Evert de Jonge

A recent paper by Taccone and coworkers showed that 15% of patients from 198 European intensive care units (ICUs) had a malignancy, mostly solid tumors but also hematological malignancies. Over the past years, the prognosis of cancer patients has improved significantly, even when ICU admission is necessary. Refusal of ICU admission should not be based on a diagnosis of cancer as the underlying condition. In contrast, these decisions should be based on the availability of treatment options, and on patients' own preferences.

Introduction: To systematically review studies evaluating the performance of Sequential Organ Failure Assessment (SOFA)-based models for predicting mortality in patients in the intensive care unit (ICU). Methods: Medline, EMBASE and other databases were searched for English-language articles with the major objective of evaluating the prognostic performance of SOFA-based models in predicting mortality in surgical and/or medical ICU admissions. The quality of each study was assessed based on a quality framework for prognostic models. Results: Eighteen articles met all inclusion criteria. The studies differed widely in the SOFA derivatives used and in their methods of evaluation. Ten studies reported about developing a probabilistic prognostic model, only five of which used an independent validation data set. The other studies used the SOFA-based score directly to discriminate between survivors and non-survivors without fitting a probabilistic model. In five of the six studies, admission-based models (Acute Physiology and Chronic Health Evaluation (APACHE) II/III) were reported to have a slightly better discrimination ability than SOFA-based models at admission (the receiver operating characteristic curve (AUC) of SOFA-based models ranged between 0.61 and 0.88), and in one study a SOFA model had higher AUC than the Simplified Acute Physiology Score (SAPS) II model. Four of these studies used the Hosmer-Lemeshow tests for calibration, none of which reported a lack of fit for the SOFA models. Models based on sequential SOFA scores were described in 11 studies including maximum SOFA scores and maximum sum of individual components of the SOFA score (AUC range: 0.69 to 0.92) and delta SOFA (AUC range: 0.51 to 0.83). Studies comparing SOFA with other organ failure scores did not consistently show superiority of one scoring system to another. Four studies combined SOFA-based derivatives with admission severity of illness scores, and they all reported on improved predictions for the combination. Quality of studies ranged from 11.5 to 19.5 points on a 20-point scale. Conclusions: Models based on SOFA scores at admission had only slightly worse performance than APACHE II/III and were competitive with SAPS II models in predicting mortality in patients in the general medical and/or surgical ICU. Models with sequential SOFA scores seem to have a comparable performance with other organ failure scores. The combination of sequential SOFA derivatives with APACHE II/III and SAPS II models clearly improved prognostic performance of either model alone. Due to the heterogeneity of the studies, it is impossible to draw general conclusions on the optimal mathematical model and optimal derivatives of SOFA scores. Future studies should use a standard evaluation methodology with a standard set of outcome measures covering discrimination, calibration and accuracy.

Introduction: The aim of this study was to investigate whether in-hospital mortality was associated with the administered fraction of oxygen in inspired air (FiO₂) and achieved arterial partial pressure of oxygen (PaO₂). Methods: This was a retrospective, observational study on data from the first 24 h after admission from 36,307 consecutive patients admitted to 50 Dutch intensive care units (ICUs) and treated with mechanical ventilation. Oxygenation data from all admission days were analysed in a subset of 3,322 patients in 5 ICUs. Results: Mean PaO₂ and FiO₂ in the first 24 h after ICU admission were 13.2 kPa (standard deviation (SD) 6.5) and 50% (SD 20%) respectively. Mean PaO₂ and FiO₂ from all admission days were 12.4 kPa (SD 5.5) and 53% (SD 18). Focusing on oxygenation in the first 24 h of admission, in-hospital mortality was shown to be linearly related to FiO₂ value and had a U-shaped relationship with PaO₂ (both lower and higher PaO₂ values were associated with a higher mortality), independent of each other and of Simplified Acute Physiology Score (SAPS) II, age, admission type, reduced Glasgow Coma Scale (GCS) score, and individual ICU. Focusing on the entire ICU stay, in-hospital mortality was independently associated with mean FiO₂ during ICU stay and with the lower two quintiles of mean PaO₂ value during ICU stay. Conclusions: Actually achieved PaO₂ values in ICU patients in The Netherlands are higher than generally recommended in the literature. High FiO₂, and both low PaO₂ and high PaO₂ in the first 24 h after admission are independently associated with in-hospital mortality in ICU patients. Future research should study whether this association is causal or merely a reflection of differences in severity of illness insufficiently corrected for in the multivariate analysis.

