapy.…”

Section: Methodsmentioning

confidence: 99%

Association of Syntax Score with Short-Term Outcomes among Acute ST Elevation Myocardial Infarction Patients Undergoing Primary PCI

Khalil,

Ammar,

Tabl

et al. 2024

Benha Medical Journal

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Background: Acute ST-elevation myocardial infarction (STEMI) demands rapid intervention, with primary percutaneous coronary intervention (PCI) playing a pivotal role. While existing risk models focus on clinical factors, the SYNTAX score assesses coronary lesion complexity. This study aimed to assess SYNTAX scores' association with short-term outcomes in STEMI patients undergoing primary PCI and compare it with SYNTAX Score II. Methods: This prospective observational study was conducted on 110 Egyptian patients diagnosed with STEMI undergoing primary PCI. Patients were evaluated based on SYNTAX scores and divided into low (≤16), intermediate (16)(17)(18)(19)(20)(21)(22), and high (>22) SYNTAX score groups. Short-term outcomes including all-cause mortality, cardiac mortality, reinfarction, and revascularization were assessed over a 1-year follow-up period. Results: Patients with higher SYNTAX scores (>31) exhibited significantly lower ejection fractions (p<0.001), longer duration of chest pain (p<0.05), higher GRACE scores (p<0.05), and increased rates of failed PCI (p=0.036) and multivessel disease (p<0.01). In-hospital mortality, all-cause mortality, and major adverse cardiac and cerebrovascular events (MACCE) during follow-up were significantly higher in patients with higher SYNTAX scores (p<0.005). Survival rates were significantly lower in patients with higher SYNTAX scores compared to lower scores (p<0.005). Conclusion: The SYNTAX score, along with its derivatives SYNTAX Score II (SSII) and Clinical SYNTAX Score (cSS), demonstrated a significant association with short-term outcomes in STEMI patients undergoing primary PCI. Higher SYNTAX scores correlated with increased rates of adverse events, emphasizing its potential utility in risk stratification.

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Risk Assessment of Patients After ST-Segment Elevation Myocardial Infarction by Killip Classification: An Institutional Experience

Cited by 9 publications

References 18 publications

Numerical Dependency Analysis (NDA): A new method for estimating the statistical dependence (not correlation) of two variables

Numerical Dependency Analysis (NDA): A new method for estimating the statistical dependence (not correlation) of two variables

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

Association of Syntax Score with Short-Term Outcomes among Acute ST Elevation Myocardial Infarction Patients Undergoing Primary PCI

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