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 how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

A doubling of the carboplatin dose-intensity did not result in any significant improvement of pathologic remission or survival. Calvert's formula can be used to give a fairly accurate estimate of the carboplatin AUC. Bone marrow toxicity increased with higher dose-intensity, and a further increase of dose is only feasible with growth-factor or stem-cell support.

Electrical impedance tomography (EIT) has the potential to become a bedside tool for monitoring and guiding ventilator therapy as well as tracking the development of chronic lung diseases. This review article summarizes recent publications (from 2011) dealing with the applications of pulmonary EIT. Original papers on EIT lung imaging in clinical settings are analyzed and divided into several categories according to the lung pathology of the study subjects. Studies on children and infants are presented separately from studies on adult patients. Information on the study objectives and main results, the number of studied patients, the performed ventilatory maneuvers or interventions and the analyzed EIT information is given. Limitations that hinder EIT to become a routinely used tool in a clinical setting are also discussed.

Background: To examine the influence of positive end-expiratory pressure (PEEP) settings on lung mechanics and oxygenation in elderly patients undergoing thoracoscopic surgery.Methods: One hundred patients aged >65 years were randomly allocated into either the PEEP 5 or the electrical impedance tomography (EIT) group (PEEP EIT ). Each group underwent volume-controlled ventilation (tidal volume 6 mL/kg predicted body weight) with the PEEP either fixed at 5 cmH 2 O or set at an individualized EIT setting. The primary endpoint was the ratio of the arterial oxygen partial pressure to the fractional inspired oxygen (PaO 2 /FiO 2 ). The secondary endpoints included the driving pressure, and dynamic respiratory system compliance (C dyn ). Other outcomes, such as the mean airway pressure (P mean ), mean arterial pressure (MAP), lung complications and the length of hospital stay were explored. Results:The optimal PEEP set by EIT was significantly higher (range from 9-13 cmH 2 O) than the fixed PEEP. PaO 2 /FiO 2 was 47 mmHg higher (95% CI: 7-86 mmHg; P=0.021), C dyn was 4.3 mL/cmH 2 O higher (95% CI: 2.1-6.7 cmH 2 O; P<0.001), and the driving pressure was 3.7 cmH 2 O lower (95% CI: 2.2-5.1 mmH 2 O; P<0.001) at 0.5 h during one-lung ventilation (OLV) in the PEEP EIT group than in the PEEP 5 group. At 1 h during OLV, PaO 2 /FiO 2 was 93 mmHg higher (95% CI: 58-128 mmHg; P<0.001), C dyn was 4.4 mL/cmH 2 O higher (95% CI: 1.9-6.9 mL/cmH 2 O; P=0.001), and the driving pressure was 4.9 cmH 2 O lower (95% CI: 3.8-6.1 cmH 2 O; P<0.001) in the PEEP EIT group than in the PEEP 5 group. PaO 2 /FiO 2 was 107 mmHg higher (95% CI: 56-158 mmHg; P<0.001) in the PEEP EIT group than in the PEEP 5 group during double-lung ventilation at the end of surgery.Conclusions: PEEP values determined with EIT effectively improved oxygenation and lung mechanics during one lung ventilation in elderly patients undergoing thoracoscopic surgery.

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.