Patient-Ventilator Interactions

Flow-Limited and Reverse-Triggered Ventilator Dyssynchrony Are Associated With Increased Tidal and Dynamic Transpulmonary Pressure

OBJECTIVES

Ventilator dyssynchrony may be associated with increased delivered tidal volumes (V t s) and dynamic transpulmonary pressure (ΔP L,dyn ), surrogate markers of lung stress and strain, despite low V t ventilation. However, it is unknown which types of ventilator dyssynchrony are most likely to increase these metrics or if specific ventilation or sedation strategies can mitigate this potential.

DESIGN

A prospective cohort analysis to delineate the association between ten types of breaths and delivered V t , ΔP L,dyn , and transpulmonary mechanical energy.

SETTING

Patients admitted to the medical ICU.

PATIENTS

Over 580,000 breaths from 35 patients with acute respiratory distress syndrome (ARDS) or ARDS risk factors.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients received continuous esophageal manometry. Ventilator dyssynchrony was identified using a machine learning algorithm. Mixed-effect models predicted V t , ΔP L,dyn , and transpulmonary mechanical energy for each type of ventilator dyssynchrony while controlling for repeated measures. Finally, we described how V t , positive end-expiratory pressure (PEEP), and sedation (Richmond Agitation-Sedation Scale) strategies modify ventilator dyssynchrony's association with these surrogate markers of lung stress and strain. Double-triggered breaths were associated with the most significant increase in V t , ΔP L,dyn , and transpulmonary mechanical energy. However, flow-limited, early reverse-triggered, and early ventilator-terminated breaths were also associated with significant increases in V t , ΔP L,dyn , and energy. The potential of a ventilator dyssynchrony type to increase V t , ΔP L,dyn , or energy clustered similarly. Increasing set V t may be associated with a disproportionate increase in high-volume and high-energy ventilation from double-triggered breaths, but PEEP and sedation do not clinically modify the interaction between ventilator dyssynchrony and surrogate markers of lung stress and strain.

CONCLUSIONS

Double-triggered, flow-limited, early reverse-triggered, and early ventilator-terminated breaths are associated with increases in V t , ΔP L,dyn , and energy. As flow-limited breaths are more than twice as common as double-triggered breaths, further work is needed to determine the interaction of ventilator dyssynchrony frequency to cause clinically meaningful changes in patient outcomes.

The Development, Optimization, and Validation of Four Different Machine Learning Algorithms to Identify Ventilator Dyssynchrony

Invasive mechanical ventilation can worsen lung injury. Ventilator dyssynchrony (VD) may propagate ventilator-induced lung injury (VILI) and is challenging to detect and systematically monitor because each patient takes approximately 25,000 breaths a day yet some types of VD are rare, accounting for less than 1% of all breaths. Therefore, we sought to develop and validate accurate machine learning (ML) algorithms to detect multiple types of VD by leveraging esophageal pressure waveform data to quantify patient effort with airway pressure, flow, and volume data generated during mechanical ventilation, building a computational pipeline to facilitate the study of VD.

MATERIALS AND METHODS

We collected ventilator waveform and esophageal pressure data from 30 patients admitted to the ICU. Esophageal pressure allows the measurement of transpulmonary pressure and patient effort. Waveform data were cleaned, features considered essential to VD detection were calculated, and a set of 10,000 breaths were manually labeled. Four ML algorithms were trained to classify each type of VD: logistic regression, support vector classification, random forest, and XGBoost.

RESULTS

We trained ML models to detect different families and seven types of VD with high sensitivity (>90% and >80%, respectively). Three types of VD remained difficult for ML to classify because of their rarity and lack of sample size. XGBoost classified breaths with increased specificity compared to other ML algorithms.

DISCUSSION

We developed ML models to detect multiple types of VD accurately. The ability to accurately detect multiple VD types addresses one of the significant limitations in understanding the role of VD in affecting patient outcomes.

CONCLUSION

ML models identify multiple types of VD by utilizing esophageal pressure data and airway pressure, flow, and volume waveforms. The development of such computational pipelines will facilitate the identification of VD in a scalable fashion, allowing for the systematic study of VD and its impact on patient outcomes.

Mechanical Ventilation and Respiratory Support in the Pediatric Intensive Care Unit

Children admitted to the pediatric intensive care unit often require respiratory support for the treatment of respiratory distress and failure. Respiratory support comprises both noninvasive modalities (ie, heated humidified high-flow nasal cannula, continuous positive airway pressure, bilevel positive airway pressure, negative pressure ventilation) and invasive mechanical ventilation. In this article, we review the various essential elements and considerations involved in the planning and application of respiratory support in the treatment of the critically ill children.

