Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

Subclinical vascular composites predict clinical cardiovascular disease, stroke, and dementia: The Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND AND AIMS

Subclinical cardiovascular disease (CVD) measures may reflect biological pathways that contribute to increased risk for coronary heart disease (CHD) events, stroke, and dementia beyond conventional risk scores.

METHODS

The Multi-Ethnic Study of Atherosclerosis (MESA) followed 6814 participants (45-84 years of age) from baseline in 2000-2002 to 2018 over 6 clinical examinations and annual follow-up interviews. MESA baseline subclinical CVD procedures included: seated and supineblood pressure, coronary calcium scan, radial artery tonometry, and carotid ultrasound. Baseline subclinical CVD measures were transformed into z-scores before factor analysis to derive composite factor scores. Time to clinical event for all-cause CVD, CHD, stroke and ICD code-based dementia events were modeled using Cox proportional hazards models reported as area under the curve (AUC) with 95% Confidence Intervals (95%CI) at 10 and 15 years of follow-up. All models included all factor scores together, and adjustment for conventional risk scores for global CVD, stroke, and dementia.

RESULTS

After factor selection, 24 subclinical measures aggregated into four distinct factors representing: blood pressure, atherosclerosis, arteriosclerosis, and cardiac factors. Each factor significantly predicted time to CVD events and dementia at 10 and 15 years independent of each other and conventional risk scores. Subclinical vascular composites of atherosclerosis and arteriosclerosis best predicted time to clinical events of CVD, CHD, stroke, and dementia. These results were consistent across sex and racial and ethnic groups.

CONCLUSIONS

Subclinical vascular composites of atherosclerosis and arteriosclerosis may be useful biomarkers to inform the vascular pathways contributing to events of CVD, CHD, stroke, and dementia.

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy

Background

Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies.

Objectives

The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM.

Methods

We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method.

Results

In total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest.

Conclusions

The proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.

Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study

Aims

Patients with atrial fibrillation (AF) have a higher risk of ischaemic stroke and death. While anticoagulants are effective at reducing these risks, they increase the risk of bleeding. Current clinical risk scores only perform modestly in predicting adverse outcomes, especially for the outcome of death. We aimed to test the multi-label gradient boosting decision tree (ML-GBDT) model in predicting risks for adverse outcomes in a prospective global AF registry.

Methods and results

We studied patients from phase II/III of the Global Registry on Long-Term Oral Anti-Thrombotic Treatment in Patients with Atrial Fibrillation registry between 2011 and 2020. The outcomes were all-cause death, ischaemic stroke, and major bleeding within 1 year following the AF. We trained the ML-GBDT model and compared its discrimination with the clinical scores in predicting patient outcomes. A total of 25 656 patients were included [mean age 70.3 years (SD 10.3); 44.8% female]. Within 1 year after AF, ischaemic stroke occurred in 215 (0.8%), major bleeding in 405 (1.6%), and death in 897 (3.5%) patients. Our model achieved an optimized area under the curve in predicting death (0.785, 95% CI: 0.757-0.813) compared with the Charlson Comorbidity Index (0.747, P = 0.007), ischaemic stroke (0.691, 0.626-0.756) compared with CHA2DS2-VASc (0.613, P = 0.028), and major bleeding (0.698, 0.651-0.745) as opposed to HAS-BLED (0.607, P = 0.002), with improvement in net reclassification index (10.0, 12.5, and 23.6%, respectively).

Conclusion

The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study

Background

Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms.

Methods

Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software.

Results

A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database

BACKGROUND

The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients.

METHODS

We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness.

RESULTS

This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts.

CONCLUSION

The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.

Analysis of the fatigue status of medical security personnel during the closed-loop period using multiple machine learning methods: a case study of the Beijing 2022 Olympic Winter Games

Using machine learning methods to analyze the fatigue status of medical security personnel and the factors influencing fatigue (such as BMI, gender, and wearing protective clothing working hours), with the goal of identifying the key factors contributing to fatigue. By validating the predicted outcomes, actionable and practical recommendations can be offered to enhance fatigue status, such as reducing wearing protective clothing working hours. A questionnaire was designed to assess the fatigue status of medical security personnel during the closed-loop period, aiming to capture information on fatigue experienced during work and disease recovery. The collected data was then preprocessed and used to determine the structural parameters for each machine learning algorithm. To evaluate the prediction performance of different models, the mean relative error (MRE) and goodness of fit (R2) between the true and predicted values were calculated. Furthermore, the importance rankings of various parameters in relation to fatigue status were determined using the RF feature importance analysis method. The fatigue status of medical security personnel during the closed-loop period was analyzed using multiple machine learning methods. The prediction performance of these methods was ranked from highest to lowest as follows: Gradient Boosting Regression (GBM) > Random Forest (RF) > Adaptive Boosting (AdaBoost) > K-Nearest Neighbors (KNN) > Support Vector Regression (SVR). Among these algorithms, four out of the five achieved good prediction results, with the GBM method performing the best. The five most critical parameters influencing fatigue status were identified as working hours in protective clothing, a customized symptom and disease score (CSDS), physical exercise, body mass index (BMI), and age, all of which had importance scores exceeding 0.06. Notably, working hours in protective clothing obtained the highest importance score of 0.54, making it the most critical factor impacting fatigue status. Fatigue is a prevalent and pressing issue among medical security personnel operating in closed-loop environments. In our investigation, we observed that the GBM method exhibited superior predictive performance in determining the fatigue status of medical security personnel during the closed-loop period, surpassing other machine learning techniques. Notably, our analysis identified several critical factors influencing the fatigue status of medical security personnel, including the duration of working hours in protective clothing, CSDS, and engagement in physical exercise. These findings shed light on the multifaceted nature of fatigue among healthcare workers and emphasize the importance of considering various contributing factors. To effectively alleviate fatigue, prudent management of working hours for security personnel, along with minimizing the duration of wearing protective clothing, proves to be promising strategies. Furthermore, promoting regular physical exercise among medical security personnel can significantly impact fatigue reduction. Additionally, the exploration of medication interventions and the adoption of innovative protective clothing options present potential avenues for mitigating fatigue. The insights derived from this study offer valuable guidance to management personnel involved in organizing large-scale events, enabling them to make informed decisions and implement targeted interventions to address fatigue among medical security personnel. In our upcoming research, we will further expand the fatigue dataset while considering higher precisionprediction algorithms, such as XGBoost model, ensemble model, etc., and explore their potential contributions to our research.

