Determining rhythmicity and determinism of temperature curves in septic and non-septic critically ill patients through chronobiological and recurrence quantification analysis: a pilot study.

BACKGROUND: A few studies have demonstrated that critically ill patients exhibit circadian deregulation and reduced complexity of different time series, such as temperature. RESULTS: In this prospective study, we enrolled 21 patients divided into three groups: group A (N = 10) included subjects who had septic shock at the time of ICU entry, group B (N = 6) included patients who developed septic shock during ICU stay, and group C consisted of 5 non-septic critically ill patients. Core body temperature (CBT) was recorded for 24 h at a rate of one sample per hour (average of CBT for that hour) and during different occasions: upon ICU entry and exit in groups A and C and upon entry, septic shock development, and exit in group B. Markers of circadian rhythmicity included mean values, amplitude that is the difference between peak and mean values, and peak time. Furthermore, recurrence quantification analysis (RQA) was employed for assessing different markers of complexity of temperature signals. Patients from group C exhibited higher temperature amplitude upon entry (0.45 ± 0.19) in relation with both groups A (0.28 ± 0.18, p < 0.05) and B (0.32 ± 0.13, p < 0.05). Circadian features did not differ within all groups. Temperature amplitude in groups B and C upon entry was negatively correlated with SAPS II (r = - 0.72 and - 0.84, p < 0.003) and APACHE II scores (r = - 0.70 and - 0.63, p < 0.003), respectively, as well as duration of ICU and hospital stay in group B (r = - 0.62 and - 0.64, p < 0.003) and entry SOFA score in group C (r = - 0.82, p < 0.003). Increased periodicity of CBT was found for all patients upon exit related to entry in the ICU. Different RQA features indicating periodic patterns of change of entry CBT were negatively correlated with severity of disease and length of ICU stay for all patients. CONCLUSIONS: Increased temperature rhythmicity during ICU entry was related with lower severity of disease and better clinical outcomes, whereas the more deterministic CBT patterns were found in less critically ill patients with shorter ICU stay.

Melatonin and cortisol exhibit different circadian rhythm profiles during septic shock depending on timing of onset: a prospective observational study.

BACKGROUND: Septic shock has been found to disrupt circadian rhythms. Moreover, timing of onset has been associated with different circadian profiles in experimental studies. RESULTS: In this prospective study, we enrolled 26 patients divided into two groups: Group A (N = 15) included subjects who had septic shock at the time of ICU admission and Group B (N = 11) included patients who developed septic shock during ICU admission. 6-Sulfatoxymelatonin (aMT6s) and cortisol levels were measured in urine samples every 4 h over a 24-h period. Two sets of samples were taken from Group A (entry/septic shock and exit) and three sets from Group B (entry, septic shock and exit). Mean, amplitude that is the difference between peak and mean values, as well as peak time, were estimated for both aMT6s and cortisol. In Group A, amplitude of aMT6s upon entry (septic shock) was reduced in relation to exit (437.2 ± 309.2 vs. 674.1 ± 657.6 ng/4 h, p < 0.05). Peak time occurred earlier (10:00 p.m. vs. 07:00 a.m, p < 0.05) and correlated with higher APACHE II score and longer ICU stay. In Group B, aMT6s mean values were significantly increased during septic shock (2492.2 ± 1709.1 ng/4 h) compared to both entry (895.4 ± 715.5 ng/4 h) and exit (1308.6 ± 1214.4 ng/4 h, p < 0.05 for all comparisons). Amplitude of aMT6s was also elevated during septic shock (794.8 ± 431.8 ng/4 h) in relation to entry (293.1 ± 275.9 ng/4 h, p < 0.05). Regarding cortisol rhythm in Group A, during septic shock amplitude was increased compared to exit (13.3 ± 31 ng/4 h vs. 8.7 ± 21.2 ng/4 h p < 0.05) and correlated with reduced hospital length of stay. In Group B, cortisol mean values and amplitude during septic shock (10 ± 5.3 and 3 ± 1.8 ng/4 h, respectively) were significantly reduced compared to both entry (30 ± 57.9 and 12.3 ± 27.3 ng/4 h) and exit (14.4 ± 20.7 and 6.6 ± 8.7 ng/4 h, p < 0.05 for all comparisons) and correlated with higher SOFA score and longer ICU and hospital stay. CONCLUSIONS: Septic shock induced inverse changes of aMT6s and cortisol circadian rhythm profiles both within and between different groups of patients, depending on timing of onset. Reduced rhythmicity was correlated with severity of disease and longer ICU stay.

Connected health and integrated care: Toward new models for chronic disease management.

The increasingly aging population in Europe and worldwide brings up the need for the restructuring of healthcare. Technological advancements in electronic health can be a driving force for new health management models, especially in chronic care. In a patient-centered e-health management model, communication and coordination between patient, healthcare professionals in primary care and hospitals can be facilitated, and medical decisions can be made timely and easily communicated. Bringing the right information to the right person at the right time is what connected health aims at, and this may set the basis for the investigation and deployment of the integrated care models. In this framework, an overview of the main technological axes and challenges around connected health technologies in chronic disease management are presented and discussed. A central concept is personal health system for the patient/citizen and three main application areas are identified. The connected health ecosystem is making progress, already shows benefits in (a) new biosensors, (b) data management, (c) data analytics, integration and feedback. Examples are illustrated in each case, while open issues and challenges for further research and development are pinpointed.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock.

BACKGROUND: Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. METHODS: Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. RESULTS: Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. CONCLUSIONS: We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness.

Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials.

BACKGROUND: Separation from mechanical ventilation is a difficult task, whereas conventional predictive indices have not been proven accurate enough, so far. A few studies have explored changes of breathing pattern variability for weaning outcome prediction, with conflicting results. In this study, we tried to assess respiratory complexity during weaning trials, using different non-linear methods derived from theory of complex systems, in a cohort of surgical critically ill patients. RESULTS: Thirty two patients were enrolled in the study. There were 22 who passed and 10 who failed a weaning trial. Tidal volume and mean inspiratory flow were analyzed for 10 minutes during two phases: 1. pressure support (PS) ventilation (15-20 cm H2O) and 2. weaning trials with PS: 5 cm H2O. Sample entropy (SampEn), detrended fluctuation analysis (DFA) exponent, fractal dimension (FD) and largest lyapunov exponents (LLE) of the two respiratory parameters were computed in all patients and during the two phases of PS. Weaning failure patients exhibited significantly decreased respiratory pattern complexity, reflected in reduced sample entropy and lyapunov exponents and increased DFA exponents of respiratory flow time series, compared to weaning success subjects (p < 0.001). In addition, their changes were opposite between the two phases of the weaning trials. A new model including rapid shallow breathing index (RSBI), its product with airway occlusion pressure at 0.1 sec (P0.1), SampEn and LLE predicted better weaning outcome compared with RSBI, P0.1 and RSBI* P0.1 (conventional model, R(2) = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05). CONCLUSIONS: We suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.