Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

CHDmap: One Step Further Toward Integrating Medicine-Based Evidence Into Practice

Evidence-based medicine, rooted in randomized controlled trials, offers treatment estimates for the average patient but struggles to guide individualized care. This challenge is amplified in complex conditions like congenital heart disease due to disease variability and limited trial applicability. To address this, medicine-based evidence was proposed to synthesize information for personalized care. A recent article introduced a patient similarity network, CHDmap, which represents a promising technical rendition of the medicine-based evidence concept. Leveraging comprehensive clinical and echocardiographic data, CHDmap creates an interactive patient map representing individuals with similar attributes. Using a k-nearest neighbor algorithm, CHDmap interactively identifies closely resembling patient groups based on specific characteristics. These approximate matches form the foundation for predictive analyses, including outcomes like hospital length of stay and complications. A key finding is the tool’s dual capacity: not only did it corroborate clinical intuition in many scenarios, but in specific instances, it prompted a reevaluation of cases, culminating in an enhancement of overall performance across various classification tasks. While an important first step, future versions of CHDmap may aim to expand mapping complexity, increase data granularity, consider long-term outcomes, allow for treatment comparisons, and implement artificial intelligence–driven weighting of various input variables. Successful implementation of CHDmap and similar tools will require training for practitioners, robust data infrastructure, and interdisciplinary collaboration. Patient similarity networks may become valuable in multidisciplinary discussions, complementing clinicians’ expertise. The symbiotic approach bridges evidence, experience, and real-life care, enabling iterative learning for future physicians.

A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study

Background

Although evidence-based medicine proposes personalized care that considers the best evidence, it still fails to address personal treatment in many real clinical scenarios where the complexity of the situation makes none of the available evidence applicable. "Medicine-based evidence" (MBE), in which big data and machine learning techniques are embraced to derive treatment responses from appropriately matched patients in real-world clinical practice, was proposed. However, many challenges remain in translating this conceptual framework into practice.

Objective

This study aimed to technically translate the MBE conceptual framework into practice and evaluate its performance in providing general decision support services for outcomes after congenital heart disease (CHD) surgery.

Methods

Data from 4774 CHD surgeries were collected. A total of 66 indicators and all diagnoses were extracted from each echocardiographic report using natural language processing technology. Combined with some basic clinical and surgical information, the distances between each patient were measured by a series of calculation formulas. Inspired by structure-mapping theory, the fusion of distances between different dimensions can be modulated by clinical experts. In addition to supporting direct analogical reasoning, a machine learning model can be constructed based on similar patients to provide personalized prediction. A user-operable patient similarity network (PSN) of CHD called CHDmap was proposed and developed to provide general decision support services based on the MBE approach.

Results

Using 256 CHD cases, CHDmap was evaluated on 2 different types of postoperative prognostic prediction tasks: a binary classification task to predict postoperative complications and a multiple classification task to predict mechanical ventilation duration. A simple poll of the k-most similar patients provided by the PSN can achieve better prediction results than the average performance of 3 clinicians. Constructing logistic regression models for prediction using similar patients obtained from the PSN can further improve the performance of the 2 tasks (best area under the receiver operating characteristic curve=0.810 and 0.926, respectively). With the support of CHDmap, clinicians substantially improved their predictive capabilities.

Conclusions

Without individual optimization, CHDmap demonstrates competitive performance compared to clinical experts. In addition, CHDmap has the advantage of enabling clinicians to use their superior cognitive abilities in conjunction with it to make decisions that are sometimes even superior to those made using artificial intelligence models. The MBE approach can be embraced in clinical practice, and its full potential can be realized.

Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study

Objectives

Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes.

Methods

A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm.

Results

Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927).

Conclusions

Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.

The paucity of high-level evidence for therapy in pediatric cardiology

Introduction

Clinical practice should be based on the highest quality of evidence available. Therefore, we aimed to classify publications in the field of pediatric cardiology in the year 2021 based on the level of scientific evidence.

