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

New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients

BACKGROUND

The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD.

METHODS

We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017-April 2019).

RESULTS

In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively.

CONCLUSION

We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov, NCT03865914.

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

Computational Signatures for Post-Cardiac Arrest Trajectory Prediction: Importance of Early Physiological Time Series

BACKGROUND

There is an unmet need for timely and reliable prediction of post-cardiac arrest (CA) clinical trajectories. We hypothesized that physiological time series (PTS) data recorded on the first day of intensive care would contribute significantly to discrimination of outcomes at discharge.

PATIENTS AND METHODS

Adult patients in the multicenter eICU database who were mechanically ventilated after resuscitation from out-of-hospital CA were included. Outcomes of interest were survival, neurological status based on Glasgow motor subscore (mGCS) and surrogate functional status based on discharge location (DL), at hospital discharge. Three machine learning predictive models were trained, one with features from the electronic health records (EHR), the second using features derived from PTS collected in the first 24 hours after ICU admission (PTS₂₄), and the third combining PTS₂₄ and EHR. Model performances were compared, and the best performing model was externally validated in the MIMIC-III dataset.

RESULTS

Data from 2,216 admissions were included in the analysis. Discrimination of prediction models combining EHR and PTS₂₄ features was higher than models using either EHR or PTS₂₄ for prediction of survival (AUROC 0.83, 0.82 and 0.79 respectively), neurological outcome (0.87, 0.86 and 0.79 respectively), and DL (0.80, 0.78 and 0.76 respectively). External validation in MIMIC-III (n = 86) produced similar model performance. Feature analysis suggested prognostic significance of previously unknown EHR and PTS₂₄ variables.

CONCLUSION

These results indicate that physiological data recorded in the early phase after CA resuscitation contain signatures that are linked to post-CA outcome. Additionally, they attest to the effectiveness of ML for post-CA predictive modeling.

Machine learning approach to predict acute kidney injury after liver surgery

BACKGROUND

Acute kidney injury (AKI) after surgery appears to increase the risk of death in patients with liver cancer. In recent years, machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis.

AIM

To develop prediction models for AKI after liver cancer resection using machine learning techniques.

METHODS

We screened a total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020. The AKI definition used was consistent with the Kidney Disease: Improving Global Outcomes. We included in our analysis preoperative data such as demographic characteristics, laboratory findings, comorbidities, and medication, as well as perioperative data such as duration of surgery. Computerized algorithms used for model development included logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and decision tree (DT). Feature importance was also ranked according to its contribution to model development.

RESULTS

AKI events occurred in 296 patients (12.1%) within 7 d after surgery. Among the original models based on machine learning techniques, the RF algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for XGBoost, 0.90 for DT, 0.90 for SVM, and 0.85 for LR. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variable that contributed the most in the RF algorithm was age, followed by cholesterol, and surgery time.

CONCLUSION

Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI. The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.

The roles of predictors in cardiovascular risk models - a question of modeling culture?

BACKGROUND

While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations.

METHODS

We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots.

RESULTS

When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models.

CONCLUSION

Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm.

Identification of hemodynamic biomarkers for bicuspid aortic valve induced aortic dilation using machine learning

Recent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic parameters from 4D Flow MRI data and find which parameters allow an accurate classification between healthy volunteers (HV) and BAV patients with dilated and non-dilated ascending aorta using machine learning. Sixteen hemodynamic parameters were calculated in the ascending aorta (AAo) and aortic arch (AArch) at peak systole from 4D Flow MRI. We used sequential forward selection (SFS) and principal component analysis (PCA) as feature selection algorithms. Then, eleven machine-learning classifiers were implemented to separate HV and BAV patients (non- and dilated ascending aorta). Multiple correlation patterns from hemodynamic parameters were extracted using hierarchical clustering. The linear discriminant analysis and random forest are the best performing classifiers, using five hemodynamic parameters selected with SFS (velocity angle, forward velocity, vorticity, and backward velocity in AAo; and helicity density in AArch) a 96.31 ± 1.76% and 96.00 ± 0.83% accuracy, respectively. Hierarchical clustering revealed three groups of correlated features. According to this analysis, we observed that features selected by SFS have a better performance than those selected by PCA because the five selected parameters were distributed according to 3 different clusters. Based on the proposed method, we concluded that the feature selection method found five potentially hemodynamic biomarkers related to this disease.

