Dataset Information

Risk Factors and Outcomes of Acute Myocardial Infarction in a Cohort of Antiphospholipid Syndrome.

ABSTRACT:

Background

Antiphospholipid syndrome (APS) is a disorder associated with thromboembolic diseases, including acute myocardial infarction (AMI). Given that AMI is a relatively common condition with poor prognostic features, identification of risk factors for AMI in APS is important.

Methods

A retrospective cohort study was performed consisting of 332 patients with APS, and 239 patients with thrombotic APS were finally included. Patients were followed up in the outpatient department for 5 years. Clinical data and laboratory parameters were analyzed to identify the risk factors for AMI in APS. The primary and secondary clinical outcomes were all-cause mortality and recurrence of thrombosis, respectively.

Results

AMI was observed in 12.1% (29/239) of patients with APS. Compared to patients without AMI, patients with AMI had multiple organ thrombosis (55.1 vs. 34.3%, p = 0.029), recurrent thrombosis (58.6 vs. 34.3%, p = 0.011), a higher incidence of atherosclerosis (62.1 vs. 23.8%, p < 0.001), higher neutrophil count (×10⁹/L) [4.68 (3.25, 8.17) vs. 3.71 (2.64, 5.80), p = 0.036], longer QT interval (ms) [438 ms (423, 454) vs. 425 ms (410, 446), p = 0.016], and fewer venous thrombosis events (27.6 vs. 63.3%, p < 0.001). Multivariate logistic regression analysis (adjusted for age and gender) identified several factors that were positively associated with AMI, including multiple organ thrombosis [odds ratio (OR) 8.862, 95% confidence interval (CI): 1.817-43.212, p = 0.007), atherosclerosis (OR 5.397, 95%CI: 1.943-14.994, p = 0.001), and elevated neutrophil count (>6.3 ×10⁹/L) (OR 3.271, 95%CI: 1.268-8.440, p = 0.014). The venous thrombosis was negatively associated with AMI (OR 0.106, 95%CI: 0.036-0.314, p < 0.001). Kaplan-Meier analysis revealed that the recurrence rates of arterial thrombosis differed significantly between patients with AMI and those without AMI [hazard ratio (HR) = 3.307, p = 0.038].

Conclusion

Atherosclerosis, multiple organ thrombosis, an increased number of neutrophils are variables positively associated with AMI in APS, and venous thrombosis had a negative association with AMI. AMI only predicts the subsequent recurrence of arterial thrombosis. These findings suggest that distinct pathophysiological mechanisms may exist and contribute to the development of venous or arterial thrombotic APS.

SUBMITTER: Gan Y

PROVIDER: S-EPMC9294316 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk Factors and Outcomes of Acute Myocardial Infarction in a Cohort of Antiphospholipid Syndrome.

Gan Yuzhou Y Zhao Yawei Y Li Gongming G Ye Hua H Zhou Yunshan Y Hou Chang C Wang Lan L Guo Jianping J Li Chun C

Frontiers in cardiovascular medicine 20220705

<h4>Background</h4>Antiphospholipid syndrome (APS) is a disorder associated with thromboembolic diseases, including acute myocardial infarction (AMI). Given that AMI is a relatively common condition with poor prognostic features, identification of risk factors for AMI in APS is important.<h4>Methods</h4>A retrospective cohort study was performed consisting of 332 patients with APS, and 239 patients with thrombotic APS were finally included. Patients were followed up in the outpatient department ...[more]

