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

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction

BACKGROUND

Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

METHODS

All adult discharges for STEMI in the National Inpatient Sample (October 2015 to December 2019) were included, excluding patients with prior myocardial infarction. Machine-learning clustering analysis was used to define clusters based on 21 clinical attributes of interest. Main outcomes of the study were cluster-based comparison of risk profile, in-hospital clinical outcomes and utilization of invasive management. Binomial hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) was used to detect the between-cluster differences.

RESULTS

Out of overall 470,960 STEMI cases, the machine-learning analysis revealed 4 different clusters with 205,640 (cluster 0: 'behavioural risk cluster'), 146,400 (cluster 1: 'least comorbidity cluster'), 45,100 (cluster 2: 'diabetes with end-organ damage cluster') and 73,820 (cluster 3: 'cardiometabolic cluster') cases. Attributes with the highest importance for clustering were hypertension and diabetes. After multivariable adjustment, patients from 'diabetes with end-organ damage cluster' exhibited the worst mortality, MACCE and ischemic stroke (p < 0.001 for all), as well as the lowest utilization of invasive management (p < 0.001 for all), in comparison to other clusters. Patients from 'behavioural risk cluster' exhibited the best in-hospital prognosis and the highest utilization of invasive management, compared to other clusters (p < 0.001 for all).

CONCLUSIONS

Machine learning driven clustering of inpatients with STEMI reveals important population subgroups with distinct prevalence, risk profile, prognosis and management. Data driven approaches may identify high risk phenogroups and warrants further study.

An integrated signature of clinical metrics and immune-related genes as a prognostic indicator for ST-segment elevation myocardial infarction patient survival

Background

The immune-inflammatory pathway plays a critical role in myocardial infarction development. However, few studies have systematically explored immune-related genes in relation to myocardial infarction prognosis using bioinformatic analysis. Our study aims to identify differentially expressed immune-related genes(DEIRGs) in ST-segment elevation myocardial infarction (STEMI) patients and investigate their association with clinical outcomes.

Materials and methods

We conducted a systematic review of Gene Expression Omnibus datasets, selecting GSE49925, GSE60993, and GSE61144 for analysis. DEIRGs were identified using GEO2R and overlapped across the chosen datasets. Functional enrichment analysis elucidated the DEIRGs' biological functions and pathways. We established an optimal prognostic prediction model using LASSO penalized Cox proportional hazards regression. The signature's clinical utility was evaluated through survival analysis, ROC curve assessment, and decision curve analysis. Additionally, we constructed a prognostic nomogram for survival rate prediction. External validation was performed using our own plasma samples.

Results

The resulting prognostic signature integrated two dysregulated DEIRGs (S100A12 and IL2RB) and two clinical variables (serum creatinine level and Gensini score). This signature effectively stratified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and decision curve analysis demonstrated its robust predictive performance and clinical utility within the first two years post-disease onset. External validation confirmed significant outcome differences between risk groups.

Conclusions

Our study establishes a prognostic signature that combines DEIRGs and clinical variables for STEMI patients. The signature exhibits promising predictive capabilities for patient stratification and survival risk assessment.

Advancements in Artificial Intelligence for Precision Diagnosis and Treatment of Myocardial Infarction: A Comprehensive Review of Clinical Trials and Randomized Controlled Trials

Coronary artery disease (CAD) is still a serious global health issue that has a substantial impact on death and illness rates. The goal of primary prevention strategies is to lower the risk of developing CAD. Nevertheless, current methods usually rely on simple risk assessment instruments that might overlook significant individual risk factors. This limitation highlights the need for innovative methods that can accurately assess cardiovascular risk and offer personalized preventive care. Recent advances in machine learning and artificial intelligence (AI) have opened up interesting new avenues for optimizing primary preventive efforts for CAD and improving risk prediction models. By leveraging large-scale databases and advanced computational techniques, AI has the potential to fundamentally alter how cardiovascular risk is evaluated and managed. This review looks at current randomized controlled studies and clinical trials that explore the application of AI and machine learning to improve primary preventive measures for CAD. The emphasis is on their ability to recognize and include a range of risk elements in sophisticated risk assessment models.

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

OBJECTIVE

Kawasaki syndrome (KS) is an acute vasculitis that affects children < 5 years of age and leads to coronary artery lesions (CAL) in about 20-25% of untreated cases. Machine learning (ML) is a branch of artificial intelligence (AI) that integrates complex data sets on a large scale and uses huge data to predict future events. The purpose of the present study was to use ML to present the model for early risk assessment of CAL in children with KS by different algorithms.

METHODS

A total of 158 children were enrolled from Women and Children's Hospital, Qingdao University, and divided into 70-30% as the training sets and the test sets for modeling and validation studies. There are several classifiers are constructed for models including the random forest (RF), the logistic regression (LR), and the eXtreme Gradient Boosting (XGBoost). Data preprocessing is analyzed before applying the classifiers to modeling. To avoid the problem of overfitting, the 5-fold cross validation method was used throughout all the data.