Predicting adverse events after thoracic endovascular aortic repair for patients with type B aortic dissection

The potential of adverse events (AEs) after thoracic endovascular aortic repair (TEVAR) in patients with type B aortic dissection (TBAD) has been reported. To avoid the occurrence of AEs, it is important to recognize high-risk population for prevention in advance. The data of 261 patients with TBAD who received TEVAR between June 2017 and June 2021 at our medical center were retrospectively reviewed. After the implementation of exclusion criteria, 172 patients were finally included, and after 2.8 years (range from 1 day to 5.8 years) of follow up, they were divided into AEs (n = 41) and non-AEs (n = 131) groups. We identified the predictors of AEs, and a prediction model was constructed to calculate the specific risk of postoperative AEs at 1, 2, and 3 years, and to stratify patients into high-risk (n = 78) and low-risk (n = 94) group. The prediction model included seven predictors: Age > 75 years, Lower extremity malperfusion (LEM), NT-proBNP > 330 pg/ml, None distal tear, the ratio between the diameter of the ascending aorta and descending aorta (A/D ratio) > 1.2, the ratio of the area of the false lumen to the total aorta (FL ratio) > 64%, and acute TEVAR, which exhibited excellent predictive accuracy performance and discriminatory ability with C statistic of 82.3% (95% CI 77.3-89.2%). The prediction model was contributed to identify high-risk patients of postoperative AEs, which may serve to achievement of personalized treatment and follow-up plans for patients.

Predicting hypertension control using machine learning

Hypertension is a widely prevalent disease and uncontrolled hypertension predisposes affected individuals to severe adverse effects. Though the importance of controlling hypertension is clear, the multitude of therapeutic regimens and patient factors that affect the success of blood pressure control makes it difficult to predict the likelihood to predict whether a patient's blood pressure will be controlled. This project endeavors to investigate whether machine learning can accurately predict the control of a patient's hypertension within 12 months of a clinical encounter. To build the machine learning model, a retrospective review of the electronic medical records of 350,008 patients 18 years of age and older between January 1, 2015 and June 1, 2022 was performed to form model training and testing cohorts. The data included in the model included medication combinations, patient laboratory values, vital sign measurements, comorbidities, healthcare encounters, and demographic information. The mean age of the patient population was 65.6 years with 161,283 (46.1%) men and 275,001 (78.6%) white. A sliding time window of data was used to both prohibit data leakage from training sets to test sets and to maximize model performance. This sliding window resulted in using the study data to create 287 predictive models each using 2 years of training data and one week of testing data for a total study duration of five and a half years. Model performance was combined across all models. The primary outcome, prediction of blood pressure control within 12 months demonstrated an area under the curve of 0.76 (95% confidence interval; 0.75-0.76), sensitivity of 61.52% (61.0-62.03%), specificity of 75.69% (75.25-76.13%), positive predictive value of 67.75% (67.51-67.99%), and negative predictive value of 70.49% (70.32-70.66%). An AUC of 0.756 is considered to be moderately good for machine learning models. While the accuracy of this model is promising, it is impossible to state with certainty the clinical relevancy of any clinical support ML model without deploying it in a clinical setting and studying its impact on health outcomes. By also incorporating uncertainty analysis for every prediction, the authors believe that this approach offers the best-known solution to predicting hypertension control and that machine learning may be able to improve the accuracy of hypertension control predictions using patient information already available in the electronic health record. This method can serve as a foundation with further research to strengthen the model accuracy and to help determine clinical relevance.

A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm

BACKGROUND

Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.

METHODS

This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.

RESULTS

The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.

CONCLUSIONS

Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Evolving mechanisms and presentations of cardiovascular disease in people with HIV: implications for management

People with HIV (PWH) are at elevated risk for cardiovascular diseases (CVDs), including myocardial infarction, heart failure, and sudden cardiac death, among other CVD manifestations. Chronic immune dysregulation resulting in persistent inflammation is common among PWH, particularly those with sustained viremia and impaired CD4+ T cell recovery. This inflammatory milieu is a major contributor to CVDs among PWH, in concert with common comorbidities (such as dyslipidemia and smoking) and, to a lesser extent, off-target effects of antiretroviral therapy. In this review, we discuss the clinical and mechanistic evidence surrounding heightened CVD risks among PWH, implications for specific CVD manifestations, and practical guidance for management in the setting of evolving data.

