Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU.

In the context of assisted ventilation in ICU, it is of vital importance to keep a high synchronization between the patient's attempt to breath and the assisted ventilation event, so that the patient receives the ventilation support requested. In this work, experimental equipment is employed, which allows for unobtrusive and continuous monitoring of a multiple relevant bioparameters. These are meant to guide the medical professionals in appropriately adapting the treatment and fine-tune the ventilation. However, synchronization phenomena of different origin (neurological, mechanical, ventilation parameters) may occur, which vary among patients, and during the course of monitoring of a single patient, the timely recognition of which is challenging even for experts. The dynamics and complex causal relations among bioparameters and the ventilation synchronization are not well studied. The purpose of this work is to elaborate on a methodology toward modeling the ventilation synchronization failures based on the evolution of monitored bioparameters. Principal component analysis is employed for the transformation into a small number of features and the investigation of repeating patterns and clusters within measurements. Using these features, nonlinear prediction models based on support vector machines regression are explored, in terms of what past knowledge is required and what is the future horizon that can be predicted. The proposed model shows good correlation (over 0.74) with the actual outputs, constituting an encouraging step toward understanding of ICU ventilation dynamic phenomena.

Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients.

A few studies estimating temperature complexity have found decreased Shannon entropy, during severe stress. In this study, we measured both Shannon and Tsallis entropy of temperature signals in a cohort of critically ill patients and compared these measures with the sequential organ failure assessment (SOFA) score, in terms of intensive care unit (ICU) mortality. Skin temperature was recorded in 21 mechanically ventilated patients, who developed sepsis and septic shock during the first 24 h of an ICU-acquired infection. Shannon and Tsallis entropies were calculated in wavelet-based decompositions of the temperature signal. Statistically significant differences of entropy features were tested between survivors and non-survivors and classification models were built, for predicting final outcome. Significantly reduced Tsallis and Shannon entropies were found in non-survivors (seven patients, 33%) as compared to survivors. Wavelet measurements of both entropy metrics were found to predict ICU mortality better than SOFA, according to a combination of area under the curve, sensitivity and specificity values. Both entropies exhibited similar prognostic accuracy. Combination of SOFA and entropy presented improved the outcome of univariate models. We suggest that reduced wavelet Shannon and Tsallis entropies of temperature signals may complement SOFA in mortality prediction, during the first 24 h of an ICU-acquired infection.