scholarly journals TCT-391 Comparision of CHAD2DS2VASc score vs GRACE score predicting value in the long term all-cause mortality in patients with st elevation myocardial infarction who undergoing primary percutaneous coronary

2017 ◽  
Vol 70 (18) ◽  
pp. B160
Author(s):  
Alfredo Redondo Diéguez ◽  
Ramiro Trillo Nouche ◽  
Alejandro Avila Carrillo ◽  
Diego Lopez Otero
2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
G Pessoa Amorim ◽  
D Santos-Ferreira ◽  
A Azul Freitas ◽  
H Santos ◽  
A Belo ◽  
...  

Abstract Introduction Frailty is common among patients presenting with acute myocardial infarction (MI), who have conflicting risks regarding benefits and harms of invasive procedures. Purpose To assess the clinical management and prognostic impact of invasive procedures in frail MI patients in a real-world scenario. Methods We analysed 5422 episodes of ST-elevation MI (STEMI) and 6692 of Non-ST-elevation MI (NSTEMI) recorded from 2010–2019 in a nationwide registry. A validated deficit-accumulation model was used to create a frailty index (FI), comprising 22 features [BMI >25kg/m2, myocardial infarction, angina, heart failure, percutaneous coronary intervention (PCI), coronary artery bypass graft surgery (CABG), valvular disease, bleeding, pacemaker/implantable cardioverter defibrillator, chronic kidney disease (creatinine >2.0mg/dL), dialysis/renal transplant, stroke/transient ischaemic attack, diabetes, hypertension, dyslipidaemia, smoking, peripheral vascular disease, dementia, chronic lung disease, malignancy, polymedication (>3 cardiovascular drugs), admission haemoglobin <10g/dL; not including age]. Episodes with missing data on any FI parameter were not included. Frailty was initially defined as FI>0.25 (i.e. ≥6 features). Results Overall, 511 (9.4%) STEMI and 1763 (26.4%) NSTEMI patients were considered frail. Angiography, PCI and CABG were less frequently performed in frail patients (p<0.001). Delayed angiography (>72h) was more common among NSTEMI frail patients (p<0.001), and radial access was less commonly used overall (p<0.001). Guideline-recommended in-hospital medical therapy, including aspirin (NSTEMI), dual-antiplatelet therapy (STEMI/NSTEMI), heparin/heparin-related agents (NSTEMI), beta-blockers (STEMI) and ACEIs/ARBs (STEMI), was less commonly used in frail patients; discharge medical therapy exhibited similar patterns. Frail patients had longer hospital stay and increased in-hospital all-cause and cardiovascular (CV) mortality, as well as 1-year all-cause and CV hospitalization and all-cause mortality (p<0.001). Using receiver-operator-characteristics curve analysis, FI cutoffs of 0.11 (STEMI) and 0.20 (NSTEMI) yielded the best accuracy to predict 1-year all-cause mortality (area under the curve: 0.629 and 0.702 respectively, p<0.001) – these cutoffs were subsequently used to define frailty. Although frailty attenuated in-hospital risk reductions from angiography (STEMI/NSTEMI) and PCI (NSTEMI only) (Wald test p<0.05), their 1-year prognostic benefit remained unaffected (Wald test p>0.05). Angiography and PCI were associated with improved in-hospital and 1-year outcomes, independently of frailty status or GRACE score (p<0.001). Conclusion Frail MI patients are less commonly offered standard therapy; however, angiography and PCI were associated with short- and long-term prognostic benefits regardless of frailty status or GRACE score. Increased adherence to current recommendations might improve post-MI outcomes in frail patients. Invasive strategy and 1-year outcomes Funding Acknowledgement Type of funding source: Other. Main funding source(s): Portuguese Society of Cardiology


Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pradyumna Agasthi ◽  
Hasan Ashraf ◽  
Chieh-Ju Chao ◽  
Panwen Wang ◽  
Mohamed Allam ◽  
...  

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (p<0.0001) ( Figure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.


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