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

Blanch, Lluís; Sales, Bernat; Montanyà, Jaume; Lucangelo, Umberto; Garcia-Esquirol, Oscar; Villagrá, Ana; Chacon, Encarna; Estruga, Anna; Borelli, Massimo; Burgueño, Ma Jose; Oliva, Joan Carles; Fernández, Rafael; Villar, Jesús; Kacmarek, Robert M.; Murias, Tiberio Feliz

doi:10.1007/s00134-012-2493-4

Cited by 121 publications

(97 citation statements)

References 21 publications

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“…Neither experienced nor less experienced physicians were specifically trained in detecting PVD, and their results were very poor (although experienced physicians' results were slightly better) compared with the results of our specifically trained nurses. This finding suggests that specific training and the use of dedicated algorithms 11 could be more important than previous experience for detecting IEE. Training nurses to detect IEE could have a marked effect on patient care: because nurses usually spend more time at an individual patient's bedside than physicians, IEE could be detected and resolved earlier.…”

Section: Discussionmentioning

confidence: 94%

Nurses' Detection of Ineffective Inspiratory Efforts During Mechanical Ventilation

Chacon¹,

Estruga²,

Murias³

et al. 2012

American Journal of Critical Care

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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. Objective To evaluate the accuracy of specifically trained critical care nurses in detecting ineffective inspiratory efforts during expiration. Methods We compared 2 nurses' evaluations of measurements from 1007 breaths in 8 patients with the evaluations of experienced critical care physicians. Sensitivity, specificity, positive predictive value, negative predictive value, and the Cohen κ for interobserver agreement were calculated. Results For the first nurse, sensitivity was 92.5%, specificity was 98.3%, positive predictive value was 95.4%, negative predictive value was 97.1%, and κ was 0.92 (95% CI, 0.89-0.94). For the second nurse, sensitivity was 98.5%, specificity was 84.7%, positive predictive value was 70.7%, negative predictive value was 99.3%, and κ was 0.74 (95% CI, 0.70-0.78). Conclusion Specifically trained nurses can reliably detect ineffective inspiratory efforts during expiration.

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Section: Discussionmentioning

confidence: 94%

Nurses' Detection of Ineffective Inspiratory Efforts During Mechanical Ventilation

Chacon¹,

Estruga²,

Murias³

et al. 2012

American Journal of Critical Care

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“…This system can also be used to control the proportional assist ventilation support level based on the patient's work of breathing (serving the goal of comfort). Blanch et al 20 have developed the Better Care system, which reliably detects ineffective respiratory efforts during expiration (ie, inability of a patient to trigger breaths) with accuracy similar to that of expert intensivists and the EAdi signal (serving the comfort goal). According to Kilic and Kilic,21 conventional weaning predictors ignore important dimensions of weaning outcome.…”

Section: The Problem Of Growing Complexitymentioning

confidence: 99%

A Taxonomy for Mechanical Ventilation: 10 Fundamental Maxims

2014

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The American Association for Respiratory Care has declared a benchmark for competency in mechanical ventilation that includes the ability to "apply to practice all ventilation modes currently available on all invasive and noninvasive mechanical ventilators." This level of competency presupposes the ability to identify, classify, compare, and contrast all modes of ventilation. Unfortunately, current educational paradigms do not supply the tools to achieve such goals. To fill this gap, we expand and refine a previously described taxonomy for classifying modes of ventilation and explain how it can be understood in terms of 10 fundamental constructs of ventilator technology: (1) defining a breath, (2) defining an assisted breath, (3) specifying the means of assisting breaths based on control variables specified by the equation of motion, (4) classifying breaths in terms of how inspiration is started and stopped, (5) identifying ventilator-initiated versus patient-initiated start and stop events, (6) defining spontaneous and mandatory breaths, (7) defining breath sequences (8), combining control variables and breath sequences into ventilatory patterns, (9) describing targeting schemes, and (10) constructing a formal taxonomy for modes of ventilation composed of control variable, breath sequence, and targeting schemes. Having established the theoretical basis of the taxonomy, we demonstrate a step-by-step procedure to classify any mode on any mechanical ventilator.

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“…More recently, several investigators have explored the use of machine learning and pattern recognition to automatically detect asynchrony. [63][64][65][66][67] In most instances these systems specifically address the issue of missed triggers. Chen et al used measurements of flow and pressure deflections to detect missed triggers in 14 mechanically ventilated patients.…”

Section: Automated Detection Of Asynchronymentioning

confidence: 99%

Asynchrony and DyspneaDiscussion

2013

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SummaryPatient-ventilator synchrony and patient comfort are assumed to go hand in hand, yet few studies provide support for this common sense idea. In reality, synchrony between the patient and ventilator is complex and can be affected by the ventilator settings, type of ventilator, patient-ventilator interface, and sedation. Inspections of airway pressure and flow waveforms are reliable methods for detecting asynchrony, and automated detection seems accurate. A number of types of asynchronies have been defined, and asynchrony during invasive and noninvasive ventilation have different calling cards. There is a clear association between asynchrony, ventilator-induced diaphragmatic dysfunction, and duration of mechanical ventilation. Whether these are cause and effect or simply associated remains to be determined.

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

Abstract: In this pilot, Better Care® classified breaths as IEE in close agreement with experts and the EAdi signal.

Cited by 121 publications

References 21 publications

Nurses' Detection of Ineffective Inspiratory Efforts During Mechanical Ventilation

Nurses' Detection of Ineffective Inspiratory Efforts During Mechanical Ventilation

A Taxonomy for Mechanical Ventilation: 10 Fundamental Maxims

Asynchrony and DyspneaDiscussion

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