Mechanical ventilation in an airborne epidemic

MircroRNA Let-7a-5p in Airway Smooth Muscle Cells is Most Responsive to High Stretch in Association With Cell Mechanics Modulation

Objective: High stretch (strain >10%) can alter the biomechanical behaviors of airway smooth muscle cells which may play important roles in diverse lung diseases such as asthma and ventilator-induced lung injury. However, the underlying modulation mechanisms for high stretch-induced mechanobiological responses in ASMCs are not fully understood. Here, we hypothesize that ASMCs respond to high stretch with increased expression of specific microRNAs (miRNAs) that may in turn modulate the biomechanical behaviors of the cells. Thus, this study aimed to identify the miRNA in cultured ASMCs that is most responsive to high stretch, and subsequently investigate in these cells whether the miRNA expression level is associated with the modulation of cell biomechanics.

Methods: MiRNAs related to inflammatory airway diseases were obtained via bioinformatics data mining, and then tested with cultured ASMCs for their expression variations in response to a cyclic high stretch (13% strain) simulating in vivo ventilator-imposed strain on airways. Subsequently, we transfected cultured ASMCs with mimics and inhibitors of the miRNA that is most responsive to the high stretch, followed by evaluation of the cells in terms of morphology, stiffness, traction force, and mRNA expression of cytoskeleton/focal adhesion-related molecules.

Results: 29 miRNAs were identified to be related to inflammatory airway diseases, among which let-7a-5p was the most responsive to high stretch. Transfection of cultured human ASMCs with let-7a-5p mimics or inhibitors led to an increase or decrease in aspect ratio, stiffness, traction force, migration, stress fiber distribution, mRNA expression of α-smooth muscle actin (SMA), myosin light chain kinase, some subfamily members of integrin and talin. Direct binding between let-7a-5p and ItgαV was also verified in classical model cell line by using dual-luciferase assays.

Conclusion: We demonstrated that high stretch indeed enhanced the expression of let-7a-5p in ASMCs, which in turn led to changes in the cells’ morphology and biomechanical behaviors together with modulation of molecules associated with cytoskeletal structure and focal adhesion. These findings suggest that let-7a-5p regulation is an alternative mechanism for high stretch-induced effect on mechanobiology of ASMCs, which may contribute to understanding the pathogenesis of high stretch-related lung diseases.

Infectious Disease Emergencies

COVID-19 has added new relevance to the relationship between the world’s public health and infectious diseases and makes timely the topic of this chapter, that is, infectious disease emergencies. When patients have infectious diseases and present in-hospital with life-threatening symptoms (e.g., septic shock), prompt response is crucial to saving their lives. This chapter approaches rapid response activation from the perspective of five infectious disease emergencies: sepsis and septic shock, acute hypoxemic respiratory failure due to severe pneumonia, hypovolemic shock due to acute infectious diarrheal illness, acute respiratory failure due to influenza virus infection, and acute respiratory failure due to COVID-19 (coronavirus disease of 2019) pneumonia. The highly detailed information (e.g., predisposing conditions, initial measures to take, diagnostic work-ups, and treatment) is presented in a fashion that immediately puts pertinent information at the fingertips of those who need it.

Factors influencing lower respiratory tract infection in older patients after general anesthesia

Objective

Pulmonary complication is common in older patients after surgery. We analyzed risk factors of lower respiratory tract infection after general anesthesia among older patients.

Methods

In this retrospective investigation, we included older patients who underwent surgery with general anesthesia. Logistic regression analyses were performed to determine risk factors of lower respiratory tract infection.

Results

A total 418 postoperative patients with general anesthesia were included; the incidence of lower respiratory tract infection was 9.33%. Ten cases were caused by gram-positive bacteria, 26 cases by gram-negative bacteria, and 2 cases by fungus. We found significant differences in age, smoking, diabetes, oral/nasal tracheal intubation, and surgery duration. Logistic regression analysis indicated that age ≥70 years (odds ratio [OR] 2.028, 95% confidence interval [CI] 1.115–3.646), smoking (OR 2.314, 95% CI 1.073–4.229), diabetes (OR 2.185, 95% CI 1.166–4.435), nasotracheal intubation (OR 3.528, 95% CI 1.104–5.074), and duration of surgery ≥180 minutes (OR 1.334, 95% CI 1.015–1.923) were independent risk factors of lower respiratory tract infections.