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

Background

Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets.

Methods

Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30–74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables.

Results

First CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R²: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001).

Conclusions

Deep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.

Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4HEN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4HEN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4HEN-COX quintiles, ranging from 0.25% to 7.8%. ML4HEN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND

There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches.

METHODS

6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated.

RESULTS

In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.80, P ≤ 0.001) and mortality (AUC: 0.87 vs. 0.84, P ≤ 0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a >40% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P ≤ 0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.22 vs. 3.61, P = 0.043) and mortality (6.81 vs. 5.52, P = 0.044). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction.

CONCLUSION

DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.

Current Progress of Studies of Coronary CT for Risk Prediction of Major Adverse Cardiovascular Event (MACE)

Cardiovascular disease is a serious threat to human health, and early risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease can help guide prevention and clinical decisions. Coronary computed tomography (CT) is a useful imaging tool for evaluation of coronary heart disease, and its ability to reflect coronary atherosclerosis shows potential value for risk prediction. In recent years, various new techniques and studies of coronary CT have emerged for risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease. We will review the background and current study advances of using coronary artery calcium score, coronary CT angiography, and artificial intelligence in this field.

Prediction of Cardiovascular Parameters With Supervised Machine Learning From Singapore "I" Vessel Assessment and OCT-Angiography: A Pilot Study

Purpose

Assessment of cardiovascular risk is the keystone of prevention in cardiovascular disease. The objective of this pilot study was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax risk, and SCORE risk score) with machine learning (ML) model based on retinal vascular quantitative parameters.

Methods

We proposed supervised ML algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography – angiography (OCT-A) scans (alone and combined). To describe retinal microvascular network, we used the Singapore “I” Vessel Assessment (SIVA), which extracts vessel parameters from fundus photography and quantitative OCT-A retinal metrics of superficial retinal capillary plexus.

Results

The retinal and cardiovascular data of 144 patients were included. This paper presented a high prediction rate of the cardiovascular risk score. By means of the Naïve Bayes algorithm and SIVA + OCT-A data, the AHA risk score was predicted with 81.25% accuracy, the SCORE risk with 75.64% accuracy, and the Syntax score with 96.53% of accuracy.

Conclusions

Performance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with SIVA software and OCT-A were able to predict cardiovascular scores with a robust rate. Quantitative retinal vascular biomarkers with the ML strategy might provide valuable data to implement predictive model for cardiovascular parameters.

Translational Relevance

Small data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters.

Predicting Incident Heart Failure in Women With Machine Learning: The Women's Health Initiative Cohort

BACKGROUND

Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women's Health Initiative (WHI).

METHODS

We used 2 machine-learning methods-Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)-to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years.

RESULTS

LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76).

CONCLUSIONS

Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

BACKGROUND

Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.

METHOD

The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models.

RESULTS

Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban-rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones.

CONCLUSION

Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review

INTRODUCTION

Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants.

METHODS

A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included.

RESULTS

Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health.

DISCUSSION

Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.

Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important.

PURPOSE

To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization.

STUDY TYPE

Retrospective.

POPULATION

A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%).

FIELD STRENGTH

A 1.5 T, balanced steady-state free precession (bSSFP) sequence.

ASSESSMENT

Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization.

STATISTICAL TESTS

ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant.

RESULTS

During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and -15%, respectively.

DATA CONCLUSIONS

Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.

