COVID-19 ICU and mechanical ventilation patient characteristics and outcomes-A systematic review and meta-analysis

Antibiotics Prescribing in Intensive Care Settings during the COVID-19 Era: A Systematic Review

The prevalence of patients admitted to intensive care units (ICUs) with SARS-CoV-2 infection who were prescribed antibiotics is undetermined and might contribute to the increased global antibiotic resistance. This systematic review evaluates the prevalence of antibiotic prescribing in patients admitted to ICUs with SARS-CoV-2 infection using PRISMA guidelines. We searched and scrutinized results from PubMed and ScienceDirect databases for published literature restricted to the English language up to 11 May 2021. In addition, we included observational studies of humans with laboratory-confirmed SARS-CoV-2 infection, clinical characteristics, and antibiotics prescribed for ICU patients with SARS-CoV-2 infections. A total of 361 studies were identified, but only 38 were included in the final analysis. Antibiotic prescribing data were available from 2715 patients, of which prevalence of 71% was reported in old age patients with a mean age of 62.7 years. From the reported studies, third generation cephalosporin had the highest frequency amongst reviewed studies (36.8%) followed by azithromycin (34.2%). The estimated bacterial infection in 12 reported studies was 30.8% produced by 15 different bacterial species, and S. aureus recorded the highest bacterial infection (75%). The fundamental outcomes were the prevalence of ICU COVID-19 patients prescribed antibiotics stratified by age, type of antibiotics prescribed, and the presence of co-infections and comorbidities. In conclusion, more than half of ICU patients with SARS-CoV-2 infection received antibiotics, and prescribing is significantly higher than the estimated frequency of identified bacterial co-infection.

Cardiovascular risk factors, cardiovascular disease, and COVID-19: an umbrella review of systematic reviews

AIMS

To consolidate evidence to determine (i) the association between cardiovascular risk factors and health outcomes with coronavirus 2019 (COVID-19); and (ii) the impact of COVID-19 on cardiovascular health.

METHODS AND RESULTS

An umbrella review of systematic reviews was conducted. Fourteen medical databases and pre-print servers were searched from 1 January 2020 to 5 November 2020. The review focused on reviews rated as moderate or high-quality using the AMSTAR 2 tool. Eighty-four reviews were identified; 31 reviews were assessed as moderate quality and one was high-quality. The following risk factors were associated with higher mortality and severe COVID-19: renal disease [odds ratio (OR) (95% confidence interval) for mortality 3.07 (2.43-3.88)], diabetes mellitus [OR 2.09 (1.80-2.42)], hypertension [OR 2.50 (2.02-3.11)], smoking history [risk ratio (RR) 1.26 (1.20-1.32)], cerebrovascular disease [RR 2.75 (1.54-4.89)], and cardiovascular disease [OR 2.65 (1.86-3.78)]. Liver disease was associated with higher odds of mortality [OR 2.81 (1.31-6.01)], but not severe COVID-19. Current smoking was associated with a higher risk of severe COVID-19 [RR 1.80 (1.14-2.85)], but not mortality. Obesity associated with higher odds of mortality [OR 2.18 (1.10-4.34)], but there was an absence of evidence for severe COVID-19. In patients hospitalized with COVID-19, the following incident cardiovascular complications were identified: acute heart failure (2%), myocardial infarction (4%), deep vein thrombosis (7%), myocardial injury (10%), angina (10%), arrhythmias (18%), pulmonary embolism (19%), and venous thromboembolism (25%).

CONCLUSION

Many of the risk factors identified as associated with adverse outcomes with COVID-19 are potentially modifiable. Primary and secondary prevention strategies that target cardiovascular risk factors may improve outcomes for people following COVID-19.

Mortality associated with early changes in ARDS severity in COVID-19 patients - Insights from the PRoVENT-COVID study

Purpose

We investigated changes in ARDS severity and associations with outcome in COVID–19 ARDS patients.

Methods

We compared outcomes in patients with ARDS classified as ‘mild’, ‘moderate’ or ‘severe’ at calendar day 1, and after reclassification at calendar day 2. The primary endpoint was 28–day mortality. We also identified which ventilatory parameters had an association with presence of severe ARDS at day 2. We repeated the analysis for reclassification at calendar day 4.