Genotypes of the UCP1 gene polymorphisms and cardiometabolic diseases: A multifactorial study of association with disease probability

Cardiometabolic diseases (CMDs) are complex disorders with a heterogenous phenotype, which are caused by multiple factors including genetic factors. Single nucleotide polymorphisms (SNPs) rs45539933 (p.Ala64Thr), rs10011540 (c.-112A>C), rs3811791 (c.-1766A>G), and rs1800592 (c.-3826A>G) in the UCP1 gene have been analyzed for association with CMDs in many studies providing controversial results. However, previous studies only considered individual UCP1 SNPs and did not evaluate them in an integrated manner, which is a more powerful approach to uncover genetic component of complex diseases. This study aimed to investigate associations between UCP1 genotype combinations and CMDs or CMD risk factors in the context of non-genetic factors. We performed multiple logistic regression analysis and proposed new methodology of testing different combinations of SNP genotypes. We found that probability of CMDs increased in presence of the three-SNP combination of genotypes with minor alleles of c.-3826A>G and p.Ala64Thr and wild allele of c.-112A>C, with increasing age, body mass index (BMI), body fat percentage (BF%) and may differ between sexes and between countries. The combination of genotypes with c.-3826A>G minor allele and wild homozygotes of c.-112A>C and p.Ala64Thr was associated with increased probability of diabetes. While combination of genotypes with minor alleles of all three SNPs reduced the CMD probability. The present results suggest that age, BMI, sex, and UCP1 three-SNP combinations of genotypes significantly contribute to CMD probability. Varying of c.-112A>C alleles in the genotype combination with minor alleles of c.-3826A>G and p.Ala64Thr markedly changes CMD probability.

Adopting artificial intelligence in cardiovascular medicine: a scoping review

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

Utility of multimodal longitudinal imaging data for dynamic prediction of cardiovascular and renal disease: the CARDIA study

Background

Medical examinations contain repeatedly measured data from multiple visits, including imaging variables collected from different modalities. However, the utility of such data for the prediction of time-to-event is unknown, and only a fraction of the data is typically used for risk prediction. We hypothesized that multimodal longitudinal imaging data could improve dynamic disease prognosis of cardiovascular and renal disease (CVRD).

Methods

In a multi-centered cohort of 5,114 CARDIA participants, we included 166 longitudinal imaging variables from five imaging modalities: Echocardiography (Echo), Cardiac and Abdominal Computed Tomography (CT), Dual-Energy x-ray Absorptiometry (DEXA), Brain Magnetic Resonance Imaging (MRI) collected from young adulthood to mid-life over 30 years (1985-2016) to perform dynamic survival analysis of CVRD events using machine learning dynamic survival analysis (Dynamic-DeepHit, LTRCforest, and Extended Cox for Time-varying Covariates). Risk probabilities were continuously updated as new data were collected. Model performance was assessed using integrated AUC and C-index and compared to traditional risk factors.

Results

Longitudinal imaging data, even when being irregularly collected with high missing rates, improved CVRD dynamic prediction (0.03 in integrated AUC, up to 0.05 in C-index compared to traditional risk factors; best model's C-index = 0.80-0.83 up to 20 years from baseline) from young adulthood followed up to midlife. Among imaging variables, Echo and CT variables contributed significantly to improved risk estimation. Echo measured in early adulthood predicted midlife CVRD risks almost as well as Echo measured 10-15 years later (0.01 C-index difference). The most recent CT exam provided the most accurate prediction for short-term risk estimation. Brain MRI markers provided additional information from cardiac Echo and CT variables that led to a slightly improved prediction.

Conclusions

Longitudinal multimodal imaging data readily collected from follow-up exams can improve CVRD dynamic prediction. Echocardiography measured early can provide a good long-term risk estimation, while CT/calcium scoring variables carry atherosclerotic signatures that benefit more immediate risk assessment starting in middle-age.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study

BACKGROUND

Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.

AIM

To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.

METHODS

This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.

RESULTS

Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice.

CONCLUSION

Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.

Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

Could Tumor Necrosis Factor Serve as a Marker for Cardiovascular Risk Factors and Left Ventricular Hypertrophy in Patients with Early-Onset Coronary Artery Disease?

Introduction: Tumor necrosis factor (TNF), a pro-inflammatory cytokine, can be produced by cardiomyocytes, leading to metabolic disorders in the myocardium. The objective of this study was to assess the relationship between plasma levels of the TNF cytokine and the presence of known biochemical and clinical risk factors for cardiovascular disease, along with the parameters of cardiac morphology in patients diagnosed with coronary artery disease (CAD) at a young age. Materials and Methods: The study group included 75 men aged up to 50 years and 25 women aged up to 55 years. The plasma TNF concentration was measured by use of the ELISA assay. Echocardiography and electrocardiographic examinations were performed in all patients. Results: We observed positive correlations for TNF with the BMI ratio, weight, waist and hip circumference. We also found negative correlations for TNF with HDL levels and ApoA concentrations, and positive correlations with the ApoB/ApoA1 ratio, Apo B, IL6, LDL and TG concentrations. These results suggest an association between higher plasma TNF concentrations and components of metabolic syndrome, including dyslipidemia. TNF may be a potential risk factor for impaired diastolic function. Conclusions: While TNF may be useful for diagnosing certain risks in CAD patients, the TNF measurement cannot be used as a surrogate test for echocardiography.