RESULTS

The area under the curve (AUC) of the RF model was 0.925 according to the validation of the test set. The average accuracy was 0.930 (95% CI, 0.905 to 0.956). The AUC of the LG model was 0.888 and the average accuracy was 0.893 (95% CI, 0,837 to 0.950). The AUC of the XGBoost model was 0.879 and the average accuracy was 0.935 (95% CI, 0.891 to 0.980).

CONCLUSION

The RF algorithm was used in the present study to construct a prediction model for CAL effectively, with an accuracy of 0.930 and AUC of 0.925. The novel model established by ML may help guide clinicians in the initial decision to make a more aggressive initial anti-inflammatory therapy. Due to the limitations of external validation and regional population characteristics, additional research is required to initiate a further application in the clinic.

A new clustering model based on the seminal plasma/serum ratios of multiple trace element concentrations in male patients with subfertility

Purpose

To investigate whether seminal plasma (SP)/serum ratios of multiple trace elements (TEs) can classify patients with male subfertility.

Methods

SP/serum ratios of 20 TEs (lithium, sodium, magnesium, phosphorus, sulfur, potassium, calcium, manganese, iron, cobalt, copper, zinc, arsenic, selenium, rubidium, strontium, molybdenum, cesium, barium, and thallium) were calculated for healthy volunteers (n = 4) and those consulting for male subfertility (n = 245). Volunteer semen samples were collected by split ejaculation into early and subsequent fractions, and SP/serum ratio data were compared between fractions. The patients' SP/serum ratio data were used in an unsupervised clustering analysis and qualitatively compared with the data from the fractions of ejaculation from the volunteers. Semen quality parameters and pregnancy outcomes were compared between patient clusters.

Results

The early fraction of volunteers was characterized by lower phosphorus and arsenic and 18 other higher TEs than the subsequent fraction. Cluster analysis classified patients into four distinct clusters, one sharing characteristics with the early fraction and another with the subsequent fraction. One cluster with the early fraction characteristics had significantly lower semen volume and higher pregnancy rates from spontaneous pregnancies or intrauterine insemination.

Conclusions

Classification of patients based on SP/serum ratios of multiple TEs represents the dominance of fractions of ejaculation samples.

Exosomal circ-0020887 and circ-0009590 as novel biomarkers for the diagnosis and prediction of short-term adverse cardiovascular outcomes in STEMI patients

This study attempted to identify exosomal circular RNAs (circRNAs) as diagnostic and prognostic biomarkers for patients with ST-segment elevation myocardial infarction (STEMI). The differentially expressed exosomal circRNAs (DEECs) were screened from microarray dataset (GSE160717 and GSE197137) and RNA-Seq dataset (GSE208194), and the expression levels of DEECs in patients with STEMI were validated using reverse transcription and quantitative real-time PCR. The diagnostic value of DEECs was assessed using receiver operating characteristic curves. The major adverse cardiovascular event (MACE)-free 1-year survival rate was evaluated using the Kaplan-Meier method, and the factors affecting prognosis were determined using Cox regression model analysis. Results showed that four DEECs were screened including exo-circ-0001490, exo-circ-0020887, exo-circ-0009590, and exo-circ-0055440, and only upregulated exo-circ-0020887 and exo-circ-0009590 expression was validated in patients with STEMI. The exo-circ-0020887 and exo-circ-0009590 expression was positively correlated with hs-CRP, LDL-C, cTnI, and CK-MB. The exo-circ-0020887 and exo-circ-0009590 showed good diagnostic efficacy to distinguish STEMI patients from healthy controls (area under the curves: 0.85 and 0.80). STEMI patients with high levels of exo-circ-0020887 and exo-circ-0009590 had lower MACE-free 1-year survival rate, and exo-circ-0020887 and exo-circ-0009590 expression was independent risk factors for adverse prognosis. In summary, upregulation of plasma exo-circ-0020887 and exo-circ-0009590 might act as potential biomarkers for the diagnosis and prediction of short-term adverse cardiovascular outcomes in patients with STEMI.

Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers in severe burns

Introduction

Viral infection, typically disregarded, has a significant role in burns. However, there is still a lack of biomarkers and immunotherapy targets related to viral infections in burns.

Methods

Virus-related genes (VRGs) that were extracted from Gene Oncology (GO) database were included as hallmarks. Through unsupervised consensus clustering, we divided patients into two VRGs molecular patterns (VRGMPs). Weighted gene co-expression network analysis (WGCNA) was performed to study the relationship between burns and VRGs. Random forest (RF), least absolute shrinkage and selection operator (LASSO) regression, and logistic regression were used to select key genes, which were utilized to construct prognostic signatures by multivariate logistic regression. The risk score of the nomogram defined high- and low-risk groups. We compared immune cells, immune checkpoint-related genes, and prognosis between the two groups. Finally, we used network analysis and molecular docking to predict drugs targeting CD69 and SATB1. Expression of CD69 and SATB1 was validated by qPCR and microarray with the blood sample from the burn patient.