Conclusions

Older patients undergoing general anesthesia after tracheal intubation have a high risk of lower respiratory tract infections. Clinical interventions should be provided to prevent pulmonary infections in patients with relevant risk factors.

Transmission of Severe Acute Respiratory Syndrome Coronavirus 1 and Severe Acute Respiratory Syndrome Coronavirus 2 During Aerosol-Generating Procedures in Critical Care: A Systematic Review and Meta-Analysis of Observational Studies

OBJECTIVES

To assess the risk of coronavirus transmission to healthcare workers performing aerosol-generating procedures and the potential benefits of personal protective equipment during these procedures.

DATA SOURCES

MEDLINE, EMBASE, and Cochrane CENTRAL were searched using a combination of related MeSH terms and keywords.

STUDY SELECTION

Cohort studies and case controls investigating common anesthetic and critical care aerosol-generating procedures and transmission of severe acute respiratory syndrome coronavirus 1, Middle East respiratory syndrome coronavirus, and severe acute respiratory syndrome coronavirus 2 to healthcare workers were included for quantitative analysis.

DATA EXTRACTION

Qualitative and quantitative data on the transmission of severe acute respiratory syndrome coronavirus 1, severe acute respiratory syndrome coronavirus 2, and Middle East respiratory syndrome coronavirus to healthcare workers via aerosol-generating procedures in anesthesia and critical care were collected independently. The Risk Of Bias In Non-randomized Studies - of Interventions tool was used to assess the risk of bias of included studies.

DATA SYNTHESIS

Seventeen studies out of 2,676 yielded records were included for meta-analyses. Endotracheal intubation (odds ratio, 6.69, 95% CI, 3.81-11.72; p < 0.001), noninvasive ventilation (odds ratio, 3.65; 95% CI, 1.86-7.19; p < 0.001), and administration of nebulized medications (odds ratio, 10.03; 95% CI, 1.98-50.69; p = 0.005) were found to increase the odds of healthcare workers contracting severe acute respiratory syndrome coronavirus 1 or severe acute respiratory syndrome coronavirus 2. The use of N95 masks (odds ratio, 0.11; 95% CI, 0.03-0.39; p < 0.001), gowns (odds ratio, 0.59; 95% CI, 0.48-0.73; p < 0.001), and gloves (odds ratio, 0.39; 95% CI, 0.29-0.53; p < 0.001) were found to be significantly protective of healthcare workers from contracting severe acute respiratory syndrome coronavirus 1 or severe acute respiratory syndrome coronavirus 2.

CONCLUSIONS

Specific aerosol-generating procedures are high risk for the transmission of severe acute respiratory syndrome coronavirus 1 and severe acute respiratory syndrome coronavirus 2 from patients to healthcare workers. Personal protective equipment reduce the odds of contracting severe acute respiratory syndrome coronavirus 1 and severe acute respiratory syndrome coronavirus 2.

Respiratory care for the critical patients with 2019 novel coronavirus

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted through respiratory droplets, aerosols and close contact. Cross infections occur because viruses spread rapidly among humans. Nineteen percent (19%) of the infected patients developed severe pneumonia and acute respiratory distress syndrome (ARDS). Hypoxemia usually occurs and patients may require oxygen therapy or mechanical ventilation (MV) support. In this article, recently published clinical experience and observational studies were reviewed. Corresponding respiratory therapy regarding different stages of infection is proposed. Infection control principles and respiratory strategies including oxygen therapy, non-invasive respiratory support (NIRS), intubation evaluation, equipment preparation, ventilator settings, special maneuvers comprise of the prone position (PP), recruitment maneuver (RM), extracorporeal membrane oxygenation (ECMO), weaning and extubation are summarized. Respiratory equipment and device disinfection recommendations are worked up. We expect this review article could be used as a reference by healthcare workers in patient care while minimizing the risk of environmental contamination.

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation

BACKGROUND

Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

RESEARCH QUESTION

Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance?

STUDY DESIGN AND METHODS

We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio2, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value.

RESULTS

We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.