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.

Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling

BACKGROUND

There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days.

METHODS

We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness.

RESULTS

The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value.

CONCLUSIONS

A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

Photodynamic Therapy for Atherosclerosis

Atherosclerosis, which currently contributes to 31% of deaths globally, is of critical cardiovascular concern. Current diagnostic tools and biomarkers are limited, emphasizing the need for early detection. Lifestyle modifications and medications form the basis of treatment, and emerging therapies such as photodynamic therapy are being developed. Photodynamic therapy involves a photosensitizer selectively targeting components of atherosclerotic plaques. When activated by specific light wavelengths, it induces localized oxidative stress aiming to stabilize plaques and reduce inflammation. The key advantage lies in its selective targeting, sparing healthy tissues. While preclinical studies are encouraging, ongoing research and clinical trials are crucial for optimizing protocols and ensuring long-term safety and efficacy. The potential combination with other therapies makes photodynamic therapy a versatile and promising avenue for addressing atherosclerosis and associated cardiovascular disease. The investigations underscore the possibility of utilizing photodynamic therapy as a valuable treatment choice for atherosclerosis. As advancements in research continue, photodynamic therapy might become more seamlessly incorporated into clinical approaches for managing atherosclerosis, providing a blend of efficacy and limited invasiveness.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

BACKGROUND

A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation.

METHODS

PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789).

RESULTS

In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively.

CONCLUSION

AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

Background

With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest.

Objectives

This study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases.

Methods

We harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects.

Results

Among the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance.

Conclusions

This study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.

Severity of coronary artery disease is associated with diminished circANRIL expression: A possible blood based transcriptional biomarker in East Africa

Antisense Noncoding RNA in the INK4 Locus (ANRIL) is the prime candidate gene at Chr9p21, the well-defined genetic risk locus associated with coronary artery disease (CAD). ANRIL and its transcript variants were investigated for the susceptibility to CAD in adipose tissues (AT) and peripheral blood mononuclear cells (PBMCs) of the study group and the impact of 9p21.3 locus mutations was further analysed. Expressions of ANRIL, circANRIL (hsa_circ_0008574), NR003529, EU741058 and DQ485454 were detected in epicardial AT (EAT) mediastinal AT (MAT), subcutaneous AT (SAT) and PBMCs of CAD patients undergoing coronary artery bypass grafting and non-CAD patients undergoing heart valve surgery. ANRIL expression was significantly upregulated, while the expression of circANRIL was significantly downregulated in CAD patients. Decreased circANRIL levels were significantly associated with the severity of CAD and correlated with aggressive clinical characteristics. rs10757278 and rs10811656 were significantly associated with ANRIL and circANRIL expressions in AT and PBMCs. The ROC-curve analysis suggested that circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). circANRIL has high diagnostic accuracy (AUC: 0.9808, cut-off: 0.33, sensitivity: 1.0, specificity: 0.88). We report the first data demonstrating the presence of ANRIL and its transcript variants expressions in the AT and PBMCs of CAD patients. circANRIL having a synergetic effect with ANRIL plays a protective role in CAD pathogenesis. Therefore, altered circANRIL expression may become a potential diagnostic transcriptional biomarker for early CAD diagnosis.

Artificial Intelligence in Heart Failure: Friend or Foe?

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.

Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study

OBJECTIVE

Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics.

MATERIALS AND METHODS

Life's Essential 8 (LE8) variables, demographics, and CVD events were queried over 15 ears in 5060 CARDIA participants with 18 years of subsequent follow-up. LE8 subgraphs were mined and a SANMF algorithm was applied to cluster frequently occurring subgraphs. K-fold cross-validation and diagnostics were performed to determine cluster assignment. Cox proportional hazard models were fit for future CV event risk and logistic regression was performed for cluster phenotyping.

RESULTS

The cohort (54.6% female, 48.7% White) produced 3 clusters of CVH metrics: Healthy & Late Obesity (HLO) (29.0%), Healthy & Intermediate Sleep (HIS) (43.2%), and Unhealthy (27.8%). HLO had 5 ideal LE8 metrics between ages 18 and 39 years, until BMI increased at 40. HIS had 7 ideal LE8 metrics, except sleep. Unhealthy had poor levels of sleep, smoking, and diet but ideal glucose. Race and employment were significantly different by cluster (P < .001) but not sex (P = .734). For 301 incident CV events, multivariable hazard ratios (HRs) for HIS and Unhealthy were 0.73 (0.53-1.00, P = .052) and 2.00 (1.50-2.68, P < .001), respectively versus HLO. A 15-year event survival was 97.0% (HIS), 96.3% (HLO), and 90.4% (Unhealthy, P < .001).

DISCUSSION AND CONCLUSION

SANMF of LE8 metrics identified 3 unique clusters of CVH behavior patterns. Clustering of longitudinal LE8 variables via SANMF is a robust tool for phenotypic risk assessment for future adverse cardiovascular events.

Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model

Introduction

The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis.

Methods

A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared.

Results

A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively.

