Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold

QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning

BACKGROUND

Prediction of mortality in hospitalized patients is a crucial and important problem. Several severity scoring systems over the past few decades and machine learning models for mortality prediction have been developed to predict in-hospital mortality. Our aim in this study was to apply machine learning (ML) algorithms using QTc interval to predict in-hospital mortality in ACS patients and compare them to the validated conventional risk scores.

RESULTS

This study was retrospective, using supervised learning, and data mining. Out of a cohort of 500 patients admitted to a tertiary care hospital from September 2018 to August 2020, who presented with ACS. Prediction models for in-hospital mortality in ACS patients were developed using 3 ML algorithms. We employed the ensemble learning random forest (RF) model, the Naive Bayes (NB) model and the rule-based projective adaptive resonance theory (PART) model. These models were compared to one another and to two conventional validated risk scores; the Global Registry of Acute Coronary Events (GRACE) risk score and Thrombolysis in Myocardial Infarction (TIMI) risk score. Out of the 500 patients included in our study, 164 (32.8%) patients presented with unstable angina, 148 (29.6%) patients with non-ST-elevation myocardial infarction (NSTEMI) and 188 (37.6%) patients were having ST-elevation myocardial infarction (STEMI). 64 (12.8%) patients died in-hospital and the rest survived. Performance of prediction models was measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.83 to 0.93 using all available variables compared to the GRACE score (0.9 SD 0.05) and the TIMI score (0.75 SD 0.02). Using QTc as a stand-alone variable yielded (0.67 SD 0.02) with a cutoff value 450 using Bazett's formula, whereas using QTc in addition to other variables of personal and clinical data and other ECG variables, the result was 0.8 SD 0.04. Results of RF and NB models were almost the same, but PART model yielded the least results. There was no significant difference of AUC values after replacing the missing values and applying class balancer.

CONCLUSIONS

The proposed method can effectively predict patients at high risk of in-hospital mortality early in the setting of ACS using only clinical and ECG data. Prolonged QTc interval can be used as a risk predictor of in-hospital mortality in ACS patients.

Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning

INTRODUCTION AND OBJECTIVES

Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data of a population-based, randomly selected cohort of Sri Lankans followed up for 10 years and to validate it in an external cohort.

MATERIAL AND METHODS

The cohort consisted of 2596 individuals between 40-65 years of age in 2007, who were followed up for 10 years. Of them, 179 developed hard CV diseases (CVD) by 2017. We developed three CV risk prediction models named model 1, 2 and 3 using ML. We compared predictive performances between models and the WHO risk charts using receiver operating characteristic curves (ROC). The most predictive and practical model for use in primary care, model 3 was named "SLCVD score" which used age, sex, smoking status, systolic blood pressure, history of diabetes, and total cholesterol level in the calculation. We developed an online platform to calculate the SLCVD score. Predictions of SLCVD score were validated in an external hospital-based cohort.

RESULTS

Model 1, 2, SLCVD score and the WHO risk charts predicted 173, 162, 169 and 10 of 179 observed events and the area under the ROC (AUC) were 0.98, 0.98, 0.98 and 0.52 respectively. During external validation, the SLCVD score and WHO risk charts predicted 56 and 18 respectively of 119 total events and AUCs were 0.64 and 0.54 respectively.

CONCLUSIONS

SLCVD score is the first and only CV risk prediction model specific for Sri Lankans. It predicts the 10-year risk of developing a hard CVD in Sri Lankans. SLCVD score was more effective in predicting Sri Lankans at high CV risk than WHO risk charts.

Artificial intelligence in cardiovascular medicine: clinical applications

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Machine-learning based risk prediction of in-hospital outcomes following STEMI: the STEMI-ML score

Background

Traditional prognostic models for ST-segment elevation myocardial infarction (STEMI) have limitations in statistical methods and usability.

Objective

We aimed to develop a machine-learning (ML) based risk score to predict in-hospital mortality, intensive care unit (ICU) admission, and left ventricular ejection fraction less than 40% (LVEF < 40%) in STEMI patients.

Methods

We reviewed 1,863 consecutive STEMI patients undergoing primary percutaneous coronary intervention (pPCI) or rescue PCI. Eight supervised ML methods [LASSO, ridge, elastic net (EN), decision tree, support vector machine, random forest, AdaBoost and gradient boosting] were trained and validated. A feature selection method was used to establish more informative and nonredundant variables, which were then considered in groups of 5/10/15/20/25/30(all). Final models were chosen to optimise area under the curve (AUC) score while ensuring interpretability.

Results

Overall, 128 (6.9%) patients died in hospital, with 292 (15.7%) patients requiring ICU admission and 373 (20.0%) patients with LVEF < 40%. The best-performing model with 5 included variables, EN, achieved an AUC of 0.79 for in-hospital mortality, 0.78 for ICU admission, and 0.74 for LVEF < 40%. The included variables were age, pre-hospital cardiac arrest, robust collateral recruitment (Rentrop grade 2 or 3), family history of coronary disease, initial systolic blood pressure, initial heart rate, hypercholesterolemia, culprit vessel, smoking status and TIMI flow pre-PCI. We developed a user-friendly web application for real-world use, yielding risk scores as a percentage.

