A novel system to collect dual pulse oximetry data for critical congenital heart disease screening research

Abstract

Introduction: Access to patient medical data is critical to building a real-time data analytic pipeline for improving care providers’ ability to detect, diagnose, and prognosticate diseases. Critical congenital heart disease (CCHD) is a common group of neonatal life-threatening defects that must be promptly diagnosed to minimize morbidity and mortality. CCHD can be diagnosed both prenatally and postnatally. However, despite current screening practices involving oxygen saturation analysis, timely diagnosis is missed in approximately 900 infants with CCHD annually in the USA and can benefit from increased data processing capabilities. Adding non-invasive perfusion measurements to oxygen saturation data can improve the timeliness and fidelity of CCHD diagnostics. However, real-time monitoring and interpretation of non-invasive perfusion data are currently limited. Methods: To address this challenge, we created a hardware and software architecture utilizing a Pi-top™ for collecting, visualizing, and storing dual oxygen saturation, perfusion indices, and photoplethysmography data. Data aggregation in our system is automated and all data files are coded with unique study identifiers to facilitate research purposes. Results: Using this system, we have collected data from 190 neonates, 130 presumably without and 60 with congenital heart disease, in total comprising 1665 min of information. From these data, we are able to extract non-invasive perfusion features such as perfusion index, radiofemoral delay, and slope of systolic rise or diastolic fall. Conclusion: This data collection and waveform analysis is relatively inexpensive and can be used to enhance future CCHD screening algorithms.

Impact of pulse oximetry screening to detect congenital heart defects: 5 years’ experience in a UK regional neonatal unit

“…Hence, further research is required to find optimal methods to enhance diagnosis of these missed cases, particularly for acyanotic serious congenital heart defects. Recently, Doshi et al have published a promising role of adding non-invasive pulse oximetry measurements such as perfusion index (PI), radiofemoral pulse delay (f-hTD) and waveform analysis in improve detection of such cases [21,22,24,37,38]. In particular, to facilitate earlier detection of left-sided obstructive lesions, perfusion index shows promise as an adjunct to POS, and most modern pulse oximeter models have built-in capability to measure real-time peripheral perfusion [21,22,23,24,39].…”

Machine Learning–Based Critical Congenital Heart Disease Screening Using Dual‐Site Pulse Oximetry Measurements

“…Our pulse oximetry recording process and system has been previously described. 10 Briefly, we used 2 Nonin WristOx2 3150 oximeters (Nonin Medical Inc, Plymouth, MN) that then connected via Bluetooth to a Pi‐top, a laptop computer that uses Raspberry Pi microcomputers (Linux based) to record pulse oximetry data labeled with study identification numbers. The Pi‐top displayed both pulse oximetry waveforms along with Sp o 2 and pulse amplitude index (PAI), which is analogous to the perfusion index, values in real time.…”

“…PAI indicates pulse amplitude index; and Sp o 2 , oxygen saturation. Partially reproduced from Doshi et al 10 under the terms and conditions of the Creative Commons Attribution‐Non Commercial No Derivatives license (http:// creativecommons.org/licenses/by‐nc‐nd/4.0 /).…”