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

Machine learning in cardiology: Clinical application and basic research

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increasingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.

Novel Anti-Inflammatory Therapies in Coronary Artery Disease and Acute Coronary Syndromes

Evidence suggests that inflammation plays an important role in atherosclerosis and the consequent clinical presentation, including stable coronary artery disease (CAD) and acute coronary syndromes (ACS). The most essential elements are cytokines, proteins with hormone-like properties that are produced by the immune cells, endothelial cells, platelets, fibroblasts, and some stromal cells. Interleukins (IL-1β and IL-6), chemokines, interferon-γ (IFN-γ), and tumor necrosis factor-alpha (TNF-α) are the cytokines commonly associated with endothelial dysfunction, vascular inflammation, and atherosclerosis. These molecules can be targeted by commonly used therapeutic substances or selective molecules that exert targeted anti-inflammatory actions. The most significant anti-inflammatory therapies are aspirin, statins, colchicine, IL-1β inhibitors, and IL-6 inhibitors, along with novel therapies such as TNF-α inhibitors and IL-1 receptor antagonists. Aspirin and statins are well-established therapies for atherosclerosis and CAD and their pleiotropic and anti-inflammatory actions contribute to their efficacy and favorable profile. Colchicine may also be considered in high-risk patients if recurrent ACS episodes occur when on optimal medical therapy according to the most recent guidelines. Recent randomized studies have also shown that therapies specifically targeting inflammatory interleukins and inflammation can reduce the risk for cardiovascular events, but these therapies are yet to be fully implemented in clinical practice. Preclinical research is also intense, targeting various inflammatory mediators that are believed to be implicated in CAD, namely repeated transfers of the soluble mutant of IFN-γ receptors, NLRP3 inflammasome inhibitors, IL-10 delivery by nanocarriers, chemokine modulatory treatments, and reacting oxygen species (ROS) targeting nanoparticles. Such approaches, although intriguing and promising, ought to be tested in clinical settings before safe conclusions can be drawn. Although the link between inflammation and atherosclerosis is significant, further studies are needed in order to elucidate this association and improve outcomes in patients with CAD.

Promise and pitfalls of ChatGPT for patient education on coronary angiogram

The past decade has seen extraordinary and rapid progress in the field of artificial intelligence (AI), which produces computer systems capable of performing tasks that typically require human intelligence.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

BACKGROUND

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

OBJECTIVE

The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

METHODS

Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map.

RESULTS

Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics.

CONCLUSION

Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Imaging Early Life Cardiovascular Phenotype

The growing epidemics of obesity, hypertension, and diabetes, in addition to worsening environmental factors such as air pollution, water scarcity, and climate change, have fueled the continuously increasing prevalence of cardiovascular diseases (CVDs). This has caused a markedly increasing burden of CVDs that includes mortality and morbidity worldwide. Identification of subclinical CVD before overt symptoms can lead to earlier deployment of preventative pharmacological and nonpharmacologic strategies. In this regard, noninvasive imaging techniques play a significant role in identifying early CVD phenotypes. An armamentarium of imaging techniques including vascular ultrasound, echocardiography, magnetic resonance imaging, computed tomography, noninvasive computed tomography angiography, positron emission tomography, and nuclear imaging, with intrinsic strengths and limitations can be utilized to delineate incipient CVD for both clinical and research purposes. In this article, we review the various imaging modalities used for the evaluation, characterization, and quantification of early subclinical cardiovascular diseases.

Recent Advances in Translational Research for Heart Failure in Japan

Despite decades of intensive research and therapeutic development, heart failure remains a leading cause of death worldwide. However, recent advances in several basic and translational research fields, such as genomic analysis and single-cell analysis, have increased the possibility of developing novel diagnostic approaches to heart failure. Most cardiovascular diseases that predispose individuals to heart failure are caused by genetic and environmental factors. It follows that genomic analysis can contribute to the diagnosis and prognostic stratification of patients with heart failure. In addition, single-cell analysis has shown great potential for unveiling the pathogenesis and/or pathophysiology and for discovering novel therapeutic targets for heart failure. Here, we summarize the recent advances in translational research on heart failure in Japan, based mainly on our studies.

Machine Learning for Prediction of Adverse Cardiovascular Events in Adults With Repaired Tetralogy of Fallot Using Clinical and Cardiovascular Magnetic Resonance Imaging Variables

BACKGROUND

Existing models for prediction of major adverse cardiovascular events (MACE) after repair of tetralogy of Fallot have been limited by modest predictive capacity and limited applicability to routine clinical practice. We hypothesized that an artificial intelligence model using an array of parameters would enhance 5-year MACE prediction in adults with repaired tetralogy of Fallot.

METHODS

A machine learning algorithm was applied to 2 nonoverlapping, institutional databases of adults with repaired tetralogy of Fallot: (1) for model development, a prospectively constructed clinical and cardiovascular magnetic resonance registry; (2) for model validation, a retrospective database comprised of variables extracted from the electronic health record. The MACE composite outcome included mortality, resuscitated sudden death, sustained ventricular tachycardia and heart failure. Analysis was restricted to individuals with MACE or followed ≥5 years. A random forest model was trained using machine learning (n=57 variables). Repeated random sub-sampling validation was sequentially applied to the development dataset followed by application to the validation dataset.