Patient-ventilator dyssynchronies: Are they all the same? A clinical classification to guide actions

Patient ventilatory dyssynchrony (PVD) is a mismatch between the respiratory drive of the patient and ventilatory assistance. It is a complex event seen in almost all ventilated patients and at any ventilator mode, with uncertain significance and prognosis. Due to its different pathophysiological mechanisms, there is still not consensual classification to guide us in selecting the best treatment. In the present review we aimed to summarize some clinical data on PVD, and to propose a clinical classification based on the type of PVD, from potentially innocuous to clearly harmful PVD, which could help clinicians in the decision-making process from adjusting ventilator settings to deeply sedate or paralyze the patient. Clearly, further studies are needed addressing risk factors, physiologic mechanisms and direct consequences of PVD in order to help clinicians to design effective and proven strategies at the bedside.

Management of anemia in patients who decline blood transfusion

Declining a treatment modality should not be considered the same as refusal of medical care as illustrated by the management of Jehovah's Witness patients who do not accept transfusions. Over the years, a comprehensive set of strategies have been developed to meet the specific needs of these patients and these strategies are collectively called "Bloodless Medicine and Surgery" (BMS). The focus in BMS is to optimize the patients' hematopoietic capacity to increase hemoglobin (Hgb) level, minimize blood loss, improve hemostasis, and provide supportive strategies to minimize oxygen consumption and maximize oxygen utilization. We present 3 case reports that illustrate some of the challenges faced and measures available to effectively treat these patients. Under BMS programs, patients with extremely low hemoglobin levels, not conducive to survival under ordinary conditions, have survived and recovered without receiving allogeneic transfusions. Additionally, the valuable experience gained from caring for these patients has paved the way to develop the concept of Patient Blood Management as a standard care to benefit all patients, and not only those for whom blood is not an option.

Patient-ventilator asynchrony

Patient-v entilator asynchrony (PVA) is a mismatch between the patient, regarding time, flow, volume, or pressure demands of the patient respiratory system, and the ventilator, which supplies such demands, during mechanical ventilation (MV). It is a common phenomenon, with incidence rates ranging from 10% to 85%. PVA might be due to factors related to the patient, to the ventilator, or both. The most common PVA types are those related to triggering, such as ineffective effort, auto-triggering, and double triggering; those related to premature or delayed cycling; and those related to insufficient or excessive flow. Each of these types can be detected by visual inspection of volume, flow, and pressure waveforms on the mechanical ventilator display. Specific ventilatory strategies can be used in combination with clinical management, such as controlling patient pain, anxiety, fever, etc. Deep sedation should be avoided whenever possible. PVA has been associated with unwanted outcomes, such as discomfort, dyspnea, worsening of pulmonary gas exchange, increased work of breathing, diaphragmatic injury, sleep impairment, and increased use of sedation or neuromuscular blockade, as well as increases in the duration of MV, weaning time, and mortality. Proportional assist ventilation and neurally adjusted ventilatory assist are modalities of partial ventilatory support that reduce PVA and have shown promise. This article reviews the literature on the types and causes of PVA, as well as the methods used in its evaluation, its potential implications in the recovery process of critically ill patients, and strategies for its resolution.

Patient-ventilator asynchrony

Patient-v entilator asynchrony (PVA) is a mismatch between the patient, regarding time, flow, volume, or pressure demands of the patient respiratory system, and the ventilator, which supplies such demands, during mechanical ventilation (MV). It is a common phenomenon, with incidence rates ranging from 10% to 85%. PVA might be due to factors related to the patient, to the ventilator, or both. The most common PVA types are those related to triggering, such as ineffective effort, auto-triggering, and double triggering; those related to premature or delayed cycling; and those related to insufficient or excessive flow. Each of these types can be detected by visual inspection of volume, flow, and pressure waveforms on the mechanical ventilator display. Specific ventilatory strategies can be used in combination with clinical management, such as controlling patient pain, anxiety, fever, etc. Deep sedation should be avoided whenever possible. PVA has been associated with unwanted outcomes, such as discomfort, dyspnea, worsening of pulmonary gas exchange, increased work of breathing, diaphragmatic injury, sleep impairment, and increased use of sedation or neuromuscular blockade, as well as increases in the duration of MV, weaning time, and mortality. Proportional assist ventilation and neurally adjusted ventilatory assist are modalities of partial ventilatory support that reduce PVA and have shown promise. This article reviews the literature on the types and causes of PVA, as well as the methods used in its evaluation, its potential implications in the recovery process of critically ill patients, and strategies for its resolution.