This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV). 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. We analyzed 7,027 h of MV comprising 8,731,981 breaths. Asynchronies were detected in all patients and in all ventilator modes. The median AI was 3.41 % [IQR 1.95–5.77]; the most common asynchrony overall and in each mode was IEE [2.38 % (IQR 1.36–3.61)]. Asynchronies were less frequent from 12 pm to 6 am [1.69 % (IQR 0.47–4.78)]. In the hours where more than 90 % of breaths were machine-triggered, the median AI decreased, but asynchronies were still present. When we compared patients with AI > 10 vs AI ≤ 10 %, we found similar reintubation and tracheostomy rates but higher ICU and hospital mortality and a trend toward longer duration of MV in patients with an AI above the cutoff. Asynchronies are common throughout MV, occurring in all MV modes, and more frequently during the daytime. Further studies should determine whether asynchronies are a marker for or a cause of mortality.

Monitoring plays an important role in the current management of patients with acute respiratory failure but sometimes lacks definition regarding which 'signals' and 'derived variables' should be prioritized as well as specifics related to timing (continuous versus intermittent) and modality (static versus dynamic). Many new techniques of respiratory monitoring have been made available for clinical use recently, but their place is not always well defined. Appropriate use of available monitoring techniques and correct interpretation of the data provided can help improve our understanding of the disease processes involved and the effects of clinical interventions. In this consensus paper, we provide an overview of the important parameters that can and should be monitored in the critically ill patient with respiratory failure and discuss how the data provided can impact on clinical management.

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. Asynchronies occur when ventilator assistance does not match the patient’s demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management. Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

Patient-ventilator asynchrony exists when the phases of breath delivered by the ventilator do not match those of the patient. Asynchronies occur throughout mechanical ventilation and negatively affect patient comfort, duration of mechanical ventilation, length of ICU stays, and mortality. Identifying asynchronies requires careful attention to patients and their ventilator waveforms. This review discusses the different types of asynchronies, how they are generated, and their impact on patient comfort and outcome. Moreover, it discusses practical approaches for detecting, correcting, and preventing asynchronies. Current evidence suggests that the best approach to managing asynchronies is by adjusting ventilator settings. Proportional modes improve patient-ventilator coupling, resulting in greater comfort and less dyspnea, but not in improved outcomes with respect to the duration of mechanical ventilation, delirium, or cognitive impairment. Advanced computational technologies will allow smart alerts, and models based on time series of asynchronies will be able to predict and prevent asynchronies, making it possible to tailor mechanical ventilation to meet each patient's needs throughout the course of mechanical ventilation.