The Effect of Clusters of Double Triggering and Ineffective Efforts in Critically Ill Patients.

Objectives: To characterize clusters of double triggering and ineffective inspiratory efforts throughout mechanical ventilation and investigate their associations with mortality and duration of ICU stay and mechanical ventilation.

Design: Registry-based, real-world study.

Background: Asynchronies during invasive mechanical ventilation can occur as isolated events or in clusters and might be related to clinical outcomes.

Longitudinal Changes in Patient-Ventilator Asynchronies and Respiratory System Mechanics Before and After Tracheostomy.

Background: This was a pilot study to analyze the effects of tracheostomy on patient-ventilator asynchronies and respiratory system mechanics. Data were extracted from an ongoing prospective, real-world database that stores continuous output from ventilators and bedside monitors. Twenty adult subjects were on mechanical ventilation and were tracheostomized during an ICU stay: 55% were admitted to the ICU for respiratory failure and 35% for neurologic conditions; the median duration of mechanical ventilation before tracheostomy was 12 d; and the median duration of mechanical ventilation was 16 d.

Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities.

Background: Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes.

Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients.

Background: In critically ill patients, poor patient-ventilator interaction may worsen outcomes. Although sedatives are often administered to improve comfort and facilitate ventilation, they can be deleterious. Whether opioids improve asynchronies with fewer negative effects is unknown.

Predicting Patient-ventilator Asynchronies with Hidden Markov Models.

In mechanical ventilation, it is paramount to ensure the patient's ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h.

Double Cycling During Mechanical Ventilation: Frequency, Mechanisms, and Physiologic Implications.

Objectives: Double cycling generates larger than expected tidal volumes that contribute to lung injury. We analyzed the incidence, mechanisms, and physiologic implications of double cycling during volume- and pressure-targeted mechanical ventilation in critically ill patients.

Design: Prospective, observational study.

Feasibility and safety of virtual-reality-based early neurocognitive stimulation in critically ill patients.

Background: Growing evidence suggests that critical illness often results in significant long-term neurocognitive impairments in one-third of survivors. Although these neurocognitive impairments are long-lasting and devastating for survivors, rehabilitation rarely occurs during or after critical illness. Our aim is to describe an early neurocognitive stimulation intervention based on virtual reality for patients who are critically ill and to present the results of a proof-of-concept study testing the feasibility, safety, and suitability of this intervention.

Automatic detection of ventilatory modes during invasive mechanical ventilation.

Background: Expert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use.

Asynchronies during mechanical ventilation are associated with mortality.

Purpose: This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV).

Methods: Prospective, noninterventional observational study of 50 patients admitted to intensive care unit (ICU) beds equipped with Better Care™ software throughout MV. The software distinguished ventilatory modes and detected ineffective inspiratory efforts during expiration (IEE), double-triggering, aborted inspirations, and short and prolonged cycling to compute the asynchrony index (AI) for each hour.

Effect of dynamic random leaks on the monitoring accuracy of home mechanical ventilators: a bench study.

Background: So far, the accuracy of tidal volume (VT) and leak measures provided by the built-in software of commercial home ventilators has only been tested using bench linear models with fixed calibrated and continuous leaks. The objective was to assess the reliability of the estimation of tidal volume (VT) and unintentional leaks in a single tubing bench model which introduces random dynamic leaks during inspiratory or expiratory phases.

Methods: The built-in software of four commercial home ventilators and a fifth ventilator-independent ad hoc designed external software tool were tested with two levels of leaks and two different models with excess leaks (inspiration or expiration).

Nurses' detection of ineffective inspiratory efforts during mechanical ventilation.

Unlabelled: BACKGROUND PATIENT: ventilator dyssynchrony is common and may influence patients' outcomes. Detection of such dyssynchronies relies on careful observation of patients and airway flow and pressure measurements. Given the shortage of specialists, critical care nurses could be trained to identify dyssynchronies.

Validation of the Better Care® system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study.

Purpose: Ineffective respiratory efforts during expiration (IEE) are a problem during mechanical ventilation (MV). The goal of this study is to validate mathematical algorithms that automatically detect IEE in a computerized (Better Care®) system that obtains and processes data from intensive care unit (ICU) ventilators in real time.

Methods: The Better Care® system, integrated with ICU health information systems, synchronizes and processes data from bedside technology.