Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients

The Digital Revolution in Medicine: Applications in Cardio-Oncology

PURPOSE OF REVIEW

A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors.

RECENT FINDINGS

Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets. Machine learning in cardio-oncology care has great potential in screening, diagnosis, monitoring, and managing cancer therapy-related cardiovascular complications. To this end, large-scale imaging data and clinical information are being leveraged in training efficient AI algorithms that may lead to effective clinical tools for caring for this vulnerable population. Telemedicine may benefit cardio-oncology patients by enhancing healthcare delivery through lowering costs, improving quality, and personalizing care. Similarly, the utilization of wearable biosensors and mobile health technology for remote monitoring holds the potential to improve cardio-oncology outcomes through early intervention and deeper clinical insight. Investigations are ongoing regarding the application of digital health tools such as telemedicine and remote monitoring devices in enhancing the functional status and recovery of cancer patients, particularly those with limited access to centralized services, by increasing physical activity levels and providing access to rehabilitation services.

SUMMARY

In recent years, advances in cancer survival have increased the prevalence of patients experiencing cancer therapy-related cardiovascular complications. Traditional cardio-oncology risk categorization largely relies on basic clinical features and physician assessment, necessitating advancements in machine learning to create objective prediction models using diverse data sources. Healthcare disparities may be perpetuated through AI algorithms in digital health technologies. In turn, this may have a detrimental effect on minority populations by limiting resource allocation. Several AI-powered innovative health tools could be leveraged to bridge the digital divide and improve access to equitable care.

Development and validation of a predictive model for cancer therapy-related cardiac dysfunction in breast cancer patients using echocardiographic indicators

OBJECTIVE

This study aimed to develop and validate a predictive model for cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients undergoing chemotherapy, targeted therapy, or immunotherapy.

METHODS

A retrospective analysis was conducted on 506 patients treated at Hunan Provincial People's Hospital (2018-2023).

RESULTS

Clinical and imaging biomarkers, including NT-proBNP (P < 0.001), left ventricular ejection fraction (LVEF; P = 0.003), and left atrial diameter (LA; P = 0.012), were evaluated. Lasso-Cox regression identified eight significant predictors (all P < 0.05), which were incorporated into a nomogram. The model exhibited excellent discrimination in both the training (AUC 0.82, 95% CI 0.78-0.86) and validation cohorts (AUC 0.79, 95% CI 0.74-0.83). Time-dependent ROC curves demonstrated consistent predictive accuracy at 4 weeks (AUC 0.80, P < 0.001), 8 weeks (AUC 0.81, P < 0.001), and 12 weeks (AUC 0.79, P = 0.002). Calibration curves indicated good agreement (Hosmer-Lemeshow test P = 0.34), and decision curve analysis confirmed the model's clinical utility (net benefit > 15% across threshold probabilities).

CONCLUSION

This validated tool facilitates early CTRCD risk stratification (C-index 0.80, P < 0.001), supporting personalized monitoring of cardiotoxicity.

County-Level Urbanization and Cardiovascular Death in Patients With Cancer

CONTEXT

Cardiovascular death (CVD) is a leading cause of mortality in patients with cancer, with sociodemographic factors such as urbanization influencing outcomes.

OBJECTIVE

To examine the impact of county-level urbanization on CVD mortality in patients with cancer in the United States from 1999 to 2020.

DESIGN

Retrospective cross-sectional analysis using CDC WONDER mortality data.

SETTING

US counties categorized as rural or urban based on the 2013 NCHS Urban-Rural Classification Scheme.

PARTICIPANTS

Patients with cardiovascular disease (ICD-10: I00-I78) and comorbid cancer (ICD-10: C00-C97), spanning all U.S. counties from 1999 to 2020.

MAIN OUTCOME MEASURES

Age-adjusted mortality rates (AAMRs) per 100 000 population and rural-to-urban rate ratios (RRs) with 95% confidence intervals.

RESULTS

The cumulative rural-to-urban RR for CVD in patients with cancer was 1.11 (95% CI: 1.10-1.11), increasing from 1.00 in 1999 to 1.20 in 2020 (β = 0.009, P < .001). Rural AAMRs were higher across demographic groups, including males (12.85 vs 11.62 per 100 000), females (6.08 vs 5.58), Black individuals (9.76 vs 9.64), and White individuals (8.79 vs 7.94). Rural Black populations showed a rising RR from 0.85 in 1999 to 1.04 in 2020 (β = 0.005, P = .01). Hispanic populations exhibited lower rural mortality, with a stable RR (0.93, P = 1.0). The most common CVD cause was ischemic heart disease (53.93% of rural and 55.9% of urban deaths).

CONCLUSIONS

An increasing rural-to-urban disparity in CVD mortality among cancer patients highlights the role of urbanization in health inequities. Interventions targeting rural health care access and socioeconomic disparities are essential to address this growing gap.

Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models

Background

This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment.

Materials and Methods

We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers - decision tree, K-nearest neighbor, and random forest (RF) - using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]).

Results

In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure.

