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INTELLiVENT®-ASV® - your bedside assistant in the fight against COVID-19

Article

Author: Kaouther Saihi, Caroline Brown

Date of first publication: 06.07.2022

As you fight to save lives, our most advanced ventilation mode can help you to save time, manpower and contamination.
INTELLiVENT®-ASV® - your bedside assistant in the fight against COVID-19

Shortage of ventilators and caregivers

The coronavirus pandemic is proving to be a global disaster of unprecedented magnitude, not only in terms of health, but also from a social and economic point of view. Particularly in those countries most badly affected, caregivers are currently stretched to the limit.  Two of the major challenges currently being faced are:

  • A shortage of mechanical ventilators in many countries
  • Having enough trained caregivers to operate the ventilators available. The number of patients admitted simultaneously to the ICU is in some cases extremely high, and caregivers in this highly infectious environment are at risk of being contaminated or suffering from burnout or other psychosocial problems (Zhang WR, Wang K, Yin L, et al. Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China. Psychother Psychosom. 2020;89(4):242-250. doi:10.1159/0005076391​).

In China, an estimated 3,000 healthcare workers have been infected (Adams JG, Walls RM. Supporting the Health Care Workforce During the COVID-19 Global Epidemic. JAMA. 2020;323(15):1439-1440. doi:10.1001/jama.2020.39722​), while the International Council of Nurses puts worldwide estimates at around 90,000 (May, 2020) (https://www.icn.ch/news/icn-calls-data-healthcare-worker-infection-rates-and-deaths3​). Special hospital protocols to protect the caregivers include measures such as limiting the number of times the caregiver enters the patient’s room.

Our intelligent ventilation mode, INTELLiVENT-ASV (Not available in the US and some other marketsA​), can help you address these issues and support you in many other ways during this stressful and exhausting time.

Exactly how can INTELLiVENT-ASV help?

  • By reducing the caregiver’s interaction with the ventilator and thus the risk of contamination
  • By continuously monitoring the patient's lung condition, and adjusting settings to support the application of individualized lung-protective strategies
  • By autonomously ventilating patients within the predefined ventilation zone to spare the caregiver
  • By serving as one mode of ventilation for both passive and active patients 
  • By keeping an eye on both hypoxemia and hyperoxemia and adjusting settings to avoid both risks
  • By promoting early weaning with the Quick Wean function that includes manual and automatic Spontaneous Breathing Trials

How does INTELLiVENT-ASV work?

INTELLiVENT-ASV acts as your personal assistant at the bedside, using physiologic data from the patient and clinician-set targets and limits to automatically and continuously regulate CO2 elimination and oxygenation for both passive and active patients. 

Several clinical studies have proven both its safety and efficacy in treating mechanically ventilated patients with different conditions and levels of severity, from a normal lung to COPD, brain injury and ARDS (mild, moderate and severe). A recent study (Arnal JM, Saoli M, Garnero A. Airway and transpulmonary driving pressures and mechanical powers selected by INTELLiVENT-ASV in passive, mechanically ventilated ICU patients. Heart Lung. 2020;49(4):427-434. doi:10.1016/j.hrtlng.2019.11.0014​) on 255 passive ICU patients with normal lungs, COPD, and ARDS showed that INTELLiVENT-ASV selected driving pressure, mechanical power and VT considered to be in the safe ranges for lung protection.
 

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Patient ventilated in INTELLiVENT-ASV mode
INTELLiVENT-ASV at work
Patient ventilated in INTELLiVENT-ASV mode
INTELLiVENT-ASV at work

How do COVID-19 patients present?

