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Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and, SCOPUS: re.j, info:eu-repo/semantics/published

Neurally adjusted ventilatory assist (NAVA) is a ventilation assist mode that delivers pressure in proportionality to electrical activity of the diaphragm (Eadi). Compared to pressure support ventilation (PS), it improves patient-ventilator synchrony and should allow a better expression of patient's intrinsic respiratory variability. We hypothesize that NAVA provides better matching in ventilator tidal volume (Vt) to patients inspiratory demand. 22 patients with acute respiratory failure, ventilated with PS were included in the study. A comparative study was carried out between PS and NAVA, with NAVA gain ensuring the same peak airway pressure as PS. Robust coefficients of variation (CVR) for Eadi and Vt were compared for each mode. The integral of Eadi (sh{phonetic}Eadi) was used to represent patient's inspiratory demand. To evaluate tidal volume and patient's demand matching, Range90 = 5-95 % range of the Vt/sh{phonetic}Eadi ratio was calculated, to normalize and compare differences in demand within and between patients and modes. In this study, peak Eadi and sh{phonetic}Eadi are correlated with median correlation of coefficients, R > 0.95. Median sh{phonetic}Eadi, Vt, neural inspiratory time (Ti_ Neural), inspiratory time (Ti) and peak inspiratory pressure (PIP) were similar in PS and NAVA. However, it was found that individual patients have higher or smaller sh{phonetic}Eadi, Vt, Ti_ Neural, Ti and PIP. CVR analysis showed greater Vt variability for NAVA (p < 0.005). Range90 was lower for NAVA than PS for 21 of 22 patients. NAVA provided better matching of Vt to sh{phonetic}Eadi for 21 of 22 patients, and provided greater variability Vt. These results were achieved regardless of differences in ventilatory demand (Eadi) between patients and modes. © 2012 Springer Science+Business Media New York.

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 (sh{phonetic}Eadi) were assessed at the different NAVA levels. A metric, Range90, was defined as the 5-95% range of Vt/sh{phonetic}Eadi 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 sh{phonetic}Eadi 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 sh{phonetic}Eadi and Vt were not different between NAVA levels.Conclusions: The patient-specific matching between sh{phonetic}Eadi 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/sh{phonetic}Eadi is a physiologic metric that could help in individual titration of NAVA level. © 2013 Chiew et al.; licensee BioMed Central Ltd.

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data.

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data.

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data.

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data.