METHODS: Databases of MEDLINE, EMBASE and CENTRAL were systematically searched from inception until March 2021. Case reports and case series were excluded.
RESULTS: Eleven studies (n = 606 patients) were eligible. Prone ventilation significantly improved PaO2/FiO2 ratio (studies: 8, n = 579, mean difference 46.75, 95% CI 33.35‒60.15, p < 0.00001; evidence: very low) and peripheral oxygen saturation (SpO2) (studies: 3, n = 432, mean difference 1.67, 95% CI 1.08‒2.26, p < 0.00001; evidence: ow), but not the arterial partial pressure of carbon dioxide (PaCO2) (studies: 5, n = 396, mean difference 2.45, 95% CI 2.39‒7.30, p = 0.32; evidence: very low), mortality rate (studies: 1, n = 215, Odds Ratio 0.66, 95% CI 0.32‒1.33, p = 0.24; evidence: very low), or number of patients discharged alive (studies: 1, n = 43, Odds Ratio 1.49, 95% CI 0.72‒3.08, p = 0.28; evidence: very low).
CONCLUSION: Prone ventilation improved PaO2/FiO2 ratio and SpO2 in intubated COVID-19 patients. Given the substantial heterogeneity and low level of evidence, more randomized- controlled trials are warranted to improve the certainty of evidence, and to examine the adverse events of prone ventilation.
METHODS: Patients that were treated at the Hospital Sultan Ismail's Burns Intensive Care (BICU) unit for acute burn injuries between 1 January 2010 to 31 December 2017 were included. Risk factors to predict in-patient burn mortality were gender, age, mechanism of injury, total body surface area burn (TBSA), inhalational injury, mechanical ventilation, presence of tracheotomy, time from of burn injury to BICU admission and initial centre of first emergency treatment was administered. These variables were analysed using univariate and multivariate analysis for the outcomes of death. All patients were scored retrospectively using the five-burn mortality prognostic scores. Predictive ability for burn mortality was analysed using the area under receiver operating curve (AUROC).
RESULTS: A total of 525 patients (372 males and 153 females) with mean age of 34.5 ± 14.6 years were included. There were 463 survivors and 62 deaths (11.8% mortality rate). The outcome of the primary objective showed that amongst the burn mortality risk factors that remained after multivariate analysis were older age (p = 0.004), wider TBSA burn (p
METHODS: This is a retrospective review of all mechanically ventilated surgical patients in the wards, in a tertiary hospital, in 2020. Sixty-two patients out of 116 patients ventilated in surgical wards fulfilled the inclusion criteria. Demography, surgical diagnosis and procedures and physiologic, biochemical and survival data were analyzed to explore the outcomes and predictors of mortality.
RESULTS: Twenty-two out of 62 patients eventually gained ICU admission. Mean time from intubation to ICU entry and mean length of ICU stay were 48 h (0 to 312) and 10 days (1 to 33), respectively. Survival for patients admitted to ICU compared to ventilation in the acute surgery wards was 54.5% (12/22) vs 17.5% (7/40). Thirty-four patients underwent surgery, and the majority were bowel-related emergency operations. SAPS2 score validation revealed AUC of 0.701. More than half of patients with mortality risk
METHODS: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort.
RESULTS AND DISCUSSION: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data.
CONCLUSION: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.
MATERIALS AND METHODS: This study is a novel retrospective study in a tertiary centre in Malaysia. Case notes of COVID- 19 patients who underwent tracheostomy in Hospital Ampang were collected using the electronic Hospital Information System. Data were analysed using the SPSS system.
RESULTS: From a total of 30 patients, 15 patients survived. All patients underwent either open or percutaneous tracheostomy. The median age is 53 (range: 28-69) with a significant p-value of 0.02. Amongst comorbidities, it was noted that diabetes mellitus was significant with a p-value of 0.014. The median time from the onset of COVID-19 to tracheostomy is 30 days. The median duration of intensive care unit (ICU) stay is 30.5 days, with the median duration of hospital length of stay of 44 days (p = 0.009 and <0.001, respectively). No complications that contributed to patient death were found. Survivors had a median of 29.5 days from tracheostomy to oxygen liberation.
CONCLUSION: Tracheostomy in COVID-19 patients that requires prolonged ventilation is unavoidable. It is a safe procedure and mortality is not related to the procedure. Mortality is primarily associated with COVID-19.