RESULTS

We identified 804 individuals (n=312 for development and n=492 for validation). Model prediction (area under the curve [95% CI]) for MACE in the validation dataset was strong (0.82 [0.74-0.89]) with superior performance to a conventional Cox multivariable model (0.63 [0.51-0.75]; P=0.003). Model performance did not change significantly with input restricted to the 10 strongest features (decreasing order of strength: right ventricular end-systolic volume indexed, right ventricular ejection fraction, age at cardiovascular magnetic resonance imaging, age at repair, absolute ventilatory anaerobic threshold, right ventricular end-diastolic volume indexed, ventilatory anaerobic threshold % predicted, peak aerobic capacity, left ventricular ejection fraction, and pulmonary regurgitation fraction; 0.81 [0.72-0.89]; P=0.232). Removing exercise parameters resulted in inferior model performance (0.75 [0.65-0.84]; P=0.002).

CONCLUSIONS

In this single-center study, a machine learning-based prediction model comprised of readily available clinical and cardiovascular magnetic resonance imaging variables performed well in an independent validation cohort. Further study will determine the value of this model for risk stratification in adults with repared tetralogy of Fallot.

Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis

PURPOSE

With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis.

METHODS

Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD.

RESULTS

After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang.

CONCLUSION

The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning

BACKGROUND

Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden.

METHODS

Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model.

RESULTS

Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F₁ score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F₁ score by 33.7%.

CONCLUSIONS

Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Using machine learning to predict cardiovascular risk using self-reported questionnaires: Findings from the 45 and up study

BACKGROUND

Machine learning has been shown to outperform traditional statistical methods for risk prediction model development. We aimed to develop machine learning-based risk prediction models for cardiovascular mortality and hospitalisation for ischemic heart disease (IHD) using self-reported questionnaire data.

METHODS

The 45 and Up Study was a retrospective population-based study in New South Wales, Australia (2005-2009). Self-reported healthcare survey data on 187,268 participants without a history of cardiovascular disease was linked to hospitalisation and mortality data. We compared different machine learning algorithms, including traditional classification methods (support vector machine (SVM), neural network, random forest and logistic regression) and survival methods (fast survival SVM, Cox regression and random survival forest).

RESULTS

A total of 3687 participants experienced cardiovascular mortality and 12,841 participants had IHD-related hospitalisation over a median follow-up of 10.4 years and 11.6 years respectively. The best model for cardiovascular mortality was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 0.3 achieved by under-sampling of the non-cases. This model had the Uno's and Harrel's concordance indexes of 0.898 and 0.900 respectively. The best model for IHD hospitalisation was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 1.0 with Uno's and Harrel's concordance indexes of 0.711 and 0.718 respectively.

CONCLUSION

Machine learning-based risk prediction models developed using self-reported questionnaire data had good prediction performance. These models may have the potential to be used in initial screening tests to identify high-risk individuals before undergoing costly investigation.

Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification

In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.

Subclinical Vascular Composites Predict Clinical Cardiovascular Disease, Stroke, and Dementia: The Multi-Ethnic Study of Atherosclerosis (MESA)

Background

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 6,814 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 supine blood 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 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, arteriosclerosis, atherosclerosis, 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 arteriosclerosis and atherosclerosis 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 arteriosclerosis and atherosclerosis may be useful biomarkers to inform the vascular pathways contributing to events of CVD, CHD, stroke, and dementia.

Stroke prevention in rural residents: development of a simplified risk assessment tool with artificial intelligence

BACKGROUND

Limited studies have focused on the risk assessment of stroke in rural regions. Moreover, the application of artificial intelligence in stroke risk scoring system is still insufficient. This study aims to develop a simplified and visualized risk score with good performance and convenience for rural stroke risk assessment, which is combined with a machine learning (ML) algorithm.

METHODS

Participants of the Henan Rural Cohort were enrolled in this study. The total participants (n = 38,322) were randomly split into a train set and a test set in the ratio of 7:3. An ML algorithm was used to select variables and the logistic regression was then applied to construct the scoring system. The C-statistic and the Brier score (BS) were used to evaluate the discrimination and calibration. The Framingham stroke risk profile (FSRP) and the self-reported stroke risk function (SRSRF) were chosen to be compared.

RESULTS

The Rural Stroke Risk Score (RSRS) was produced in this study, including age, drinking status, triglyceride, type 2 diabetes mellitus, hypertension, waist circumference, and family history of stroke. On validation, the C-statistic was 0.757 (95% CI 0.749-0.765) and the BS was 0.058 in the test set. In addition, the discrimination of RSRS was 6.02% and 7.34% higher than that of the FSRP and SRSRF, respectively.

CONCLUSIONS

A well-performed scoring system for assessing stroke risk in rural residents was developed in this study. This risk score would facilitate stroke screening and the prevention of cardiovascular disease in economically underdeveloped areas.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

A streamlined, machine learning-derived approach to risk-stratification in heart failure patients with secondary tricuspid regurgitation

AIMS

Secondary tricuspid regurgitation (sTR) is the most frequent valvular heart disease and has a significant impact on mortality. A high burden of comorbidities often worsens the already dismal prognosis of sTR, while tricuspid interventions remain underused and initiated too late. The aim was to examine the most powerful predictors of all-cause mortality in moderate and severe sTR using machine learning techniques and to provide a streamlined approach to risk-stratification using readily available clinical, echocardiographic and laboratory parameters.