Molecular Insights in Atrial Fibrillation Pathogenesis and Therapeutics: A Narrative Review

The prevalence of atrial fibrillation (AF) is bound to increase globally in the following years, affecting the quality of life of millions of people, increasing mortality and morbidity, and beleaguering health care systems. Increasingly effective therapeutic options against AF are the constantly evolving electroanatomic substrate mapping systems of the left atrium (LA) and ablation catheter technologies. Yet, a prerequisite for better long-term success rates is the understanding of AF pathogenesis and maintenance. LA electrical and anatomical remodeling remains in the epicenter of current research for novel diagnostic and treatment modalities. On a molecular level, electrical remodeling lies on impaired calcium handling, enhanced inwardly rectifying potassium currents, and gap junction perturbations. In addition, a wide array of profibrotic stimuli activates fibroblast to an increased extracellular matrix turnover via various intermediaries. Concomitant dysregulation of the autonomic nervous system and the humoral function of increased epicardial adipose tissue (EAT) are established mediators in the pathophysiology of AF. Local atrial lymphomononuclear cells infiltrate and increased inflammasome activity accelerate and perpetuate arrhythmia substrate. Finally, impaired intracellular protein metabolism, excessive oxidative stress, and mitochondrial dysfunction deplete atrial cardiomyocyte ATP and promote arrhythmogenesis. These overlapping cellular and molecular alterations hinder us from distinguishing the cause from the effect in AF pathogenesis. Yet, a plethora of therapeutic modalities target these molecular perturbations and hold promise in combating the AF burden. Namely, atrial selective ion channel inhibitors, AF gene therapy, anti-fibrotic agents, AF drug repurposing, immunomodulators, and indirect cardiac neuromodulation are discussed here.

Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.

Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study

AIMS

Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects.

METHODS AND RESULTS

Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 ± 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 ± 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P < 0.05 for all). Subjects with a higher ML score (by Youden's index) had high hazard of suffering events (HR: 10.38, P < 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men.

CONCLUSIONS

In this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment.

LightGBM: an efficient and accurate method for predicting pregnancy diseases

As machine learning is becoming the fashion in disease prediction while no prediction model has performed very efficiently and accurately on predicting pregnancy diseases up to now, it's necessary to compare several common machine learning methods' performance on pregnancy diseases prediction and select out the best one. The data of two common pregnancy complications, pregnancy-induced hypertension (PIH) and Intrahepatic cholestasis of pregnancy (ICP), based on various maternal characteristics measured in patients' routine blood examination in 10-19 weeks of gestation are considered to be suitable to be learned. This is a retrospective study of 320 healthy pregnancies in 10-19 weeks, with 149 patients who subsequently developed PIH and 250 patients who subsequently developed ICP. Nine machine learning methods were used to predict PIH and ICP and their performance was compared via 8 evaluation indexes. Finally, the light Gradient Boosting Machine (lightGBM) is considered to be the best method to predict gestational diseases.Impact statementWhat is already known on this subject? As a kind of commonly used method in disease prediction, machine learning could be applied to clinical data for developing robust risk models and many achievements have been made. Also, machine learning can be used to predict pregnancy diseases. Although some machine learning methods have been used for screening gestational diseases, methods based on simple theories, such as logistic regression and decision tree, are frequently used. They don't always have a very satisfactory prediction results. Besides, only a few types of pregnancy diseases can be predicted.What do the results of this study add? LightGBM has the best prediction results of PIH and ICP among 9 machine learning methods in this study. It can predict PIH (AUC = 81.72%) with a sensitivity of 70.59%, and ICP (AUC = 95.91%) with a sensitivity of 97.91%.What are the implications of these findings for clinical practice and/or further research? A new model has been developed for effective first-trimester screening for two common pregnancy diseases, PIH and ICP. This lightGBM model can be used in relative hospitals and population of the research, and provide references for doctors' diagnosis and treatment of pregnant women. In further research, the predicted effect of lightGBM on daily practice and other pregnancy diseases such as pregnancy diabetes, will be verified.

Artificial intelligence and cardiovascular imaging: A win-win combination

Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

BACKGROUND

Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined.

OBJECTIVES

Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality.

METHODS

Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years' follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome.

RESULTS

There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m²) and small (LVEDVi ≤55 mL/m²) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort.

CONCLUSIONS

Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR.

