Investigating Capnography Innovation for Better Patient Monitoring in the Resource Limited Surgical Setting

Abstract

Background. Access to basic anesthetic monitoring in the developing world is lacking, which contributes to the 100 times greater anesthesia-related mortality in low- and middle-income countries. We hypothesize that an environmental sensor with a lower sampling rate could provide some clinical utility by providing CO₂ levels, respiratory rate, and support in detection of clinical abnormalities. Materials and Methods. A bench-top lung simulation was created to replicate CO₂ waveforms, and an environmental sensor was compared with industry-available technology. Sensor response time and respiratory rates were compared between devices. Additionally, an in silico model was created to replicate capnography pathology as waveforms would appear using the environmental sensor. Results and Conclusion. Breath simulations using the bench-top lung simulation produced similar results to industry standards with a degree of variability. Respiratory rates did not differ between the environmental sensor and all other devices tested. Finally, pathological waveforms created in silico carried a certain level of detail regarding ventilatory pathology, which could provide some clinical insight to an anesthesiologist. We believe our prototype is the first step toward making low-cost and portable capnography available in the resource-limited setting, and future efforts should focus on bridging the gap to safer anesthesia and surgery globally.