METHODS AND RESULTS

This large-scale, long-term observational study included 3359 moderate and 1509 severe sTR patients encompassing the entire heart failure spectrum (preserved, mid-range and reduced ejection fraction). A random survival forest was applied to investigate the most important predictors and group patients according to their number of adverse features.The identified predictors and thresholds, that were associated with significantly worse mortality were lower glomerular filtration rate (<60 mL/min/1.73m2), higher NT-proBNP, increased high sensitivity C-reactive protein, serum albumin < 40 g/L and hemoglobin < 13 g/dL. Additionally, grouping patients according to the number of adverse features yielded important prognostic information, as patients with 4 or 5 adverse features had a fourfold risk increase in moderate sTR [4.81(3.56-6.50) HR 95%CI, P < 0.001] and fivefold risk increase in severe sTR [5.33 (3.28-8.66) HR 95%CI, P < 0.001].

CONCLUSION

This study presents a streamlined, machine learning-derived and internally validated approach to risk-stratification in patients with moderate and severe sTR, that adds important prognostic information to aid clinical-decision-making.

Machine Learning and the Conundrum of Stroke Risk Prediction

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Objective:Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

Methods:This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

Conclusion:ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

Machine learning-based prediction of infarct size in patients with ST-segment elevation myocardial infarction: A multi-center study

BACKGROUND

Cardiac magnetic resonance imaging (CMR) is the gold standard for measuring infarct size (IS). However, this method is expensive and requires a specially trained technologist to administer. We therefore sought to quantify the IS using machine learning (ML) based analysis on clinical features, which is a convenient and cost-effective alternative to CMR.

METHODS AND RESULTS

We included 315 STEMI patients with CMR examined one week after morbidity in final analysis. After feature selection by XGBoost on fifty-six clinical features, we used five ML algorithms (random forest (RF), light gradient boosting decision machine, deep forest, deep neural network, and stacking) to predict IS with 26 (selected by XGBoost with information gain greater than average level of 56 features) and the top 10 features, during which 5-fold cross-validation were used to train and optimize models. We then evaluated the value of actual and ML-IS for the prediction of adverse remodeling. Our finding indicates that MLs outperform the linear regression in predicting IS. Specifically, the RF with five predictors identified by the exhaustive method performed better than linear regression (LR) with 10 indicators (R² of RF: 0.8; LR: 0). The finding also shows that both actual and ML-IS were independently associated with adverse remodeling. ML-IS ≥ 21% was associated with a twofold increase in the risk of LV remodeling (P < 0.01) compared with patients with reference IS (1st tertile).

CONCLUSION

ML-based methods can predict IS with widely available clinical features, which provide a proof-of-concept tool to quantitatively assess acute phase IS.

Role of artificial intelligence and machine learning in interventional cardiology

Directed by two decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, AI has shown potential in providing data interpretation and automated analysis from electrocardiogram (ECG), echocardiography, computed tomography angiography (CTA), magnetic resonance imaging (MRI), and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention (R-PCI), along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning (ML) algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of ML has emerged as a deep learning algorithm for numerous cardiovascular (CV) applications. However, the impact DL on the future of cardiology practice is not clear. Predictive models based on DL have several limitations including low generalizability and decision processing in cardiac anatomy.

Incremental Value of Noncontrast Chest Computed Tomography-derived Parameters in Predicting Subclinical Carotid Atherosclerosis: From the PERSUADE Study

PURPOSE

To investigate the incremental value of noncontrast chest computed tomography (CT)-derived parameters, such as coronary artery calcium score (CACS) and epicardial adipose tissue volume (EATv), in predicting subclinical carotid atherosclerosis above traditional risk factors in community-based asymptomatic populations of northern China.

MATERIALS AND METHODS

A total of 2195 community-based asymptomatic individuals were enrolled from Jidong Oilfield in accordance with the PERSUADE study. CACS and EATv were measured on noncontrast chest CT. Demographics and ideal cardiovascular health score (ICHS) were collected through questionnaires. We recalculated the ideal cardiovascular health risk score (ICHRS) (ICHRS=14-ICHS) and standardized the parameters as log-CACS and body mass index adjusted EATv (i-EATv). Subclinical carotid atherosclerosis was assessed by Doppler sonography and defined as any prevalence of average carotid intima-media thickness ≥1.00 mm, appearance of carotid plaque, and carotid arterial stenosis in the areas of extracranial carotid arteries on both sides.

RESULTS

A total of 451 (20.55%) individuals presented subclinical carotid atherosclerosis. CACS and EATv were significantly greater in the subclinical group, while ICHS was lower. In multivariate logistic regression, ICHRS (odds ratio [OR]=1.143, 95% confidence interval [CI]: 1.080-1.210, P <0.001), log-CACS (OR=1.701, 95% CI: 1.480-1.955, P <0.001), and i-EATv (OR=1.254, 95% CI: 1.173-1.341, P <0.001) were found to be independent risk predictors for subclinical carotid atherosclerosis. In receiver-operating characteristic curve analysis, when combined with male sex and age level, the area under the curve of the ICHRS basic model increased from 0.627 (95% CI: 0.599-0.654) to 0.757 (95% CI: 0.732-0.781) ( P <0.0001). Further adding log-CACS and i-EATv, the area under the curve demonstrated a statistically significant improvement (0.788 [95% CI: 0.765-0.812] vs. 0.757 [95% CI: 0.732-0.781], P <0.0001).

CONCLUSION

Noncontrast chest CT-derived parameters, including CACS and EATv, could provide significant incremental improvement for predicting subclinical carotid atherosclerosis beyond the conventional risk assessment model based on ICHRS.

