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

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

Performance of lung recruitment model in healthy anesthetised pigs

Patients with acute respiratory failure are given mechanical ventilation (MV) for treatment and breathing support. During MV, positive end-expiratory pressure (PEEP) is applied to recruit collapsed alveoli and maximized oxygenation. However, there are no well-established methods for quantifying alveoli recruitment with PEEP increase

Pilot Proof of Concept Clinical Trials of Stochastic Targeted (STAR) Glycemic Control

(open access)Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials. Methods: Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. Results: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. Conclusions: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

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

First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

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