Development and validation of an android-based application for anaesthesia neuromuscular monitoring

Mobile Applications in Clinical and Perioperative Care for Anesthesia: Narrative Review

BACKGROUND

The increasing use of smartphones by providers and patients alike demonstrates that digital health utilizing mobile applications has the potential to transform perioperative care and education in anesthesia.

OBJECTIVE

This literature review describes the current scope of the use of mobile applications in anesthesiology.

METHODS

Literature was searched using PubMed, Scopus, and clinicaltrials.gov for articles published from January 1, 2010, through April 1, 2020. Only English language studies were included. Articles were included if they examined the use of a mobile health application in the setting of anesthesia or the perioperative (immediate preoperative, intraoperative, and postoperative) period. Studies were excluded if they explored video interventions or did not examine the feasibility or efficacy of the mobile app.

RESULTS

We included 29 articles, and three areas of clinical functionality were identified: patient-centered care (preoperative, intraoperative, and postoperative), systems-based improvement, and medical education. Several studies demonstrate the feasibility and reliability of mobile apps in these areas, but many are only tested for efficacy in simulated environments or with small patient samples.

CONCLUSIONS

Mobile health applications show promise in improving communication between anesthesiologists, improving workflow efficiency, enhancing medical education, and reducing hospital costs. However, there is a need for validation and improvement before full implementation by the provider, patients, and hospital systems. Future studies are needed to demonstrate meaningful health outcomes to create guidelines and recommendations specific to the application of mobile technology to health care.

Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach

Background

Perioperative quantitative monitoring of neuromuscular function in patients receiving neuromuscular blockers has become internationally recognized as an absolute and core necessity in modern anesthesia care. Because of their kinetic nature, artifactual recordings of acceleromyography-based neuromuscular monitoring devices are not unusual. These generate a great deal of cynicism among anesthesiologists, constituting an obstacle toward their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities. Outlier analysis (or anomaly detection) refers to the problem of finding patterns in data that do not conform to expected behavior.

Objective

This study was motivated by the development of a smartphone app intended for neuromuscular monitoring based on combined accelerometric and angular hand movement data. During the paired comparison stage of this app against existing acceleromyography monitoring devices, it was noted that the results from both devices did not always concur. This study aims to engineer a set of features that enable the detection of outliers in the form of erroneous train-of-four (TOF) measurements from an acceleromyographic-based device. These features are tested for their potential in the detection of erroneous TOF measurements by developing an outlier detection algorithm.

Methods

A data set encompassing 533 high-sensitivity TOF measurements from 35 patients was created based on a multicentric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring app. A basic set of features was extracted based on raw data while a second set of features was purpose engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression (CSLR) models were deployed to evaluate the performance of these features. The final output of the developed models was a binary classification, indicating if a TOF measurement was an outlier or not.

Results

A total of 7 basic features were extracted based on raw data, while another 8 features were engineered based on TOF pattern characteristics. The model training and testing were based on separate data sets: one with 319 measurements (18 outliers) and a second with 214 measurements (12 outliers). The F1 score (95% CI) was 0.86 (0.48-0.97) for the CSLR model with engineered features, significantly larger than the CSLR model with the basic features (0.29 [0.17-0.53]; P<.001).

Conclusions

The set of engineered features and their corresponding incorporation in an outlier detection algorithm have the potential to increase overall neuromuscular monitoring data consistency. Integrating outlier flagging algorithms within neuromuscular monitors could potentially reduce overall acceleromyography-based reliability issues.

Trial Registration

ClinicalTrials.gov NCT03605225; https://clinicaltrials.gov/ct2/show/NCT03605225