RESUMO
To measure inspiratory airflow resistance in patients with acute respiratory distress syndrome (ARDS) due to COVID-19. DESIGN: Observational cohort of a convenience sample. SETTING: Three community ICUs. SUBJECTS: Fifty-five mechanically ventilated patients with COVID-19. INTERVENTIONS: Measurements of ventilatory mechanics during volume control ventilation. MEASUREMENTS: Flow-time and pressure-time scalars were used to measure inspiratory airways resistance. RESULTS: The median inspiratory airflow resistance was 12 cm H2O/L/s (interquartile range, 10-16). Inspiratory resistance was not significantly different among patients with asthma or chronic obstructive pulmonary disease compared with those without a history of obstructive airways disease (median 12.5 vs 12 cm H2O/L/s, respectively; p = 0.66). Survival to 90 days among patients with inspiratory resistance above 12 cm H2O/L/s was 68% compared with 60% for patients below 12 cm H2O/L/s (p = 0.58). Inspiratory resistance did not correlate with C-reactive protein, ferritin, Pao2/Fio2 ratio, or static compliance. CONCLUSIONS: Inspiratory airflow resistance was normal to slightly elevated among mechanically ventilated patients with ARDS due to COVID-19. Airways resistance was independent of a history of obstructive airways disease, did not correlate with biomarkers of disease severity, and did not predict mortality.
RESUMO
BACKGROUND: The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations. AIM: The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic. METHODS: We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses. RESULTS: The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm. CONCLUSIONS: A discrete-event simulation model is developed, validated, and used to carry out "what-if" scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.