PMID: 35865377

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Project description:ImportanceStudies of acute myocardial infarction (AMI) occurring outside the hospital have informed approaches to addressing risk, treatment, and patient outcomes. Similar insights for in-hospital AMI are lacking.ObjectiveTo determine the incidence, risk factors, and outcomes associated with in-hospital AMI.Design, setting, and participantsCohort, nested case-control, and matched cohort study of patients hospitalized in US Veterans Health Administration facilities between July 2007 and September 2009. The incidence of in-hospital AMI was determined from a complete cohort of in-hospital AMI relative to the total number of inpatient admissions. From the in-hospital AMI cohort, detailed medical record review was performed on 687 cases and 687 individually matched controls. Risk factors and outcomes associated with in-hospital AMI were determined from matched comparison of in-hospital AMI cases to hospitalized controls.ExposuresCandidate risk factors for in-hospital AMI included characteristics at the time of admission and in-hospital variables prior to the index date.Main outcomes and measuresIn the determination of the incidence and risk factors associated with in-hospital AMI, the outcome of interest was in-hospital AMI. All-cause mortality was the main outcome of interest following in-hospital AMI.ResultsA total of 5556 patients with in-hospital AMI (mean [SD] age, 73 [10] years; 5456 [98.2%] male) were identified among 1.3 million admissions, with an incidence of 4.27 in-hospital AMI events per 1000 admissions. Independent risk factors associated with in-hospital AMI included intensive care unit setting, history of coronary artery disease, heart rate greater than 100 beats/min, hemoglobin level less than 8 g/dL, and white blood cell count 14 000/μL or greater. Compared with the matched control group, mortality was significantly higher for patients with in-hospital AMI (in-hospital mortality, 26.4% vs 4.2%; 30-day mortality, 33.0% vs 10.0%; 1-year mortality, 59.2% vs 34.4%).Conclusions and relevanceIn-hospital AMI was common and associated with common cardiovascular risk factors and markers of acute illness. Patient outcomes following in-hospital AMI were poor, with 1-year mortality approaching 60%. Further study of in-hospital AMI may yield opportunities to reduce in-hospital AMI risk and improve patient outcomes.

Project description:BackgroundGastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.ObjectiveThis study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.MethodsA multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms-logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks-were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables.ResultsThe RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups.ConclusionsThe ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies.

Project description:BackgroundUnderstanding the trajectories of metabolic risk factors for acute myocardial infarction (AMI) is necessary for healthcare policymaking. We estimated future projections of the incidence of metabolic diseases in a multi-ethnic population with AMI.MethodsThe incidence and mortality contributed by metabolic risk factors in the population with AMI (diabetes mellitus [T2DM], hypertension, hyperlipidemia, overweight/obesity, active/previous smokers) were projected up to year 2050, using linear and Poisson regression models based on the Singapore Myocardial Infarction Registry from 2007 to 2018. Forecast analysis was stratified based on age, sex and ethnicity.FindingsFrom 2025 to 2050, the incidence of AMI is predicted to rise by 194.4% from 482 to 1418 per 100,000 population. The largest percentage increase in metabolic risk factors within the population with AMI is projected to be overweight/obesity (880.0% increase), followed by hypertension (248.7% increase), T2DM (215.7% increase), hyperlipidemia (205.0% increase), and active/previous smoking (164.8% increase). The number of AMI-related deaths is expected to increase by 294.7% in individuals with overweight/obesity, while mortality is predicted to decrease by 11.7% in hyperlipidemia, 29.9% in hypertension, 32.7% in T2DM and 49.6% in active/previous smokers, from 2025 to 2050. Compared with Chinese individuals, Indian and Malay individuals bear a disproportionate burden of overweight/obesity incidence and AMI-related mortality.InterpretationThe incidence of AMI is projected to continue rising in the coming decades. Overweight/obesity will emerge as fastest-growing metabolic risk factor and the leading risk factor for AMI-related mortality.FundingThis research was supported by the NUHS Seed Fund (NUHSRO/2022/058/RO5+6/Seed-Mar/03) and National Medical Research Council Research Training Fellowship (MOH-001131). The SMIR is a national, ministry-funded registry run by the National Registry of Diseases Office and funded by the Ministry of Health, Singapore.

Dataset Information

Risk Factors and Outcomes of Acute Myocardial Infarction in a Cohort of Antiphospholipid Syndrome.

Background

Methods

Results

Conclusion

Publications

Risk Factors and Outcomes of Acute Myocardial Infarction in a Cohort of Antiphospholipid Syndrome.

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