Awake Prone Positioning for Patients with Covid-19 and Acute Respiratory Failure: A Meta-Analysis

INTRODUCTION: Awake prone positioning has been broadly utilised for non-intubated patients with COVID-19- related acute hypoxemic respiratory failure but the results from randomised controlled trials (RCTs) are inconsistent. Hence, we aimed to perform an updated meta-analysis to assess the efficacy and safety of awake prone positioning and identify the subpopulations which are likely to benefit the most. METHOD(S): We followed the PRISMA guidelines and an a priori protocol (PROSPERO CRD42022342426) to conduct our study. An electronic search was carried out on several databases including PubMed, Embase and ClinicalTrials.gov from inception to June 2022. We included only RCTs comparing awake prone position (intervention) with the supine positioning or standard of care with no prone positioning (control). Our primary outcomes were risk of intubation and all-cause mortality. Secondary outcomes included the need for escalating respiratory support, length of ICU and hospital stay, ventilation-free days and adverse events. RevMan 5.4 was used to conduct meta-analyses using a random-effects model. Risk ratios (RRs) and mean differences (MDs) were used as effect measures. RESULT(S): Eleven RCTs were included in our study with a cumulative sample size of 2385 patients. Our meta-analysis showed that awake prone positioning reduced the risk of intubation in the overall population (RR 0.84, 95% CI: 0.74- 0.95). In subgroup analyses, a greater benefit was observed among patients who received advanced respiratory support (i.e., high-flow nasal cannula or nasal intermittent positive pressure ventilation at enrolment) compared with patients receiving conventional oxygen therapy and in intensive care unit (ICU) settings compared with non-ICU settings. Awake prone positioning did not decrease the risk of mortality (RR 0.94, 95% CI: 0.78-1.12) and had no effect on any of the secondary outcomes. CONCLUSION(S): This meta-analysis demonstrated that in patients with COVID-19-related acute hypoxemic respiratory failure, awake prone positioning reduced the risk of intubation, particularly in those patients requiring advanced respiratory support and in those enrolled in the ICU setting but did not decrease the risk of death.

Can Procalcitonin Values Track the Presence of Secondary Infections in Covid-19 Patients?

INTRODUCTION: We set out to analyse the efficacy of Procalcitonin in identifying secondary infections in Covid-19 patients. METHOD(S): In this Retrospective Observational Study, inclusion criteria comprised patients admitted to the Grange University Hospital intensive care unit with SARS-CoV-2 infection throughout the second and third waves of the pandemic. Data collected included daily biomarkers of inflammation such as white blood cell (WBC) counts and C-Reactive Protein (CRP) values and the presence of microbiologically proven secondary infections, quantifying the clinical burden of co-infections in these patients and calculating the microorganism prevalence. Continuous variables are presented as median (interquartile ranges). Differences in continuous variables were analysed using independent Mann-Whitney-U tests and one-way Analysis of Variances (ANOVA). RESULT(S): 121 patients were analysed in Wave 2 and 118 in Wave 3. Of these patients, 69 (57%) tested positive for an infection in Wave 2 and 52 (44%) in Wave 3. The median Procalcitonin levels (ng/mL) on Day 0 were 0.21 (IQR 0.1-1.44) in the No infection Group, compared to 0.24 (0.1-0.44) in the Infection Group for Wave 2. For Wave 3, the Procalcitonin levels (ng/mL) on Day 0 were 0.23 (0.1-.5325) in the No infection Group, compared to 0.14 (0.09-0.44) in the Infection Group. There was no significant difference between the two cohorts over the 14 days period. Similarly, no significant difference was observed in Wave 3 between the Infection vs No infection group. WCB and CRP values were also almost identical in the groups in both waves. Length of ICU stay was higher in the Infection Group in both Wave 2 (median 15 [IQR 0-48]) and Wave 3 (12.05 [0-72.5]) compared to the No Infection Group (Wave 2: 5 [0.08-23.8], Wave 3: 4 [0.6-12.9]). The mean 1st day of infection was Day 6 (SD 3.9) in Wave 2 and Day 4 (SD 3) in Wave 3. The most common pathogen associated with a bloodstream infection was Escherichia Coli (ECOL) in Wave 2 (n=3) and Staphylococcus Aureus (SAUR) in Wave 3 (n=3). The most common pathogen found in sputum culture results was Candida Albicans (CALB) in both Wave 2 (n=32) and Wave 3 (n=19). CONCLUSION(S): Despite initial promise, absolute Procalcitonin values failed to indicate the emergence of critical care-acquired infection in COVID-19 Patients.

