Statins: a role in infected critically ill patients?

Terblanche and colleagues add to the ongoing controversy over the role, if any, for statins in patients with sepsis. The authors note that statins fail to prevent progression to organ dysfunction in critically ill patients. However, like most publications, the study is retrospective and stimulates the controversy but fails to resolve it. The time has come for robust randomized controlled clinical trials.     

Leptin in sepsis: a well-suited biomarker in critically ill patients?

The value of monitoring serum leptin in critically ill patients is important for early diagnosis and differentiation between sepsis and non-infectious systemic inflammatory response syndrome (SIRS). The early diagnosis of sepsis, the identification of its origin, and an adequate therapeutic management are crucial to overcome sepsis-associated mortality. Cytokine levels are an obvious choice as sepsis markers, since cytokines are key mediators of the inflammatory response to sepsis. Leptin, a hormone mainly generated by adipocytes, acts centrally in the hypothalamus to regulate body weight and energy expenditure. There is, however, strong evidence that leptin is also involved in cell-mediated immunity and cytokine crosstalk. The finding that a serum leptin threshold of 38 μg/l can distinguish between sepsis and non-infectious SIRS (sensitivity 91.2%, specificity 85%) is the major finding in the article by Yousef and colleagues (in this issue). Much remains to be learned about the precise mechanisms by which leptin signaling participates in sepsis and non-infectious SIRS. This knowledge will potentially contribute to new therapeutic approaches.     

Does gender influence outcomes in critically ill patients?

ABSTRACT: Investigators continue to debate whether gender plays any role in patient outcome following injury/critical illness. We submit that age and hormonal milieu at the time of injury, rather than gender, are the critical factors influencing patient outcome under those conditions.     

Inflammation biomarkers and delirium in critically ill patients: new insights?

The pathophysiological mechanism of the serious and frequently occurring disorder delirium is poorly understood. Inflammation and sepsis are known risk factors for ICU delirium and therefore these patients are highly susceptible to delirium. Several studies have been performed to determine which cytokines are most associated with delirium but the results are inconclusive. Also, new biomarkers associated with brain dysfunction and cognitive impairment are still recognized and need to be studied to determine their relation with delirium. In this commentary we address some limitations concerning an interesting new study that warrants directions for future studies.We read with interest the study by Ritter and colleagues in which the relationship between inflammatory biomarkers and the development of delirium was investigated [1]. While delirium in critically ill patients is recognized as a major problem associated with deleterious outcome, the pathophysiology is still poorly understood. Apart from a role in further unraveling pathophysiological pathways in delirium, biomarkers could possibly also be used as diagnostic or prognostic disease markers. This task might prove to be difficult, as delirium is a multifactorial disorder and thus several pathways are probably involved in its development [2]. Studying the underlying mechanism of delirium in a relatively homogeneous study population, as Ritter and colleagues did in 78 inflamed patients [1], could minimize the interaction between different pathways. Patients with systemic inflammation or sepsis are highly vulnerable to developing brain dysfunction and delirium, also defined as sepsis-associated delirium or sepsis-associated encephalopathy [3], and could therefore serve this purpose.Ritter and colleagues studied TNFα, soluble TNF receptor (STNFR)-1, STNFR2, IL-1β, IL-6, IL-10 and adiponectin in systemic inflamed patients in relation to delirium [1]. In their prospective cohort study they found significant associations between STNFR1, STNFR2, IL-1β and adiponectin concentrations and the development of delirium.We previously found that delirium was associated with IL-8 (odds ratio, 9.0; 95% confidence interval, 1.8 to 44.0) and IL-10 (odds ratio, 2.6; 95% confidence interval, 1.1 to 5.9) but not with TNFα in inflamed ICU patients [4]. In contrast, Ritter and colleagues found no association between IL-10 and delirium. This discrepancy might be explained by differences in study design: Ritter and colleagues collected blood samples within 12 hours of ICU admission, while in our study blood was drawn within 24 hours after the onset of delirium regardless of ICU length of stay. In view of the changes over time in the concentration of cytokines and the development of delirium, serial determinations of circulating inflammatory markers and the relationship with the development of delirium would be of great interest, but this has not been carried out in ICU patients up to now.Surprisingly, Ritter and colleagues found no differences in several patient characteristics such as age, severity of illness scores, and duration of mechanical ventilation, but also not in the presence of sepsis, between delirious and nondelirious ICU patients, while these characteristics are clearly recognized as risk factors for delirium [5-7]. A possible explanation for why they did not find these differences could be the frequency of delirium assessment. Due to its fluctuating course, delirium can be missed when patients are assessed with the Confusion Assessment Method for the Intensive Care Unit only twice a day – especially when it is recognized that on average 20% of delirium is missed when using the Confusion Assessment Method for the Intensive Care Unit [8]. This fact may also explain the observed relatively low delirium incidence (39.7%) in these highly susceptible patients suffering from systemic inflammation.Another interesting and very relevant point is that Ritter and colleagues also studied, besides the common proinflammatory and anti-inflammatory cytokines, the hormone adiponectin. Adiponectin was recently determined to interact with the brain [9] and to play a role in neuroprotection and energy expenditure. Levels of adiponectin are elevated in critically ill patients [10] and even higher in delirious ICU patients, as Ritter and colleagues determined [1]. These authors are the first to determine an association between adiponectin levels (adjusted for weight) and delirium.Although not a primary study aim, Ritter and colleagues also determined the accuracy of the prediction of delirium using these biomarkers. Even though IL-β, STNFR1, STNFR2 and adiponectin individually predict delirium moderately well (area under the receiver operating characteristics curve: 0.70 to 0.84), the authors did not mention the overall area under the receiver operating characteristics curve of the model using multivariate regression analysis. Unfortunately the sample size in their study did not allow inclusion of other relevant risk factors. In daily clinical practice, therefore, using a delirium prediction model specifically developed for prediction based on clinical risk factors appears better [11]. Despite these shortcomings, their study was well designed and their findings are very relevant to further progress this field.Currently, the role of biomarkers as Ritter and colleagues determined is, although relevant, still limited for daily clinical practice. Serial measurements of inflammatory markers in ICU patients and further research into the role of adiponectin in the development of delirium warrant further investigation in future studies.     