Activation of estrogen receptor α inhibits TLR4 signaling in macrophages and alleviates the instability of atherosclerotic plaques in the postmenopausal stage

Acute cardiovascular events increase significantly in postmenopausal women. The relationship between estrogen receptor (ER) and plaque stability in the postmenopausal stage remains to be elucidated. We aimed to explore whether ERα activation improves plaque instability in the postmenopausal stage. Here, we report that postmenopausal women showed increased macrophage activation and plaque instability with increased MCP-1, MMP9, TLR4, MYD88 and NF-κB p65 and decreased ERα and TIMP1 expression in the vascular endothelium. Moreover, ovariectomy in LDLR-/- mice resulted in a significant increase in plaque area and necrotic core area, as well as a significant decrease in collagen content and an increase in macrophage accumulation in the artery. Ovariectomy also reduced serum estrogen levels and ERα expression and upregulated TLR4 and MMP9 expression in arteries in LDLR-/- mice. Estrogen or phytoestrogen therapy upregulated the expression level of ERα in ovariectomized mice and increased plaque stability by inhibiting macrophage accumulation and TLR4 signaling. In vitro, LPS incubation of RAW264.7 cells resulted in a significant decrease in ERα and TIMP1 expression and an increase in TLR4 activation, and estrogen or phytoestrogen treatment increased ERα and TIMP1 expression and inhibited TLR4 activation and MMP9 expression in LPS-treated RAW264.7 cells. Compared to control siRNA transfected RAW264.7 cells, TLR4 siRNA promoted TIMP1 expression in RAW264.7 cells with LPS incubation, but did not affect ERα expression in RAW264.7 cells with or without LPS treatment. The ERα inhibitor MPP abolished the regulatory effect of estrogen or phytoestrogen on LPS-induced RAW264.7 cells. In conclusion, the present study demonstrates that decreased ERα expression promotes macrophage infiltration and plaque instability in the postmenopausal stage, and activation of ERα in the postmenopausal stage alleviates atherosclerotic plaque instability by inhibiting TLR4 signaling and macrophage-related inflammation.

Analyzing and predicting the risk of death in stroke patients using machine learning

Background

Stroke is an acute disorder and dysfunction of the focal neurological system that has long been recognized as one of the leading causes of death and severe disability in most regions globally. This study aimed to supplement and exploit multiple comorbidities, laboratory tests and demographic factors to more accurately predict death related to stroke, and furthermore, to make inferences about the heterogeneity of treatment in stroke patients to guide better treatment planning.

Methods

We extracted data from the Medical Information Mart from the Intensive Care (MIMIC)-IV database. We compared the distribution of the demographic factors between the control and death groups. Subsequently, we also developed machine learning (ML) models to predict mortality among stroke patients. Furthermore, we used meta-learner to recognize the heterogeneity effects of warfarin and human albumin. We comprehensively evaluated and interpreted these models using Shapley Additive Explanation (SHAP) analysis.

Results

We included 7,483 patients with MIMIC-IV in this study. Of these, 1,414 (18.9%) patients died during hospitalization or 30 days after discharge. We found that the distributions of age, marital status, insurance type, and BMI differed between the two groups. Our machine learning model achieved the highest level of accuracy to date in predicting mortality in stroke patients. We also observed that patients who were consistent with the model determination had significantly better survival outcomes than the inconsistent population and were better than the overall treatment group.

Conclusion

We used several highly interpretive machine learning models to predict stroke prognosis with the highest accuracy to date and to identify heterogeneous treatment effects of warfarin and human albumin in stroke patients. Our interpretation of the model yielded a number of findings that are consistent with clinical knowledge and warrant further study and verification.

Multi-Ethnic Study of Atherosclerosis: Relationship between Left Ventricular Shape at Cardiac MRI and 10-year Outcomes

Background Left ventricular (LV) subclinical remodeling is associated with adverse outcomes and indicates mechanisms of disease development. Standard metrics such as LV mass and volumes may not capture the full range of remodeling. Purpose To quantify the relationship between LV three-dimensional shape at MRI and incident cardiovascular events over 10 years. Materials and Methods In this retrospective study, 5098 participants from the Multi-Ethnic Study of Atherosclerosis who were free of clinical cardiovascular disease underwent cardiac MRI from 2000 to 2002. LV shape models were automatically generated using a machine learning workflow. Event-specific remodeling signatures were computed using partial least squares regression, and random survival forests were used to determine which features were most associated with incident heart failure (HF), coronary heart disease (CHD), and cardiovascular disease (CVD) events over a 10-year follow-up period. The discrimination improvement of adding LV shape to traditional cardiovascular risk factors, coronary artery calcium scores, and N-terminal pro-brain natriuretic peptide levels was assessed using the index of prediction accuracy and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to illustrate the ability of remodeling signatures to predict the end points. Results Overall, 4618 participants had sufficient three-dimensional MRI information to generate patient-specific LV models (mean age, 60.6 years ± 9.9 [SD]; 2540 women). Among these participants, 147 had HF, 317 had CHD, and 455 had CVD events. The addition of LV remodeling signatures to traditional cardiovascular risk factors improved the mean AUC for 10-year survival prediction and achieved better performance than LV mass and volumes; HF (AUC, 0.83 ± 0.01 and 0.81 ± 0.01, respectively; P < .05), CHD (AUC, 0.77 ± 0.01 and 0.75 ± 0.01, respectively; P < .05), and CVD (AUC, 0.78 ± 0.0 and 0.76 ± 0.0, respectively; P < .05). Kaplan-Meier analysis demonstrated that participants with high-risk HF remodeling signatures had a 10-year survival rate of 56% compared with 95% for those with low-risk scores. Conclusion Left ventricular event-specific remodeling signatures were more predictive of heart failure, coronary heart disease, and cardiovascular disease events over 10 years than standard mass and volume measures and enable an automatic personalized medicine approach to tracking remodeling. © RSNA, 2022 Online supplemental material is available for this article.

Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: Integrative analysis of eight clinical trials

BACKGROUND

Few models exist to predict mortality in cancer patients receiving immunotherapy. Our aim was to build a machine learning-based risk stratification model for predicting mortality in atezolizumab-treated cancer patients.

METHODS

Data from 2538 patients in eight atezolizumab-treated cancer clinical trials across three cancer types (non-small-cell lung cancer, bladder transitional cell carcinoma, and renal cell carcinoma) were included. The whole cohort was randomly split into development and validation cohorts in a 7:3 ratio. Machine-learning algorithms (extreme gradient boosting, random forest, logistic regression with lasso regularization, support vector machine, and K-nearest neighbor) were applied to develop prediction models. Model performance was mainly assessed by area under the receiver operating characteristic curve (AUC) value, calibration plot, and decision curve analysis. The probability of death risk was then stratified.

RESULTS

One thousand and three hundred and seventy-nine (54.33%) patients died. The random forest (RF) model was overall the best in terms of predictive performance, with the AUC of 0.844 (95% confidence interval [CI]: 0.826-0.862) in the development cohort and 0.786 (95% CI: 0.754-0.818) in the validation cohort for predicting mortality. Twelve baseline variables contributing to mortality prediction in the RF model were C-reactive protein, PD-L1 level, cancer type, prior liver metastasis, derived neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, hemoglobin, white blood cell count, number of metastatic sites, pulse rate, and Eastern Cooperative Oncology Group (ECOG) performance status. A total of 1782 (70.2%) patients were separated into the high-risk and 756 (29.8%) low-risk groups. Patients in the high-risk group were significantly more likely to die, experience disease progression, discontinue study, and discontinue treatment than patients in the low-risk group (all p values < 0.001). Risk groups were not associated with immune-related adverse events and grades 3-5 treatment-related adverse events (all p values > 0.05).

CONCLUSION

RF model has good performance in mortality prediction and risk stratification for cancer patients receiving atezolizumab monotherapy.

Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study

BACKGROUND

Multivariate longitudinal data are under-utilized for survival analysis compared to cross-sectional data (CS - data collected once across cohort). Particularly in cardiovascular risk prediction, despite available methods of longitudinal data analysis, the value of longitudinal information has not been established in terms of improved predictive accuracy and clinical applicability.

METHODS

We investigated the value of longitudinal data over and above the use of cross-sectional data via 6 distinct modeling strategies from statistics, machine learning, and deep learning that incorporate repeated measures for survival analysis of the time-to-cardiovascular event in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort. We then examined and compared the use of model-specific interpretability methods (Random Survival Forest Variable Importance) and model-agnostic methods (SHapley Additive exPlanation (SHAP) and Temporal Importance Model Explanation (TIME)) in cardiovascular risk prediction using the top-performing models.

RESULTS

In a cohort of 3539 participants, longitudinal information from 35 variables that were repeatedly collected in 6 exam visits over 15 years improved subsequent long-term (17 years after) risk prediction by up to 8.3% in C-index compared to using baseline data (0.78 vs. 0.72), and up to approximately 4% compared to using the last observed CS data (0.75). Time-varying AUC was also higher in models using longitudinal data (0.86-0.87 at 5 years, 0.79-0.81 at 10 years) than using baseline or last observed CS data (0.80-0.86 at 5 years, 0.73-0.77 at 10 years). Comparative model interpretability analysis revealed the impact of longitudinal variables on model prediction on both the individual and global scales among different modeling strategies, as well as identifying the best time windows and best timing within that window for event prediction. The best strategy to incorporate longitudinal data for accuracy was time series massive feature extraction, and the easiest interpretable strategy was trajectory clustering.

CONCLUSION

Our analysis demonstrates the added value of longitudinal data in predictive accuracy and epidemiological utility in cardiovascular risk survival analysis in young adults via a unified, scalable framework that compares model performance and explainability. The framework can be extended to a larger number of variables and other longitudinal modeling methods.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00005130, Registration Date: 26/05/2000.

Improving the Prediction of Death from Cardiovascular Causes with Multiple Risk Markers

Background

Traditional risk factors including demographics, blood pressure, cholesterol, and diabetes status are successfully able to predict a proportion of cardiovascular disease (CVD) events. Whether including additional routinely measured factors improves CVD prediction is unclear. To determine whether a comprehensive risk factor list, including clinical blood measures, blood counts, anthropometric measures, and lifestyle factors, improves prediction of CVD deaths beyond traditional factors.

Methods

The analysis comprised of 21,982 participants aged 40 years and older (mean age=59.4 years at baseline) from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2016 survey cycles. Data were linked with the National Death Index mortality data through 2019 and split into 80:20 training and testing sets. Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, antihypertensive medications, and diabetes), we compared models with an additional 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, 13 cardiovascular health-related questions, and all 113 predictors together. To build prediction models for CVD mortality, we performed Cox proportional hazards regression, elastic-net (ENET) penalized Cox regression, and random survival forest, and compared classification using C-index and net reclassification improvement.

