Ole Bayer

Markers of endothelial damage in organ dysfunction and sepsis.

ObjectivesTo review the literature on direct and indirect markers of endothelial activation and damage in patients with sepsis and systemic inflammation and to assess their clinical usefulness for diagnosis and outcome. Various markers derived from or activated by endothelial cells are described, such as adhesion molecules, thrombomodulin, von Willebrand factor, parameters of the coagulation system, and interleukin-6. Furthermore, the association of these markers with the severity of sepsis, systemic inflammation, and outcome is evaluated. Data Extraction and SynthesisPublished research and review articles related to these parameters, with special emphasis on clinical studies. ConclusionsEndothelial activation and damage occur early during sepsis and play a major role in the pathophysiology of systemic inflammation. Various markers of endothelial activation are increased during sepsis and systemic inflammation, and in most studies, the level of markers such as soluble intercellular adhesion molecule, vascular cell adhesion molecule, and E selectin correlate well with the severity of inflammation and the course of the disease. However, to date, it remains unclear whether adhesion molecules and coagulation parameters are superior in this respect to interleukin-6 and procalcitonin, as direct comparisons are lacking. In addition, it is evident that markers of endothelial activation and coagulation parameters lack specificity for infection-induced endothelial damage and organ dysfunction.

Effects of fluid resuscitation with synthetic colloids or crystalloids alone on shock reversal, fluid balance, and patient outcomes in patients with severe sepsis: A prospective sequential analysis*

Objective:To assess shock reversal and required fluid volumes in patients with septic shock. Design:Prospective before and after study comparing three different treatment periods. Setting:Fifty-bed single-center surgical intensive care unit. Patients:Consecutive patients with severe sepsis. Interventions:Fluid therapy directed at preset hemodynamic goals with hydroxyethyl starch (predominantly 6% hydroxyethyl starch 130/0.4) in the first period, 4% gelatin in the second period, and only crystalloids in the third period. Measurements and Main Results:Main outcome was time to shock reversal (serum lactate <2.2 mmol/L and discontinuation of vasopressor use). Hemodynamic goals were mean arterial pressure >70 mm Hg; ScvO2 <70%; central venous pressure >8 mm Hg. Safety outcomes were acute kidney injury defined by Risk, Injury, Failure, Loss, and End-stage kidney disease criteria and new need for renal replacement therapy. Hemodynamic measures, serum lactate, and creatinine were comparable at baseline in all study periods (hydroxyethyl starch n = 360, gelatin n = 352, only crystalloids n = 334). Severity scores, hospital length of stay, and intensive care unit or hospital mortality did not differ significantly among groups. All groups showed similar time to shock reversal. More fluid was needed over the first 4 days in the crystalloid group (fluid ratios 1.4:1 [crystalloids to hydroxyethyl starch] and 1.1:1 [crystalloids to gelatin]). After day 5, fluid balance was more negative in the crystalloid group. Hydroxyethyl starch and gelatin were independent risk factors for acute kidney injury (odds ratio, 95% confidence interval 2.55, 1.76–3.69 and 1.85, 1.31–2.62, respectively). Patients receiving synthetic colloids received significantly more allogeneic blood products. Conclusions:Shock reversal was achieved equally fast with synthetic colloids or crystalloids. Use of colloids resulted in only marginally lower required volumes of resuscitation fluid. Both low molecular weight hydroxyethyl starch and gelatin may impair renal function.

Renal effects of synthetic colloids and crystalloids in patients with severe sepsis: a prospective sequential comparison.