Conclusions

The STEMI-ML score effectively predicts in-hospital outcomes in STEMI patients and may assist with risk stratification and individualising patient management.

Algorithms advise, humans decide: the evidential role of the patient preference predictor

An AI-based 'patient preference predictor' (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP-that between algorithmic prediction and decision-making-and argue that much of the recent philosophical disagreement stems from this oversight. I show how three prominent objections to the PPP only challenge its use as the sole determinant of a choice, and actually support its use as a source of evidence about patient preferences to inform human decision-making. The upshot is that we should adopt the evidential conception of the PPP and shift our evaluation of this technology towards the ethics of algorithmic prediction, rather than decision-making.

The Rise of Virtual Health Care: Transforming the Health Care Landscape in the Kingdom of Saudi Arabia: A Review Article

Background: The rise of virtual healthcare underscores the transformative influence of digital technologies in reshaping the healthcare landscape. As technology advances and the global demand for accessible and convenient healthcare services escalates, the virtual healthcare sector is gaining unprecedented momentum. Saudi Arabia, with its ambitious Vision 2030 initiative, is actively embracing digital innovation in the healthcare sector. Methods: In this narrative review, we discussed the key drivers and prospects of virtual healthcare in Saudi Arabia, highlighting its potential to enhance healthcare accessibility, quality, and patient outcomes. We also summarized the role of the COVID-19 pandemic in the digital transformation of healthcare in the country. Healthcare services provided by Seha Virtual Hospital in Saudi Arabia, the world's largest and Middle East's first virtual hospital, were also described. Finally, we proposed a roadmap for the future development of virtual health in the country. Results and conclusions: The integration of virtual healthcare into the existing healthcare system can enhance patient experiences, improve outcomes, and contribute to the overall well-being of the population. However, careful planning, collaboration, and investment are essential to overcome the challenges and ensure the successful implementation and sustainability of virtual healthcare in the country.

Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare ) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Analysis of the Larissa Heart Failure Risk Score: Predictive Value in 9207 Patients Hospitalized for Heart Failure from a Single Center

Early risk stratification is of outmost clinical importance in hospitalized patients with heart failure (HHF). We examined the predictive value of the Larissa Heart Failure Risk Score (LHFRS) in a large population of HHF patients from the Cleveland Clinic. A total of 13,309 admissions for heart failure (HF) from 9207 unique patients were extracted from the Cleveland Clinic's electronic health record system. For each admission, components of the 3-variable simple LHFRS were obtained, including hypertension history, myocardial infarction history, and red blood cell distribution width (RDW) ≥ 15%. The primary outcome was a HF readmission and/or all-cause mortality at one year, and the secondary outcome was all-cause mortality at one year of discharge. For both outcomes, all variables were statistically significant, and the Kaplan-Meier curves were well-separated and in a consistent order (Log-rank test p-value < 0.001). Higher LHFRS values were found to be strongly related to patients experiencing an event, showing a clear association of LHFRS with this study outcomes. The bootstrapped-validated area under the curve (AUC) for the logistic regression model for each outcome revealed a C-index of 0.64 both for the primary and secondary outcomes, respectively. LHFRS is a simple risk model and can be utilized as a basis for risk stratification in patients hospitalized for HF.

Personalized Approaches to Antiplatelet Treatment for Cardiovascular Diseases: An Umbrella Review

Antiplatelet therapy is the cornerstone of antithrombotic prevention in patients with established atherosclerosis, since it has been proven to reduce coronary, cerebrovascular, and peripheral thrombotic events. However, the protective effect of antiplatelet agents is counterbalanced by an increase of bleeding events that impacts on patients' mortality and morbidity. Over the last years, great efforts have been made toward personalized antithrombotic strategies according to the individual bleeding and ischemic risk profile, aiming to maximizing the net clinical benefit. The development of risk scores, consensus definitions, and the new promising artificial intelligence tools, as well as the assessment of platelet responsiveness using platelet function and genetic testing, are now part of an integrated approach to tailored antithrombotic management. Moreover, novel strategies are available including dual antiplatelet therapy intensity and length modulation in patients undergoing myocardial revascularization, the use of P2Y12 inhibitor monotherapy for long-term secondary prevention, the implementation of parenteral antiplatelet agents in high-ischemic risk clinical settings, and combination of antiplatelet agents with low-dose factor Xa inhibitors (dual pathway inhibition) in patients suffering from polyvascular disease. This review summarizes the currently available evidence and provides an overview of the principal risk-stratification tools and antiplatelet strategies to inform treatment decisions in patients with cardiovascular disease.

AI/ML in Precision Medicine: A Look Beyond the Hype

Artificial Intelligence (AI) and Machine Learning (ML) are making headlines in medical research, especially in drug discovery, digital imaging, disease diagnostics, genetic testing, and optimal care pathway (personalized care). However, the potential uses and benefits of AI/ML applications need to be distinguished from hype. In the 2022 American Statistical Association Biopharmaceutical Section Regulatory-Industry Statistical Workshop, we convened a panel of experts from FDA and industry to talk about the challenges of successfully applying AI/ML in precision medicine and how to overcome those challenges. This paper provides a summary and expansion on the topics discussed in the panel: the application of AI/ML, bias, and data quality.