[Machine learning in anesthesiology]

The application of artificial intelligence (AI) is currently changing very different areas of life. Artificial intelligence involves the emulation of human behavior with the aid of methods from mathematics and informatics. Machine learning (ML) represents a subdivision of AI. Algorithms for ML have the potential to optimize patient care, in that they can be utilized in a supportive way in personalized medicine, decision making and risk prediction. Although the majority of the applications in medicine are still limited to data analysis and research, it is certain that ML will become increasingly more important in scientific and clinical aspects in this supportive function. Therefore, it is necessary for clinicians to have at least a basic understanding of the functional principles, strengths and weaknesses of ML.

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

OBJECTIVE

The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.

METHODS

Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.

RESULTS

Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.

CONCLUSIONS

Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Temporal change in inflammatory biomarkers and risk of cardiovascular events: the Multi-ethnic Study of Atherosclerosis

Aims

Little is known about the association of temporal changes in inflammatory biomarkers and the risk of death and cardiovascular diseases. We aimed to evaluate the association between temporal changes in C‐reactive protein (CRP), fibrinogen, and interleukin‐6 (IL‐6) and risk of heart failure (HF), cardiovascular disease (CVD), and all‐cause mortality in individuals without a history of prior CVD.

Methods and results

Participants from the Multi‐Ethnic Study of Atherosclerosis (MESA) cohort with repeated measures of inflammatory biomarkers and no CVD event prior to the second measure were included. Quantitative measures, annual change, and biomarker change categories were used as main predictors in Cox proportional hazard models stratified based on sex and statin use. A total of 2258 subjects (50.6% female, mean age of 62 years) were studied over an average of 8.1 years of follow‐up. The median annual decrease in CRP levels was 0.08 mg/L. Fibrinogen and IL‐6 levels increased by a median of 30 mg/dL and 0.24 pg/mL annually. Temporal changes in CRP were positively associated with HF risk among females (HR: 1.18 per each standard deviation increase, P < 0.001) and other CVD in both female (HR: 1.12, P = 0.004) and male participants (HR: 1.24, P = 0.003). The association of CRP change with HF and other CVD was consistently observed in statin users (HR: 1.23 per SD increase, P = 0.001 for HF and HR: 1.19 per SD increase, P < 0.001 for other CVD). There were no significant associations between temporal changes of fibrinogen or IL‐6 with HF or other CVD. Men with sustained high values of IL‐6 had a 2.3‐fold higher risk of all‐cause mortality (P < 0.001) compared with those with sustained low values.

Conclusions

Temporal change in CRP is associated with HF only in women and statin users, and other CVD in both women and men, and statin users. Annual changes in fibrinogen and IL‐6 were not predictive of cardiovascular outcomes in either sex.

Artificial intelligence and the future of life sciences

Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.

Machine learning does not improve upon traditional regression in predicting outcomes in atrial fibrillation: an analysis of the ORBIT-AF and GARFIELD-AF registries

AIMS

Prediction models for outcomes in atrial fibrillation (AF) are used to guide treatment. While regression models have been the analytic standard for prediction modelling, machine learning (ML) has been promoted as a potentially superior methodology. We compared the performance of ML and regression models in predicting outcomes in AF patients.

METHODS AND RESULTS

The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF) and Global Anticoagulant Registry in the FIELD (GARFIELD-AF) are population-based registries that include 74 792 AF patients. Models were generated from potential predictors using stepwise logistic regression (STEP), random forests (RF), gradient boosting (GB), and two neural networks (NNs). Discriminatory power was highest for death [STEP area under the curve (AUC) = 0.80 in ORBIT-AF, 0.75 in GARFIELD-AF] and lowest for stroke in all models (STEP AUC = 0.67 in ORBIT-AF, 0.66 in GARFIELD-AF). The discriminatory power of the ML models was similar or lower than the STEP models for most outcomes. The GB model had a higher AUC than STEP for death in GARFIELD-AF (0.76 vs. 0.75), but only nominally, and both performed similarly in ORBIT-AF. The multilayer NN had the lowest discriminatory power for all outcomes. The calibration of the STEP modelswere more aligned with the observed events for all outcomes. In the cross-registry models, the discriminatory power of the ML models was similar or lower than the STEP for most cases.