Materials and Methods

A PubMed search was performed to identify pediatric cardiology articles published in the calendar year 2021. The abstract or manuscript of each study was reviewed. Each study was categorized as high, medium, or low level of evidence based on the study design. Disease investigated, treatment studied, and country of publication were recorded. Randomized control trials (RCTs) in similar fields of neonatology and adult cardiology were identified for comparison. Descriptive statistics were performed on the level of evidence, type of disease, country of publication, and therapeutic intervention.

Results

In 2021, 731 studies were identified. A decrease in prevalence for the level of evidence as a function of low, medium, and high was found (50.1%, 44.2%, and 5.8%, respectively). A low level of evidence studies was the majority for all types of cardiac disease identified, including acquired heart disease, arrhythmias, congenital heart disease, and heart failure, and for treatment modalities, including circulatory support, defibrillator, percutaneous intervention, medicine, and surgery. In a subgroup analysis, most high-level evidence studies were from the USA (31%), followed by China (26.2%) and India (14.3%). Comparing RCTs, 21 RCTs were identified in pediatric cardiology compared to 178 in neonatology and 413 in adult ischemic heart disease.

Conclusions

There is a great need for the conduct of studies that offer a high level of evidence in the discipline of pediatric cardiology.

Charting the Path: Navigating Embryonic Development to Potentially Safeguard against Congenital Heart Defects

Congenital heart diseases (CHDs) are structural or functional defects present at birth due to improper heart development. Current therapeutic approaches to treating severe CHDs are primarily palliative surgical interventions during the peri- or prenatal stages, when the heart has fully developed from faulty embryogenesis. However, earlier interventions during embryonic development have the potential for better outcomes, as demonstrated by fetal cardiac interventions performed in utero, which have shown improved neonatal and prenatal survival rates, as well as reduced lifelong morbidity. Extensive research on heart development has identified key steps, cellular players, and the intricate network of signaling pathways and transcription factors governing cardiogenesis. Additionally, some reports have indicated that certain adverse genetic and environmental conditions leading to heart malformations and embryonic death may be amendable through the activation of alternative mechanisms. This review first highlights key molecular and cellular processes involved in heart development. Subsequently, it explores the potential for future therapeutic strategies, targeting early embryonic stages, to prevent CHDs, through the delivery of biomolecules or exosomes to compensate for faulty cardiogenic mechanisms. Implementing such non-surgical interventions during early gestation may offer a prophylactic approach toward reducing the occurrence and severity of CHDs.

The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data

BACKGROUND

Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices.

RESULTS

Data from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into "highest score" and "lowest score", and "best response to elamipretide" and "worst response to elamipretide". Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60-93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%).

CONCLUSIONS

In this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

Changing epidemiology of congenital heart disease: effect on outcomes and quality of care in adults

The epidemiology of congenital heart disease (CHD) has changed in the past 50 years as a result of an increase in the prevalence and survival rate of CHD. In particular, mortality in patients with CHD has changed dramatically since the latter half of the twentieth century as a result of more timely diagnosis and the development of interventions for CHD that have prolonged life. As patients with CHD age, the disease burden shifts away from the heart and towards acquired cardiovascular and systemic complications. The societal costs of CHD are high, not just in terms of health-care utilization but also with regards to quality of life. Lifespan disease trajectories for populations with a high disease burden that is measured over prolonged time periods are becoming increasingly important to define long-term outcomes that can be improved. Quality improvement initiatives, including advanced physician training for adult CHD in the past 10 years, have begun to improve disease outcomes. As we seek to transform lifespan into healthspan, research efforts need to incorporate big data to allow high-value, patient-centred and artificial intelligence-enabled delivery of care. Such efforts will facilitate improved access to health care in remote areas and inform the horizontal integration of services needed to manage CHD for the prolonged duration of survival among adult patients.

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.

The horizon of pediatric cardiac critical care

Pediatric Cardiac Critical Care (PCCC) is a challenging discipline where decisions require a high degree of preparation and clinical expertise. In the modern era, outcomes of neonates and children with congenital heart defects have dramatically improved, largely by transformative technologies and an expanding collection of pharmacotherapies. Exponential advances in science and technology are occurring at a breathtaking rate, and applying these advances to the PCCC patient is essential to further advancing the science and practice of the field. In this article, we identified and elaborate on seven key elements within the PCCC that will pave the way for the future.