Association between admission systolic blood pressure and major adverse cardiovascular events in patients with acute myocardial infarction

Zhang

et al. 2023

BMC Cardiovasc Disord

Background Patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) are at high risk of major adverse cardiovascular events (MACE) despite timely treatment. This study aimed to investigate the independent predictors and their predictive value of in-hospital MACE after primary PCI in patients with acute STEMI under the China chest pain center (standard center) treatment system. Methods We performed a single-center, retrospective study of 151 patients with acute STEMI undergoing primary PCI. All patients were treated under the China chest pain center (standard center) treatment system. The data collected included general data, vital signs, auxiliary examination results, data related to interventional therapy, and various treatment delays. The primary endpoint was the in-hospital MACE defined as the composite of all-cause death, stroke, nonfatal recurrent myocardial infarction, new-onset heart failure, and malignant arrhythmias. Results In-hospital MACE occurred in 71 of 151 patients with acute STEMI undergoing primary PCI. Logistic regression analysis showed that age, cardiac troponin I (cTnI), serum creatinine (sCr), multivessel coronary artery disease, and Killip class III/IV were risk factors for in-hospital MACE, whereas estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), systolic blood pressure (SBP), diastolic blood pressure (DBP), were protective factors, with eGFR, LVEF, cTnI, SBP, and Killip class III/IV being independent predictors of in-hospital MACE. The prediction model had good discrimination with an area under the curve = 0. 778 (95%CI: 0.690–0.865). Good calibration and clinical utility were observed through the calibration and decision curves, respectively. Conclusions Our data suggest that eGFR, LVEF, cTnI, SBP, and Killip class III/IV independently predict in-hospital MACE after primary PCI in patients with acute STEMI, and the prediction model constructed based on the above factors could be useful for individual risk assessment and early management guidance.

Section: Discussionmentioning

confidence: 99%

In-hospital major adverse cardiovascular events after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction: a retrospective study under the China chest pain center (standard center) treatment system

Zhang

et al. 2023

BMC Cardiovasc Disord

“…Acute myocardial infarction (AMI) is the death or necrosis of myocardial cells caused by the occlusion of a coronary artery and is the most serious subtype of coronary heart disease [ 1 ]. In recent years, as AMI treatment options have improved, inpatient mortality rates have declined year on year, ranging from 2.5 to 8% [ 2 – 4 ].…”

Section: Introductionmentioning

confidence: 99%

A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction

Song

Yan

et al. 2022

BMC Cardiovasc Disord

Background Early risk stratification is important for patients with acute myocardial infarction (AMI). We aimed to develop a simple APACHE IV dynamic nomogram, combined with easily available clinical parameters within 24 h of admission, thus improving its predictive power to assess the risk of mortality at 28 days. Methods Clinical information on AMI patients was extracted from the eICU database v2.0. A preliminary XGBoost examination of the degree of association between all variables in the database and 28-day mortality was conducted. Univariate and multivariate logistic regression analysis were used to perform screening of variables. Based on the multifactorial analysis, a dynamic nomogram predicting 28-day mortality in these patients was developed. To cope with missing data in records with missing variables, we applied the multiple imputation method. Predictive models are evaluated in three main areas, namely discrimination, calibration, and clinical validity. The discrimination is mainly represented by the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Calibration is represented by the calibration plot. Clinical validity is represented by the decision curve analysis (DCA) curve. Results A total of 504 people were included in the study. All 504 people were used to build the predictive model, and the internal validation model used a 500-bootstrap method. Multivariate analysis showed that four variables, APACHE IV, the first sample of admission lactate, prior atrial fibrillation (AF), and gender, were included in the nomogram as independent predictors of 28-day mortality in AMI. The prediction model had an AUC of 0.819 (95%CI 0.770–0.868) whereas the internal validation model had an AUC of 0.814 (95%CI 0.765–0.860). Calibration and DCA curves indicated that the dynamic nomogram in this study were reflective of real-world conditions and could be applied clinically. The predictive model composed of these four variables outperformed a single APACHE IV in terms of NRI and IDI. The NRI was 16.4% (95% CI: 6.1–26.8%; p = 0.0019) and the IDI was 16.4% (95% CI: 6.0–26.8%; p = 0.0020). Lactate accounted for nearly half of the total NRI, which showed that lactate was the most important of the other three variables. Conclusion The prediction model constructed by APACHE IV in combination with the first sample of admission lactate, prior AF, and gender outperformed the APACHE IV scoring system alone in predicting 28-day mortality in AMI. The prediction dynamic nomogram model was published via a website app, allowing clinicians to improve the predictive efficacy of the APACHE IV score by 16.4% in less than 1 min.

“…It is the most serious subtype of coronary heart disease. 1 According to NHANES data from 2013 to 2016, the overall prevalence of AMI among adults over 20 years old in the United States is 3.0%, and an American will suffer from AMI approximately every 40 s. 2 Moreover, the prognosis of AMI is poor, with a 5-year mortality rate as high as 51%, thereby heavily placing a huge health and socioeconomic burden. 3 Thus, risk factors of or models that can identify high-risk patients must be identified and developed to improve the prognosis of patients as timely and advanced interventions can administered.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

Tang¹,

Chen²,

Zha³

et al. 2021

IJGM

Purpose: Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the longterm survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis. Patients and Methods: The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves. Results: A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765-0.813) and 0.762 (95% CI: 0.725-0.799), respectively. Conclusion:The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.