Community Detection in Medicine: Preserved Ejection Fraction Heart Failure (HFpEF)

Background and objectives: The COVID-19 pandemic raised awareness of the complexities of the patient, the disease, and the practice of medicine. The impact of these reaches beyond healthcare (e.g., supply chains, politics, socioeconomic factors) to include nations, individuals, and molecules. In personalized medicine, "accurate diagnosis" is critical as it affects patient management, clinical trial recruitment, regulatory approval, and reimbursement policies for payers. Conventional statistics evaluate hypothesis-driven reductionist practices in medicine, e.g., the use of "scores" combining individual measurements, and are often limited by the data:variables ratio. True personalization (N of 1) is not practical but better stratification of diseases and patients can improve diagnoses. This work describes our approach and tests its ability to identify patient complexity and clinical markers in the trial of a candidate HFpEF drug better than prior methods. Methods: This study evaluated discovery or data-driven approaches, by applying community detection (CD), forgoing statistical significance to identify unknown relationships. We reanalyzed data from the I-PRESERVE study of heart failure with preserved-ejection fraction, where subgroup analysis was unsuccessful. We initially performed unipartite CD analysis and evolved to address the complexity in real-world data using a bipartite model. The mathematically grounded modularity metric enabled greater confidence in community assignments. Results: This reanalysis with CD revealed novel patient subgroups with stronger supporting rationale for group assignments, pointing to further refined and targeted studies. Conclusions: We believe that generalization of the CD approach to higher-dimensional data can lead to a "next generation of phenotyping" that encompasses the temporal progression of the patient