Introduction: Prolonged mechanical ventilation (MV) is associated with increased patient morbidity and mortality. Medical University of South Carolina (MUSC) PICU is above Solutions for Patient Safety (SPS) national average for unplanned extubations (UE) (In 2020, 0.870 vs 0.5, respectively). Upon review, the majority of UEs in our PICU (67%) do not require reintubation, suggesting an opportunity for earlier extubation attempts. To address this concern, a protocol to increase SBT with the goal to shorten length of MV was implemented. Methods: MUSC PICU SBT protocol was implemented in January 2021. Using QI methodologies, we compared pre and post SBT protocol data. Our SBT process includes daily screening of all intubated patients. Patients who meet criteria complete an SBT the next day. If passed, patient is prepared for extubation. If not successful, areas for optimizing chance of successful SBT are reviewed. Metrics assessed include percentage who qualified for SBT, percentage who completed an SBT, and percentage who were extubated following successful SBT. Secondary measures include rate of UE and rate of reintubation after SBT guided extubation. Data analysis for UE rate includes comparison of pre-protocol data (4/2020 - 12/2020) to post SBT protocol data (cycle 1 1/21 - 9/21 and cycle 2 10/21 - 6/22). Results: A total of 1193 screenings were performed (Cycle A: 612, Cycle B: 581). Cycle A data includes 220 (36%) SBTs, and 85 (39%) had successful SBT. Of the patients who had successful SBT 47 (55%) were extubated and 2 (4%) required reintubation within 24 hours. Cycle B data includes 176 (30%) SBTs, and 156 (89%) had successful SBT. Of the patients who had successful SBT 101 (65%) were extubated and 5 (11%) required reintubation within 24 hours. After implementation of SBT protocol UE rates were reduced by 31% (0.91 to 0.75). Conclusions: MUSC PICU SBT program was feasible and achieved reduction in UE rates. There are opportunities to improve the rate of SBT trial completion and extubation attempts after a successful SBT. Continuing to monitor data for longer period as interventions become more standard practice will determine true outcome.
Determining accurate counts and size distributions for biological particles (bioparticles) is crucial in wide-ranging fields, but current ensemble methods to this end are susceptible to bias from polydispersity in size. This bias can be mitigated by incorporating a separation step prior to characterization. For this reason, asymmetrical flow field-flow fractionation (AF4) with on-line multiangle light scattering (MALS) has become an important platform for determining particle size. AF4-MALS has been used to report particle concentration, particularly for complex biological particles, yet the impact of light scattering models and particle refractive indices (RI) have not been quantitatively assessed. Here, we develop an analysis workflow using AF4-MALS to simultaneously separate and determine particles sizes and concentrations. The impacts of the MALS particle counting model used to process data and the chosen RI value(s) on particle counts are systematically assessed for polystyrene latex (PSL) particles and bacterial outer membrane vesicles (OMVs) in the 20-500 nm size range. Across spherical models, PSL and OMV particle counts varied up to 13% or 200%, respectively. For the coated-sphere model used in the analysis of OMV samples, the sphere RI value greatly impacts particle counts. As the sphere RI value approaches the RI of the suspending medium, the model becomes increasingly sensitive to the light scattering signal-to-noise ultimately causing erroneous particle counts. Overall, this work establishes the importance of selecting appropriate MALS models and RI values for bioparticles to obtain accurate counts and provides an AF4-MALS method to separate, enumerate, and size polydisperse bioparticles.
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