Early detection of critical pulmonary shunts in infants

This paper aims to improve the design of modern Medical Cyber Physical Systems through the addition of supplemental noninvasive monitors. Specifically, we focus on monitoring the arterial blood oxygen content (CaO₂). one of the most closely observed vital signs in operating rooms, currently measured by a proxy - peripheral hemoglobin oxygen saturation (SpO₂). While SpO₂ is a good estimate of O₂content in the finger where it is measured, it is a delayed measure of its content in the arteries. In addition, it does not incorporate system dynamics and is a poor predictor of future CaO₂ values. Therefore, as a first step towards sup-plementing the usage of SpO₂, this work introduces a predictive monitor designed to provide early detection of critical drops in CaO₂ caused by a pulmonary shunt in infants. To this end, we develop a formal model of the circulation of oxygen and carbon dioxide in the body, characterized by unknown patient-unique parameters. Employing the model, we design a matched subspace detector to provide a near constant false alarm rate invariant to these parameters and modeling uncertainties. Finally, we validate our approach on real-patient data from lung lobectomy surgeries performed at the Children's Hospital of Philadelphia. Given 198 infants, the detector predicted 81% of the critical drops in CaO₂at an average of about 65 seconds earlier than the SpO₂-based monitor, while achieving a 0.9% false alarm rate (representing about 2 false alarms per hour).