Introduction: The objectives of this study were to systematically identify and summarize quality indicators of tight glycaemic control in critically ill patients, and to inspect the applicability of their definitions. Methods: We searched in MEDLINE^® for all studies evaluating a tight glycaemic control protocol and/or quality of glucose control that reported original data from a clinical trial or observational study on critically ill adult patients. Results: Forty-nine studies met the inclusion criteria; 30 different indicators were extracted and categorized into four nonorthogonal categories: blood glucose zones (for example, 'hypoglycaemia'); blood glucose levels (for example, 'mean blood glucose level'); time intervals (for example, 'time to occurrence of an event'); and protocol characteristics (for example, 'blood glucose sampling frequency'). Hypoglycaemia-related indicators were used in 43 out of 49 studies, acting as a proxy for safety, but they employed many different definitions. Blood glucose level summaries were used in 41 out of 49 studies, reported as means and/or medians during the study period or at a certain time point (for example, the morning blood glucose level or blood glucose level upon starting insulin therapy). Time spent in the predefined blood glucose level range, time needed to reach the defined blood glucose level target, hyperglycaemia-related indicators and protocol-related indicators were other frequently used indicators. Most indicators differ in their definitions even when they are meant to measure the same underlying concept. More importantly, many definitions are not precise, prohibiting their applicability and hence the reproducibility and comparability of research results. Conclusions: An unambiguous indicator reference subset is necessary. The result of this systematic review can be used as a starting point from which to develop a standard list of well defined indicators that are associated with clinical outcomes or that concur with clinicians' subjective views on the quality of the regulatory process.

Introduction: The aim of the study was to assess the influence of annual volume and factors related to intensive care unit (ICU) organization on in-hospital mortality among patients admitted to the ICU with severe sepsis. Methods: A retrospective cohort study was conducted using the database of the Dutch National Intensive Care Evaluation (NICE) registry. Analyses were based on consecutive patients admitted between 1 January 2003 and 30 June 2005 who fulfilled criteria for severe sepsis within the first 24 hours of admission. A 13-item questionnaire was sent to all 32 ICUs across The Netherlands that participated in the NICE registry within this period in order to obtain information on ICU organization and staffing. The association between in-hospital mortality and factors related to ICU organization was investigated using logistic regression analysis, combined with generalized estimation equations to account for potential correlations of outcomes within ICUs. Correction for patient-related factors took place by including Simplified Acute Physiology Score II, age, sex and number of dysfunctioning organ systems in the analyses. Results: Analyses based on 4,605 patients from 28 ICUs (questionnaire response rate 90.6%) revealed that a higher annual volume of severe sepsis patients is associated with a lower in-hospital mortality (P = 0.029). The presence of a medium care unit (MCU) as a step-down facility with intermediate care is associated with a higher in-hospital mortality (P = 0.013). For other items regarding ICU organization, no independent significant relationships with in-hospital mortality were found. Conclusion: A larger annual volume of patients with severe sepsis admitted to Dutch ICUs is associated with lower in-hospital mortality in this patient group. The presence of a MCU as a step-down facility is associated with greater in-hospital mortality. No other significant associations between in-hospital mortality and factors related to ICU organization were found.

Placement of a central venous catheter is one of the most common invasive procedures and is associated with septic and mechanical complications, such as bleeding and pneumothorax. Up to 30% of attempts to cannulate the central vein fail. Correct positioning of the patient can help to maximise the success rate. For placement of catheters in the subclavian vein, patients should be in the Trendelenburg position without the use of a shoulder roll to retract the shoulders. Traditionally, central venous catheters are placed using a 'blind' technique that relies on external anatomical reference marks to localise the vein. However, unnoticed anatomical variations or central venous thrombosis may contribute to cannulation failure with this technique. The use of ultrasound has been shown to increase the success rate and avoid mechanical complications when placing a catheter in the internal jugular vein. It may also increase the success rate in subclavian vein catheterisation. To increase patient safety, the use of ultrasound when placing a central venous catheter should be embraced and become the standard of care.

Introduction: The mechanism of coagulation activation during continuous venovenous hemofiltration (CVVH) has not yet been elucidated. Insight into the mechanism(s) of hemostatic activation within the extracorporeal circuit could result in a more rational approach to anticoagulation. The aim of the present study was to investigate whether CVVH using cellulose triacetate filters causes activation of the contact factor pathway or of the tissue factor pathway of coagulation. In contrast to previous studies, CVVH was performed without anticoagulation. Methods: Ten critically ill patients were studied prior to the start of CVVH and at 5, 15 and 30 minutes and 1, 2, 3 and 6 hours thereafter, for measurement of prothrombin fragment F1+2, soluble tissue factor, activated factor VII, tissue factor pathway inhibitor, kallikrein–C1-inhibitor and activated factor XII–C1-inhibitor complexes, tissue-type plasminogen activator, plasminogen activator inhibitor type I, plasmin–antiplasmin complexes, protein C and antithrombin. Results: During the study period the prothrombin fragment F1+2 levels increased significantly in four patients (defined as group A) and did not change in six patients (defined as group B). Group A also showed a rapid increase in transmembrane pressure, indicating clotting within the filter. At baseline, the activated partial thromboplastin time, the prothrombin time and the kallikrein–C1-inhibitor complex and activated factor XII–C1-inhibitor complex levels were significantly higher in group B, whereas the platelet count was significantly lower in group B. For the other studied markers the differences between group A and group B at baseline were not statistically significant. During CVVH the difference in the time course between group A and group B was not statistically significant for the markers of the tissue factor system (soluble tissue factor, activated factor VII and tissue factor pathway inhibitor), for the markers of the contact system (kallikrein–C1-inhibitor and activated factor XII–C1-inhibitor complexes) and for the markers of the fibrinolytic system (plasmin–antiplasmin complexes, tissue-type plasminogen activator and plasminogen activator inhibitor type I). Conclusion: Early thrombin generation was detected in a minority of intensive care patients receiving CVVH without anticoagulation. Systemic concentrations of markers of the tissue factor system and of the contact system did not change during CVVH. To elucidate the mechanism of clot formation during CVVH we suggest that future studies are needed that investigate the activation of coagulation directly at the site of the filter. Early coagulation during CVVH may be related to lower baseline levels of markers of contact activation.