Conclusion

The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review

Machine Learning (ML) and Deep Learning (DL) models show potential in surpassing traditional methods including generalised linear models for healthcare predictions, particularly with large, complex datasets. However, low interpretability hinders practical implementation. To address this, Explainable Artificial Intelligence (XAI) methods are proposed, but a comprehensive evaluation of their effectiveness is currently limited. The aim of this scoping review is to critically appraise the application of XAI methods in ML/DL models using Electronic Health Record (EHR) data. In accordance with PRISMA scoping review guidelines, the study searched PUBMED and OVID/MEDLINE (including EMBASE) for publications related to tabular EHR data that employed ML/DL models with XAI. Out of 3220 identified publications, 76 were included. The selected publications published between February 2017 and June 2023, demonstrated an exponential increase over time. Extreme Gradient Boosting and Random Forest models were the most frequently used ML/DL methods, with 51 and 50 publications, respectively. Among XAI methods, Shapley Additive Explanations (SHAP) was predominant in 63 out of 76 publications, followed by partial dependence plots (PDPs) in 11 publications, and Locally Interpretable Model-Agnostic Explanations (LIME) in 8 publications. Despite the growing adoption of XAI methods, their applications varied widely and lacked critical evaluation. This review identifies the increasing use of XAI in tabular EHR research and highlights a deficiency in the reporting of methods and a lack of critical appraisal of validity and robustness. The study emphasises the need for further evaluation of XAI methods and underscores the importance of cautious implementation and interpretation in healthcare settings.

Eveningness is associated with coronary artery calcification in a middle-aged Swedish population

Coronary artery calcification (CAC) is an established imaging biomarker of subclinical atherosclerosis, but its relationship to diurnal preference is not well studied. We investigated the association between chronotype and CAC in the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot cohort. Participants aged 50-64 years were randomly recruited and underwent extensive examination including imaging and accelerometry-assessed physical activity. 771 participants (47.3 % male, 57.6 ± 4.4 years) were included in this cross-sectional analysis. CAC was assessed by non-contrast computed tomography, and a CAC score > 10 was considered significant calcification. Self-assessed chronotype was classified as extreme morning, moderate morning, intermediate, moderate evening, or extreme evening. 10-year risk of first-onset cardiovascular disease was estimated by the Systemic Coronary Risk Evaluation 2 (SCORE2). Significant CAC was present in 29 % of the cohort. CAC prevalence increased from extreme morning to extreme evening type (22 %, 28 %, 29 %, 27 %, 41 % respectively, p = 0.018). In a multivariate logistic regression model controlling for confounders, extreme evening chronotype was independently associated with increased CAC prevalence compared to extreme morning type (OR 1.90, [95%CI 1.04-3.46], p = 0.037). When stratified by SCORE2 risk category (low: <5 %; moderate: 5 to <10 %; high: ≥10 %), significant CAC was most prevalent among extreme evening chronotypes in the low and moderate-risk groups, while chronotype seemed less important in the high-risk group (p = 0.011, p = 0.023, p = 0.86, respectively). Our findings suggest circadian factors may play an important role in atherosclerosis and should be considered in early cardiovascular prevention.

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Aims

Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population.

Methods and results

A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively.

Conclusion

Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.

Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature

AIM

This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane.

METHODS

After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation.

RESULTS

For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06).

CONCLUSION

Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.

Noninvasive Coronary Atherosclerotic Plaque Imaging

Coronary artery disease is the leading cause of morbidity and mortality worldwide. Despite remarkable advances in the management of coronary artery disease, the prediction of adverse coronary events remains challenging. Over the preceding decades, considerable effort has been made to improve risk stratification using noninvasive imaging. Recently, these efforts have increasingly focused on the direct imaging of coronary atherosclerotic plaque. Modern imaging now allows imaging of coronary plaque burden, plaque type, atherosclerotic plaque activity, and plaque thrombosis, which have major potential to refine patient risk stratification, aid decision making, and advance future clinical practice.

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges

Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis. However, low reproducibility of research findings and lack of standardization of procedures pose significant challenges in this field. In this review, we discuss how transcriptomic research can help in understanding the different phenotypes of the atherosclerotic plaque that contribute to the development and progression of atherosclerosis. We highlight the methodological challenges that need to be addressed to improve research outputs, and emphasize the importance of research protocols harmonization. We also discuss recent advances in transcriptomic research, including bulk or single-cell sequencing, and their added value in plaque phenotyping. Finally, we explore how integrated multiomics data and machine learning improve understanding of atherosclerosis and provide directions for future research.

Ischemic stroke prediction using machine learning in elderly Chinese population: The Rugao Longitudinal Ageing Study

OBJECTIVE

Compared logistic regression (LR) with machine learning (ML) models, to predict the risk of ischemic stroke in an elderly population in China.

METHODS

We applied 2208 records from the Rugao Longitudinal Ageing Study (RLAS) for ischemic stroke risk prediction assessment. Input variables included 103 phenotypes. For 3-year ischemic stroke risk prediction, we compared the discrimination and calibration of LR model and ML methods, where ML methods include Random Forest (RF), Gaussian kernel Support Vector Machines (SVM), Multilayer perceptron (MLP), K-Nearest Neighbors Algorithm (KNN), and Gradient Boosting Decision Tree (GBDT) to develop an ischemic stroke risk prediction model.

RESULTS

Age, pulse, waist circumference, education level, β2-microglobulin, homocysteine, cystatin C, folate, free triiodothyronine, platelet distribution width, QT interval, and QTc interval were significant induced predictors of ischemic stroke. For ischemic stroke prediction, the ML approach was able to tap more biochemical and ECG-related multidimensional phenotypic indicators compared to the LR model, which placed more importance on general demographic indicators. Compared to the LR model, SVM provided the best discrimination and calibration (C-index: 0.79 vs. 0.71, 11.27% improvement in model utility), with the best performance in both validation and test data.

