Artificial Intelligence in Critical Care: Transforming Early Detection and Management of Febrile Neutropenia in Immunocompromised Patients

Abstract

Background: Febrile neutropenia (FN) is a common and potentially life-threatening complication in immunocompromised patients, particularly those undergoing hematopoietic stem cell transplantation (HSCT). Timely identification and intervention are essential to prevent progression to sepsis or multi-organ dysfunction. While conventional monitoring relies on intermittent vital sign assessment, artificial intelligence (AI)-driven remote patient monitoring (RPM) offers a promising adjunct to enhance early detection and clinical decision-making.

Case Insight: We present a case involving a young adult female post-HSCT who was monitored using an AI-enabled RPM system. Over a 29-day post-transplant period, the system generated real-time alerts indicating clinical deterioration, including febrile episodes, hypotension, tachycardia, and respiratory distress. These alerts were substantiated by clinical and diagnostic evaluations, leading to prompt escalation of antibiotics and supportive care. The AI system’s continuous surveillance and predictive capability enabled earlier recognition of complications, even when standard bedside monitoring showed minimal change.

Conclusion: This paper highlights the potential use of AI-integrated RPM systems to transform the management of FN by enabling earlier intervention especially in high-risk immunocompromised patients. Although the preliminary observations are promising, further prospective studies are certainly required to evaluate and assess the accuracy, clinical outcomes, and resource implications of integration of AI into routine critical care monitoring protocols.