Results

During follow-up (median, 9.3 years), 3,075 participants died; 30.9% (1,372/3,075) deaths were from cardiovascular causes. In Cox proportional hazards models with traditional risk factors (C-index=0.850), CVD mortality classification improved with incorporation of clinical blood biomarkers (C-index=0.867), blood counts (C-index=0.861), and all predictors (C-index=0.871). Net CVD mortality reclassification improved 13.2% by adding clinical blood biomarkers and 12.2% by adding all predictors. Results for ENET-penalized Cox regression and random survival forest were similar. No improvement was observed in separate models for anthropometric measures, dietary nutrient intake, or cardiovascular health-related questions.

Conclusions

The addition of clinical blood biomarkers and blood counts substantially improves CVD mortality prediction, beyond traditional risk factors. These biomarkers may serve as an important clinical and public health screening tool for the prevention of CVD deaths.

Artificial intelligence in atherosclerotic disease: Applications and trends

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

Are Non-Invasive Modalities for the Assessment of Atherosclerosis Useful for Heart Failure Predictions?

Heart failure (HF) is becoming an increasingly common issue worldwide and is associated with significant morbidity and mortality, making its prevention an important clinical goal. The criteria evaluated using non-invasive modalities such as coronary artery calcification, the ankle-brachial index, and carotid intima-media thickness have been proven to be effective in determining the relative risk of atherosclerotic cardiovascular disease. Notably, risk assessments using these modalities have been proven to be superior to the traditional risk predictors of cardiovascular disease. However, the ability to assess HF risk has not yet been well-established. In this review, we describe the clinical significance of such non-invasive modalities of atherosclerosis assessments and examine their ability to assess HF risk. The predictive value could be influenced by the left ventricular ejection fraction. Specifically, when the ejection fraction is reduced, its predictive value increases because this condition is potentially a result of coronary artery disease. In contrast, using these measures to predict HF with a preserved ejection fraction may be difficult because it is a heterogeneous condition. To overcome this issue, further research, especially on HF with a preserved ejection fraction, is required.

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke

Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls (P < 0.05) and can effectively discriminate AF-CE from LAA stroke (P < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.

Network Biology and Medicine to Rescue: Applications for Retinal Disease Mechanisms and Therapy

Inherited retinal degenerations (IRDs) are clinically and genetically heterogenous blinding diseases that manifest through dysfunction of target cells, photoreceptors, and retinal pigment epithelium (RPE) in the retina. Despite knowledge of numerous underlying genetic defects, current therapeutic approaches, including gene centric applications, have had limited success, thereby asserting the need of new directions for basic and translational research. Human diseases have commonalities that can be represented in a network form, called diseasome, which captures relationships among disease genes, proteins, metabolites, and patient meta-data. Clinical and genetic information of IRDs suggest shared relationships among pathobiological factors, making these a model case for network medicine. Characterization of the diseasome would considerably improve our understanding of retinal pathologies and permit better design of targeted therapies for disrupted regions within the integrated disease network. Network medicine in synergy with the ongoing artificial intelligence revolution can boost therapeutic developments, especially gene agnostic treatment opportunities.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

AI in medicine: Where are we now and where are we going?

Advancements in AI enable personalizing healthcare, for example by investigating disease origins at the genetic or molecular level, understanding intraindividual drug effects, and fusing multi-modal personal physiological, behavioral, laboratory, and clinical data to uncover new aspects of pathophysiology. Future efforts should address equity, fairness, explainability, and generalizability of AI models.

Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning

BACKGROUND

Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients.

METHODS

Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score.

RESULTS

The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction.

CONCLUSIONS

This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.

Conventional Biomarkers for Predicting Clinical Outcomes in Patients with Heart Disease

Atherosclerosis is the main cause of cardiovascular disease worldwide. The progression of coronary atherosclerosis leads to coronary artery disease, with impaired blood flow to the myocardium and subsequent development of myocardial ischemia. Acute coronary syndromes and post-myocardial infarction heart failure are two of the most common complications of coronary artery disease and are associated with worse outcomes. In order to improve the management of patients with coronary artery disease and avoid major cardiovascular events, several risk assessment tools have been developed. Blood and imaging biomarkers, as well as clinical risk scores, are now available and validated for clinical practice, but research continues. The purpose of the current paper is to provide a review of recent findings regarding the use of humoral biomarkers for risk assessment in patients with heart disease.

Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort

Background: Cardiovascular disease (CVD) screening entails precise event prediction to orient risk stratification, resource allocation, and insurance policy. We used random survival forests (RSF) to identify markers of incident CVD among Japanese adults enrolled in an employer-mandated screening program. Methods and Results: We examined biomarker, health history, medication use, and lifestyle data from 155,108 adults aged ≥40 years. The occurrence of coronary artery disease (CAD) or atherosclerotic CVD (ASCVD) events was examined over 6 years of follow-up. The analysis used RSF to identify predictors, then investigated simplified RSF models with fewer predictors for individual-level risk prediction. Data were split into training (70%) and test (30%) datasets. At baseline, the median patient age was 47 years (interquartile range 41-56 years), with 65% males. In all, 1,642 CAD and 2,164 ASCVD events were observed. RSF identified history of heart disease, age, self-reported blood pressure medication, HbA1c, fasting blood sugar, and high-density lipoprotein as important markers of both endpoints. RSF analyses with only the top 20 predictors demonstrated good performance, with areas under the curve of >84% for CAD and >82% for ASCVD in test data across 6 years. Conclusions: We present a machine learning technique for accurate assessment of cardiovascular risk using employer-mandated annual health checkup information. The algorithm produces individual-level risk curves over time, empowering clinicians to efficiently implement prevention strategies in a low-risk population.

Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease

Background

Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF.

Methods

Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis.