Conclusion

Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due to both the disease and its treatments. The rising prevalence of cancer and the availability of multiple new therapeutic options has resulted in improved survival among patients with cancer and has expanded the scope of cardio-oncology to not only short-term but also long-term cardiovascular risks resulting from both cancer and its treatments. However, there is considerable heterogeneity in cardiovascular risk, driven by the nature of the malignancy as well as each individual's unique characteristics. The use of novel therapies, such as targeted therapies and immune checkpoint inhibitors, across multiple cancer groups has also broadened the populations among which cardiotoxicity has become an important consideration of therapy. Therefore, the ability to understand and personalize cardiovascular risk management in patients with cancer is a key target for artificial intelligence, which can deduce and respond to complex patterns within the data. These advances necessitate an overview of established biomarkers of risk, spanning advanced imaging, diagnostic testing, and multi-omics, the evidence supporting their use, and the proven and proposed role of artificial intelligence in refining this risk to attain greater precision in risk prediction and management in cardio-oncologic care.

Applications, challenges and future directions of artificial intelligence in cardio-oncology

BACKGROUND

The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects.

OBJECTIVE

This review investigates the potential of artificial intelligence (AI) to revolutionize cardio-oncology by integrating diverse data sources to address the challenges of cardiotoxicity management.

METHODS

We explore applications of AI in cardio-oncology, focusing on its ability to leverage multiple data sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, and circulating serum biomarkers.

RESULTS

AI has demonstrated significant potential in improving risk stratification and longitudinal monitoring of cardiotoxicity. By optimizing the use of electrocardiograms, non-invasive imaging, and circulating biomarkers, AI facilitates earlier detection, better prediction of outcomes, and more personalized therapeutic interventions. These advancements are poised to enhance patient outcomes and streamline clinical decision-making.

CONCLUSIONS

AI represents a transformative opportunity in cardio-oncology by advancing diagnostic and therapeutic capabilities. However, successful implementation requires addressing practical challenges such as data integration, model interpretability, and clinician training. Continued collaboration between clinicians and AI developers will be essential to fully integrate AI into routine clinical workflows.

AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring

The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.

Cancer-Therapy-Related Cardiac Dysfunction: Latest Advances in Prevention and Treatment

The increasing efficacy of cancer therapies has significantly improved survival rates, but it has also highlighted the prevalence of cancer-therapy-related cardiac dysfunction (CTRCD). This review provides a comprehensive overview of the identification, monitoring, and management of CTRCD, a condition resulting from several treatments, such as anthracyclines, HER2-targeted therapies, target therapies, and radiotherapy. The paper includes a discussion of the mechanisms of CTRCD associated with various cancer treatments. Early detection through serum biomarkers and advanced imaging techniques is crucial for effective monitoring and risk stratification. Preventive strategies include pharmacological interventions such as ACE inhibitors/angiotensin receptor blockers, beta-blockers, and statins. Additionally, novel agents like sacubitril/valsartan, sodium-glucose co-transporter type 2 inhibitors, and vericiguat show promise in managing left ventricular dysfunction. Lifestyle modifications, including structured exercise programs and optimized nutritional strategies, further contribute to cardioprotection. The latest treatments for both asymptomatic and symptomatic CTRCD across its various stages are also discussed. Emerging technologies, including genomics, artificial intelligence, novel biomarkers, and gene therapy, are paving the way for personalized approaches to CTRCD prevention and treatment. These advancements hold great promise for improving long-term outcomes in cancer patients by minimizing cardiovascular complications.

Diagnosis of cancer therapy-related cardiovascular toxicities: A multimodality integrative approach and future developments

Diagnosing cancer therapy-related cardiovascular toxicities may be a challenge. The interplay between cancer and cardiovascular diseases, beyond shared cardiovascular and cancer risk factors, and the increasingly convoluted cancer therapy schemes have complicated cardio-oncology. Biomarkers used in cardio-oncology include serum, imaging and rhythm modalities to ensure proper diagnosis and prognostic stratification of cardiovascular toxicities. For now, troponin and natriuretic peptides, multimodal cardiovascular imaging (led by transthoracic echocardiography combined with cardiac magnetic resonance or computed tomography angiography) and electrocardiography (12-lead or Holter monitor) are cornerstones in cardio-oncology. However, the imputability of cancer therapies is sometimes difficult to assess, and more refined biomarkers are currently being studied to increase diagnostic accuracy. Advances reside partly in pathophysiology-based serum biomarkers, improved cardiovascular imaging through new technical developments and remote monitoring for rhythm disorders. A multiparametric omics approach, enhanced by deep-learning techniques, should open a new era for biomarkers in cardio-oncology in the years to come.

Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review

PURPOSE OF REVIEW

This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns.

RECENT FINDINGS

AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.

Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients

BACKGROUND

Cancer-therapy related cardiac dysfunction (CTRCD) remains a significant cause of morbidity and mortality in cancer survivors. In this study, we aimed to identify differential plasma proteins and metabolites associated with left ventricular dysfunction (LVD) in cancer patients.

