The Algorithm: A Rule-Based System for Identifying Heart Failure Phenotype with a Precision Public Health Approach.

Modern medicine remains dependent on the accurate evaluation of a patient's health state, recognizing that disease is a process that evolves over time and interacts with many factors unique to that patient. The project represents a concrete attempt to address these issues by developing reproducible algorithms to support the accuracy in detection of complex diseases. This study aims to establish and validate the approach and algorithm for identifying those patients presenting with or at risk of heart failure (HF) by studying 153,393 subjects in Italy, based on patient information flow databases and is not reliant on the electronic health record to accomplish its goals. The resulting algorithm has been validated in a two-stage process, comparing predicted results with (1) HF diagnosis as identified by general practitioners (GPs) among the reference cohort and (2) HF diagnosis as identified by cardiologists within a randomly sampled subpopulation of 389 patients. The sources of data used to detect HF cases are numerous and were standardized for this study. The accuracy and the predictive values of the algorithm with respect to the GPs and the clinical standards are highly consistent with those from previous studies. In particular, the algorithm is more efficient in detecting the more severe cases of HF according to the GPs' validation (specificity increases according to the number of comorbidities) and external validation (NYHA: II-IV; HF severity index: 2, 3). Positive and negative predictive values reveal that the algorithm is most consistent with clinical evaluation performed in the specialist setting, while it presents a greater ability to rule out false-negative HF cases within the GP practice, probably as a consequence of the different HF prevalence in the two different care settings. Further development includes analyzing the clinical features of false-positive and -negative predictions, to explore the natural clustering of markers of chronic conditions by adding additional methodologies, e.g., Social Network Analysis. establishes the potential that an algorithmic approach, based on integrating administrative data with other public data sources, can enable the development of low cost and high value population-based evaluations for improving public health and impacting public health policies.