Journal of clinical monitoring and computing
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J Clin Monit Comput · Dec 2022
A model-based approach to generating annotated pressure support waveforms.
Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. ⋯ Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher's exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.
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J Clin Monit Comput · Dec 2022
Validation of the Masimo O3™ regional oximetry device in pediatric patients undergoing cardiac surgery.
We assessed the accuracy of Masimo O3™ regional cerebral oxygen saturation (rSO2) readings by comparing them with reference values and evaluated the relationship between rSO2 and somatic tissue oxygen saturation (StO2) in children undergoing cardiac surgery. After anesthesia induction, pediatric sensors were applied to the forehead and foot sole, and rSO2 and StO2 values were monitored continuously. Before cardiopulmonary bypass (CPB), FIO2 was set to 0.2, 0.5, and 0.8 serially every 15 min. ⋯ According to multiple linear regression analysis, the application of CPB, FIO2, Hb level, and tip location of the central venous catheter influenced the bias (all P < 0.05). Furthermore, the correlation between rSO2 and StO2 was weak (r = 0.254). rSO2 readings by the Masimo O3™ device and pediatric sensor had good absolute and trending accuracies with respect to the calculated reference values in children undergoing cardiac surgery. rSO2 and StO2 cannot be used interchangeably. Clinical trial registration http://clinicaltrials.gov (number: NCT04208906).
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J Clin Monit Comput · Dec 2022
Prediction of sepsis onset in hospital admissions using survival analysis.
To determine the efficacy of modern survival analysis methods for predicting sepsis onset in ICU, emergency, medical/surgical, and TCU departments. We performed a retrospective analysis on ICU, med/surg, ED, and TCU cases from multiple Mercy Health hospitals from August 2018 to March 2020. Patients in these departments were monitored by the Mercy Virtual vSepsis team and sepsis cases were determined and documented in the Mercy EHR via a rule-based engine utilizing clinical data. ⋯ This methodology improves upon previous work by demonstrating excellent model performance when generalizing survival-based prediction methods to both severe sepsis and septic shock as well as non-ICU departments. IRB InformationTrial Registration ID: 1,532,327-1. Trial Effective Date: 12/02/2019.