Dataset Information

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

ABSTRACT:

Background

Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms.

Methods

A total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015-2018. Unsupervised machine learning (consensus clustering) was applied to the present cohort to classify patients into different lipid expression phenogroups, without the guidance of clinical outcomes. Kaplan-Meier curves were implemented to show prognosis during a 904-day median follow-up (interquartile range: 587-1316). In the adjusted Cox model, the association of cluster membership with all adverse events including all-cause mortality, all-cause rehospitalization, and cardiac rehospitalization was evaluated.

Results

All patients were classified into three phenogroups, 1, 2, and 3. Patients in phenogroup 1 with the highest Lp(a) and the lowest HDL-C and apoA1 were recognized as the statin-modified cardiovascular risk group. Patients in phenogroup 2 had the highest HDL-C and apoA1 and the lowest TG, TC, LDL-C and apoB. Conversely, patients in phenogroup 3 had the highest TG, TC, LDL-C and apoB and the lowest Lp(a). Additionally, phenogroup 1 had the worst prognosis. Furthermore, a multivariate Cox analysis revealed that patients in phenogroup 1 were at significantly higher risk for all adverse outcomes.

Conclusion

Machine learning-based cluster analysis indicated that STEMI patients with increased concentrations of Lp(a) and decreased concentrations of HDL-C and apoA1 are likely to have adverse clinical outcomes due to statin-modified cardiovascular risks.

Trial registration

ChiCTR1900028516 ( http://www.chictr.org.cn/index.aspx ).

SUBMITTER: Xue Y

PROVIDER: S-EPMC8101132 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Publications

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Xue Yuzhou Y Shen Jian J Hong Weifeng W Zhou Wei W Xiang Zhenxian Z Zhu Yuansong Y Huang Chuiguo C Luo Suxin S

Lipids in health and disease 20210506 1

<h4>Background</h4>Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms.<h4>Methods</h4>A total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015-2018. Unsupervised machine learning (consensus cl ...[more]

PMID: 33957898

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Project description:ImportanceChallenges to improving ST-segment elevation myocardial infarction (STEMI) care are formidable in low- to middle-income countries because of several system-level factors.ObjectiveTo examine access to reperfusion and percutaneous coronary intervention (PCI) during STEMI using a hub-and-spoke model.Design, setting, and participantsThis multicenter, prospective, observational study of a quality improvement program studied 2420 patients 20 years or older with symptoms or signs consistent with STEMI at primary care clinics, small hospitals, and PCI hospitals in the southern state of Tamil Nadu in India. Data were collected from the 4 clusters before implementation of the program (preimplementation data). We required a minimum of 12 weeks for the preimplementation data with the period extending from August 7, 2012, through January 5, 2013. The program was then implemented in a sequential manner across the 4 clusters, and data were collected in the same manner (postimplementation data) from June 12, 2013, through June 24, 2014, for a mean 32-week period.ExposuresCreation of an integrated, regional quality improvement program that linked the 35 spoke health care centers to the 4 large PCI hub hospitals and leveraged recent developments in public health insurance schemes, emergency medical services, and health information technology.Main outcomes and measuresPrimary outcomes focused on the proportion of patients undergoing reperfusion, timely reperfusion, and postfibrinolysis angiography and PCI. Secondary outcomes were in-hospital and 1-year mortality.ResultsA total of 2420 patients with STEMI (2034 men [84.0%] and 386 women [16.0%]; mean [SD] age, 54.7 [12.2] years) (898 in the preimplementation phase and 1522 in the postimplementation phase) were enrolled, with 1053 patients (43.5%) from the spoke health care centers. Missing data were common for systolic blood pressure (213 [8.8%]), heart rate (223 [9.2%]), and anterior MI location (279 [11.5%]). Overall reperfusion use and times to reperfusion were similar (795 [88.5%] vs 1372 [90.1%]; P = .21). Coronary angiography (314 [35.0%] vs 925 [60.8%]; P < .001) and PCI (265 [29.5%] vs 707 [46.5%]; P < .001) were more commonly performed during the postimplementation phase. In-hospital mortality was not different (52 [5.8%] vs 85 [5.6%]; P = .83), but 1-year mortality was lower in the postimplementation phase (134 [17.6%] vs 179 [14.2%]; P = .04), and this difference remained consistent after multivariable adjustment (adjusted odds ratio, 0.76; 95% CI, 0.58-0.98; P = .04).Conclusions and relevanceA hub-and-spoke model in South India improved STEMI care through greater use of PCI and may improve 1-year mortality. This model may serve as an example for developing STEMI systems of care in other low- to middle-income countries.

Project description:BackgroundST-segment elevation myocardial infarction (STEMI) is a clinically distinguishable yet lethal sequela of ischaemic heart disease (IHD). In sub-Saharan Africa (SSA), death due to acute STEMI is increasing. In South Africa, there is a paucity of data available on the clinical outcomes of acute STEMI within one year for individuals treated in the public healthcare sector. This study primarily seeks to determine the one-year all-cause mortality rate of acute STEMI. The study also assesses the value of serum cardiac biomarkers of myocardial damage and serum uric acid in predicting all-cause mortality in STEMI.MethodsThis is a single-centre observational prospective cohort of all consecutive individuals presenting with an acute STEMI to the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) in Johannesburg, South Africa. Research data will be sourced on admission through electronic medical records, blood laboratory results and coronary angiography reports, and at follow-up through periodic telephonic interviews and standardised echocardiograms. At least 355 eligible participants will be continuously followed over one year, and clinical outcomes will be measured 30 days, three months, six months and one year after the index hospitalisation.DiscussionThis study provides insights into the demographic, risk factors and clinical profiles of individuals with STEMI in South Africa. Its findings may improve the risk stratification, prognostication, and therapeutic management of STEMI patients in our setting. By comparing the clinical outcomes between the different coronary reperfusion strategies, our results may guide clinicians in providing better patient treatment, particularly in sub-Saharan Africa, where access to percutaneous coronary intervention may be limited. Furthermore, the study offers insights into the routine use of baseline serum uric acid as a potential low-cost prognostic biomarker of all-cause mortality in STEMI. Finally, this study's findings may be of public health significance to local policymakers to aid in reinforcing primary prevention strategies and developing structured referral networks for timely coronary reperfusion of acute STEMI.

Dataset Information

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Background

Methods

Results

Conclusion

Trial registration

Publications

Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Similar Datasets

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