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Scoring Tools for the Analysis of Infant Respiratory Inductive Plethysmography Signals
Carlos Alejandro Robles-Rubio1, Gianluca Bertolizio2, Karen A Brown2
1Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
Insights
New manual scoring tools improve the analysis of infant cardiorespiratory data for Postoperative Apnea (POA) studies. These tools enhance accuracy and consistency in scoring respiratory patterns, crucial for rare event analysis.
Area of Science:
- Medical Informatics
- Respiratory Physiology
- Pediatric Anesthesiology
Background:
- Infants recovering from anesthesia face risks of life-threatening Postoperative Apnea (POA).
- Studying rare POA events necessitates analyzing extensive cardiorespiratory records.
- Current manual scoring methods lack scorer repeatability and comprehensive respiratory pattern descriptions.
Purpose of the Study:
- To develop and validate a set of manual scoring tools to improve the analysis of infant cardiorespiratory patterns.
- To address limitations in repeatability and descriptive accuracy of existing scoring methods for Postoperative Apnea research.
Main Methods:
- Developed definitions and scoring rules for 6 unique infant respiratory patterns using respiratory inductive plethysmography (RIP).
- Created RIPScore software for manual scoring, a data segment library, and a training protocol.
- Implemented a quality control method for ongoing scorer performance monitoring.
Main Results:
- Trained scorers achieved expert-level performance with high accuracy and consistency.
- Demonstrated very good intra- and inter-scorer repeatability using the developed tools.
- Scorers showed high efficiency and only minor confusion between respiratory patterns.
Conclusions:
- The developed manual scoring tools offer an effective method for analyzing respiratory patterns in long cardiorespiratory records.
- These tools are valuable for Postoperative Apnea research and extend to other studies using RIP signals.
- The tools facilitate large-scale, multi-center studies by ensuring standardized and repeatable data analysis.
Abstract:
Infants recovering from anesthesia are at risk of life threatening Postoperative Apnea (POA). POA events are rare, and so the study of POA requires the analysis of long cardiorespiratory records. Manual scoring is the preferred method of analysis for these data, but it is limited by low intra- and inter-scorer repeatability. Furthermore, recommended scoring rules do not provide a comprehensive description of the respiratory patterns. This work describes a set of manual scoring tools that address these limitations. These tools include: (i) a set of definitions and scoring rules for 6 mutually exclusive, unique patterns that fully characterize infant respiratory inductive plethysmography (RIP) signals; (ii) RIPScore, a graphical, manual scoring software to apply these rules to infant data; (iii) a library of data segments representing each of the 6 patterns; (iv) a fully automated, interactive formal training protocol to standardize the analysis and establish intra- and inter-scorer repeatability; and (v) a quality control method to monitor scorer ongoing performance over time. To evaluate these tools, three scorers from varied backgrounds were recruited and trained to reach a performance level similar to that of an expert. These scorers used RIPScore to analyze data from infants at risk of POA in two separate, independent instances. Scorers performed with high accuracy and consistency, analyzed data efficiently, had very good intra- and inter-scorer repeatability, and exhibited only minor confusion between patterns. These results indicate that our tools represent an excellent method for the analysis of respiratory patterns in long data records. Although the tools were developed for the study of POA, their use extends to any study of respiratory patterns using RIP (e.g., sleep apnea, extubation readiness). Moreover, by establishing and monitoring scorer repeatability, our tools enable the analysis of large data sets by multiple scorers, which is essential for longitudinal and multicenter studies.
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