METHODS

We analyzed data from 50 patients referred to the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment, integrating electronic health records, proteomic, and metabolomic profiles. LVD was defined as an ejection fraction ≤ 55% based on echocardiographic evaluation. Classification-based machine learning models were used to predict LVD using plasma metabolites and proteins as input features.

RESULTS

We identified 13 plasma proteins (P < 0.05) and 14 plasma metabolites (P < 0.05) associated with LVD. Key proteins included markers of inflammation (ST2, TNFRSF14, OPN, and AXL) and chemotaxis (RARRES2, MMP-2, MEPE, and OPN). Notably, sex-specific associations were observed, such as uridine (P = 0.003) in males. Furthermore, metabolomic features significantly associated with LVD included 1-Methyl-4-imidazoleacetic acid (P = 0.015), COL1A1 (P = 0.009), and MMP-2 (P = 0.016), and pointing to metabolic shifts and heightened inflammation in patients with LVD.

CONCLUSION

Our findings suggest that circulating metabolites may non-invasively detect clinical and molecular differences in patients with LVD, providing insights into underlying disease pathways and potential therapeutic targets.

Cardiovascular Risk Assessment and Prevention in Cardio-Oncology: Beyond Traditional Risk Factors

This review goes beyond traditional approaches in cardio-oncology, highlighting often-neglected factors impacting patient care. Social determinants, environment, health care access, and gut microbiome significantly influence patient outcomes. Powerful tools like multi-omics and wearable technologies offer deeper insights into real-world experiences. The future lies in integrating these advancements with established practices to achieve precision cardio-oncology care. By crafting tailored therapies and continuously updating comprehensive management plans based on real-time data, we can unlock the full potential of personalized care for all patients.

Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review

BACKGROUND

Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.

OBJECTIVE

This scoping review depicts the emerging use of AI on structured RWD for pharmacovigilance purposes to identify relevant trends and potential research gaps.

METHODS

The scoping review methodology is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We queried the MEDLINE database through the PubMed search engine. Relevant scientific manuscripts published from January 2010 to January 2024 were retrieved. The included studies were "mapped" against a set of evaluation criteria, including applied AI approaches, code availability, description of the data preprocessing pipeline, clinical validation of AI models, and implementation of trustworthy AI criteria following the guidelines of the FUTURE (Fairness, Universality, Traceability, Usability, Robustness, and Explainability)-AI initiative.

RESULTS

The scoping review ultimately yielded 36 studies. There has been a significant increase in relevant studies after 2019. Most of the articles focused on adverse drug reaction detection procedures (23/36, 64%) for specific adverse effects. Furthermore, a substantial number of studies (34/36, 94%) used nonsymbolic AI approaches, emphasizing classification tasks. Random forest was the most popular machine learning approach identified in this review (17/36, 47%). The most common RWD sources used were electronic health care records (28/36, 78%). Typically, these data were not available in a widely acknowledged data model to facilitate interoperability, and they came from proprietary databases, limiting their availability for reproducing results. On the basis of the evaluation criteria classification, 10% (4/36) of the studies published their code in public registries, 16% (6/36) tested their AI models in clinical environments, and 36% (13/36) provided information about the data preprocessing pipeline. In addition, in terms of trustworthy AI, 89% (32/36) of the studies followed at least half of the trustworthy AI initiative guidelines. Finally, selection and confounding biases were the most common biases in the included studies.

CONCLUSIONS

AI, along with structured RWD, constitutes a promising line of work for drug safety and pharmacovigilance. However, in terms of AI, some approaches have not been examined extensively in this field (such as explainable AI and causal AI). Moreover, it would be helpful to have a data preprocessing protocol for RWD to support pharmacovigilance processes. Finally, because of personal data sensitivity, evaluation procedures have to be investigated further.

Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI

Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD phenotype compared to Dahl salt-sensitive (SS) rats, were treated with localized cardiac RT or sham at 10 weeks of age. Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. Gradient boosting achieved an F1 score of 0.94 and an ROC value of 0.95, while random forest showed an accuracy of 88%. These findings suggest that ML, combined with cardiac MRI, can effectively detect early preclinical changes in RIHD, particularly alterations in regional myocardial contractility, highlighting the potential of these techniques for early detection and monitoring of radiation-induced cardiac dysfunction.

A roadmap for modelling radiation-induced cardiac disease

Cardiac risk mitigation is a major priority in improving outcomes for cancer survivors as advances in cancer screening and treatments continue to decrease cancer mortality. More than half of adult cancer patients will be treated with radiotherapy (RT); therefore it is crucial to develop a framework for how to assess and predict radiation-induced cardiac disease (RICD). Historically, RICD was modelled solely using whole heart metrics such as mean heart dose. However, data over the past decade has identified cardiac substructures which outperform whole heart metrics in predicting for significant cardiac events. Additionally, non-RT factors such as pre-existing cardiovascular risk factors and toxicity from other therapies contribute to risk of future cardiac events. In this review, we aim to discuss the current evidence and knowledge gaps in predicting RICD and provide a roadmap for the development of comprehensive models based on three interrelated components, (1) baseline CV risk assessment, (2) cardiac substructure radiation dosimetry linked with cardiac-specific outcomes and (3) novel biomarker development.

Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities

Numerous cancer therapies have detrimental cardiovascular effects on cancer survivors. Cardiovascular toxicity can span the course of cancer treatment and is influenced by several factors. To mitigate these risks, cardio-oncology has evolved, with an emphasis on prevention and treatment of cardiovascular complications resulting from the presence of cancer and cancer therapy. Artificial intelligence (AI) holds multifaceted potential to enhance cardio-oncologic outcomes. AI algorithms are currently utilizing clinical data input to identify patients at risk for cardiac complications. Additional application opportunities for AI in cardio-oncology involve multimodal cardiovascular imaging, where algorithms can also utilize imaging input to generate predictive risk profiles for cancer patients. The impact of AI extends to digital health tools, playing a pivotal role in the development of digital platforms and wearable technologies. Multidisciplinary teams have been formed to implement and evaluate the efficacy of these technologies, assessing AI-driven clinical decision support tools. Other avenues similarly support practical application of AI in clinical practice, such as incorporation into electronic health records (EHRs) to detect patients at risk for cardiovascular diseases. While these AI applications may help improve preventive measures and facilitate tailored treatment to patients, they are also capable of perpetuating and exacerbating healthcare disparities, if trained on limited, homogenous datasets. However, if trained and operated appropriately, AI holds substantial promise in positively influencing clinical practice in cardio-oncology. In this review, we explore the impact of AI on cardio-oncology care, particularly regarding predicting cardiotoxicity from cancer treatments, while addressing racial and ethnic biases in algorithmic implementation.

Artificial intelligence machine learning based evaluation of elevated left ventricular end-diastolic pressure: a Cleveland Clinic cohort study

Background

Left ventricular end-diastolic pressure (LVEDP) is a key indicator of cardiac health. The gold-standard method of measuring LVEDP is invasive intra-cardiac catheterization. Echocardiography is used for non-invasive estimation of left ventricular (LV) filling pressures; however, correlation with invasive LVEDP is variable. We sought to use machine learning (ML) algorithms to predict elevated LVEDP (>20 mmHg) using clinical, echocardiographic, and biomarker parameters.

Methods

We identified a cohort of 460 consecutive patients from the Cleveland Clinic, without atrial fibrillation or significant mitral valve disease who underwent transthoracic echocardiography within 24 hours of elective heart catheterization between January 2008 and October 2010. We included patients' clinical (e.g., heart rate), echocardiographic (e.g., E/e'), and biomarker [e.g., N-terminal brain natriuretic peptide (NT-proBNP)] profiles. We fit logistic regression (LR), random forest (RF), gradient boosting (GB), support vector machine (SVM), and K-nearest neighbors (KNN) algorithms in a 20-iteration train-validate-test workflow and measured performance using average area under the receiver operating characteristic curve (AUROC). We also predicted elevated tau (>45 ms), the gold-standard parameter for LV diastolic dysfunction, and performed multi-class classification of the patients' cardiac conditions. For each outcome, LR weights were used to identify clinically relevant variables.

Results

ML algorithms predicted elevated LVEDP (>20 mmHg) with good performance [AUROC =0.761, 95% confidence interval (CI): 0.725-0.796]. ML models showed excellent performance predicting elevated tau (>45 ms) (AUROC =0.832, 95% CI: 0.700-0.964) and classifying cardiac conditions (AUROC =0.757-0.975). We identified several clinical variables [e.g., diastolic blood pressure, body mass index (BMI), heart rate, left atrial volume, mitral valve deceleration time, and NT-proBNP] relevant for LVEDP prediction.

Conclusions

Our study shows ML approaches can robustly predict elevated LVEDP and tau. ML may assist in the clinical interpretation of echocardiographic data.

Trastuzumab-Induced Cardiotoxicity: When and How Much Should We Worry?

This review critically analyzes the incidence of trastuzumab-induced left ventricular systolic dysfunction and congestive heart failure (CHF), distinguishing between cases with and without prior anthracycline exposure. It highlights the fact that the elevated risk of trastuzumab-induced cardiotoxicity is closely associated with prior anthracycline exposure. In the absence of prior anthracycline exposure, the incidence rates of trastuzumab-induced cardiotoxicity, particularly CHF (ranging from 0% to 0.5%), are largely comparable with those reported in the general population, especially when reversibility is taken into account. Current cardiac surveillance recommendations during trastuzumab treatment have not yet adapted to the increasing adoption of nonanthracycline treatment strategies and the associated low risk of cardiotoxicity. We propose a refined monitoring protocol to reduce the frequency of cardiac evaluations for low-risk to moderate-risk patients, especially those receiving nonanthracycline treatments. By focusing on patients at high risk or those with prior anthracycline exposure, this strategy seeks to optimize the cost-effectiveness of cardiac care in oncology.

