1. Can visual inspection of the electrical activity of the diaphragm improve the detection of patient-ventilator asynchronies by pediatric critical care physicians?
- Author
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Rosa Di Mussi, Corrado Cecchetti, Margherita Lonero, Francesco Murgolo, Sergio Picardo, Tai Pham, Matteo Di Nardo, Francesco Staffieri, Pantaleo Lorusso, Lucilla Ravà, Daniela Perrotta, and Salvatore Grasso
- Subjects
medicine.medical_specialty ,Critical Care ,medicine.medical_treatment ,Diaphragm ,Pressure support ventilation ,Random order ,03 medical and health sciences ,0302 clinical medicine ,030202 anesthesiology ,Physicians ,medicine ,Humans ,Child ,Mechanical ventilation ,Ventilators, Mechanical ,business.industry ,030208 emergency & critical care medicine ,Respiration, Artificial ,Diaphragm (structural system) ,Visual inspection ,Anesthesiology and Pain Medicine ,Emergency medicine ,Cohort ,Pediatric critical care ,Airway ,business - Abstract
BACKGROUND Patient-ventilator asynchronies are challenging during pediatric mechanical ventilation. We hypothesized that monitoring the electrical activity of the diaphragm (EAdi) together with the "standard" airway opening pressure (Pao) and flow-time waveforms during pressure support ventilation would improve the ability of a cohort of critical care physicians to detect asynchronies in ventilated children. METHODS We recorded the flow, Pao and EAdi waveforms in ten consecutive patients. The recordings were split in periods of 15 s, each reproducing a ventilator screenshot. From this pool, a team of four experts selected the most representative screenshots including at least one of the three most common asynchronies (missed efforts, auto-triggering and double triggering) and split them into two versions, respectively showing or not the EAdi waveforms. The screenshots were shown in random order in a questionnaire to sixty experienced pediatric intensivists that were asked to identify any episode of patient-ventilator asynchrony. RESULTS Among the ten patients included in the study, only eight had EAdi tracings without artifacts and were analyzed. When the Eadi waveform was shown, the auto-triggering detection improved from 13% to 67% (P
- Published
- 2021
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