CONCLUSION

When developed from two large, community-based AF registries, ML techniques did not improve prediction modelling of death, major bleeding, or stroke.

Data-driven point-of-care risk model in patients with acute myocardial infarction and cardiogenic shock

BACKGROUND

Prognosis models based on stepwise regression methods show modest performance in patients with cardiogenic shock (CS). Automated variable selection allows data-driven risk evaluation by recognizing distinct patterns in data. We sought to evaluate an automated variable selection method (least absolute shrinkage and selection operator, LASSO) for predicting 30-day mortality in patients with acute myocardial infarction and CS (AMICS) receiving acute percutaneous coronary intervention (PCI) compared to two established scores.

METHODS AND RESULTS

Consecutive patients with AMICS receiving acute PCI at one of two tertiary heart centres in Denmark 2010-2017. Patients were divided according to treatment with mechanical circulatory support (MCS); PCI-MCS cohort (n = 220) versus PCI cohort (n = 1180). The latter was divided into a development (2010-2014) and a temporal validation cohort (2015-2017). Cohort-specific LASSO models were based on data obtained before PCI. LASSO models outperformed IABP-SHOCK II and CardShock risk scores in discriminative ability for 30-day mortality in the PCI validation [receiver operating characteristics area under the curve (ROC AUC) 0.80 (95% CI 0.76-0.84) vs 0.73 (95% CI 0.69-0.77) and 0.70 (95% CI 0.65-0.75), respectively, P < 0.01 for both] and PCI-MCS development cohort [ROC AUC 0.77 (95% CI 0.70-0.83) vs 0.64 (95% CI 0.57-0.71) and 0.64 (95% CI 0.57-0.71), respectively, P < 0.01 for both]. Variable influence differed depending on MCS, with age being the most influential factor in the LASSO-PCI model, whereas haematocrit and estimated glomerular filtration rate were the highest-ranking factors in the LASSO-PCI-MCS model.

CONCLUSION

Data-driven prognosis models outperformed established risk scores in patients with AMICS receiving acute PCI and exhibited good discriminative abilities. Observations indicate a potential use of machinelearning to facilitate individualized patient care and targeted interventions in the future.

Influence of coronary dominance on coronary artery calcification burden

OBJECTIVE

To evaluate the influence of coronary artery dominance on observed coronary artery calcification burden in outpatients presenting for coronary computed tomography angiography (CCTA).

METHODS

A 12-month retrospective review was performed of all CCTAs at a single institution. Coronary arterial dominance, Agatston score and presence or absence of cardiovascular risk factors including hypertension (HTN), hyperlipidemia (HLD), diabetes and smoking were recorded. Dominance groups were compared in terms of calcium score adjusted for covariates using analysis of covariance based on ranks. Only covariates observed to be significant independent predictors of the relevant outcome were included in each analysis. All statistical tests were conducted at the two-sided 5% significance level.

RESULTS

1223 individuals, 618 women and 605 men were included, mean age 60 years (24-93 years). Right coronary dominance was observed in 91.7% (n = 1109), left dominance in 8% (n = 98), and codominance in 1.3% (n = 16). The distribution of patients among Agatston score severity categories significantly differed between codominant and left (p = 0.008), and codominant and right (p = 0.022) groups, with higher prevalence of either zero or severe CAC in the codominant patients. There was no significant difference in Agatston score between dominance groups. In the subset of individuals with coronary artery calcification, Agatston score was significantly higher in codominant versus left dominant patients (mean Agatston score 595 ± 520 vs. mean 289 ± 607, respectively; p = 0.049), with a trend towards higher scores in comparison to the right-dominant group (p = 0.093). Significance was not maintained upon adjustment for covariates.

CONCLUSIONS

While the distribution of Agatston score severity categories differed in codominant versus right- or left-dominant patients, there was no significant difference in Agatston score based on coronary dominance pattern in our cohort. Reporting and inclusion of codominant subsets in larger investigations may elucidate whether codominant anatomy is associated with differing risk.