Chemotherapy Related Cardiotoxicity Evaluation-A Contemporary Review with a Focus on Cardiac Imaging

The long-term survivorship of patients diagnosed with cancer has improved due to accelerated detection and rapidly evolving cancer treatment strategies. As such, the evaluation and management of cancer therapy related complications has become increasingly important, including cardiovascular complications. These have been captured under the umbrella term "cardiotoxicity" and include left ventricular dysfunction and heart failure, acute coronary syndromes, valvular abnormalities, pericardial disease, arrhythmia, myocarditis, and vascular complications. These complications add to the burden of cardiovascular disease (CVD) or are risk factors patients with cancer treatment are presenting with. Of note, both pre- and newly developing CVD is of prognostic significance, not only from a cardiovascular perspective but also overall, potentially impacting the level of cancer therapy that is possible. Currently, there are varying recommendations and practices regarding CVD risk assessment and mitigating strategies throughout the cancer continuum. This article provides an overview on this topic, in particular, the role of cardiac imaging in the care of the patient with cancer. Furthermore, it summarizes the current evidence on the spectrum, prevention, and management of chemotherapy-related adverse cardiac effects.

S, Poddubskaya E, Novikova A, Bykova A, Kopylov P. A single-lead ECG based cardiotoxicity detection in patients on polychemotherapy

Background

Anti-cancer treatment can be fraught with cardiovascular complications, which is the most common cause of death among oncological survivors. Without appropriate cardiomonitoring during anti-cancer treatment, it becomes challenging to detect early signs of cardiovascular complications. In order to achieve higher survival rates, it is necessary to monitor oncological patients outpatiently after anti-cancer treatment administration. In this regard, we aim to evaluate the efficacy of single-lead ECG remote monitoring to detect cardiotoxicity in cancer patients with minimal cardiovascular diseases after the first cycle of polychemotherapy.

Materials and methods

The study included patients 162 patients over 18 years old with first diagnosed different types of solid tumors, planed for adjuvant (within 8 weeks after surgery) or neoadjuvant polychemotherapy. All patients were monitored, outpatiently, during 14-21 days (depending on the regimen of polychemotherapy) after polychemotherapy administration using single-lead ECG.

Results

QTc > 500 mc prolongation was detected in 8 patients (6.6 %), first-diagnosed arial fibrillation was detected in 11 patients (9 %) in period after chemotherapy administration. Moreover, left ventricular diastolic dysfunction using single-lead ECG after polychemotherapy was detected in 49 (40.1 %) patients with sensitivity 80 %, specificity 95 %, AUC 0.88 (95 % CI, 0.82-0.93).

Conclusions

The side effects of cancer treatment may cause life-threatening risks. Early identification of cardiotoxicity plays a vital role in the solution of this problem. Using portable devices to detect early cardiotoxicity is a simple, convenient and affordable screening method, that can be used for promptly observation of patients.

Advancing the care of individuals with cancer through innovation & technology: Proceedings from the cardiology oncology innovation summit 2020 and 2021

As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review

BACKGROUND

Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.

OBJECTIVE

The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.

METHODS

We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.

RESULTS

We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.

CONCLUSIONS

The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.

Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients

Aims

There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.

Methods and results

De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals.

Conclusion

Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

OBJECTIVE

We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.

MATERIALS AND METHODS

We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS).

RESULTS

Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated.

CONCLUSIONS

Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

Advancing Cardio-Oncology in Asia

Cardio-oncology is an emerging multi-disciplinary field, which aims to reduce morbidity and mortality of cancer patients by preventing and managing cancer treatment-related cardiovascular toxicities. With the exponential growth in cancer and cardiovascular diseases in Asia, there is an emerging need for cardio-oncology awareness among physicians and country-specific cardio-oncology initiatives. In this state-of-the-art review, we sought to describe the burden of cancer and cardiovascular disease in Asia, a region with rich cultural and socio-economic diversity. From describing the uniqueness and challenges (such as socio-economic disparity, ethnical and racial diversity, and limited training opportunities) in establishing cardio-oncology in Asia, and outlining ways to overcome any barriers, this article aims to help advance the field of cardio-oncology in Asia.

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

BACKGROUND

The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important.

OBJECTIVES

To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease.

DESIGN

This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines.

TRIAL REGISTRATION

ClinicalTrials.Gov Identifier: NCT05377320.

Cardio oncology: Digital innovations, precision medicine and health equity

The rapid emergence of cardio-oncology has resulted in a rapid growth of cardio-oncology programs, dedicated professional societies sections and committees, and multiple collaborative networks that emerged to amplify the access to care in this new subspecialty. However, most existing data, position statements and guidelines are limited by the lack of availability of large clinical trials to support these recommendations. Furthermore, there are significant challenges regarding proper access to cardio-oncology care and treatment, particularly in marginalized and minority populations. The emergence and evolution of personalized medicine, artificial intelligence (AI), and machine learning in medicine and in cardio-oncology provides an opportunity for a more targeted, personalized approach to cardiovascular complications of cancer treatment. The proper implementation of these new modalities may facilitate a more equitable approach to adequate and universal access to cardio-oncology care, improve health related outcomes, and enable health care systems to eliminate the digital divide. This article reviews and analyzes the current status on these important issues.

