Complex Decision Making in the Pediatric Catheterization Laboratory: Catheterizer, Know Thyself and the Data

The potential use of game theory in decision-making in CHD

Background: Congenital cardiac care involves multiple stakeholders including patients and their families, surgeons, cardiologists, anaesthetists, the wider multidisciplinary team, healthcare providers, and manufacturers, all of whom are involved in the decision-making process to some degree. Game theory utilises human behaviour to address the dynamics involved in a decision and what the best payoff is depending on the decision of other players. Aim: By presenting these interactions as a strategic game, this paper aims to provide a descriptive analysis on the utility and effectiveness of game theory in optimising decision-making in congenital cardiac care. Methodology: The comprehensive literature was searched to identify papers on game theory, and its application within surgery. Results: The analysis demonstrated that by utilising game theories, decision-making can be more aligned with patient-centric approaches, potentially improving clinical outcomes. Conclusion: Game theory is a useful tool for improving decision-making and may pave the way for more efficient and improved patient-centric approaches.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Congenital heart disease: addressing the need for novel lower-risk percutaneous interventional strategies

INTRODUCTION

With the advent of improved neonatal care, increasingly vulnerable higher-risk patients with complex congenital heart anomalies are presenting for intervention. This group of patients will always have a higher risk of an adverse event during a procedure but by recognising this risk and with the introduction risk scoring systems and thus the development of novel lower risk procedures, the rate of adverse events can be reduced.

AREA COVERED

This article reviews risk scoring systems for congenital catheterization and demonstrates how they can be used to reduce the rate of adverse events. Then novel low risk strategies are discussed for low weight infants e.g. patent ductus arteriosus (PDA) stent insertion; premature infants e.g. PDA device closure; and transcatheter pulmonary valve replacement. Finally, how risk is assessed and managed within the inherent bias of an institution is discussed.

EXPERT OPINION

There has been a remarkable improvement in the rate of adverse events in congenital cardiac interventions but now, as the benchmark of mortality rate is switched to morbidity and quality of life, continued innovation into lower risk strategies and understanding inherent bias when assessing risk will be key to continuing this improvement.

Managing uncertainty in decision-making of common congenital cardiac defects

Decision-making in congenital cardiac care, although sometimes appearing simple, may prove challenging due to lack of data, uncertainty about outcomes, underlying heuristics, and potential biases in how we reach decisions. We report on the decision-making complexities and uncertainty in management of five commonly encountered congenital cardiac problems: indications for and timing of treatment of subaortic stenosis, closure or observation of small ventricular septal defects, management of new-onset aortic regurgitation in ventricular septal defect, management of anomalous aortic origin of a coronary artery in an asymptomatic patient, and indications for operating on a single anomalously draining pulmonary vein. The strategy underpinning each lesion and the indications for and against intervention are outlined. Areas of uncertainty are clearly delineated. Even in the presence of "simple" congenital cardiac lesions, uncertainty exists in decision-making. Awareness and acceptance of uncertainty is first required to facilitate efforts at mitigation. Strategies to circumvent uncertainty in these scenarios include greater availability of evidence-based medicine, larger datasets, standardised clinical assessment and management protocols, and potentially the incorporation of artificial intelligence into the decision-making process.

Decision-Making in the Catheter Laboratory: The Most Important Variable in Successful Outcomes

Increasingly the importance of how and why we make decisions in the medical arena has been questioned. Traditionally the aeronautical and business worlds have shed a light on this complex area of human decision-making. In this review we reflect on what we already know about the complexity of decision-making in addition to directing particular focus on the challenges to decision-making in the high-intensity environment of the pediatric cardiac catheterization laboratory. We propose that the most critical factor in outcomes for children in the catheterization lab may not be technical failures but rather human factors and the lack of preparation and robust shared decision-making process between the catheterization team. Key technical factors involved in the decision-making process include understanding the anatomy, the indications and objective to be achieved, equipment availability, procedural flow, having a back-up plan and post-procedural care plan. Increased awareness, pre-catheterization planning, use of standardized clinical assessment and management plans and artificial intelligence may provide solutions to pitfalls in decision-making. Further research and efforts should be directed towards studying the impact of human factors in the cardiac catheterization laboratory as well as the broader medical environment.

Role of Doppler echocardiography for assessing right ventricular cardiac output in patients with atrial septal defect

BACKGROUND

Although Doppler echocardiography is routinely used to assess left ventricle cardiac output, there are limited data about the feasibility of Doppler echocardiography for right ventricular (RV) cardiac output assessment in patients with left-to-right shunt. The purpose of the study was to determine the correlation between Doppler-derived and Fick-derived RV cardiac index (CI), and the interobserver correlation in Doppler-derived RV CI assessment.

METHODS

Retrospective study of patients (age ≥18 years) with unrepaired atrial septal defect who underwent cardiac catheterization and echocardiography (within 3 days), 2004-2017. RV CI was calculated using the hydraulic orifice formula: [.785 × (right ventricle outflow tract diameter)²  × right ventricular outflow tract (RVOT) time velocity integral × heart rate]/body surface area.

RESULTS

A total of 128 patients (age 52 ± 17 years; female 88 [69%]) met the inclusion criteria. There was a modest correlation between Doppler-derived and Fick-derived RV CI (r = .57, P < .001), and the mean difference between Doppler-derived and Fick-derived RV CI was -.3 (95% confidence interval of agreement, -.8 to +.9) L/min/m² . There was also a modest correlation between Doppler-derived RV CI from observer #1 and observer #2 (r = .62, P < .001), and the mean difference between Doppler-derived RV CI from observer #1 and observer #2 was -.2 (95% confidence interval of agreement, -.9 to +.6).

CONCLUSIONS

The current study demonstrated a modest correlation between Doppler-derived and Fick-derived RV cardiac output, and a modest interobserver correlation in Doppler-derived RV cardiac output assessment. Further studies are required to validate these results and to explore other potential applications such as in patients with chronic pulmonary regurgitation.