Results

Of 895 patients, 8.5%, 60.1% and 31.4% had mild, moderate and severe ARDS at day 1. These proportions were 13.5%, 72.6% and 13.9% at day 2. 28–day mortality was 25.3%, 31.3% and 32.0% in patients with mild, moderate and severe ARDS at day 1 (p = 0.537), compared to 28.6%, 29.2% and 44.3% in patients reclassified at day 2 (p = 0.005). No ventilatory parameter had an independent association with presence of severe ARDS at day 2. Findings were not different reclassifying at day 4.

Conclusions

In this cohort of COVID–19 patients, ARDS severity and mortality between severity classes changed substantially over the first 4 days of ventilation. These findings are important, as reclassification could help identify target patients that may benefit from alternative approaches.

The role of emergency department triage early warning score (TREWS) and modified early warning score (MEWS) to predict in-hospital mortality in COVID-19 patients

Background

It is necessary to identify critical patients requiring hospitalization early due to the rapid increase in the number of COVID-19 cases.

Aim

This study aims to evaluate the effectiveness of scoring systems such as emergency department triage early warning score (TREWS) and modified early warning score (MEWS) in predicting mortality in COVID-19 patients.

Methods

In this retrospective cohort study, PCR positive patients evaluated for COVID-19 and decided to be hospitalized were evaluated. During the first evaluation, MEWS and TREWS scores of the patients were calculated. Intensive care needs as well as 24-h and 28-day mortality rates were evaluated.

Results

A total of 339 patients were included in the study. While 30 (8.8%) patients were hospitalized in the intensive care unit, 4 (1.2%) died in the emergency. The number of patients who died within 28 days was found to be 57 (16.8%). In 24-h mortality, the median MEWS value was found to be 7 (IQR 25–75) while the TREWS value was 11.5 (IQR 25–75). In the ROC analysis made for the diagnostic value of 28-day mortality of MEWS and TREWS scores, the area under the curve (AUC) for the MEWS score was found to be 0.833 (95% CI 0.777–0.888, p < 0.001) while it was identified as 0.823 (95% CI 0.764–0.882, p < 0.001) for the TREWS.

Conclusion

MEWS and TREWS calculated at emergency services are effective in predicting 28-day mortality in patients requiring hospitalization due to COVID-19.

Telerobotic Operation of Intensive Care Unit Ventilators

Since the first reports of a novel coronavirus (SARS-CoV-2) in December 2019, over 33 million people have been infected worldwide and approximately 1 million people worldwide have died from the disease caused by this virus, COVID-19. In the United States alone, there have been approximately 7 million cases and over 200,000 deaths. This outbreak has placed an enormous strain on healthcare systems and workers. Severe cases require hospital care, and 8.5% of patients require mechanical ventilation in an intensive care unit (ICU). One major challenge is the necessity for clinical care personnel to don and doff cumbersome personal protective equipment (PPE) in order to enter an ICU unit to make simple adjustments to ventilator settings. Although future ventilators and other ICU equipment may be controllable remotely through computer networks, the enormous installed base of existing ventilators do not have this capability. This paper reports the development of a simple, low cost telerobotic system that permits adjustment of ventilator settings from outside the ICU. The system consists of a small Cartesian robot capable of operating a ventilator touch screen with camera vision control via a wirelessly connected tablet master device located outside the room. Engineering system tests demonstrated that the open-loop mechanical repeatability of the device was 7.5 mm, and that the average positioning error of the robotic finger under visual servoing control was 5.94 mm. Successful usability tests in a simulated ICU environment were carried out and are reported. In addition to enabling a significant reduction in PPE consumption, the prototype system has been shown in a preliminary evaluation to significantly reduce the total time required for a respiratory therapist to perform typical setting adjustments on a commercial ventilator, including donning and doffing PPE, from 271 to 109 s.

High-Flow Nasal Cannula Therapy in COVID-19: Using the ROX Index to Predict Success

BACKGROUND

Optimal timing of mechanical ventilation in COVID-19 is uncertain. We sought to evaluate outcomes of delayed intubation and examine the ROX index (ie, [[Formula: see text]]/breathing frequency) to predict weaning from high-flow nasal cannula (HFNC) in patients with COVID-19.

METHODS

We performed a multicenter, retrospective, observational cohort study of subjects with respiratory failure due to COVID-19 and managed with HFNC. The ROX index was applied to predict HFNC success. Subjects that failed HFNC were divided into early HFNC failure (≤ 48 h of HFNC therapy prior to mechanical ventilation) and late failure (> 48 h). Standard statistical comparisons and regression analyses were used to compare overall hospital mortality and secondary end points, including time-specific mortality, need for extracorporeal membrane oxygenation, and ICU length of stay between early and late failure groups.

