Association between ventilator-associated events and implementation of acute respiratory distress syndrome (ARDS) ventilator weaning protocol

Ventilator-associated events in adults: A secondary analysis assessing the impact of monitoring ventilator settings on outcomes

BACKGROUND

Ventilator-associated events (VAE) is a tier implemented for surveillance by the CDC in the USA. Implementation usefulness for clinical decisions is unknown.

METHODS

We conducted a secondary analysis from a prospective, multicentre, international study, to assess the impact on outcomes of using tiers with shorter follow-up (VAE24), lower oxygenation requirements (light-VAE) or both (light VAE24).

RESULTS

A cohort of 261 adults with 2706 ventilator-days were included. The median (IQR) duration of mechanical ventilation (MV) was 9 days (5-21), and the median (IQR) length of stay in the intensive care unit (ICU) was 14 days (8-26). A VAE tier was associated with a trend to increase from 32% to 44% in the ICU mortality rates. VAE Incidence was 24 per 1,000 ventilator-days, being increased when reduced the oxygenation settings requirement (35 per 1,000 ventilator-days), follow-up (41 per 1,000 ventilator-days) or both (55 per 1,000 ventilator-days). A VAE tier was associated with 13 extra (21 vs. 8) days of ventilation, 11 (23 vs. 12) ICU days and 7 (31 vs. 14) hospitalization days, outperforming the modified tiers' performance.

CONCLUSIONS

The modification of ventilator settings (consistent with ventilator-associated events) was associated with worse outcomes among adults with prolonged mechanical ventilation. Monitoring ventilator-associated events at the bedside represents a new tool for quality improvement.

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.