Objectives: Hydroxyethyl starch 200 is associated with renal impairment in sepsis, but hydroxyethyl starch 130/0.4 and gelatin are considered to be less harmful. We hypothesized that fluid therapy with only crystalloids would decrease the incidence of acute kidney injury. Design: Prospective sequential comparison during intensive care unit stay. Setting: Surgical intensive care unit. Patients: Patients with severe sepsis. Interventions: Changes in standard fluid therapy, with predominantly 6% hydroxyethyl starch from January 2005 to June 2005, 4% gelatin from January 2006 to June 2006, and only crystalloids from September 2008 to June 2009. Measurements and Main Results: Acute kidney injury was defined by the presence of at least one RIFLE class; 118 patients received hydroxyethyl starch, 87 patients received gelatin, 141 patients received only crystalloids. Baseline serum creatinine values were similar. Patients received median cumulative doses of 46 (interquartile range, 18–92) mL/kg hydroxyethyl starch and 43 (interquartile range, 18–76) mL/kg gelatin. Total median fluid amounts were 649 (interquartile range, 275–1098) mL/kg in the hydroxyethyl starch group, 525 (237–868) mL/kg in the gelatin group, and 355 (173–911) mL/kg in the crystalloid group. The difference was statistically significant for hydroxyethyl starch after adjustment for multiple testing. Mean daily fluid intake and fluid balance were higher on days 0 and 1 in the crystalloid group. Acute kidney injury occurred in 70% of patients receiving hydroxyethyl starch (adjusted p = .002) and in 68% of patients receiving gelatin (adjusted p = .025) vs. 47% patients receiving crystalloids. Need for renal replacement therapy tended to be higher in the hydroxyethyl starch group (34%; adjusted p = .086) and in the gelatin group (34%; adjusted p = .162) in comparison to the crystalloid group (20%). Intensive care unit and hospital mortality were similar in each group (hydroxyethyl starch: 35% and 43%; gelatin: 26% and 31%; crystalloids: 30% and 37%). Conclusion: Fluid resuscitation with only crystalloids was equally effective, resulted in a more positive fluid balance only on the first 2 days, and was associated with a lesser incidence of acute kidney injury.

Perioperative fluid therapy with tetrastarch and gelatin in cardiac surgery--a prospective sequential analysis*.

Objectives:To determine clinical outcomes of synthetic colloids, tetrastarch, and gelatin, used before and after cardiac surgery. Design:Prospective observational cohort study. Setting:Fifty-bed cardiac ICU. Patients:Six thousand four hundred seventy-eight consecutive patients with cardiopulmonary bypass surgery. Interventions:Fluid therapy in the operating room and on the ICU directed at preset hemodynamic goals: 1) hydroxyethyl starch (predominantly 6% hydroxyethyl starch 130/0.4) in 2004–2006, n = 2,137; 2) 4% gelatin in 2006–2008, n = 2,324; and 3) only crystalloids in 2008–2010, n = 2,017. Measurements and Main Results:Renal replacement therapy was more common during periods when patients received synthetic colloids compared to only crystalloids. Risk of renal replacement therapy was greater after hydroxyethyl starch (odds ratio, 2.29; 95% CI, 1.47–3.60) and gelatin (odds ratio, 2.75; 95% CI, 1.84–4.16; both p < 0.001) compared to crystalloid. Propensity score stratification confirmed greater use of renal replacement therapy in the hydroxyethyl starch and gelatin periods compared to the crystalloid period (odds ratio, 1.46 [1.08, 1.97]; p = 0.013 and odds ratio, 1.72 [1.33, 2.24]; p < 0.001, respectively). Time to vasopressor cessation, normalization of serum lactate, and mean arterial pressure did not differ among groups. Total fluid requirement was 163 mL/kg in the hydroxyethyl starch period, 207 mL/kg in the gelatin period, and 224 mL/kg in the crystalloid period. Fluid intake was higher in the crystalloid group only during the first 20 hours. Conclusions:In cardiac surgery patients, fluid therapy with perioperative administration of synthetic colloids carries a high risk of renal replacement therapy and is not more effective than treating with only crystalloids.

Extravasation injury in the perioperative setting.

Extravasation is an unintentional injection or leakage of fluid in the perivascular or subcutaneous space. Extravasation injury results from a combination of factors, including solution cytotoxicity, osmolality, vasoconstrictor properties, infusion pressure, regional anatomical peculiarities, and other patient factors. We reviewed the hospital files of patients who had sustained a significant extravasation injury in the perioperative setting at two German hospitals. These cases highlight the risk of devastating consequences from extravasation injury. Vasoactive drugs and hyperosmolar and concentrated electrolyte solutions are the predominant vesicants in the perioperative setting. Prompt and appropriate intervention is important for avoiding or minimizing extensive tissue injury.