RESULTS

272 subjects with COVID-19 were managed with HFNC. One hundred sixty-four (60.3%) were successfully weaned from HFNC, and 111 (67.7%) of those weaned were managed solely in non-ICU settings. ROX index >3.0 at 2, 6, and 12 hours after initiation of HFNC was 85.3% sensitive for identifying subsequent HFNC success. One hundred eight subjects were intubated for failure of HFNC (61 early failures and 47 late failures). Mortality after HFNC failure was high (45.4%). There was no statistical difference in hospital mortality (39.3% vs 53.2%, P = .18) or any of the secondary end points between early and late HFNC failure groups. This remained true even when adjusted for covariates.

CONCLUSIONS

In this retrospective review, HFNC was a viable strategy and mechanical ventilation was unecessary in the majority of subjects. In the minority that progressed to mechanical ventilation, duration of HFNC did not differentiate subjects with worse clinical outcomes. The ROX index was sensitive for the identification of subjects successfully weaned from HFNC. Prospective studies in COVID-19 are warranted to confirm these findings and to optimize patient selection for use of HFNC in this disease.

Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning

Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient’s lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient’s survival once the relevant data are available.

Ventilator-Associated Pneumonia in Patients with COVID-19: A Systematic Review and Meta-Analysis

The aim of this systematic review and meta-analysis was to estimate the pooled occurrence of ventilator-associated pneumonia (VAP) among patients admitted to an intensive care unit with COVID-19 and mortality of those who developed VAP. We performed a systematic search on PubMed, EMBASE and Web of Science from inception to 2^nd March 2021 for nonrandomized studies specifically addressing VAP in adult patients with COVID-19 and reporting data on at least one primary outcome of interest. Random effect single-arm meta-analysis was performed for the occurrence of VAP and mortality (at the longest follow up) and ICU length of stay. Twenty studies were included in the systematic review and meta-analysis, for a total of 2611 patients with at least one episode of VAP. The pooled estimated occurrence of VAP was of 45.4% (95% C.I. 37.8–53.2%; 2611/5593 patients; I² = 96%). The pooled estimated occurrence of mortality was 42.7% (95% C.I. 34–51.7%; 371/946 patients; I² = 82%). The estimated summary estimated metric mean ICU LOS was 28.58 days (95% C.I. 21.4–35.8; I² = 98%). Sensitivity analysis showed that patients with COVID-19 may have a higher risk of developing VAP than patients without COVID-19 (OR 3.24; 95% C.I. 2.2–4.7; P = 0.015; I² = 67.7%; five studies with a comparison group).

On the analysis of mortality risk factors for hospitalized COVID-19 patients: A data-driven study using the major Brazilian database

BACKGROUND

Brazil became the epicenter of the COVID-19 epidemic in a brief period of a few months after the first officially registered case. The knowledge of the epidemiological/clinical profile and the risk factors of Brazilian COVID-19 patients can assist in the decision making of physicians in the implementation of early and most appropriate measures for poor prognosis patients. However, these reports are missing. Here we present a comprehensive study that addresses this demand.

METHODS

This data-driven study was based on the Brazilian Ministry of Health Database (SIVEP-Gripe) regarding notified cases of hospitalized COVID-19 patients during the period from February 26th to August 10th, 2020. Demographic data, clinical symptoms, comorbidities and other additional information of patients were analyzed.

RESULTS

The hospitalization rate was higher for male gender (56.56%) and for older age patients of both sexes. Overall, the lethality rate was quite high (41.28%) among hospitalized patients, especially those over 60 years of age. Most prevalent symptoms were cough, dyspnoea, fever, low oxygen saturation and respiratory distress. Cardiac disease, diabetes, obesity, kidney disease, neurological disease, and pneumopathy were the most prevalent comorbidities. A high prevalence of hospitalized COVID-19 patients with cardiac disease (65.7%) and diabetes (53.55%) and with a high lethality rate of around 50% was observed. The intensive care unit (ICU) admission rate was 39.37% and of these 62.4% died. 24.4% of patients required invasive mechanical ventilation (IMV), with high mortality among them (82.98%). The main mortality risk predictors were older age and IMV requirement. In addition, socioeconomic conditions have been shown to significantly influence the disease outcome, regardless of age and comorbidities.

CONCLUSION

Our study provides a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors. The analysis also evidenced that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.