SARS-CoV-2, the causative agent of COVID-19, is a coronavirus severely affecting the respiratory system of a growing number of people. Although COVID-19 has been shown to meet the ARDS Berlin definition (ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526-2533. doi:10.1001/jama.2012.56695​), COVID-19 pneumonia is a specific disease with particular phenotypes. Its main characteristic is the dissociation between the severity of the hypoxemia and the maintenance of relatively good respiratory mechanics. There are two types of patients, depending on the severity of the pneumonia (Gattinoni L, Chiumello D, Caironi P, et al. COVID-19 pneumonia: different respiratory treatments for different phenotypes?. Intensive Care Med. 2020;46(6):1099-1102. doi:10.1007/s00134-020-06033-26​, Gattinoni L, Chiumello D, Rossi S. COVID-19 pneumonia: ARDS or not?. Crit Care. 2020;24(1):154. Published 2020 Apr 16. doi:10.1186/s13054-020-02880-z7​, Marini JJ, Gattinoni L. Management of COVID-19 Respiratory Distress. JAMA. 2020;323(22):2329-2330. doi:10.1001/jama.2020.68258​).

Type 1: Near normal pulmonary compliance with isolated viral pneumonia

Type 2: Decreased pulmonary compliance

How do you treat them with INTELLiVENT-ASV?

INTELLiVENT-ASV selects VT according to three parameters:

  • Minute volume required to reach the PETCO2 set by the user
  • Anatomical dead space
  • Respiratory mechanics 

For any given minute volume and dead space, the selected VT depends on the resistance and the compliance. If the resistance remains stable, a decrease in compliance will result in a lower VT and higher rate, whereas an increase in compliance will result in a higher VT and lower rate.

Type 1 corresponds with normal lung compliance:

  • %MinVol, FiO2 and PEEP can be regulated automatically
  • Maximum PEEP setting might be limited to 8–10 cmH2O depending on the clinical setting 

Type 2 corresponds with low lung compliance (ARDS):

  • %MinVol, FiO2 and PEEP can be regulated automatically
  • Maximum PEEP setting limited to 15 cmH2O depending on the clinical setting

So with INTELLiVENT-ASV, you can select your patient's lung condition and your clinical targets and limits, define your weaning strategy, and then start lung-protective ventilation!

Follow the link below to see the range training materials and video tutorials on the use of INTELLiVENT-ASV.
 

Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China.

Zhang WR, Wang K, Yin L, et al. Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China. Psychother Psychosom. 2020;89(4):242-250. doi:10.1159/000507639



OBJECTIVE

We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak.

METHODS

An online survey was run from February 19 to March 6, 2020; a total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2).

RESULTS

Compared with nonmedical health workers (n = 1,255), medical health workers (n = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, p < 0.01), anxiety (13.0 vs. 8.5%, p < 0.01), depression (12.2 vs. 9.5%; p< 0.04), somatization (1.6 vs. 0.4%; p < 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; p < 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (p ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (p < 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (p < 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (p < 0.01 or 0.05).

CONCLUSIONS

During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.

Supporting the Health Care Workforce During the COVID-19 Global Epidemic.

Adams JG, Walls RM. Supporting the Health Care Workforce During the COVID-19 Global Epidemic. JAMA. 2020;323(15):1439-1440. doi:10.1001/jama.2020.3972

ICN calls for data on healthcare worker infection rates and deaths

https://www.icn.ch/news/icn-calls-data-healthcare-worker-infection-rates-and-deaths

Airway and transpulmonary driving pressures and mechanical powers selected by INTELLiVENT-ASV in passive, mechanically ventilated ICU patients.

Arnal JM, Saoli M, Garnero A. Airway and transpulmonary driving pressures and mechanical powers selected by INTELLiVENT-ASV in passive, mechanically ventilated ICU patients. Heart Lung. 2020;49(4):427-434. doi:10.1016/j.hrtlng.2019.11.001



BACKGROUND

Driving pressure (ΔP) and mechanical power (MP) are predictors of the risk of ventilation- induced lung injuries (VILI) in mechanically ventilated patients. INTELLiVENT-ASV® is a closed-loop ventilation mode that automatically adjusts respiratory rate and tidal volume, according to the patient's respiratory mechanics.

OBJECTIVES

This prospective observational study investigated ΔP and MP (and also transpulmonary ΔP (ΔPL) and MP (MPL) for a subgroup of patients) delivered by INTELLiVENT-ASV.

METHODS

Adult patients admitted to the ICU were included if they were sedated and met the criteria for a single lung condition (normal lungs, COPD, or ARDS). INTELLiVENT-ASV was used with default target settings. If PEEP was above 16 cmH2O, the recruitment strategy used transpulmonary pressure as a reference, and ΔPL and MPL were computed. Measurements were made once for each patient.

