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Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome.

ABSTRACT: BACKGROUND AND OBJECTIVES:The risk of major adverse cardiac and cerebrovascular events following acute coronary syndrome is increased in people with diabetes. Predicting out-of-hospital outcomes upon follow-up remains difficult, and no simple, well-validated tools exist for this population at present. We aim to evaluate several factors in a competing risks model for actionable evaluation of the incidence of major adverse cardiac and cerebrovascular events in diabetic outpatients following acute coronary syndrome. METHODS:Retrospective analysis of consecutive patients admitted for acute coronary syndrome in two centres. A Fine-Gray competing risks model was adjusted to predict major adverse cardiac and cerebrovascular events and all-cause mortality. A point-based score is presented that is based on this model. RESULTS:Out of the 1400 patients, there were 783 (55.9%) with at least one major adverse cardiac and cerebrovascular event (417 deaths). Of them, 143 deaths were due to non-major adverse cardiac and cerebrovascular events. Predictive Fine-Gray models show that the 'PG-HACKER' risk factors (gender, age, peripheral arterial disease, left ventricle function, previous congestive heart failure, Killip class and optimal medical therapy) were associated to major adverse cardiac and cerebrovascular events. CONCLUSION:The PG-HACKER score is a simple and effective tool that is freely available and easily accessible to physicians and patients. The PG-HACKER score can predict major adverse cardiac and cerebrovascular events following acute coronary syndrome in patients with diabetes.

SUBMITTER: Baluja A

PROVIDER: S-EPMC7510367 | biostudies-literature | 2020 Jan-Feb

REPOSITORIES: biostudies-literature

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Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome.

Baluja Aurora A Rodríguez-Mañero Moisés M Cordero Alberto A Kreidieh Bahij B Iglesias-Alvarez Diego D García-Acuña Jose M JM Martínez-Gómez Alvaro A Agra-Bermejo Rosa R Alvarez-Rodríguez Leyre L Abou-Jokh Charigan C López-Ratón Mónica M Gude-Sampedro Francisco F Alvarez-Escudero Julián J González-Juanatey Jose R JR

Diabetes & vascular disease research 20191216 1

<h4>Background and objectives</h4>The risk of major adverse cardiac and cerebrovascular events following acute coronary syndrome is increased in people with diabetes. Predicting out-of-hospital outcomes upon follow-up remains difficult, and no simple, well-validated tools exist for this population at present. We aim to evaluate several factors in a competing risks model for actionable evaluation of the incidence of major adverse cardiac and cerebrovascular events in diabetic outpatients followin ...[more]

PMID: 31841030

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Project description: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.

Project description:ImportanceGuidelines currently recommend ticagrelor over clopidogrel for patients with acute coronary syndrome (ACS) based on randomized clinical trial data in which ticagrelor reduced major adverse coronary events (MACE) vs clopidogrel but increased bleeding and dyspnea.ObjectiveTo compare the risk of MACE with ticagrelor vs clopidogrel in patients with ACS treated with percutaneous coronary intervention (PCI), to compare major bleeding and dyspnea, and to evaluate the association between P2Y12 inhibitor adherence and MACE.Design, setting, and participantsPopulation-based cohort study using data of patients discharged alive after PCI for ACS from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry from April 1, 2012, to March 31, 2016, with follow-up to 1 year. Analysis began April 2018.ExposuresOutpatient prescription for ticagrelor or clopidogrel within 31 days after PCI. Adherence was defined as a medication refill adherence value of 80% or higher.Main outcomes and measuresMajor adverse coronary events, a composite of all-cause death, hospitalization for ACS, unplanned coronary revascularization, or stent thrombosis within 365 days after index PCI. Secondary outcomes included hospitalization for major bleeding and emergency department visit for dyspnea.ResultsOf 11 185 individuals who underwent PCI, the median (interquartile range) age was 61 (54-71) years, and 2760 (24.7%) were women. Ticagrelor users (4076 [36.4%]) were generally younger and had fewer cardiac and noncardiac comorbidities than clopidogrel users. Ticagrelor was not associated with lower risk of MACE (adjusted hazard ratio [aHR], 0.97; 95% CI, 0.85-1.10); however, it was associated with an increased risk of major bleeding (aHR, 1.51; 95% CI, 1.29-1.78) and dyspnea (aHR, 1.98; 95% CI, 1.47-2.65). A total of 3328 ticagrelor users (81.6%) were adherent during the study vs 5256 of clopidogrel users (73.9%) (P < .001; χ2 = 86.4). In the full cohort, adherence was associated with a lower MACE risk (aHR, 0.79; 95% CI, 0.69-0.90 for adherence of ≥80% vs <80%). Differences in other secondary outcomes were not statistically significant. Sensitivity and subgroup analyses were consistent with primary analyses.Conclusions and relevanceIn this population-based cohort study of patients with ACS who underwent PCI, outpatient use of ticagrelor was not associated with a statistically significant reduction in MACE vs clopidogrel; however, it was associated with more major bleeding and dyspnea.

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 The Healthy Aging Index (HAI) has been regarded as useful in capturing the health status of multiple organ systems. However, to what extent the HAI is associated with major cardiovascular events remains largely unknown. The authors constructed a modified HAI (mHAI) to quantify the association of physiological aging with major vascular events and explored how the effects of a healthy lifestyle can modify this association. Methods and Results The participants with either missing values of any individual mHAI component or major illnesses such as heart attack, angina and stroke, and self-reported cancer at baseline were excluded. The mHAI components include systolic blood pressure, reaction time, forced vital capacity, serum cystatin c, and serum glucose. The authors used Cox proportional hazard models to quantify the association of mHAI with major adverse cardiac events, major coronary events, and ischemic heart disease. Cumulative incidence at 5 and 10 years was estimated, and joint analyses were stratified by age group and 4 mHAI categories. The mHAI was significantly correlated with major cardiovascular events, which is a better reflection of the aging level of the body than chronological age. An mHAI was calculated in 338 044 participants aged 38 to 73 years in the UK Biobank. Each point increase in the mHAI was associated with a 44% higher risk of major adverse cardiac events (adjusted hazard ratio [aHR], 1.44 [95% CI, 1.40-1.49]), 44% higher risk of major coronary events (aHR, 1.44 [95% CI, 1.40-1.48]), and 36% higher risk of ischemic heart disease (aHR, 1.36 [95% CI, 1.33-1.39]). The percentage of population-attribution risk was 51% (95% CI, 47-55) for major adverse cardiac events, 49% (95% CI, 45-53) for major coronary events, and 47% (95% CI, 44-50) for ischemic heart disease, which means that a substantial portion of these events could be prevented. Systolic blood pressure was the factor most significantly associated with major adverse cardiac events (aHR, 1.94 [95% CI, 1.82-2.08]; percentage of population-attribution risk, 36%), major coronary events (aHR, 2.01 [95% CI, 1.85-2.17]; percentage of population-attribution risk, 38%), and ischemic heart disease (aHR, 1.80 [95% CI, 1.71-1.89]; percentage of population-attribution risk, 32%). A healthy lifestyle significantly attenuated mHAI associations with incidence of vascular events. Conclusions Our findings indicate that higher mHAI is associated with increased major vascular events. A healthy lifestyle may attenuate these associations.

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Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome.

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Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome.

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