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

## TL;DR

CHDmap is a tool that uses patient similarity networks to support personalized care in congenital heart disease by integrating clinical data and predictive analytics.

## Contribution

CHDmap introduces a patient similarity network that enables personalized care by leveraging clinical data and k-nearest neighbor algorithms.

## Key findings

- CHDmap corroborated clinical intuition and improved performance in classification tasks.
- The tool identified patient groups with similar attributes for predictive analyses like hospital length of stay.
- CHDmap prompted reevaluation of cases, leading to better decision-making in specific scenarios.

## Abstract

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.