RESULTS

Of the 255 patients included, 98 patients were classified as normal-lungs, 28 as COPD, and 129 as ARDS patients. The median ΔP was 8 (7 - 10), 10 (8 - 12), and 9 (8 - 11) cmH2O for normal-lungs, COPD, and ARDS patients, respectively. The median MP was 9.1 (4.9 - 13.5), 11.8 (8.6 - 16.5), and 8.8 (5.6 - 13.8) J/min for normal-lungs, COPD, and ARDS patients, respectively. For the 19 patients managed with transpulmonary pressure ΔPL was 6 (4 - 7) cmH2O and MPL was 3.6 (3.1 - 4.4) J/min.

CONCLUSIONS

In this short term observation study, INTELLiVENT-ASV selected ΔP and MP considered in safe ranges for lung protection. In a subgroup of ARDS patients, the combination of a recruitment strategy and INTELLiVENT-ASV resulted in an apparently safe ΔPL and MPL.

Acute respiratory distress syndrome: the Berlin Definition.

ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526-2533. doi:10.1001/jama.2012.5669

The acute respiratory distress syndrome (ARDS) was defined in 1994 by the American-European Consensus Conference (AECC); since then, issues regarding the reliability and validity of this definition have emerged. Using a consensus process, a panel of experts convened in 2011 (an initiative of the European Society of Intensive Care Medicine endorsed by the American Thoracic Society and the Society of Critical Care Medicine) developed the Berlin Definition, focusing on feasibility, reliability, validity, and objective evaluation of its performance. A draft definition proposed 3 mutually exclusive categories of ARDS based on degree of hypoxemia: mild (200 mm Hg < PaO2/FIO2 ≤ 300 mm Hg), moderate (100 mm Hg < PaO2/FIO2 ≤ 200 mm Hg), and severe (PaO2/FIO2 ≤ 100 mm Hg) and 4 ancillary variables for severe ARDS: radiographic severity, respiratory system compliance (≤40 mL/cm H2O), positive end-expiratory pressure (≥10 cm H2O), and corrected expired volume per minute (≥10 L/min). The draft Berlin Definition was empirically evaluated using patient-level meta-analysis of 4188 patients with ARDS from 4 multicenter clinical data sets and 269 patients with ARDS from 3 single-center data sets containing physiologic information. The 4 ancillary variables did not contribute to the predictive validity of severe ARDS for mortality and were removed from the definition. Using the Berlin Definition, stages of mild, moderate, and severe ARDS were associated with increased mortality (27%; 95% CI, 24%-30%; 32%; 95% CI, 29%-34%; and 45%; 95% CI, 42%-48%, respectively; P < .001) and increased median duration of mechanical ventilation in survivors (5 days; interquartile [IQR], 2-11; 7 days; IQR, 4-14; and 9 days; IQR, 5-17, respectively; P < .001). Compared with the AECC definition, the final Berlin Definition had better predictive validity for mortality, with an area under the receiver operating curve of 0.577 (95% CI, 0.561-0.593) vs 0.536 (95% CI, 0.520-0.553; P < .001). This updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition. The approach of combining consensus discussions with empirical evaluation may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.

COVID-19 pneumonia: different respiratory treatments for different phenotypes?

Gattinoni L, Chiumello D, Caironi P, et al. COVID-19 pneumonia: different respiratory treatments for different phenotypes?. Intensive Care Med. 2020;46(6):1099-1102. doi:10.1007/s00134-020-06033-2

COVID-19 pneumonia: ARDS or not?

Gattinoni L, Chiumello D, Rossi S. COVID-19 pneumonia: ARDS or not?. Crit Care. 2020;24(1):154. Published 2020 Apr 16. doi:10.1186/s13054-020-02880-z

Management of COVID-19 Respiratory Distress.

Marini JJ, Gattinoni L. Management of COVID-19 Respiratory Distress. JAMA. 2020;323(22):2329-2330. doi:10.1001/jama.2020.6825

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