Respiratory system elastance monitoring during PEEP titration

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Aortic dP/dtmax accurately reflects left ventricular contractility when effective preload independence is achieved

peer reviewe

Glucose control positively influences patient outcome: a retrospective study

The goal of this research is to demonstrate that well-regulated glycemia is beneficial to patient outcome, regardless of how it is achieved

Effects of Neurally Adjusted Ventilatory Assist (NAVA) levels in non-invasive ventilated patients: titrating NAVA levels with electric diaphragmatic activity and tidal volume matching

BACKGROUND: Neurally adjusted ventilatory assist (NAVA) delivers pressure in proportion to diaphragm electrical activity (Eadi). However, each patient responds differently to NAVA levels. This study aims to examine the matching between tidal volume (Vt) and patients' inspiratory demand (Eadi), and to investigate patient-specific response to various NAVA levels in non-invasively ventilated patients. METHODS: 12 patients were ventilated non-invasively with NAVA using three different NAVA levels. NAVA100 was set according to the manufacturer's recommendation to have similar peak airway pressure as during pressure support. NAVA level was then adjusted ±50% (NAVA50, NAVA150). Airway pressure, flow and Eadi were recorded for 15 minutes at each NAVA level. The matching of Vt and integral of Eadi (ʃEadi) were assessed at the different NAVA levels. A metric, Range90, was defined as the 5-95% range of Vt/ʃEadi ratio to assess matching for each NAVA level. Smaller Range90 values indicated better matching of supply to demand. RESULTS: Patients ventilated at NAVA50 had the lowest Range90 with median 25.6 uVs/ml [Interquartile range (IQR): 15.4-70.4], suggesting that, globally, NAVA50 provided better matching between ʃEadi and Vt than NAVA100 and NAVA150. However, on a per-patient basis, 4 patients had the lowest Range90 values in NAVA100, 1 patient at NAVA150 and 7 patients at NAVA50. Robust coefficient of variation for ʃEadi and Vt were not different between NAVA levels. CONCLUSIONS: The patient-specific matching between ʃEadi and Vt was variable, indicating that to obtain the best possible matching, NAVA level setting should be patient specific. The Range90 concept presented to evaluate Vt/ʃEadi is a physiologic metric that could help in individual titration of NAVA level.Peer reviewe

Model-based patient matching for in-parallel pressure-controlled ventilation

Background: Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. Methods: The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. Results: The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. Conclusion: This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials

Validation of a virtual patient and virtual trials method for accurate prediction of tight glycemic control protocol performance

Peer reviewe

Variability of insulin sensitivity during the first 4 days of critical illness

1-pageSafe, effective tight glycaemic control (TGC) can improve outcomes in critical care patients, but is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between insulin concentration and insulin mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycaemia. This study investigates the daily evolution of model-based insulin sensitivity level and variability for critical care patients receiving TGC during the first four days of their ICU stay

Does tight glycemic control positively impact on patient mortality?

peer reviewe

Pilot Trials of STAR Target to Range Glycemic Control

ESICM 2011 programme is available in files INTRODUCTION. Tight glycemic control (TGC) has shown benefits in cardiac surgery ICU patients. STAR (Stochastic TARgeted) is a flexible, model-based TGC protocol accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 90 mg/dL. OBJECTIVES. To assess the safety, efficacy and clinical workload of the STAR TGC controller in pilot trials