Vasilios Papaioannou

Relation of heart rate variability to serum levels of C-reactive protein, interleukin 6, and 10 in patients with sepsis and septic shock

PURPOSE The aim of the study was to investigate possible associations between different heart rate variability (HRV) indices and various biomarkers of inflammation in 45 septic patients. MATERIALS AND METHODS We daily assessed HRV in the time domain (SD of RR intervals [SDNN]), frequency domain (low [LF], high frequency [HF], LF/HF as an indicator of sympathovagal balance); the 2 values of SD (SD1, SD2) from the Poincaré plot; and measured C-reactive protein, interleukin 6, and interleukin 10 serum levels in patients with sepsis and mean Sequential Organ Failure Assessment score (SOFA) 10 or lower (n = 25) and septic shock (SOFA > 10, n = 20) for 6 days. RESULTS C-reactive protein exhibited significant negative correlations with LF (r = -0.78), LF/HF (r = -0.61), and SDNN (r = -0.79) and positive correlations with HF (r = 0.80) and SD1/SD2 (r = 0.66), whereas interleukin 10 was positively correlated with HF (r = 0.71) and negatively with LF (r = -0.89) and LF/HF (r = -0.66) in septic shock patients (P < .05 for all comparisons). Standard deviation of RR intervals and HF proved to be independent predictors of the severity of disease (beta slope [B] = -1.091; P = .013; 95% confidence interval [CI], -1.43 to -0.74, and B = 0.78; P = .022; 95% CI, 0.21-1.35, respectively). CONCLUSIONS Our data suggest that low HRV and sympathovagal balance during septic shock are associated with both an increased hyperinflammatory and antiinflammatory response.

Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: A study of physiologic complexity in surgical critically ill patients

PURPOSE The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques. MATERIALS AND METHODS Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H(2)O) and (2) weaning trials with PS (5 cm H(2)O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α1 exponent were computed in all patients and during the 2 phases of PS. RESULTS Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α1 exponent, compared with weaning success subjects (P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups (P < .001). A new model including rapid shallow breathing index (RSBI), α1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P(0.1)), and RSBI × P(0.1) (conventional model, R(2) = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively (P < .005). CONCLUSIONS We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.

Procalcitonin and procalcitonin kinetics for diagnosis and prognosis of intravascular catheter-related bloodstream infections in selected critically ill patients: a prospective observational study

BackgroundProcalcitonin (PCT) has emerged as a valuable marker of sepsis. The potential role of PCT in diagnosis and therapy monitoring of intravascular catheter-related bloodstream infections (CRBSI) in intensive care unit (ICU) is still unclear and was evaluated.MethodsForty-six patients were included in the study, provided they were free of infection upon admission and presented the first episode of suspected CRBSI during their ICU stay. Patients who had developed any other infection were excluded. PCT was measured daily during the ICU hospitalization. Primary endpoint was proven CRBSI. Therapy monitoring as according to infection control was also evaluated.ResultsAmong the 46 patients, 26 were diagnosed with CRBSI. Median PCT on the day of infection suspicion (D0) was 7.70 and 0.10 ng/ml for patients with and without proven CRBSI, respectively (p < 0.001). The area under the curve (AUC) for PCT was 0.990 (95% CI; 0.972 – 1.000), whereas a cut-off value of 0.70 ng/ml provided sensitivity and specificity of 92.3 and 100% respectively. In contrast, the AUC for white blood cells (WBC) was 0.539 (95% CI; 0.369 – 0.709), and for C-reactive protein (CRP), 0.603 (95% CI; 0.438 – 0.768). PCT was the best predictor of proven infection. Moreover, an increase >0.20 ng/ml of PCT between the D0 and any of the 4 preceding days was associated with a positive predictive value exceeding 96%. PCT concentrations from the D2 to D6 after suspected infection tended to decrease in controlled patients, whereas remained stable in non-controlled subjects. A PCT concentration exceeding 1.5 ng/ml during D3 was associated with lack of responsiveness to therapy (p = 0.028).ConclusionsWe suggest that PCT could be a helpful diagnostic and prognostic marker of CRBSI in critically ill patients. Both absolute values and variations should be considered.