A comparative study of four intensive care outcome prediction models in cardiac surgery patients

BackgroundOutcome prediction scoring systems are increasingly used in intensive care medicine, but most were not developed for use in cardiac surgery patients. We compared the performance of four intensive care outcome prediction scoring systems (Acute Physiology and Chronic Health Evaluation II [APACHE II], Simplified Acute Physiology Score II [SAPS II], Sequential Organ Failure Assessment [SOFA], and Cardiac Surgery Score [CASUS]) in patients after open heart surgery.MethodsWe prospectively included all consecutive adult patients who underwent open heart surgery and were admitted to the intensive care unit (ICU) between January 1st 2007 and December 31st 2008. Scores were calculated daily from ICU admission until discharge. The outcome measure was ICU mortality. The performance of the four scores was assessed by calibration and discrimination statistics. Derived variables (Mean- and Max- scores) were also evaluated.ResultsDuring the study period, 2801 patients (29.6% female) were included. Mean age was 66.9 ± 10.7 years and the ICU mortality rate was 5.2%. Calibration tests for SOFA and CASUS were reliable throughout (p-value not < 0.05), but there were significant differences between predicted and observed outcome for SAPS II (days 1, 2, 3 and 5) and APACHE II (days 2 and 3). CASUS, and its mean- and maximum-derivatives, discriminated better between survivors and non-survivors than the other scores throughout the study (area under curve ≥ 0.90). In order of best discrimination, CASUS was followed by SOFA, then SAPS II, and finally APACHE II. SAPS II and APACHE II derivatives had discrimination results that were superior to those of the SOFA derivatives.ConclusionsCASUS and SOFA are reliable ICU mortality risk stratification models for cardiac surgery patients. SAPS II and APACHE II did not perform well in terms of calibration and discrimination statistics.

An Early Warning Scoring System to Identify Septic Patients in the Prehospital Setting: The PRESEP Score

OBJECTIVES The objective was to develop and evaluate an early sepsis detection score for the prehospital setting. METHODS A retrospective analysis of consecutive patients who were admitted by emergency medical services (EMS) to the emergency department of the Jena University Hospital was performed. Because potential predictors for sepsis should be based on consensus criteria, the following parameters were extracted from the EMS protocol for further analysis: temperature, heart rate (HR), respiratory rate (RR), oxygen saturation (SaO2 ), Glasgow Coma Scale score, blood glucose, and systolic blood pressure (sBP). Potential predictors were stratified based on inspection of Loess graphs. Backward model selection was performed to select risk factors for the final model. The Prehospital Early Sepsis Detection (PRESEP) score was calculated as the sum of simplified regression weights. Its predictive validity was compared to the Modified Early Warning Score (MEWS), the Robson screening tool, and the BAS 90-30-90. RESULTS A total of 375 patients were included in the derivation sample; 93 (24.8%) of these had sepsis, including 60 patients with severe sepsis and 12 patients with septic shock. Backward model selection identified temperature, HR, RR, SaO2 , and sBP for inclusion in the PRESEP score. Simplified weights were as follows: temperature > 38°C = 4, temperature < 36°C = 1, HR > 90 beats/min = 2, RR > 22 breaths/min = 1, SaO2 < 92% = 2, and sBP < 90 mm Hg = 2. The cutoff value for a possible existing septic disease based on maximum Youdens index was ≥4 (sensitivity 0.85, specificity 0.86, positive predictive value [PPV] 0.66, and negative predictive value [NPV] 0.95). The area under the receiver operating characteristic curve (AUC) of the PRESEP score was 0.93 (95% confidence interval [CI] = 0.89 to 0.96) and was larger than the AUC of the MEWS (0.93 vs. 0.77, p < 0.001). The PRESEP score surpassed MEWS and BAS 90-60-90 for sensitivity (0.74 and 0.62, respectively), specificity (0.75 and 0.83), PPV (0.45 and 0.51), and NPV (0.91 and 0.89). The Robson screening tool had a higher sensitivity and NPV (0.95 and 0.97), but its specificity and PPV were lower (0.43 and 0.32). CONCLUSIONS The PRESEP score could be a valuable tool for identifying septic patients in the prehospital setting in the case of suspected infection. It should be prospectively validated.