Immune checkpoint inhibitor induced myocarditis, myasthenia gravis, and myositis: A single-center case series

BACKGROUND

Immune checkpoint inhibitors can result in overlap syndrome comprised of myasthenia gravis, myositis and myocarditis. However, the mortality predictors have not been clearly delineated.

METHODS

We examined the characteristics of 11 patients diagnosed with overlap syndrome at Cleveland Clinic. All the available clinical, diagnostic, biochemical and disease specific factors were examined. Clinical predictors of increased mortality were using student t-test for parametric data and Wilcoxon-signed rank testing for nonparametric data.

RESULTS

Seven patients out of eleven patients were alive during the analysis. Our study did confirm that troponins were indicator of early demise. However, study showed that elevated creatinine, BUN, and decreased hemoglobin were also observed in patients who met early demise. Unlike previously published studies, elevated NT Pro-BNP and reduced left ventricular ejection fraction were not a seen in this study. However, there were higher incidence of electrical abnormalities in deceased patients when compared to alive.

CONCLUSION

Our study is first to examine various clinical parameters of overlap syndrome that might be predictive of mortality. This study confirms troponin as possible predictor and adds elevated creatinine, BUN and reduced hemoglobin as possible early biomarkers in deceased patients. The analysis showed that reduced LVEF was not a seen in deceased patients.

Cardiac imaging techniques for the assessment of immune checkpoint inhibitor-induced cardiotoxicity and their potential clinical applications

Immune checkpoint inhibitors (ICIs) have encouraged a paradigm shift in the clinical management of patients with cancer. Despite the dramatically improved tumor response and patient prognosis, ICIs have been associated with ICI-related myocarditis, which has a high fatality rate. Cardiac imaging plays a critical role in the assessment of cardiac injury. Echocardiography, cardiac magnetic resonance imaging, and targeted tracer-based cardiac molecular imaging techniques alone or in combination reflect pathophysiology and depict different aspects of lesions at different clinical stages, i.e., they have potentially complementary value. Imaging techniques for identifying ICI-induced cardiotoxicity at the early stage may reduce the incidence of adverse cardiovascular events. Particularly in planned ICI therapy among patients with cancer, improved monitoring approaches to identify patients who are at the highest risk of ICI-related myocarditis may help in refining clinical decisions, allowing treatment to be more accurately targeted toward patients who are most likely to benefit. In this study, we systematically reviewed the studies on cardiac imaging techniques for assessing ICI-induced cardiotoxicity. We elaborated about the potential applications of cardiac imaging techniques for the optimized management of patients with ICI-related myocarditis, including risk stratification, diagnosis, and prognosis.

An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline

Although anti-cancer therapy-induced cardiotoxicity is known, until now it lacks a reliable risk predictive model of the subsequent cardiotoxicity in breast cancer patients receiving anthracycline therapy. An artificial intelligence (AI) with a machine learning approach has yet to be applied in cardio-oncology. Herein, we aimed to establish a predictive model for differentiating patients at a high risk of developing cardiotoxicity, including cancer therapy-related cardiac dysfunction (CTRCD) and symptomatic heart failure with reduced ejection fraction. This prospective single-center study enrolled patients with newly diagnosed breast cancer who were preparing for anthracycline therapy from 2014 to 2018. We randomized the patients into a 70%/30% split group for ML model training and testing. We used 15 variables, including clinical, chemotherapy, and echocardiographic parameters, to construct a random forest model to predict CTRCD and heart failure with a reduced ejection fraction (HFrEF) during the 3-year follow-up period (median, 30 months). Comparisons of the predictive accuracies among the random forest, logistic regression, support-vector clustering (SVC), LightGBM, K-nearest neighbor (KNN), and multilayer perceptron (MLP) models were also performed. Notably, predicting CTRCD using the MLP model showed the best accuracy compared with the logistic regression, random forest, SVC, LightGBM, and KNN models. The areas under the curves (AUC) of MLP achieved 0.66 with the sensitivity and specificity as 0.86 and 0.53, respectively. Notably, among the features, the use of trastuzumab, hypertension, and anthracycline dose were the major determinants for the development of CTRCD in the logistic regression. Similarly, MLP, logistic regression, and SVM also showed higher AUCs for predicting the development of HFrEF. We also validated the AI prediction model with an additional set of patients developing HFrEF, and MLP presented an AUC of 0.81. Collectively, an AI prediction model is promising for facilitating physicians to predict CTRCD and HFrEF in breast cancer patients receiving anthracycline therapy. Further studies are warranted to evaluate its impact in clinical practice.