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

BackgroundEven 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.MethodsTwenty-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.ResultsStatistically 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.ConclusionsWe 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

BackgroundSeparation 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.ResultsThirty 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, R2 = 0.874 vs 0.643, p < 0.001). Areas under the curve were 0.916 vs 0.831, respectively (p < 0.05).ConclusionsWe suggest that complexity analysis of respiratory signals can assess inherent breathing pattern dynamics and has increased prognostic impact upon weaning outcome in surgical patients.

Negative-pressure acute tracheobronchial hemorrhage and pulmonary edema

Negative-pressure pulmonary edema is a well-known complication of an acute upper airway obstruction, which may rarely present as acute alveolar hemorrhage in cases of severe capillary stress failure. Hemorrhage from the central airways has also been reported as a rare manifestation of acute tracheobronchial injury, associated with severe disruption of the bronchial vasculature due to highly negative inspiratory pressure. In this clinical report, we describe a case of both acute tracheobronchial and alveolar hemorrhage in a young man, occurring immediately after extubation due to laryngospasm, diagnosed by bronchoscopy with bronchoalveolar lavage (BAL), measurement of the pulmonary edema fluid/plasma protein ratio, and by thoracic computed tomography (CT) scan. We propose that the patient experienced severe postobstructive negative-pressure pulmonary edema, related to increased permeability of the alveolar capillary membrane and a parallel loss of integrity of the bronchial vascular network. Our findings suggest that both changes in the bronchial circulation and mechanical stress failure of the more distal alveolar-capillary system may be induced by severe and acute upper-airway obstruction.

Traumatic asphyxia due to blunt chest trauma: a case report and literature review

IntroductionCrush asphyxia is different from positional asphyxia, as respiratory compromise in the latter is caused by splinting of the chest and/or diaphragm, thus preventing normal chest expansion. There are only a few cases or small case series of crush asphyxia in the literature, reporting usually poor outcomes.Case presentationWe present the case of a 44-year-old Caucasian man who developed traumatic asphyxia with severe thoracic injury and mild brain edema after being crushed under heavy auto vehicle mechanical parts. He remained unconscious for an unknown time. The treatment included oropharyngeal intubation and mechanical ventilation, bilateral chest tube thoracostomies, treatment of brain edema and other supportive measures. Our patient’s outcome was good. Traumatic asphyxia is generally under-reported and most authors apply supportive measures, while the final outcome seems to be dependent on the length of time of the chest compression and on the associated injuries.ConclusionTreatment for traumatic asphyxia is mainly supportive with special attention to the re-establishment of adequate oxygenation and perfusion; treatment of the concomitant injuries might also affect the final outcome.

Biosignal analysis techniques for weaning outcome assessment

Discontinuation of mechanical ventilation in critically ill patients is a challenging task and involves a careful weighting of the benefits of early extubation and the risks of premature spontaneous breathing trial. Recently, apart from studying different physiological variables by means of descriptive statistical tests, breathing pattern variability analysis has been performed for the assessment of weaning readiness. A limited number of clinical studies implementing different weaning protocols in heterogeneous groups of patients and using a variable set of signal processing techniques have appeared in the critical care literature, with varying results. The purpose of this review article is 3-fold: (1) to describe the different signal processing techniques being implemented for the assessment of weaning readiness, (2) to provide insight into the pathophysiological mechanisms that may govern breath-to-breath variability/complexity in health and disease, and (3) to present results from the critical care literature derived from the application of biosignal analysis tools for the identification of possible weaning indices.

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.

Predicting the Ineffective Efforts in ICU Assisted Ventilation via Multiparametric Models

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. A series of relevant bioparameters continuously monitored, are meant to guide the medical professionals in appropriately adapting the operation and treatment, in this direction. Still, 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 towards predicting level of ventilation synchronization based on the previous known evolution of monitored bioparameters. A nonlinear model is thus proposed, based on support vector machines regression, and the best input memory is investigated. The proposed model shows good correlation (over 0.7) with the actual outputs, and low error, constituting an encouraging step towards understanding of ICU ventilation dynamic phenomena.