Tele-Intensive Care Unit Program in Brazil: Implementation and Expansion.

In this scientific report, we aimed to describe the implementation and expansion of a Tele-Intensive Care Unit (Tele-ICU) program in Brazil, highlighting the pillars of success, improvements, and perspectives. Tele-ICU program emerged during the COVID-19 pandemic at the Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), focusing on clinical case discussions and training of health practitioners in public hospitals of the state of São Paulo in Brazil, to support health care professionals for treating COVID-19 patients. The success of implementing this initiative endorsed the project expansion to other five hospitals from different macroregions of the country, leading to the Tele-ICU-Brazil. These projects assisted 40 hospitals, allowing more than 11,500 teleinterconsultations (exchange of medical information between health care professionals using a licensed online platform) and training more than 14,800 health care professionals, reducing mortality and length of hospitalized patients. A segment in telehealth for the obstetrics health care was implemented after detecting these were a susceptible group of patients to COVID-19 severity. As a perspective, this segment will be expanded to 27 hospitals in the country. The Tele-ICU projects reported here were the largest digital health ICU programs ever established in Brazilian National Health System until know. Their results were unprecedented and proved to be crucial for supporting health care professionals nationwide during the COVID-19 pandemic and guide future initiatives in digital health in Brazil's National Health System.

A multi-intervention protocol to improve sleep quality in a coronary care unit.

AIMS: Poor sleep is a frequent occurrence in the critical illness. Evaluate sleep quality and test the effect of a multi-intervention sleep care protocol in improving sleep quality in a coronary care unit (CCU). METHODS AND RESULTS: Quasi-experimental study, carried out in two phases. During the first phase, the control group (n = 58 patients) received usual care. Baseline sleep data were collected through the Richards-Campbell Sleep Questionnaire (RCSQ) and the Sleep in the Intensive Care Unit Questionnaire (SICUQ). During the second phase (n = 55 patients), a sleep care protocol was implemented. Interventions included actions to promote analgesia, reduce noise, brightness, and other general measures. Sleep data were collected again to assess the impact of these interventions. The intervention group had better scores in overall sleep depth [median (interquartile range)] [81 (65-96.7) vs. 69.7 (50-90); P = 0.046]; sleep fragmentation [90 (65-100) vs. 69 (42.2-92.7); P = 0.011]; return to sleep [90 (69.7-100) vs. 71.2 (40.7-96.5); P = 0.007]; sleep quality [85 (65-100) vs. 71.1 (49-98.1); P = 0.026]; and mean RCSQ score [83 (66-94) vs. 66.5 (45.7-87.2); P = 0.002] than the baseline group. The main barriers to sleep were pain [1 (1.0-5.5)], light [1 (1.0-5.0)], and noise [1 (1.0-5.0)]. The most rated sources of sleep-disturbing noise were heart monitor alarm [3 (1.0-5.25)], intravenous pump alarm [1.5 (1.0-5.00)]. and mechanical ventilator alarm [1 (1.0-5.0)]. All were significantly lower in the intervention group than in the baseline group. CONCLUSION: A multi-intervention protocol was feasible and effective in improving different sleep quality parameters and reducing some barriers to sleep in CCU patients.

Performance of Noninvasive Ventilation Masks in a Lung Model of COPD Exacerbation.

BACKGROUND: Noninvasive ventilation (NIV) reduces intubation and mortality in patients with COPD exacerbation who present with respiratory failure, and the type of mask may affect its success. Our objective was to compare the performance of 3 different NIV masks in a lung model. METHODS: We set the lung simulator mechanics and respiratory rate, and tested a small oronasal mask, a total face mask, and a large oronasal mask. We added CO2 at a constant rate into the system and monitored the end-tidal carbon dioxide. We used a mechanical ventilator to deliver NIV in 8 different combinations of inspiratory effort, pressure support, and expiratory positive airway pressure. We measured end-tidal carbon dioxide mask leakage, tidal volume, trigger time, time to achieve 90% of the inspiratory target during inspiration, and excess inspiratory time. RESULTS: We presented the mean ± SD of the 8 simulated conditions for each mask. The mean ± SD leakage was higher for the total face mask (51 ± 6 L/min) than for the small oronasal mask (37 ± 5 L/min) and for the large oronasal mask (21 ± 3 L/min), P < .001; but end-tidal carbon dioxide and tidal volume were similar. The mean ± SD 90% of the inspiratory target during inspiration was faster for the small oronasal mask (585 ± 49 ms) compared with the large oronasal (647 ± 107 ms) and total face mask (851 ± 105 ms), P < .001, all other variables were similar. CONCLUSIONS: In this model, we found that the type of mask had no impact on CO2 washout or on most synchrony variables.