Shared Genetic Risk Factors Between Cancer and Cardiovascular Diseases

Cancer and cardiovascular diseases (CVD) account for approximately 27.5 million deaths every year. While they share some common environmental risk factors, their shared genetic risk factors are not yet fully understood. The aim of the present study was to aggregate genetic risk factors associated with the comorbidity of cancer and CVDs. For this purpose, we: (1) created a catalog of genes associated with cancer and CVDs, (2) visualized retrieved data as a gene-disease network, and (3) performed a pathway enrichment analysis. We performed screening of PubMed database for literature reporting genetic risk factors in patients with both cancer and CVD. The gene-disease network was visualized using Cytoscape and the enrichment analysis was conducted using Enrichr software. We manually reviewed the 181 articles fitting the search criteria and included 13 articles in the study. Data visualization revealed a highly interconnected network containing a single subnetwork with 56 nodes and 146 edges. Genes in the network with the highest number of disease interactions were JAK2, TTN, TET2, and ATM. The pathway enrichment analysis revealed that genes included in the study were significantly enriched in DNA damage repair (DDR) pathways, such as homologous recombination. The role of DDR mechanisms in the development of CVDs has been studied in previously published research; however, additional functional studies are required to elucidate their contribution to the pathophysiology to CVDs.

Pursuing Connectivity in Cardio-Oncology Care-The Future of Telemedicine and Artificial Intelligence in Providing Equity and Access to Rural Communities

The aim of this review is to discuss the current health disparities in rural communities and to explore the potential role of telehealth and artificial intelligence in providing cardio-oncology care to underserviced communities. With advancements in early detection and cancer treatment, survivorship has increased. The interplay between cancer and cardiovascular disease, which are the leading causes of morbidity and mortality in this population, has been increasingly recognized. Worldwide, cardio-oncology clinics (COCs) have emerged to deliver a multidisciplinary approach to the care of patients with cancer to mitigate cardiovascular risks while minimizing interruptions in cancer treatment. Despite the value of COCs, the accessibility gap between urban and rural communities in both oncology and cardio-oncology contributes to health care disparities and may be an underrecognized determinant of health globally. Telehealth and artificial intelligence offer opportunities to provide timely care irrespective of rurality. We therefore explore current developments within this sphere and propose a novel model of care to address the disparity in urban vs. rural cardio-oncology using the experience in Canada, a geographically large country with many rural communities.

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data

Cancer mortality has improved due to earlier detection via screening, as well as due to novel cancer therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitions. However, similarly to older cancer therapies such as anthracyclines, these therapies have also been documented to cause cardiotoxic events including cardiomyopathy, myocardial infarction, myocarditis, arrhythmia, hypertension, and thrombosis. Imaging modalities such as echocardiography and magnetic resonance imaging (MRI) are critical in monitoring and evaluating for cardiotoxicity from these treatments, as well as in providing information for the assessment of function and wall motion abnormalities. MRI also allows for additional tissue characterization using T1, T2, extracellular volume (ECV), and delayed gadolinium enhancement (DGE) assessment. Furthermore, emerging technologies may be able to assist with these efforts. Nuclear imaging using targeted radiotracers, some of which are already clinically used, may have more specificity and help provide information on the mechanisms of cardiotoxicity, including in anthracycline mediated cardiomyopathy and checkpoint inhibitor myocarditis. Hyperpolarized MRI may be used to evaluate the effects of oncologic therapy on cardiac metabolism. Lastly, artificial intelligence and big data of imaging modalities may help predict and detect early signs of cardiotoxicity and response to cardioprotective medications as well as provide insights on the added value of molecular imaging and correlations with cardiovascular outcomes. In this review, the current imaging modalities used to assess for cardiotoxicity from cancer treatments are discussed, in addition to ongoing research on targeted molecular radiotracers, hyperpolarized MRI, as well as the role of artificial intelligence (AI) and big data in imaging that would help improve the detection and prognostication of cancer-treatment cardiotoxicity.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity

Study objective

A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial.

Design/setting/participants

Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology.

Results

The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects.

Conclusion

Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Machine Learning-based Correlation Study between Perioperative Immunonutritional Index and Postoperative Anastomotic Leakage in Patients with Gastric Cancer

Backgrounds: The immunonutritional index showed great potential for predicting postoperative complications in various malignant diseases, while risk assessment based on machine learning (ML) methods is becoming popular in clinical practice. Early detection and prevention for postoperative anastomotic leakage (AL) play an important role in prognosis improvement among patients with gastric cancer (GC). Methods: This retrospective study included 297 patients with gastric cancer receiving gastrectomy between 2018 and 2021 in general surgery department of Xinhua Hospital. Perioperative clinical variables were collected to evaluate the predictive value for postoperative AL with 5 ML models. Then, AUROC was applied to identify the optimal perioperative clinical index and ML model for predicting postoperative AL. Results: The incidence of postoperative AL was 6.1% (n=18). After the training of 5 ML classification models, we found that immunonutritional index had significantly better classification ability than inflammatory or nutritional index alone separately (AUROC=0.87 vs. 0.83, P=0.01; AUROC=0.87 vs. 0.68, P<0.01). Next, we found that support vector machine (SVM), one of the ML methods, with selected immunonutritional index showed significantly greater classification ability than optimal univariant parameter [CRP on postoperative day 4 (AUROC=0.89 vs.0.86, P=0.02)]. Also, statistical analysis revealed multiple variables with significant relevance to postoperative AL, including serum CRP and albumin on postoperative day 4, NLR and SII etc. Conclusion: This study showed that perioperative immunonutritional index could act as an indicator for postoperative AL. Also, ML methods could significantly enhance the classification ability, and therefore, could be applied as a powerful tool for postoperative risk assessment for patients with GC.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

PURPOSE

The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice.

