Modelling thirty-day mortality in the acute respiratory distress syndrome (ARDS) in an adult ICU

Publisher's copy made available with the permission of the publisher © Australian Society of AnaesthetistsVariables predicting thirty-day outcome from Acute Respiratory Distress Syndrome (ARDS) were analysed using Cox regression structured for time-varying covariates. Over a three-year period, 1996-1998, consecutive patients with ARDS (bilateral chest X-ray opacities, PaO₂/FiO₂ ratio of <200 and an acute precipitating event) were identified using a prospective computerized data base in a university teaching hospital ICU. The cohort, 106 mechanically ventilated patients, was of mean (SD) age 63.5 (15.5) years and 37% were female. Primary lung injury occurred in 45% and 24% were postoperative. ICU-admission day APACHE II score was 25 (8); ARDS onset time from ICU admission was 1 day (median: range 0-16) and 30 day mortality was 41% (95% CI: 33%-51%). At ARDS onset, PaO₂/FiO₂ ratio was 92 (31), 81% had four-quadrant chest X-ray opacification and lung injury score was 2.75 (0.45). Average mechanical ventilator tidal volume was 10.3 ml/ predicted kg weight. Cox model mortality predictors (hazard ratio, 95% CI) were: APACHE II score, 1.15 (1.09-1.21); ARDS lag time (days), 0.72 (0.58-0.89); direct versus indirect injury, 2.89 (1.45-5.76); PaO₂/FiO₂ ratio, 0.98 (0.97-0.99); operative versus non-operative category, 0.24 (0.09-0.63). Time-varying effects were evident for PaO₂/FiO₂ ratio, operative versus non-operative category and ventilator tidal volume assessed as a categorical predictor with a cut-point of 8 ml/kg predicted weight (mean tidal volumes, 7.1 (1.9) vs 10.7 (1.6) ml/kg predicted weight). Thirty-day survival was improved for patients ventilated with lower tidal volumes. Survival predictors in ARDS were multifactorial and related to patient-injury-time interaction and level of mechanical ventilator tidal volume.J. L. Moran, P. J. Solomon, V. Fox, M. Salagaras, P. J. Williams, K. Quinlan, A. D. Berstenhttp://www.aaic.net.au/Article.asp?D=200332

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York

Patient-specific bone modelling and remodelling simulation of hypoparathyroidism based on human iliac crest biopsies

We previously developed a load-adaptive bone modelling and remodelling simulation model that can predict changes in the bone micro-architecture as a result of changes in mechanical loading or cell activity. In combination with a novel algorithm to estimate loading conditions, this offers the possibility for patient-specific predictions of bone modelling and remodelling. Based on such models, the underlying mechanisms of bone diseases and/or the effects of certain drugs and their influence on the bone micro-architecture can be investigated. In the present study we test the ability of this approach to predict changes in bone micro-architecture during hypoparathyroidism (HypoPT), as an illustrative example. We hypothesize that, apart from reducing bone turnover, HypoPT must also lead to increased osteocyte mechanosensitivity in order to explain the changes in bone mass seen in patients. Healthy human iliac crest biopsies were used as the starting point for the simulations that mimic HypoPT conditions and the resultant micro-architectures were compared to age-matched clinical HypoPT biopsies. Simulation results were in good agreement with the clinical data when osteocyte mechanosensitivity was increased by 40%. In conclusion, the results confirm our hypothesis, and also demonstrate that patient-specific bone modelling and remodelling simulations are feasible

Sto(ry)chastics: a Bayesian Network Architecture for User Modeling and Computational Storytelling for Interactive Spaces

This paper presents sto(ry)chastics, a user-centered approach for computational storytelling for real-time sensor-driven multimedia audiovisual stories, such as those that are triggered by the body in motion in a sensor-instrumented interactive narrative space. With sto(ry)chastics the coarse and noisy sensor inputs are coupled to digital media outputs via a user model, which is estimated probabilistically by a Bayesian network. To illustrate sto(ry)chastics, this paper describes the museum wearable, a device which delivers an audiovisual narration interactively in time and space to the visitor as a function of the estimated visitor type. The wearable relies on a custom-designed long-range infrared locationidentification sensor to gather information on where and how long the visitor stops in the museum galleries and uses this information as input to, or observations of, a (dynamic) Bayesian network. The network has been tested and validated on observed visitor tracking data by parameter learning using the Expectation Maximization (EM) algorithm, and by performance analysis of the model with the learned parameters