Results

The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18-1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09-1.96, p = 0.01; HR: 1.54, 95% CI: 1.22-1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46-7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50-64 years were HBP (HR: 3.75 95% CI: 1.08-13, p = 0.04) CAD and SA in the group of 60-75 years were (HR: 2.4 95% CI: 1.18-4.86, p = 0.02; HR: 2.51, 95% CI: 1.14-5.52, p = 0.02, respectively).

Conclusion

Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin-American population.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

Use of Machine Learning to Develop Prediction Models for Mortality and Stroke in Patients Undergoing Balloon Aortic Valvuloplasty

OBJECTIVE

To develop an artificial intelligence, machine learning prediction model for estimating in-hospital mortality and stroke in patients undergoing balloon aortic valvuloplasty (BAV).

METHODS

The National Inpatient Sample (NIS) database was used to identify patients who underwent BAV from 2005 to 2017. Outcomes analyzed were in-hospital all-cause mortality and stroke after BAV. Predictors of mortality and stroke were selected using LASSO regularization. A conventional logistic regression and a random forest machine learning algorithm were used to train the models for predicting outcomes. The performance of all the modeling algorithms for predicting in-hospital mortality and stroke was compared between models using c-statistic, F1 score, brier score loss, diagnostic accuracy, and Kolmogorov-Smirnov plots.

RESULTS

A total of 6962 patients with severe aortic stenosis who underwent BAV were identified. The performance of random forest classifier was comparable with logistic regression for predicting in-hospital mortality for all measures of performance (F1 score 0.422 vs 0.409, ROC-AUC 0.822 [95 % CI 0.787-0.855] vs 0.815 [95 % CI 0.779-0.849], diagnostic accuracy 70.42 % vs 70.93 %, KS-statistic 0.513 vs 0.494 and brier score loss 0.295 vs 0.291). The random forest algorithm significantly outperformed logistic regression in predicting in-hospital stroke with respect to all performance metrics: F1 score 0.225 vs 0.095, AUC 0.767 [0.662-0.858] vs 0.637 [0.499-0.754], brier score loss [0.399 vs 0.407], and KS-statistic [0.465 vs 0.254].

CONCLUSIONS

The good discrimination of machine learning models reveal the potential of artificial intelligence to improve patient risk stratification for BAV.

Machine learning-based identification of the novel circRNAs circERBB2 and circCHST12 as potential biomarkers of intracerebral hemorrhage

BACKGROUND

The roles and potential diagnostic value of circRNAs in intracerebral hemorrhage (ICH) remain elusive.

METHODS

This study aims to investigate the expression profiles of circRNAs by RNA sequencing and RT-PCR in a discovery cohort and an independent validation cohort. Bioinformatics analysis was performed to identify the potential functions of circRNA host genes. Machine learning classification models were used to assess circRNAs as potential biomarkers of ICH.

RESULTS

A total of 125 and 284 differentially expressed circRNAs (fold change > 1.5 and FDR < 0.05) were found between ICH patients and healthy controls in the discovery and validation cohorts, respectively. Nine circRNAs were consistently altered in ICH patients compared to healthy controls. The combination of the novel circERBB2 and circCHST12 in ICH patients and healthy controls showed an area under the curve of 0.917 (95% CI: 0.869-0.965), with a sensitivity of 87.5% and a specificity of 82%. In combination with ICH risk factors, circRNAs improved the performance in discriminating ICH patients from healthy controls. Together with hsa_circ_0005505, two novel circRNAs for differentiating between patients with ICH and healthy controls showed an AUC of 0.946 (95% CI: 0.910-0.982), with a sensitivity of 89.1% and a specificity of 86%.

CONCLUSION

We provided a transcriptome-wide overview of aberrantly expressed circRNAs in ICH patients and identified hsa_circ_0005505 and novel circERBB2 and circCHST12 as potential biomarkers for diagnosing ICH.

A Machine-Learning Model for the Prognostic Role of C-Reactive Protein in Myocarditis

Aims: The role of inflammation markers in myocarditis is unclear. We assessed the diagnostic and prognostic correlates of C-reactive protein (CRP) at diagnosis in patients with myocarditis. Methods and results: We retrospectively enrolled patients with clinically suspected (CS) or biopsy-proven (BP) myocarditis, with available CRP at diagnosis. Clinical, laboratory and imaging data were collected at diagnosis and at follow-up visits. To evaluate predictors of death/heart transplant (Htx), a machine-learning approach based on random forest for survival data was employed. We included 409 patients (74% males, aged 37 ± 15, median follow-up 2.9 years). Abnormal CRP was reported in 288 patients, mainly with CS myocarditis (p < 0.001), recent viral infection, shorter symptoms duration (p = 0.001), chest pain (p < 0.001), better functional class at diagnosis (p = 0.018) and higher troponin I values (p < 0.001). Death/Htx was reported in 13 patients, of whom 10 had BP myocarditis (overall 10-year survival 94%). Survival rates did not differ according to CRP levels (p = 0.23). The strongest survival predictor was LVEF, followed by anti-nuclear auto-antibodies (ANA) and BP status. Conclusions: Raised CRP at diagnosis identifies patients with CS myocarditis and less severe clinical features, but does not contribute to predicting survival. Main death/Htx predictors are reduced LVEF, BP diagnosis and positive ANA.

Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension

The structure of a back propagation neural network was optimized by a particle swarm optimization (PSO) algorithm, and a back propagation neural network model based on a PSO algorithm was constructed. By comparison with a general back propagation neural network and logistic regression, the fitting performance and prediction performance of the PSO algorithm is discussed. Furthermore, based on the back propagation neural network optimized by the PSO algorithm, the risk factors related to hypertension were further explored through the mean influence value algorithm to construct a risk prediction model. In the evaluation of the fitting effect, the root mean square error and coefficient of determination of the back propagation neural network based on the PSO algorithm were 0.09 and 0.29, respectively. In the comparison of prediction performance, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the back propagation neural network based on PSO algorithm were 85.38%, 43.90%, 96.66%, and 0.86, respectively. The results showed that the backpropagation neural network optimized by PSO had the best fitting effect and prediction performance. Meanwhile, the mean impact value algorithm could screen out the risk factors related to hypertension and build a disease prediction model, which can provide clues for exploring the pathogenesis of hypertension and preventing hypertension.

Associations of Inflammation with Risk of Cardiovascular and All-Cause Mortality in Adults with Hypertension: An Inflammatory Prognostic Scoring System

BACKGROUND

Inflammation is one of the major pathways in the progression of hypertension (HTN), and the related inflammatory markers have demonstrated certain predictive values. The current study aimed to integrate these markers to construct an inflammatory prognostic scoring (IPS) system and to assess the prognostic values of IPS in patients with HTN.

METHODS

A total of 9846 adult participants with HTN from NHANES 1999-2010 were enrolled and followed up. Demographic characteristics and the related laboratory results for the 12 inflammatory markers were collected. LASSO-COX regression, Kaplan-Meier, restricted cubic spline COX regression (RCS), receiver operator characteristic curve (ROC), and random survival forests (RSF) analysis were applied to explore the values of individual and IPS parameters.

RESULTS

At the census date of follow-up, 2387 (24.2%) were identified as all-cause deaths and 484 (4.9%) as cardiovascular deaths. All inflammatory markers showed certain prognostic values. Then, based on the LAASO analysis, LDH, ALP, LYM, NLR, MLR, SIRI, and RDW were included in the construction of the IPS system. The higher IPS had significantly worse long-term prognosis in Kaplan-Meier analysis (p log-rank <0.001). Also, IPS remained an independent prognosticator compared to the lowest quartile (All p for trend <0.001), and the ROC showed satisfactory values in the long-term prognosis of both cardiovascular and all-cause mortality. RCS further showed a linear association of IPS with cardiovascular mortality and all-cause mortality (p for non-linearity >0.05). Two different algorithms of RSF, variable importance and minimal depth, to evaluate the prognostic importance showed that IPS was the best in survival prediction.

CONCLUSION

Our results highlight that a higher IPS (system integrating the inflammatory markers) was associated with the increased risk of cardiovascular and all-cause mortality in patients with HTN, suggesting that IPS is a useful method for risk stratification in HTN.

Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features

BACKGROUND

Precise localization of the site of origin of premature ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure.

OBJECTIVE

The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ventricular outflow tract and right ventricular outflow tract (RVOT) using surface electrocardiogram characteristics.

METHODS

A total of 851 patients undergoing radiofrequency ablation of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features.

RESULTS

In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accuracy 94.23%; sensitivity 97.10%; specificity 88.57%). In the prospective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%).

CONCLUSION

Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.

Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF.

OBJECTIVES

This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF.

METHODS

The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort.

RESULTS

HF hospitalization occurred in 606 patients (14%) during a median follow-up period of 4.4 years in the entire registry. Data of transthoracic echocardiography and biomarkers were frequently nominated as important predictive variables across all 6 ML models. The ML model based on a random forest algorithm using 7 variables (age, history of HF, creatinine clearance, cardiothoracic ratio on x-ray, left ventricular [LV] ejection fraction, LV end-systolic diameter, and LV asynergy) had high prediction performance (area under the receiver operating characteristics curve [AUC]: 0.75) and was significantly superior to the Framingham HF risk model (AUC: 0.67; P < 0.001). Based on Kaplan-Meier curves, the ML model could stratify the risk of HF hospitalization during the follow-up period (log-rank; P < 0.001).

CONCLUSIONS

The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Improving predictive performance in incident heart failure using machine learning and multi-center data

Objective

To mitigate the burden associated with heart failure (HF), primary prevention is of the utmost importance. To improve early risk stratification, advanced computational methods such as machine learning (ML) capturing complex individual patterns in large data might be necessary. Therefore, we compared the predictive performance of incident HF risk models in terms of (a) flexible ML models and linear models and (b) models trained on a single cohort (single-center) and on multiple heterogeneous cohorts (multi-center).

Design and methods

In our analysis, we used the meta-data consisting of 30,354 individuals from 6 cohorts. During a median follow-up of 5.40 years, 1,068 individuals experienced a non-fatal HF event. We evaluated the predictive performance of survival gradient boosting (SGB), CoxNet, the PCP-HF risk score, and a stacking method. Predictions were obtained iteratively, in each iteration one cohort serving as an external test set and either one or all remaining cohorts as a training set (single- or multi-center, respectively).

Results

Overall, multi-center models systematically outperformed single-center models. Further, c-index in the pooled population was higher in SGB (0.735) than in CoxNet (0.694). In the precision-recall (PR) analysis for predicting 10-year HF risk, the stacking method, combining the SGB, CoxNet, Gaussian mixture and PCP-HF models, outperformed other models with PR/AUC 0.804, while PCP-HF achieved only 0.551.

Conclusion

With a greater number and variety of training cohorts, the model learns a wider range of specific individual health characteristics. Flexible ML algorithms can be used to capture these diverse distributions and produce more precise prediction models.

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography

For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People's Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.