Antimicrobial Use in the Second and Third Wave of Covid-19 Infections in a Uk Intensive Care Unit

INTRODUCTION: COVID-19 imposed a great amount of challenge and stress on global health care professionals. Critically ill patients were started on empirical antibiotics and antimicrobials at the beginning of the pandemic. However, studies have later shown that less than 10% of the patients with COVID-19 had a co-existing bacterial infection. METHOD(S): The aim of this study was to calculate the prevalence of antimicrobial use on patients with COVID-19 in both waves two and three. The prime objective was to audit the use of antibiotics in patients with COVID-19 using the UK NICE guidelines and to observe the initial antimicrobials started for the patients in both waves. Information on patient demographic, antibiotics, laboratory results, and patient outcome were collected. Microsoft Excel Version 16.62 and IBM SPSS Statistics Version 28.0.1.1 (14) were used for statistical analysis. RESULT(S): In total 57.02% of patients from wave 2 had a confirmed infection. However, 80.2% of the wave 2 cohort were prescribed antibiotics. The antibiotics density for wave 2 averaged 0.92. 44.07% of the patients admitted during wave 3 had a laboratory-confirmed infection and 52.1% of the cohort were prescribed antibiotics. The average antibiotic density for wave three patients was 0.71. A higher proportion of this cohort had less cumulative days on antibiotics. 90 patients had 0-1 density in wave 3 compared to 84 in wave 2. Tazocin (Tazobactam and Pipercillin) was used the most in both cohort. It was used much more in wave 2 than wave 3. Tazocin is recommended by NICE as the first-line antimicrobial if the patient has severe symptoms, signs of sepsis, or is at higher risk of resistance. The second most common combination of initial antimicrobial was Amoxicillin and Clarithromycin. CONCLUSION(S): Physicians have become increasingly confident in diagnosing and managing patients with COVID-19 however, identification of a biomarker that can aid in distinguishing between viral and bacterial infections is necessary. During times of intense pressure and high demand, an objective marker, infection control liaison, and early microbiological input, helps minimise unnecessary antibiotic use.

Icu risk prediction from commonly measured clinical data using artificial neural network tools

INTRODUCTION: COVID19 has challenged the already established predictive tools in critical care. There is a plethora of unintegrated, untested, underpowered risk-stratification tools, derived using traditional linear modelling with limited predictability for the wider population. Artificial neural network approaches could help developing tools which can be integrated into EMRs. METHODS: We have developed a proof of concept algorithm based on our proprietary artificial neural network (ANN) methodology using 8 markers derived from clinical and laboratory data of 40 critical care patients obtained within 24 hours of ICU admission. The methodology uses a proprietary stepwise ANN algorithm with extensive regularisation and cross validation to identify an optimised panel of markers having maximum sensitivity and specificity. The crossvalidation strategy does not rely on large data sets but rather analyses smaller data sets in parallel. In this process, markers are added sequentially, and their classification performance monitored. The optimised marker panel is then incorporated into a tuned classifier further optimising performance. The final stage takes the validated classifier and converts it to a piece of software. RESULTS: Our ANN derived clinical risk-stratification model predicts ICU mortality with a sensitivity of 96% and a specificity of 94%. The optimal performance was reached after adding eight parameters: LDH, Bilirubin, Heart rate, platelet count, sex, age, white blood cell count and number of co-morbidities. The model resulted in two misclassifications: The one false positive case was in the training set and one false negative in the test set. The model AUC overall was 0.98 with a corrected threshold of 0.54. CONCLUSIONS: During the pandemic, significant amounts of health data covering admission, monitoring and outcome have been collected. Routine ICU data capture is well developed and standardised. Our preliminary results support the use of the ANN derived algorithm for mortality prediction, derived from commonly available clinical data. Our model will need external validation in larger datasets from diverse geographic regions, however has the potential to be integrated in electronic clinical information systems to aid resource utilisation and recruitment of high-risk patients to clinical trials.