Dataset Information

Development and Validation of a Nomogram of In-hospital Major Adverse Cardiovascular and Cerebrovascular Events in Patients With Acute Coronary Syndrome.

ABSTRACT: Background and Objective: This study aims to develop and validate a nomogram for the occurrence of in-hospital major adverse cardiovascular and cerebrovascular events (MACCE) in acute coronary syndrome (ACS) patients. Methods: A total of 1,360 ACS patients admitted between November 2014 and October 2019 from Zhongda Hospital and Yancheng Third People's Hospital were included. Patients admitted in Zhongda Hospital before 2018 were split into the training cohort (n = 793). Those admitted after 2018 in Zhongda Hospital and patients from Yancheng Third People's Hospital were split into the validation cohort (n = 567). Twenty eight clinical features routinely assessed including baseline characteristics, past medical history and auxiliary examinations were used to inform the models to predict in-hospital MACCE (all-cause mortality, reinfarction, stroke, and heart failure) in ACS patients. The best-performing model was tested in the validation cohort. The accuracy and clinical applicability were tested by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses (DCA). Results: The in-hospital MACCE occurred in 93 (6.83%) patients. The final prediction model consists of four variables: age, Killip grading, fasting blood-glucose (FBG) and whether percutaneous coronary intervention (PCI) was performed at early stage. A nomogram was used to present the final result. Individualized nomogram exhibited comparable discrimination to the Global Registry of Acute Coronary Events (GRACE) score [AUC: 0.807 (95% CI 0.736-0.878) vs. 0.761 (95% CI 0.69-0.878)], P = 0.10) and a better discrimination than the Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) score [AUC: 0.807 (95% CI 0.736-0.878) vs. 0.723(95% CI 0.648-0.798), P = 0.01] in predicting the risk of in-hospital MACCE in ACS patients. A good prediction performance was maintained in the validation cohort (AUC =0.813, 95% CI 0.738-0.889). The prediction model also exhibited decent calibration (P = 0.972) and clinical usefulness. Conclusion: The nomogram may be a simple and effective tool in predicting the occurrence of in-hospital MACCE in ACS patients. Further longitudinal studies are warranted to validate its value in guiding clinical decision-making and optimizing the treatment of high-risk patients.

SUBMITTER: Bo X

PROVIDER: S-EPMC8380764 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Importance:Major adverse cardiovascular and cerebrovascular events (MACCE) are a significant source of perioperative morbidity and mortality following noncardiac surgery. Objective:To evaluate national trends in perioperative cardiovascular outcomes and mortality after major noncardiac surgery and to identify surgical subtypes associated with cardiovascular events using a large administrative database of United States hospital admissions. Design, Setting, Participants:Patients who underwent major noncardiac surgery from January 2004 to December 2013 were identified using the National Inpatient Sample. Main Outcomes and Measures:Perioperative MACCE (primary outcome), defined as in-hospital, all-cause death, acute myocardial infarction (AMI), or acute ischemic stroke, were evaluated over time. Results:Among 10?581?621 hospitalizations (mean [SD] patient age, 65.74 [12.32] years; 5?975?798 female patients 56.60%]) for major noncardiac surgery, perioperative MACCE occurred in 317?479 hospitalizations (3.0%), corresponding to an annual incidence of approximately 150?000 events after applying sample weights. Major adverse cardiovascular and cerebrovascular events occurred most frequently in patients undergoing vascular (7.7%), thoracic (6.5%), and transplant surgery (6.3%). Between 2004 and 2013, the frequency of MACCE declined from 3.1% to 2.6% (P for trend <.001; adjusted odds ratio [aOR], 0.95; 95% CI, 0.94-0.97) driven by a decline in frequency of perioperative death (aOR, 0.79; 95% CI, 0.77-0.81) and AMI (aOR, 0.87; 95% CI, 0.84-0.89) but an increase in perioperative ischemic stroke from 0.52% in 2004 to 0.77% in 2013 (P for trend <.001; aOR 1.79; CI 1.73-1.86). Conclusions and Relevance:Perioperative MACCE occurs in 1 of every 33 hospitalizations for noncardiac surgery. Despite reductions in the rate of death and AMI among patients undergoing major noncardiac surgery in the United States, perioperative ischemic stroke increased over time. Additional efforts are necessary to improve cardiovascular care in the perioperative period of patients undergoing noncardiac surgery.

Project description:BackgroundAcute 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.ObjectiveThe 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.MethodsTo predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. In doing so, PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The checklist "Quality assessment of machine learning studies" was used to assess the quality of eligible studies. The findings of the studies are presented in the form of a narrative synthesis of evidence.ResultsIn total, among 2,558 retrieved articles, 22 studies were qualified for analysis. Major adverse cardiovascular events and mortality were predicted in 5 and 17 studies, respectively. 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 and 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.ConclusionMachine 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.

Project description:Background & aimsHepatic steatosis has been associated with increased risk of major adverse cardiovascular events (MACE) but it is not clear whether steatosis is independently associated with risk of MACE. We investigated whether steatosis is associated with risk of MACE independently of the presence and extent of baseline coronary artery disease, assessed by comprehensive contrast-enhanced computed tomography angiography (CTA).MethodsWe conducted a nested cohort study of 3756 subjects (mean age, 60.6 years; 48.4% men) who underwent coronary CTA at 193 sites in North America, from July 2010 through September 2013, as part of the PROMISE study, which included noninvasive cardiovascular analyses of symptomatic outpatients without coronary artery disease. Independent core laboratory readers measured hepatic and splenic attenuation, using non-contrast computed tomography images to identify steatosis, and evaluated coronary plaques and stenosis in coronary CTA images. We collected data on participants' cardiovascular risk factors, presence of metabolic syndrome, and body mass index. The primary endpoint was an adjudicated composite of MACE (death, myocardial infarction, or unstable angina) during a median follow-up time of 25 months.ResultsAmong the 959 subjects who had steatosis (25.5% of the cohort), 42 had MACE (4.4%), whereas among the 2797 subjects without steatosis, 73 had MACE (2.6%) (hazard ratio [HR] for MACE in subjects with steatosis, 1.69; 95% CI, 1.16-2.48; P = .006 for MACE in subjects with vs without steatosis). This association remained after adjustment for atherosclerotic cardiovascular disease risk scores, significant stenosis, and metabolic syndrome (adjusted HR, 1.72; 95% CI, 1.16-2.54; P = .007) or obesity (adjusted HR, 1.75; 95% CI, 1.19-2.59; P = .005). Steatosis remained independently associated with MACE after adjustment for all CTA measures of plaques and stenosis.ConclusionsHepatic steatosis is associated with MACE independently of other cardiovascular risk factors or extent of coronary artery disease. Strategies to reduce steatosis might reduce risk of MACE. ClinicalTrials.gov no: NCT01174550.

Dataset Information

Development and Validation of a Nomogram of In-hospital Major Adverse Cardiovascular and Cerebrovascular Events in Patients With Acute Coronary Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets