Ventilator Advisory System Employing Load and Tolerance Strategy Recommends Appropriate Pressure Support Ventilation Settings

Banner, Michael J.; Euliano, Neil R.; MacIntyre, Neil R.; Layon, A. Joseph; Bonett, S; Gentile, Michael A.; Bshouty, Zoheir; Peters, Carl W.; Gabrielli, A.

doi:10.1378/chest.07-2011

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…Table 1 summarizes and shows the characteristics of the studies. Fourteen studies evaluated the functionality of CDSSs in ICUs [51][52][53][54][55][56][57][58][59][60][61][62][63][64], three examined the applicability of databases in ICUs [65][66][67], one studied investigated the usefulness of EHRs [68], and another one considered CPOE [69]. Lastly, three analyzed the function of combinations of CDSS/CPOE and CDSS/EHR [70][71][72].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Fifteen studies involved patients over 18 years [51][52][53][54][55][57][58][59]61,[64][65][66][67][68][69], five studies focused on pediatric patients [56,63,[70][71][72], one used data from both populations [62], and examined professionals [60]. Most studies were conducted in the United States [51,52,55,56,58,62,[65][66][67][68], Germany [64,72], and the United Kingdom [53,59,63]. Moreover, one was developed each in Spain [69], Israel [71], France [70], Australia [60], Sweden [61], Japan [54], and Canada [57].…”

Section: Study Characteristicsmentioning

confidence: 99%

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Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

et al. 2021

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The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

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Section: Study Characteristicsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

et al. 2021

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“…The effect of propofol, or sedation in general, on respiratory effort has not previously been incorporated in a ventilation model or clinical decision support system. Since propofol has a clinically important effect on respiratory effort [20,21], and is often used as a sedative in the management of critically ill patients [44], its inclusion enhances the clinical relevance of the model. In addition, the LDPV model uses a flow-cycle threshold based on patient generated flow when simulating pressure support ventilation, a feature that has not been incorporated previously.…”

Section: Model Rationalementioning

confidence: 99%

“…Finally, the LDPV model is unique in that the outputs of the model directly reflect patient effort and can potentially be used as indicators of lung and diaphragm stress and strain. While previous ventilation models have included patient effort, they either do not incorporate them within a physiology-based model [44] or use a simplified measure based on the questionable assumption that a ratio of tidal volume and pressure support level can be used to describe patient effort [12,43]. The model could be implemented as a CDSS to help clinicians rapidly identify the combination of ventilation and sedation settings most likely to achieve lung and diaphragm-protective targets in patients, overcoming the complexity and time-consumption involved in titrating ventilation and sedation to target by trial and error.…”

Section: Model Rationalementioning

confidence: 99%

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Zhang

Ratano

Brochard

et al. 2020

J Clin Monit Comput

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Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung-and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid-base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient's transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO 2 respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

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“…4 The PSV advisory system provided valid recommendations for setting PSV to appropriately unload the inspiratory muscles, which were as good as the clinical judgment of attending physicians. Another group of clinicians who may make recommendations for setting PSV is critical-care Registered Respiratory Therapists (RRTs).…”

Section: Introductionmentioning

confidence: 99%

Pressure Support Ventilation Advisory System Provides Valid Recommendations for Setting Ventilator

et al. 2011

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BACKGROUND: Pressure support ventilation (PSV) should be applied so that the inspiratory muscles are unloaded appropriately. We developed a computerized advisory system that assesses the load on the inspiratory muscles to spontaneously inhale, reflected by the automatically and noninvasively measured work of breathing per minute, and tolerance for that load, reflected by spontaneous breathing frequency and tidal volume, in a fuzzy-logic algorithm that provides recommendations for setting PSV. We call this a load and tolerance strategy for determining PSV. METHODS: In a clinical validation study, we compared the recommendations from our PSV advisory system to the recommendations of experienced critical-care Registered Respiratory Therapists (RRTs) for setting PSV in patients with respiratory failure. With 76 adult patients in a university medical center surgical intensive care unit receiving PSV, a combined pressure/flow sensor, positioned between the endotracheal tube and patient Y-piece, sent measurements to the PSV advisory system. We compared the advisory system's recommendations (increase, maintain, or decrease the pressure support) to the RRTs' recommendations at the bedside. RESULTS: There were no significant differences between the RRTs' and the advisory system's recommendations (n ‫؍‬ 109) to increase, maintain, or decrease PSV. The RRTs agreed with 91% of the advisory system's recommendations (kappa statistic 0.85, P < .001). The advisory system was very good at predicting the RRTs' pressure support recommendations (r 2 ‫؍‬ 0.87, P < .02). CONCLUSIONS: A load and tolerance strategy with a computerized PSV advisory system provided valid recommendations for setting PSV to unload the inspiratory muscles, and the recommendations were essentially the same as the recommendations from experienced critical-care RRTs. The PSV advisory system operates continuously and automatically and may be useful in clinical environments where experts are not always available.

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Ventilator Advisory System Employing Load and Tolerance Strategy Recommends Appropriate Pressure Support Ventilation Settings

Cited by 14 publications

References 12 publications

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Pressure Support Ventilation Advisory System Provides Valid Recommendations for Setting Ventilator

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