The risk of ischemic stroke and hemorrhagic stroke in Chinese adults with low-density lipoprotein cholesterol concentrations < 70 mg/dL

BACKGROUND

The risk of stroke in individuals with very low low-density lipoprotein cholesterol (LDL-C) concentrations remains high. We sought to prioritize predictive risk factors for stroke in Chinese participants with LDL-C concentrations < 70 mg/dL using a survival conditional inference tree, a machine learning method.

METHODS

The training dataset included 9327 individuals with LDL-C concentrations < 70 mg/dL who were free of cardiovascular diseases and did not use lipid-modifying drugs from the Kailuan I study (N = 101,510). We examined the validity of this algorithm in a second Chinese cohort of 1753 participants with LDL-C concentrations < 70 mg/dL from the Kailuan II study (N = 35,856).

RESULTS

During a mean 8.5-9.0-year follow-up period, we identified 388 ischemic stroke cases and 145 hemorrhagic stroke cases in the training dataset and 20 ischemic stroke cases and 8 hemorrhagic stroke cases in the validation dataset. Of 15 examined predictors, poorly controlled blood pressure and very low LDL-C concentrations (≤ 40 mg/dL) were the top hierarchical predictors of both ischemic stroke risk and hemorrhagic stroke risk. The groups, characterized by the presence of 2-3 of aforementioned risk factors, were associated with a higher risk of ischemic stroke (hazard ratio (HR) 7.03; 95% confidence interval (CI) 5.01-9.85 in the training dataset; HR 4.68, 95%CI 1.58-13.9 in the validation dataset) and hemorrhagic stroke (HR 3.94, 95%CI 2.54-6.11 in the training dataset; HR 4.73, 95%CI 0.81-27.6 in the validation dataset), relative to the lowest risk groups (presence of 0-1 of these factors). There was a linear association between cumulative average LDL-C concentrations and stroke risk. LDL-C concentrations ≤ 40 mg/dL was significantly associated with increased risk of ischemic stroke (HR 2.07, 95%CI 1.53, 2.80) and hemorrhagic stroke (HR 2.70, 95%CI 1.70, 4.30) compared to LDL-C concentrations of 55-70 mg/dL, after adjustment for age, hypertension status, and other covariates.

CONCLUSION

Individuals with extremely low LDL-C concentrations without previous lipid-modifying treatment could still be at high stroke risk.

TRIAL REGISTRATION

Chinese Clinical Trial Register, ChiCTR-TNRC-11001489 . Registered on 24-08-2011.

Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis

BACKGROUND

Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races.

METHODS

We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D'Agostino χ2 tests were used to assess discrimination and calibration, respectively.

RESULTS

The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults.

CONCLUSIONS

Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.

Applications of machine learning in the chemical pathology laboratory

Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory's total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.

Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China

Background

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks.

Methods

The final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of included variables. Twenty-two variables, including sociodemographic characteristics, medical history, cytokines, and synthetic indices, were used for model development.

Results

Among 1508 subjects, 203 were diagnosed with CVD over a median follow-up of 5.17 years. All 7 models had moderate to excellent discrimination (AUC ranged from 0.770 to 0.872) and were well calibrated. LR and SVM performed identically with an AUC of 0.872 (95% CI: 0.829–0.907) and 0.868 (95% CI: 0.825–0.904), respectively. LR had the lowest Brier score (0.078) and the highest sensitivity (97.1%). Decision curve analysis indicated that SVM was slightly better than LR. The inflammatory cytokines, such as hs-CRP and IL-6, were identified as strong predictors of CVD.

Conclusion

SVM and LR can be applied to guide clinical decision-making in the Kazakh Chinese population, and further study is required to ensure their accuracies.

Metabolic Syndrome in an Aging Society - Role of Oxidant-Antioxidant Imbalance and Inflammation Markers in Disentangling Atherosclerosis

INTRODUCTION

The prevalence of metabolic syndrome among the elderly population is growing. The elements of metabolic syndrome in an aging society are currently being researched. Atherosclerosis is a slow process in which the first symptoms may be observed after many years. The mechanisms underlying the progression of atherosclerosis are oxidative stress and inflammation. Inflammation and oxidative stress are associated with the increased incidence of metabolic syndrome. Taking the above into consideration, metabolic syndrome is thought to be a clinical equivalent of atherosclerosis.