METHODS

Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered.

RESULTS

Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice.

CONCLUSIONS

ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

What can machines learn about heart failure? A systematic literature review

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.

A retrospective analysis of cardiovascular adverse events associated with immune checkpoint inhibitors

Background

Modern therapies in oncology have increased cancer survivorship, as well as the incidence of cardiovascular adverse events. While immune checkpoint inhibitors have shown significant clinical impact in several cancer types, the incidence of immune-related cardiovascular (CV) adverse events poses an additional health concern and has been reported.

Methods

We performed a retrospective analysis of the FDA Adverse Event Reporting System data of suspect product reports for immunotherapy and classical chemotherapy from January 2010–March 2020. We identified 90,740 total adverse event reports related to immune checkpoint inhibitors and classical chemotherapy.

Results

We found that myocarditis was significantly associated with patients receiving anti-program cell death protein 1 (PD-1) or anti-program death ligand 1 (PD-L1), odds ratio (OR) = 23.86 (95% confidence interval [CI] 11.76–48.42, (adjusted p-value) q <  0.001), and combination immunotherapy, OR = 7.29 (95% CI 1.03–51.89, q = 0.047). Heart failure was significantly associated in chemotherapy compared to PD-(L)1, OR = 0.50 (95% CI 0.37–0.69, q <  0.001), CTLA4, OR = 0.08 (95% CI 0.03–0.20, q <  0.001), and combination immunotherapy, OR = 0.25 (95% CI 0.13–0.48, q <  0.001). Additionally, we observe a sex-specificity towards males in cardiac adverse reports for arrhythmias, OR = 0.81 (95% CI 0.75–0.87, q <  0.001), coronary artery disease, 0.63 (95% CI 0.53–0.76, q <  0.001), myocardial infarction, OR = 0.60 (95% CI 0.53–0.67, q <  0.001), myocarditis, OR = 0.59 (95% CI 0.47–0.75, q <  0.001) and pericarditis, OR = 0.5 (95% CI 0.35–0.73, q <  0.001).

Conclusion

Our study provides the current risk estimates of cardiac adverse events in patients treated with immunotherapy compared to conventional chemotherapy. Understanding the clinical risk factors that predispose immunotherapy-treated cancer patients to often fatal CV adverse events will be crucial in Cardio-Oncology management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40959-021-00106-x.

Effects of Carfilzomib Therapy on Left Ventricular Function in Multiple Myeloma Patients

Background: Carfilzomib improves the prognosis of multiple myeloma (MM) patients but significantly increases cardiovascular toxicity. The timing and effect of Carfilzomib therapy on the left ventricular function is still under investigation. We sought to assess the echocardiographic systo-diastolic changes, including global longitudinal strain (GLS), in patients treated with Carfilzomib and to identify predictors of increased risk of cardiovascular adverse events (CVAEs) during therapy.

Methods: Eighty-eight patients with MM performed a baseline cardiovascular evaluation comprehensive of transthoracic echocardiogram (TTE) before the start of Carfilzomib therapy and after 6 months. All patients were clinically followed up to early identify the occurrence of CVAEs during the whole therapy duration.

Results: After Carfilzomib treatment, mean GLS slightly decreased (−22.2% ± 2.6 vs. −21.3% ± 2.5; p < 0.001). Fifty-eight percent of patients experienced CVAEs during therapy: 71% of them had uncontrolled hypertension, and 29% had major CVAEs or CV events not related to arterial hypertension. GLS variation during therapy was not related to an increased risk of CVAEs; however, patients with baseline GLS ≥ −21% and/or left ventricular ejection fraction (LVEF) ≤ 60% had a greater risk of major CVAEs (OR = 6.2, p = 0.004; OR = 3.7, p = 0.04, respectively). Carfilzomib led to a higher risk of diastolic dysfunction (5.6 vs. 13.4%, p = 0.04) and to a rise in E/e′ ratio (8.9 ± 2.7 vs. 9.7 ± 3.7; p = 0.006).

Conclusion: Carfilzomib leads to early LV function impairment early demonstrated by GLS changes and diastolic dysfunction. Baseline echocardiographic parameters, especially GLS and LVEF, might improve cardiovascular risk stratification before treatment.

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients

Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy-related cardiac dysfunction (CTRCD) play important roles in precision cardio-oncology. Methods and Results This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815-0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782-0.792), heart failure (AUROC, 0.882; 95% CI, 0.878-0.887), stroke (AUROC, 0.660; 95% CI, 0.650-0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799-0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797-0.807). Model generalizability was further confirmed using time-split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. Conclusions This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large-scale, longitudinal patient data from healthcare systems.