Introduction: Recombinant human activated protein C (rhAPC) is the first drug for which a reduction of mortality in severe sepsis has been demonstrated. However, the mechanism by which this reduction in mortality is achieved is still not clearly defined. The aim of the present study was to evaluate the dynamics of the anticoagulant, anti-inflammatory and pro-fibrinolytic action of rhAPC in patients with severe sepsis, by comparing rhAPC-treated patients with case controls. Methods: In this prospectively designed multicenter case control study, 12 patients who were participating in the ENHANCE study, an open-label study of rhAPC in severe sepsis, were treated intravenously with rhAPC at a constant rate of 24 μg/kg/h for a total of 96 h. Twelve controls with severe sepsis matching the inclusion criteria received standard therapy. The treatment was started within 48 h after the onset of organ failure. Blood samples were taken before the start of the infusion and at 4, 8, 24, 48, 96 and 168 h, for determination of parameters of coagulation and inflammation. Results: Sepsis-induced thrombin generation as measured by thrombin-antithrombin complexes and prothrombin fragment F1+2, was reset by rhAPC within the first 8 h of infusion. The administration of rhAPC did not influence parameters of fibrinolysis and inflammation. There was no difference in outcome or occurrence of serious adverse events between the treatment group and the control group. Conclusion: Sepsis-induced thrombin generation in severely septic patients is reset by rhAPC within the first 8 h of infusion without influencing parameters of fibrinolysis and inflammation.

Introduction: Prognostic models, such as the Acute Physiology and Chronic Health Evaluation (APACHE) II or III, the Simplified Acute Physiology Score (SAPS) II, and the Mortality Probability Models (MPM) II were developed to quantify the severity of illness and the likelihood of hospital survival for a general intensive care unit (ICU) population. Little is known about the performance of these models in specific populations, such as patients with cancer. Recently, specific prognostic models have been developed to predict mortality for cancer patients who are admitted to the ICU. The present analysis reviews the performance of general prognostic models and specific models for cancer patients to predict in-hospital mortality after ICU admission. Methods: Studies were identified by searching the Medline databases from 1994 to 2004. We included studies evaluating the performance of mortality prediction models in critically ill cancer patients. Results: Ten studies were identified that evaluated prognostic models in cancer patients. Discrimination between survivors and non-survivors was fair to good, but calibration was insufficient in most studies. General prognostic models uniformly underestimate the likelihood of hospital mortality in oncological patients. Two versions of a specific oncological scoring systems (Intensive Care Mortality Model (ICMM)) were evaluated in five studies and showed better discrimination and calibration than the general prognostic models. Conclusion: General prognostic models generally underestimate the risk of mortality in critically ill cancer patients. Both general prognostic models and specific oncology models may reliably identify subgroups of patients with a very high risk of mortality.

Introduction: Advanced age is thought to be associated with increased mortality in critically ill patients. This report reviews available data on factors that determine outcome, on the value of prognostic models, and on preferences regarding life-sustaining treatments in (very) elderly intensive care unit (ICU) patients. Methods: We searched the Medline database (January 1966 to January 2005) for English language articles. Selected articles were cross-checked for other relevant publications. Results: Mortality rates are higher in elderly ICU patients than in younger patients. However, it is not age per se but associated factors, such as severity of illness and premorbid functional status, that appear to be responsible for the poorer prognosis. Patients' preferences regarding life-sustaining treatments are importantly influenced by the likelihood of a beneficial outcome. Commonly used prognostic models have not been calibrated for use in the very elderly. Furthermore, they do not address long-term survival and functional outcome. Conclusion: We advocate the development of new prognostic models, validated in elderly ICU patients, that predict not only survival but also functional and cognitive status after discharge. Such a model may support informed decision making with respect to patients' preferences.