AIM

The aim of this paper is to review the impact of the interplay of oxidant-antioxidant and inflammation markers in metabolic syndrome in general as well as its components in the pathophysiology which underlies development of atherosclerosis in elderly individuals.

METHODS

A systematic scan of online resources designed for elderly (≥65 years) published from 2005 to the end of 2020 were reviewed. This was supplemented with grey literature and then all resources were narratively analyzed. The analysis included the following terms: "atherosclerosis or metabolic syndrome" and "oxidative stress or inflammation" and "elderly" to find reports of atherosclerotic disease from asymptomatic to life-threatening among the elderly population with metabolic syndrome .

RESULTS

The work summarizes articles that were applicable to this study, including systematic reviews, qualitative studies and opinion pieces. Current knowledge focuses on monitoring the inflammation and oxidant-antioxidant imbalance in disentangling atherosclerosis in patients diagnosed with metabolic syndrome. The population-based studies described inflammation, increased oxidative stress and weak antioxidant defense systems as the mechanisms underlying atherosclerosis development. Moreover, there are discussions that these targets could potentially be a point of intervention to reduce the development of atherosclerosis in the elderly, especially those with altered glucose and lipid metabolism. Specific markers may be used as an approach for the prevention and lifestyle modification of atherosclerotic disease in such population.

CONCLUSION

Metabolic syndrome and its components are important contributors in the progression of atherosclerotic disease in the elderly population but constant efforts should be made to broaden our knowledge of elderly groups who are the most susceptible for the development of atherosclerosis complications.

Machine ​learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure

Aims

Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in‐hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models.

Methods and results

Inpatient cases with primary International Statistical Classification of Diseases and Related Health Problems (ICD‐10) encoded discharge diagnosis of HF non‐electively admitted to 86 German Helios hospitals between 1 January 2016 and 31 December 2018 were identified. The dataset was randomly split 75%/25% for model development and testing. Highly unbalanced variables were removed. Four ML algorithms were applied, and all algorithms were tuned using a grid search with multiple repetitions. Model performance was evaluated by computing receiver operating characteristic areas under the curve. In total, 59 125 cases (69.8% aged 75 years or older, 51.9% female) were investigated, and in‐hospital mortality was 6.20%. Areas under the curve of all ML algorithms outperformed regression analysis in the testing dataset with values of 0.829 [95% confidence interval (CI) 0.814–0.843] for logistic regression, 0.875 (95% CI 0.863–0.886) for random forest, 0.882 (95% CI 0.871–0.893) for gradient boosting machine, 0.866 (95% CI 0.854–0.878) for single‐layer neural networks, and 0.882 (95% CI 0.872–0.893) for extreme gradient boosting. Brier scores demonstrated a good calibration especially of the latter three models.

Conclusions

We introduced reliable models to calculate expected in‐hospital mortality based only on administrative routine data using ML algorithms. A broad application could supplement quality measurement programs and therefore improve future HF patient care.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Multi-Ethnic Study of Atherosclerosis (MESA): JACC Focus Seminar 5/8

The MESA (Multi-Ethnic Study of Atherosclerosis) is a National Heart, Lung, and Blood Institute-sponsored prospective study aimed at studying the prevalence, progression, determinants, and prognostic significance of subclinical cardiovascular disease in a sex-balanced, multiethnic, community-dwelling U.S. cohort. MESA helped usher in an era of noninvasive evaluation of subclinical atherosclerosis presence, burden, and progression for the evaluation of atherosclerotic cardiovascular disease risk, beyond what could be predicted by traditional risk factors alone. Concepts developed in MESA have informed international patient care guidelines, providing new tools to effectively guide public health policy, population screening, and clinical decision-making. MESA is grounded in an open science model that continues to be a beacon for collaborative science. In this review, we detail the original goals of MESA, and describe how the scope of MESA has evolved over time. We highlight 10 significant MESA contributions to cardiovascular medicine, and chart the path forward for MESA in the year 2021 and beyond.

