Enhancing Heart Failure Diagnosis Accuracy and Distinguishing It From Other Pulmonary Conditions: A Retrospective Case Series Study Leveraging the HeartLogic Parameters.

Introduction Heart failure (HF) poses a substantial and escalating medical and economic challenge, marked by significant morbidity and mortality. It stands as the primary cause of hospital admissions among the elderly, contributing significantly to healthcare expenditures in developed nations. Evaluating cardiac and pulmonary function remains challenging, necessitating careful interpretation to mitigate misdiagnosis and inappropriate treatment. Remote monitoring has emerged as a preventive strategy to curb HF-related hospitalizations, emphasizing the importance of early detection of impending acute HF decompensation. Implantable cardiac defibrillators (ICDs) capture various parameters, including heart rhythm, pacing percentages, thoracic impedance, and physical activity. Objective In this study, we aim to investigate the effectiveness of HeartLogic (Boston Scientific, Marlborough, Massachusetts) parameters in accurately distinguishing HF patients from individuals with alternative diagnoses. Methods This cross-sectional study was conducted at Cabell Huntington Hospital, St. Mary's Medical Center in Huntington, West Virginia, between 2021 and 2022. The study involved a retrospective chart review of electronic medical records, approved by the institutional review board, encompassing patients aged >18 admitted with Heartlogic-capable devices. The analysis included demographic variables, admission and discharge diagnoses, length of hospital stays, health literacy index, and thoracic impedance. Results Of the initially included 26 patients, 19 met all inclusion criteria. The demographic profile highlighted a predominantly older population with a male preponderance and a notable incidence of congestive heart failure (CHF). Physiological changes, particularly in thoracic impedance and the HeartLogic Index, demonstrated significant alterations. Logistic regression analysis revealed that changes in health literacy index and thoracic impedance significantly contributed to predicting the change in CHF diagnosis. Conclusion This study, conducted in a rural setting, demonstrates the capability of the HeartLogic algorithm in predicting HF events, providing valuable insights into its utility in diverse clinical environments. The findings emphasize the potential of this technology to enhance diagnostic accuracy and improve patient outcomes. Despite inherent limitations, this analysis contributes unique perspectives, particularly in the context of a specific and underexplored rural population in West Virginia.