Results

We established two VRGMPs, which differed in monocytes, neutrophils, dendritic cells, and T cells. In WGCNA, genes were divided into 14 modules, and the black module was correlated with VRGMPs. A total of 65 genes were selected by WGCNA, STRING, and differential expression analysis. The results of GO enrichment analysis were enriched in Th1 and Th2 cell differentiation, B cell receptor signaling pathway, alpha-beta T cell activation, and alpha-beta T cell differentiation. Then the 2-gene signature was constructed by RF, LASSO, and LOGISTIC regression. The signature was an independent prognostic factor and performed well in ROC, calibration, and decision curves. Further, the expression of immune cells and checkpoint genes differed between high- and low-risk groups. CD69 and SATB1 were differentially expressed in burns.

Discussion

This is the first VRG-based signature (including 2 key genes validated by qPCR) for predicting survival, and it could provide vital guidance to achieve optimized immunotherapy for immunosuppression in burns.

Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram

We aimed to measure the diagnostic accuracy of the deep learning model (DLM) for ST-elevation myocardial infarction (STEMI) on a 12-lead electrocardiogram (ECG) according to culprit artery sorts. From January 2017 to December 2019, we recruited patients with STEMI who received more than one stent insertion for culprit artery occlusion. The DLM was trained with STEMI and normal sinus rhythm ECG for external validation. The primary outcome was the diagnostic accuracy of DLM for STEMI according to the three different culprit arteries. The outcomes were measured using the area under the receiver operating characteristic curve (AUROC), sensitivity (SEN), and specificity (SPE) using the Youden index. A total of 60,157 ECGs were obtained. These included 117 STEMI-ECGs and 60,040 normal sinus rhythm ECGs. When using DLM, the AUROC for overall STEMI was 0.998 (0.996–0.999) with SEN 97.4% (95.7–100) and SPE 99.2% (98.1–99.4). There were no significant differences in diagnostic accuracy within the three culprit arteries. The baseline wanders in false positive cases (83.7%, 345/412) significantly interfered with the accurate interpretation of ST elevation on an ECG. DLM showed high diagnostic accuracy for STEMI detection, regardless of the type of culprit artery. The baseline wanders of the ECGs could affect the misinterpretation of DLM.

Targeting Epigenetics and Non-coding RNAs in Myocardial Infarction: From Mechanisms to Therapeutics

Myocardial infarction (MI) is a complicated pathology triggered by numerous environmental and genetic factors. Understanding the effect of epigenetic regulation mechanisms on the cardiovascular disease would advance the field and promote prophylactic methods targeting epigenetic mechanisms. Genetic screening guides individualised MI therapies and surveillance. The present review reported the latest development on the epigenetic regulation of MI in terms of DNA methylation, histone modifications, and microRNA-dependent MI mechanisms and the novel therapies based on epigenetics.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

BACKGROUND

The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.

METHODS

Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed.

RESULTS

In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality.

CONCLUSION

Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

BACKGROUND

Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes.

METHODS

We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups.

RESULTS

We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality.

CONCLUSIONS

Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.

Main differences between two highly effective lipid-lowering therapies in subclasses of lipoproteins in patients with acute myocardial infarction

Background

Large observational studies have shown that small, dense LDL subfractions are related to atherosclerotic cardiovascular disease. This study assessed the effects of two highly effective lipid-lowering therapies in the atherogenic subclasses of lipoproteins in subjects with ST-segment elevation myocardial infarction (STEMI).

Methods

Patients of both sexes admitted with their first myocardial infarction and submitted to pharmacoinvasive strategy (N = 101) were included and randomized using a central computerized system to receive a daily dose of simvastatin 40 mg plus ezetimibe 10 mg or rosuvastatin 20 mg for 30 days. Intermediate-density lipoprotein (IDL) and low-density lipoprotein (LDL) subfractions were analysed by polyacrylamide gel electrophoresis (Lipoprint System) on the first (D1) and 30th days (D30) of lipid-lowering therapy. Changes in LDL and IDL subfractions between D1 and D30 were compared between the lipid-lowering therapies (Mann-Whitney U test).

Results

The classic lipid profile was similar in both therapy arms at D1 and D30. At D30, the achievement of lipid goals was comparable between lipid-lowering therapies. Cholesterol content in atherogenic subclasses of LDL (p = 0.043) and IDL (p = 0.047) decreased more efficiently with simvastatin plus ezetimibe than with rosuvastatin.

Conclusions

Lipid-lowering therapy with simvastatin plus ezetimibe was associated with a better pattern of lipoprotein subfractions than rosuvastatin monotherapy. This finding was noted despite similar effects in the classic lipid profile and may contribute to residual cardiovascular risk.

Trial registration

ClinicalTrials.gov, NCT02428374, registered on 28/09/2014.

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia

Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.