Using Anti-Malondialdehyde Modified Peptide Autoantibodies to Import Machine Learning for Predicting Coronary Artery Stenosis in Taiwanese Patients with Coronary Artery Disease

Machine learning (ML) algorithms have been applied to predicting coronary artery disease (CAD). Our purpose was to utilize autoantibody isotypes against four different unmodified and malondialdehyde (MDA)-modified peptides among Taiwanese with CAD and healthy controls (HCs) for CAD prediction. In this study, levels of MDA, MDA-modified protein (MDA-protein) adducts, and autoantibody isotypes against unmodified peptides and MDA-modified peptides were measured with enzyme-linked immunosorbent assay (ELISA). To improve the performance of ML, we used decision tree (DT), random forest (RF), and support vector machine (SVM) coupled with five-fold cross validation and parameters optimization. Levels of plasma MDA and MDA-protein adducts were higher in CAD patients than in HCs. IgM anti-IGKC^76-99 MDA and IgM anti-A1AT^284-298 MDA decreased the most in patients with CAD compared to HCs. In the experimental results of CAD prediction, the decision tree classifier achieved an area under the curve (AUC) of 0.81; the random forest classifier achieved an AUC of 0.94; the support vector machine achieved an AUC of 0.65 for differentiating between CAD patients with stenosis rates of 70% and HCs. In this study, we demonstrated that autoantibody isotypes imported into machine learning algorithms can lead to accurate models for clinical use.

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy

Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF.

Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms.

Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation.

Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups.

Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.

Machine Learning Applications in Heart Failure Disease Management: Hype or Hope?

Purpose of the review

Machine learning (ML) approaches have emerged as powerful tools in medicine. This review focuses on the use ML to assess risk of events in patients with heart failure (HF). It provides an overview of the ML process, challenges in developing risk scores, and strategies to mitigate problems.

Recent findings

Risk scores developed using standard statistical methods have limited accuracy, particularly when they are applied to populations other than the one in which they were developed. Computerized ML algorithms which identify correlations between descriptive variables in complex, non-linear, multi-dimensional systems provide an alternative approach to predicting risk of events. The MARKER-HF mortality risk score was developed using data from the electronic health record of HF patients followed at a large academic medical center. The risk score, which uses eight commonly available variables, proved to be highly accurate in predicting mortality across the spectrum of risk. It retained accuracy in independent populations and was superior to other risk scores.

Summary

Machine learning approaches can be used to develop risk scores that are superior to ones based on standard statistical methods. Careful attention to detail in curating data, selecting covariates, and trouble-shooting the process is required to optimize results.

Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke

BACKGROUND

Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.

AIMS

582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, 79 were excluded due to using cohorts from outside the United States or being review articles or editorials. 27 studies were thus included in this analysis.

SUMMARY OF REVIEW

Of the 27 studies included, 7 (25.9%) used patient data from California, 6 (22.2%) were multicenter, 3 (11.1%) were in Massachusetts, 2 (7.4%) each in Illinois, Missouri, and New York, and 1 (3.7%) each from South Carolina, Washington, West Virginia, and Wisconsin. 1 (3.7%) study used data from Utah and Texas. These were qualitatively compared to a CDC study showing the highest distribution of stroke in Mississippi (4.3%) followed by Oklahoma (3.4%), Washington D.C. (3.4%), Louisiana (3.3%), and Alabama (3.2%) while the prevalence in California was 2.6%.

CONCLUSIONS

It is clear that a strong disconnect exists between the datasets and patient cohorts used in training machine learning algorithms in clinical research and the stroke distribution in which clinical tools using these algorithms will be implemented. In order to ensure a lack of bias and increase generalizability and accuracy in future machine learning studies, datasets using a varied patient population that reflects the unequal distribution of stroke risk factors would greatly benefit the usability of these tools and ensure accuracy on a nationwide scale.

Pre-existing and machine learning-based models for cardiovascular risk prediction

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70-0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer-Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer-Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.