INTERPRETATION

A transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

The I-READI Quality and Safety Framework: A Health System’s Response to Airway Complications in Mechanically Ventilated Patients with Covid-19

Health care institutions responding to quality and safety challenges during times of crisis, such as emerging infectious diseases or natural disasters, can follow the I-READI conceptual framework: Integration, Root Cause Analysis, Evidence Review, Adaptation, Dissemination, and Implementation. The University of Pennsylvania Health System developed this approach by drawing on lessons learned from rapidly coordinating changes to their ventilator management practices. They modified their practices to improve patient safety after recognizing high rates of airway complications among mechanically ventilated patients with Covid-19. Vertical and horizontal integration of their quality and safety teams helped streamline problem solving, enrich collaboration, and coordinate implementation. Root cause analysis and evidence review framed their practice adaptation, ensuring that they prioritized patient and health care worker safety. Daily safety huddles engaged frontline providers and promoted dissemination of the revised interventions. Telemedicine oversight and real-time ICU dashboards enabled system-wide implementation, goal setting, and continuous performance feedback. Under their revised guidelines, the rate of endotracheal tube obstruction among mechanically ventilated patients with Covid-19 decreased from 9.2% to less than 1%, and reintubation rates decreased from 36% to 9%.

Physician preparedness for resource allocation decisions under pandemic conditions: A cross-sectional survey of Canadian physicians, April 2020

BACKGROUND

Under the pandemic conditions created by the novel coronavirus of 2019 (COVID-19), physicians have faced difficult choices allocating scarce resources, including but not limited to critical care beds and ventilators. Past experiences with severe acute respiratory syndrome (SARS) and current reports suggest that making these decisions carries a heavy emotional toll for physicians around the world. We sought to explore Canadian physicians' preparedness and attitudes regarding resource allocation decisions.

METHODS

From April 3 to April 13, 2020, we conducted an 8-question online survey of physicians practicing in the region of Ottawa, Ontario, Canada, organized around 4 themes: physician preparedness for resource rationing, physician preparedness to offer palliative care, attitudes towards resource allocation policy, and approaches to resource allocation decision-making.

RESULTS

We collected 219 responses, of which 165 were used for analysis. The majority (78%) of respondents felt "somewhat" or "a little prepared" to make resource allocation decisions, and 13% felt "not at all prepared." A majority of respondents (63%) expected the provision of palliative care to be "very" or "somewhat difficult." Most respondents (83%) either strongly or somewhat agreed that there should be policy to guide resource allocation. Physicians overwhelmingly agreed on certain factors that would be important in resource allocation, including whether patients were likely to survive, and whether they had dementia and other significant comorbidities. Respondents generally did not feel confident that they would have the social support they needed at the time of making resource allocation decisions.

INTERPRETATION

This rapidly implemented survey suggests that a sample of Canadian physicians feel underprepared to make resource allocation decisions, and desire both more emotional support and clear, transparent, evidence-based policy.

Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation

IMPORTANCE

Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.

OBJECTIVE

To develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.

DESIGN

Observational cohort study SETTING: Two academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).

PARTICIPANTS

Over 31,000 admissions to the intensive care units (ICUs) at two hospitals. Additionally, 777 patients with COVID-19 patients were used for prospective validation. Patients who were placed on mechanical ventilation within four hours of their admission were excluded. MAIN OUTCOME(S) and MEASURE(S): Electronic health record (EHR) data were extracted on an hourly basis, and a set of 40 features were calculated and passed to an interpretable deep-learning algorithm to predict the future need for mechanical ventilation 24 hours in advance. Additionally, commonly used clinical criteria (based on heart rate, oxygen saturation, respiratory rate, FiO2 and pH) was used to assess future need for mechanical ventilation. Performance of the algorithms were evaluated using the area under receiver-operating characteristic curve (AUC), sensitivity, specificity and positive predictive value.

RESULTS

After applying exclusion criteria, the external validation cohort included 3,888 general ICU and 402 COVID-19 patients. The performance of the model (AUC) with a 24-hour prediction horizon at the validation site was 0.882 for the general ICU population and 0.918 for patients with COVID-19. In comparison, commonly used clinical criteria and the ROX score achieved AUCs in the range of 0.773 - 0.782 and 0.768 - 0.810 for the general ICU population and patients with COVID-19, respectively.

CONCLUSIONS AND RELEVANCE

A generalizable and transparent deep-learning algorithm improves on traditional clinical criteria to predict the need for mechanical ventilation in hospitalized patients, including those with COVID-19. Such an algorithm may help clinicians with optimizing timing of tracheal intubation, better allocation of mechanical ventilation resources and staff, and improve patient care.