CONCLUSION

In a comparison of LR with five ML models, the accuracy of ischemic stroke prediction was higher by combining ML with multiple phenotypes. Combined with other studies based on elderly populations in China, ML techniques, especially SVM, have shown good long-term predictive performance, inspiring the potential value of ML use in clinical practice.

Urinary cortisol and cardiovascular events in women vs. men: The multi-ethnic study of atherosclerosis

Research suggests that women experience greater cardiovascular ischemic effects from stress than men. Visceral adiposity is an endocrine tissue that differs by sex and interacts with stress hormones. We hypothesized that urinary cortisol would be associated with increased cardiovascular events and change in coronary artery calcium score (CAC) in women, and these relationships would vary by central obesity. In the Multi-Ethnic Study of Atherosclerosis Stress Ancillary study, cortisol was quantified by 12-h overnight urine collection. Central obesity was estimated by waist-hip ratio (WHR). Multivariable Cox models estimated the relationship between cortisol and cardiovascular events and assessed for moderation by WHR. The relationship between cortisol and change in CAC Agatston score was assessed by Tobit regression models. 918 patients were analyzed with median follow up of 11 years. There was no association between urinary cortisol and cardiovascular events in the cohort. However, in individuals with below median WHR, higher urinary cortisol levels (upper tertile) were associated with higher cardiovascular event rates in the full cohort, women, and men, but not in groups with above median WHR. There was significant moderation by WHR in women, but not men, whereby the association between elevated cortisol and increased cardiovascular events diminished as WHR increased. Urinary cortisol was associated with increased change in CAC in women (P = 0.003) but not men, without moderation by WHR. Our study highlights associations between cortisol and subclinical atherosclerosis in women, and moderation of the relationship between cortisol and cardiovascular events by central obesity in both genders.

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery

BACKGROUND

This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC).

METHODS

The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition.

RESULTS

The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683).

CONCLUSIONS

The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

[Yacon root extract improves lipid metabolism in hyperlipidemic rats by inhibiting HMGCR expression and activating the PPARα/CYP7A1/CPT-1 pathway]

OBJECTIVE

To investigate the effect of yacon root extract on lipid metabolism in rats with hyperlipidemia (HLP) and its underlying mechanisms.

METHODS

SD rat models of HLP induced by high- fat diet feeding for 8 weeks were randomized into the model group, fenofibrate treatment group (27 mg/kg), and yacon extract treatment groups at doses of 5, 2.5 and 1.25 g/kg (n=10). The rats were given corresponding drug treatments via gavage for 8 weeks. After the treatments, the rats were observed for body weight changes, liver coefficient, liver pathology, and serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C). The mRNA and protein expressions of HMGCR, PPARα, CYP7A1, and CPT-1 in the liver were detected using RT-qPCR and Western blotting.

RESULTS

Compared with those in the model group, the rats treated with fenofibrate and 5 g/kg yacon root extract showed significantly slower body weight gain and lower liver coefficient (P < 0.05) with lower serum levels of TG, TC, and LDL- C (P < 0.05) but higher HDL- C level (P < 0.05). The HLP rat models showed obvious fatty degeneration and vacuolar changes in the liver, which were significantly alleviated by fenofibrate treatment and by treatment with yacon root extract in a dose-dependent manner. Both fenofibrate and 5 g/kg yacon root extract significantly lowered the mRNA and protein expression levels of HMGCR (P < 0.001) and increased the expressions of PPARα, CYP7A1, and CPT-1 in the liver of HLP rats (P < 0.001).

CONCLUSION

Yacon root extract can reduce serum TG and TC levels in HLP rats possibly by inhibiting HMGCR expression and activating the PPARα/CYP7A1/CPT-1 signaling pathway, thereby promoting fatty acid β oxidation and bile acid metabolism.

Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure

OBJECTIVE

Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF.

METHOD

The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers.

RESULTS

The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified.

CONCLUSION

The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.

Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: Assessment using machine learning algorithms

Assessment of the influence of cardiovascular risk factors (CVRF) on cardiovascular event (CVE) using machine learning algorithms offers some advantages over preexisting scoring systems, and better enables personalized medicine approaches to cardiovascular prevention. Using data from four different sources, we evaluated the outcomes of three machine learning algorithms for CVE prediction using different combinations of predictive variables and analysed the influence of different CVRF-related variables on CVE prediction when included in these algorithms. A cohort study based on a male cohort of workers applying populational data was conducted. The population of the study consisted of 3746 males. For descriptive analyses, mean and standard deviation were used for quantitative variables, and percentages for categorical ones. Machine learning algorithms used were XGBoost, Random Forest and Naïve Bayes (NB). They were applied to two groups of variables: i) age, physical status, Hypercholesterolemia (HC), Hypertension, and Diabetes Mellitus (DM) and ii) these variables plus treatment exposure, based on the adherence to the treatment for DM, hypertension and HC. All methods point out to the age as the most influential variable in the incidence of a CVE. When considering treatment exposure, it was more influential than any other CVRF, which changed its influence depending on the model and algorithm applied. According to the performance of the algorithms, the most accurate was Random Forest when treatment exposure was considered (F1 score 0.84), followed by XGBoost. Adherence to treatment showed to be an important variable in the risk of having a CVE. These algorithms could be applied to create models for every population, and they can be used in primary care to manage interventions personalized for every subject.

Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study

The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Background

Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature.

Methods

We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type.

Results

After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables.

Conclusions

ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Machine Learning Models for ASCVD Risk Prediction in an Asian Population - How to Validate the Model is Important

Introduction

Atherosclerotic cardiovascular disease (ASCVD) is prevalent worldwide including Taiwan, however widely accepted tools to assess the risk of ASCVD are lacking in Taiwan. Machine learning models are potentially useful for risk evaluation. In this study we used two cohorts to test the feasibility of machine learning with transfer learning for developing an ASCVD risk prediction model in Taiwan.

Methods

Two multi-center observational registry cohorts, T-SPARCLE and T-PPARCLE were used in this study. The variables selected were based on European, U.S. and Asian guidelines. Both registries recorded the ASCVD outcomes of the patients. Ten-fold validation and temporal validation methods were used to evaluate the performance of the binary classification analysis [prediction of major adverse cardiovascular (CV) events in one year]. Time-to-event analyses were also performed.

Results

In the binary classification analysis, eXtreme Gradient Boosting (XGBoost) and random forest had the best performance, with areas under the receiver operating characteristic curve (AUC-ROC) of 0.72 (0.68-0.76) and 0.73 (0.69-0.77), respectively, although it was not significantly better than other models. Temporal validation was also performed, and the data showed significant differences in the distribution of various features and event rate. The AUC-ROC of XGBoost dropped to 0.66 (0.59-0.73), while that of random forest dropped to 0.69 (0.62-0.76) in the temporal validation method, and the performance also became numerically worse than that of the logistic regression model. In the time-to-event analysis, most models had a concordance index of around 0.70.

Conclusions

Machine learning models with appropriate transfer learning may be a useful tool for the development of CV risk prediction models and may help improve patient care in the future.

Electrocardiographic Associations of Cardiac Biomarkers and Cardiac Magnetic Resonance Measures of Fibrosis in the Multiethnic Study of Atherosclerosis (MESA)

Abnormalities in myocardial substrate, including diffuse and replacement fibrosis, increase the risk of cardiovascular disease (CVD). Data are sparse on whether electrocardiogram (ECG) measures, coupled with circulating biomarkers, may aid in identifying cardiac fibrosis. This study aimed to determine whether 12-lead ECG and biomarkers together augment the prediction of cardiac fibrosis in participants who are free of known CVD. This is a cross-sectional analysis in the MESA (Multiethnic Study of Atherosclerosis) study at visit 5 (2010 to 2012), with measurements of biomarkers (cardiac troponin T and growth differentiation factor-15), gadolinium-enhanced cardiac magnetic resonance imaging, and ECG. Logistic regression associations of ECG measures with cardiac magnetic resonance surrogates of fibrosis (highest quartile extracellular volume [interstitial fibrosis] and late gadolinium enhancement [replacement fibrosis]) were adjusted for demographics and risk factors. Using the C-statistic, we evaluated whether adding ECG measures and biomarkers to clinical characteristics improved the prediction of either type of fibrosis. There were 1,170 eligible participants (aged 67.1 ± 8.6 years). Among the ECG measures, QRS duration (odds ratio [OR] 1.41 per 10 ms, 95% confidence interval [CI] 1.10 to 1.81), major ST-T abnormalities (OR 3.03, 95%CI 1.20, 7.65), and abnormal QRS-T angle (OR 6.32, 95%CI 3.00, 13.33) were associated with replacement fibrosis, whereas only abnormal QRS-T angle (OR 3.05, 95%CI,1.69, 5.48) was associated with interstitial fibrosis. ECG markers, in addition to clinical characteristics, improved the prediction of replacement fibrosis (p = 0.002) but not interstitial fibrosis. The addition of cardiac troponin T and growth differentiation factor-15 to the ECG findings did not significantly improve the model discrimination for either type of cardiac fibrosis. In CVD free participants, simple ECG measures are associated with replacement fibrosis and interstitial fibrosis. The addition of these measures improves identification of replacement but not interstitial fibrosis. These findings may help refine the identification of myocardial scar in the general population.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.

Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population

OBJECTIVES

Chronic total occlusion (CTO) is a form of coronary artery disease (CAD) requiring percutaneous coronary intervention. There has been minimal research regarding CTO-specific risk factors and predictive models. We developed machine learning predictive models based on clinical characteristics to identify patients with CTO before coronary angiography.

METHODS

Data from 1473 patients with CAD, including 317 patients with and 1156 patients without CTO, were retrospectively analyzed. Partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) models were used to identify CTO-specific risk factors and predict CTO development. Receiver operating characteristic (ROC) curve analysis was performed for model validation.

RESULTS

For CTO prediction, the PLS-DA model included 10 variables; the ROC value was 0.706. The RF model included 42 variables; the ROC value was 0.702. The SVM model included 20 variables; the ROC value was 0.696. DeLong's test showed no difference among the three models. Four variables were present in all models: sex, neutrophil percentage, creatinine, and brain natriuretic peptide (BNP).

CONCLUSIONS

Validation of machine learning prediction models for CTO revealed that the PLS-DA model had the best prediction performance. Sex, neutrophil percentage, creatinine, and BNP may be important risk factors for CTO development.

Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models

OBJECTIVE

To explore the correlation between neutrophil-to-lymphocyte ratio (NLR) and contrast-induced acute kidney injury (CI-AKI). To develop machine-learning (ML) methods based on NLR and other relevant high-risk factors to establish new and effective predictive models of CI-AKI. Methods: The data of 2230 patients, who underwent elective vascular intervention, coronary angiography and percutaneous coronary intervention were retrospectively collected. The patients were divided into a CI-AKI group and a non-CI-AKI group. Logistic regression was used to analyze the correlation of NLR with CI-AKI and high-risk factors for CI-AKI, and logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and naïve Bayes (NB) models based on NLR and the high-risk factors were established.

RESULTS

A high NLR(>2.844) was an independent risk factor for CI-AKI (odds ratio = 2.304, p < 0.001). The area under the ROC curve (AUC) of the NB model was the largest (0.774), indicating that it had the best performance. NLR, serum creatinine concentration, fasting plasma glucose concentration, and use of β-blocker all accounted for a large proportion of the predictive performance of each model and were the four most important factors affecting the occurrence of CI-AKI.

CONCLUSIONS

There was a significant correlation between NLR and CI-AKI The NB model exhibited the best predictive performance out of the five ML models based on NLR exhibited the best predictive performance out of the five ML models.

Application and risk prediction of thrombolytic therapy in cardio-cerebrovascular diseases: a review

Cardiocerebrovascular diseases (CVDs) are the leading cause of death worldwide, consuming huge healthcare budget. For CVD patients, the prompt assessment and appropriate administration is the crux to save life and improve prognosis. Thrombolytic therapy, as a non-invasive approach to achieve recanalization, is the basic component of CVD treatment. Still, there are risks that limits its application. The objective of this review is to give an introduction on the utilization of thrombolytic therapy in cardiocerebrovascular blockage diseases, including coronary heart disease and ischemic stroke, and to review the development in risk assessment of thrombolytic therapy, comparing the performance of traditional scales and novel artificial intelligence-based risk assessment models.

Reduction of Calcium Scores Using Intravenous Chelation: A Retrospective Pilot Study

This pilot study presents a retrospective analysis of 10 asymptomatic patients with a positive calcium score who received a series of intravenous calcium ethylenediaminetetraacetic acid (EDTA) chelations. Current standards for cardiovascular risk stratification include assessments of cholesterol, blood pressure, blood sugar, lifestyle, obesity, and family history. Despite addressing traditional risk factors, myocardial infarctions and cerebrovascular accidents remain the leading causes of death and disability worldwide. Asymptomatic decay of the vascular system is a prelude to catastrophic events, and calcium scores are emerging as a significant adjunct for risk assessment. Positive calcium scores correlate with an increased risk of cardiovascular events. However, there are no therapies known to reliably reverse calcium scores. Previous studies have demonstrated that intravenous chelation therapy reduces cardiovascular morbidity and mortality in patients with a prior history of myocardial infarction; however, its mechanism of action is unknown. One theory is that chelation therapy would reverse calcium buildup in coronary arteries, which is known to have a positive correlation with the risk of having a cardiovascular event. The 10 patients had no prior history of coronary artery disease. Infusions were administered in an outpatient setting. Patients were encouraged to receive a treatment every month. No other supplements or prescriptions were required as part of the treatment. An average of 26.9 chelations were administered over an average of 37.9 months. Calcium scores decreased by an average of 27.38%, and all 10 patients experienced a reduction in scores.  This study demonstrates that chelation has the potential to reduce calcium scores. Since calcium scores correlate with cardiovascular risk, reducing the calcium score may reduce the risk of an event. If these results are supported by larger, placebo-controlled studies, chelation therapy may become an option that could be added to statins and other FDA-approved therapies for primary prevention in patients with a positive calcium score.

Data-driven mortality risk prediction of severe degenerative mitral regurgitation patients undergoing mitral valve surgery

AIMS

The outcomes of mitral valve replacement/repair (MVR) in severe degenerative mitral regurgitation (MR) patients depend on various risk factors. We aimed to develop a risk prediction model for post-MVR mortality in severe degenerative MR patients using machine learning.

METHODS AND RESULTS

Consecutive severe degenerative MR patients undergoing MVR were analysed (n = 1521; 70% training/30% test sets). A random survival forest (RSF) model was constructed, with 3-year post-MVR all-cause mortality as the outcome. Partial dependency plots were used to define the thresholds of each risk factor. A simple scoring system (MVR-score) was developed to stratify post-MVR mortality risk. At 3 years following MVR, 90 patients (5.9%) died in the entire cohort (59 and 31 deaths in the training and test sets). The most important predictors of mortality in order of importance were age, haemoglobin, valve replacement, glomerular filtration rate, left atrial dimension, and left ventricular (LV) end-systolic diameter. The final RSF model with these six variables demonstrated high predictive performance in the test set (3-year C-index 0.880, 95% confidence interval 0.834-0.925), with mortality risk increased strongly with left atrial dimension >55 mm, and LV end-systolic diameter >45 mm. MVR-score demonstrated effective risk stratification and had significantly higher predictability compared to the modified Mitral Regurgitation International Database score (3-year C-index 0.803 vs. 0.750, P = 0.034).

CONCLUSION

A data-driven machine learning model provided accurate post-MVR mortality prediction in severe degenerative MR patients. The outcome following MVR in severe degenerative MR patients is governed by both clinical and echocardiographic factors.