Attributable costs of patients with candidemia and potential implications of polymerase chain reaction–based pathogen detection on antifungal therapy in patients with sepsis

PURPOSE The purposes of this study were to calculate attributable costs of candidemia in patients with severe sepsis and to obtain preliminary data regarding the potential effects of polymerase chain reaction-based pathogen detection on antifungal therapy for these patients. METHODS Patients treated between 2004 and 2010 because of severe sepsis were included into this retrospective analysis. The hospital management provided annual fixed costs per patient-day; data for variable intensive care unit costs were taken from the literature. Multiplex polymerase chain reaction (PCR) was used (VYOO, SIRS-Lab, Jena, Germany) for pathogen detection in the blood. RESULTS Thirty-two patients with candidemia were identified. Of 874 patients with sepsis, propensity score matching found 32 corresponding patients with sepsis but without candida infection but similar risk factors for developing candidemia. Attributable costs of candidemia were 7713.79 Euro (cost increase, 19.4%). Initiation of antifungal therapy was reduced from 67.5 (52.4, 90) hours in the group, where candida infection was determined by blood culture, to 31.0 (28.0, 37.5; P < .01) hours after detection by multiplex PCR. CONCLUSIONS Candidemia increases costs of care in patients with septic shock. Polymerase chain reaction-based pathogen detection significantly reduces the time to initiation of antifungal therapy. This might impact on the clinical course of the disease but need to be confirmed in further trials.

Combination of European System for Cardiac Operative Risk Evaluation (EuroSCORE) and Cardiac Surgery Score (CASUS) to Improve Outcome Prediction in Cardiac Surgery.

Background We hypothesized that the combination of a preoperative and a postoperative scoring system would improve the accuracy of mortality prediction and therefore combined the preoperative ‘additive EuroSCORE‘ (European system for cardiac operative risk evaluation) with the postoperative ‘additive CASUS’ (Cardiac Surgery Score) to form the ‘modified CASUS’. Material/Methods We included all consecutive adult patients after cardiac surgery during January 2007 and December 2010 in our prospective study. Our single-centre study was conducted in a German general referral university hospital. The original additive and the ‘modified CASUS’ were tested using calibration and discrimination statistics. We compared the area under the curve (AUC) of the receiver characteristic curves (ROC) by DeLong’s method and calculated overall correct classification (OCC) values. Results The mean age among the total of 5207 patients was 67.2±10.9 years. Whilst the ICU mortality was 5.9% we observed a mean length of ICU stay of 4.6±7.0 days. Both models demonstrated excellent discriminatory power (mean AUC of ‘modified CASUS’: ≥0.929; ‘additive CASUS’: ≥0.920), with no significant differences according to DeLong. Neither model showed a significant p-value (<0.05) in calibration. We detected the best OCC during the 2nd day (modified: 96.5%; original: 96.6%). Conclusions Our ‘additive’ and ‘modified’ CASUS are reasonable overall predictors. We could not detect any improvement in the accuracy of mortality prediction in cardiac surgery by combining a preoperative and a postoperative scoring system. A separate calculation of the two individual elements is therefore recommended.

Inclusion of 'ICU-Day' in a Logistic Scoring System Improves Mortality Prediction in Cardiac Surgery.

Background Prolonged intensive care unit (ICU) stay is a predictor of mortality. The length of ICU stay has never been considered as a variable in an additive scoring system. How could this variable be integrated into a scoring system? Does this integration improve mortality prediction? Material/Methods The ‘modified CArdiac SUrgery Score’ (CASUS) was generated by implementing the length of stay as a new variable to the ‘additive CASUS’. The ‘logistic CASUS’ already considers this variable. We defined outcome as ICU mortality and statistically compared the three CASUS models. Discrimination, comparison of receiver operating characteristic curves (DeLong’s method), and calibration (observed/expected ratio) were analyzed on days 1–13. Results Between 2007 and 2010, we included 5207 cardiac surgery patients in this prospective study. The mean age was 67.2±10.9 years. The mean length of ICU stay was 4.6±7.0 days and ICU mortality was 5.9%. All scores had good discrimination, with a mean area under the curve of 0.883 for the additive and modified, and 0.895 for the ‘logistic CASUS’. DeLong analysis showed superiority in favor of the logistic model as from day 5. The calibration of the logistic model was good. We identified overestimation (days 1–5) and accurate (days 6–9) calibration for the additive and ‘modified CASUS’. The ‘modified CASUS’ remained accurate but the ‘additive CASUS’ tended to underestimate the risk of mortality (days 10–13). Conclusions The integration of length of ICU stay as a variable improves mortality prediction significantly. An ‘ICU-day’ variable should be included into a logistic but not an additive model.