Consensus français sur la réalisation de trachéotomies et les soins de trachéotomies pendant la pandémie de COVID-19

Les soins de l'orifice de trachéotomie/stomie et la trachéotomie sont considérés à fort risque de contamination par le coronavirus 19 (SARS-CoV-2). La propagation de cette infection étant rapide, tous les patients sur le territoire français doivent, en cette période de pandémie, être considérés comme potentiellement porteurs du virus. Néanmoins, les patients ne présentant pas de manifestations cliniques ni radiologiques (scanner thoracique) du COVID-19 et dont le prélèvement viral nasopharyngé est négatif, 24h avant le geste sont considérés à faible risque. Les consignes de protections reposent sur un habillage et une désinfection de tout matériel utilisé ou souillé spécifiques. La pièce utilisée doit être aérée après les soins et la pression de celle-ci nulle ou négative. La technique percutanée est privilégiée à la cervicotomie pour réduire l'aérosolisation et éviter de déplacer le patient d'une unité de réanimation au bloc opératoire. La désaturation des patients étant souvent rapide, l'oxygénation doit être optimisée lors de la trachéotomie. Un blocage neuromusculaire médicamenteux est conseillé pour diminuer la toux lors de l'insertion de la canule. Une équipe expérimentée est indispensable pour que les gestes soient fluides, sécurisés et peu contaminants.

French consensus regarding precautions during tracheostomy and post-tracheostomy care in the context of COVID-19 pandemic

Tracheostomy post-tracheostomy care are regarded as at high risk for contamination of health care professionals with the new coronavirus (SARS-CoV-2). Considering the rapid spread of the infection, all patients in France must be considered as potentially infected by the virus. Nevertheless, patients without clinical or radiological (CT scan) markers of COVID-19, and with negative nasopharyngeal sample within 24h of surgery, are at low risk of being infected. Instructions for personal protection include specific wound dressings and decontamination of all material used. The operating room should be ventilated after each tracheostomy and the pressure of the room should be neutral or negative. Percutaneous tracheostomy is to be preferred over surgical cervicotomy in order to reduce aerosolization and to avoid moving patients from the intensive care unit to the operating room. Ventilation must be optimized during the procedure, to limit patient oxygen desaturation. Drug assisted neuromuscular blockage is advised to reduce coughing during tracheostomy tube insertion. An experienced team is mandatory to secure and accelerate the procedure as well as to reduce risk of contamination.

Preventing Airborne Disease Transmission: Implications for Patients During Mechanical Ventilation

The organisms causing respiratory infections such as influenza are spread in droplets or aerosols or by direct or indirect contact with contaminated surfaces. Certain medical procedures have been termed aerosol generating because they are associated with high or augmented inspiratory and expiratory flows, which can increase microbial dissemination. Invasive ventilation maneuvers and noninvasive ventilation (NIV) fall into that category. We discuss the risk of transmitting these procedures and the strategies for mechanical ventilation in future airborne epidemics with special consideration given to the issue of protecting health care workers (HCWs).

Mechanical ventilators in the hot zone: effects of a CBRN filter on patient protection and battery life

OBJECTIVE

In a contaminated environment, respiratory protection for ventilator dependent patients can be achieved by attaching a chemical, biological, radiological, or nuclear (CBRN) filter to the air intake port of a portable ventilator. We evaluated the effect of the filter on battery performance of four portable ventilators in a laboratory setting.

METHODS

Each ventilator was attached to a test lung. Ventilator settings were: assist control (AC) mode, respiratory rate 35 bpm, tidal volume 450 ml, positive end-expiratory pressure (PEEP) 10 cm H(2)O, inspiratory time 0.8 s, and FIO(2) 0.21. Ventilators were operated until the battery was fully discharged. We also evaluated the ventilators' ability to deliver all the gas through the CBRN filter and analyzed the pressures required to breathe through the anti-asphyxiation valve of a failed device.

RESULTS

The range of battery life varied widely across different ventilator models (99.8-562.6 min). There was no significant difference in battery life (p<0.01) when operating with or without the CBRN filter attached. Only the Impact 731 routed all inspired gases through the CBRN filter. The pressure required to breathe though the failed device was -4 cm H(2)O to -9 cm H(2)O.

CONCLUSIONS

Duration of operation from the internal battery was not altered by attachment of the CBRN filter. The use of a CBRN filter is necessary for protection of ventilator dependent patients when environmental contamination is present, although conditions exist where all gas does not pass through the filter with some ventilators under normal operating conditions.