Hyperglycaemia in critically ill patients: marker or mediator of mortality?

Acute hyperglycaemia has been associated with complications, prolonged intensive care unit and hospital stay, and increased mortality. We made an inventory of the prevalence and prognostic value of hyperglycaemia, and of the effects of glucose control in different groups of critically ill patients. The prevalence of hyperglycaemia in critically ill patients, using stringent criteria, approaches 100%. An unambiguous negative correlation between hyperglycaemia and mortality has been described in various groups of critically ill patients. Although the available evidence remains inconsistent, there appears to be a favourable effect of glucose regulation. This effect on morbidity and mortality depends on patient characteristics. To be able to compare results of future studies involving glucose regulation, better definitions of hyperglycaemia (and consequently of normoglycaemia) and patient populations are needed.     

Unmeasured anions in critically ill patients: can they predict mortality?

OBJECTIVE: To determine whether base excess, base excess caused by unmeasured anions, and anion gap can predict lactate in adult critically ill patients, and also to determine whether acid-base variables can predict mortality in these patients. DESIGN: Retrospective study. SETTING: Adult intensive care unit of tertiary hospital. PATIENTS: Three hundred adult critically ill patients admitted to the intensive care unit. INTERVENTIONS: Retrieval of admission biochemical data from computerized records, quantitative biophysical analysis of data with the Stewart-Figge methodology, and statistical analysis. MEASUREMENTS AND MAIN RESULTS: We measured plasma Na+, K+, Mg2+, Cl-, HCO3-, phosphate, ionized Ca2+, albumin, lactate, and arterial pH and Paco2. All three variables (base excess, base excess caused by unmeasured anions, anion gap) were significantly correlated with lactate (r2 =.21, p <.0001; r2 =.30, p <.0001; and r2 =.31. p <.0001, respectively). Logistic regression analysis showed that the area under the receiver operating characteristic (AUROC) curves had moderate to high accuracy for the prediction of a lactate concentration >5 mmol/L: AUROC curves, 0.86 (95% confidence interval [CI], 0.78-0.94), 0.86 (95% CI, 0.78-0.93), and 0.85 (95% CI, 0.77-0.92), respectively.Logistic regression analysis showed that hospital mortality rate correlated significantly with Acute Physiology and Chronic Health Evaluation (APACHE) II score, anion gap corrected (anion gap corrected by albumin), age, lactate, anion gap, chloride, base excess caused by unmeasured anions, strong ion gap, sodium, bicarbonate, strong ion difference effective, and base excess. However, except for APACHE II score, AUROC curves for mortality prediction were relatively small: 0.78 (95% CI, 0.72-0.84) for APACHE II, 0.66 (95% CI, 0.59-0.73) for lactate, 0.64 (95% CI, 0.57-0.71) for base excess caused by unmeasured anions, and 0.63 (95% CI, 0.56-0.70) for strong ion gap. CONCLUSIONS: Base excess, base excess caused by unmeasured anions, and anion gap are good predictors of hyperlactatemia (>5 mmol/L). Acid-base variables and, specifically, "unmeasured anions" (anion gap, anion gap corrected, base excess caused by unmeasured anions, strong ion gap), irrespective of the methods used to calculate them, are not accurate predictors of hospital mortality rate in critically ill patients.     

Scoring systems in critically ill: Which one to use in cancer patients?

BACKGROUNDScoring systems have not been evaluated in oncology patients. We aimed to assess the performance of Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, APACHE IV, Simplified Acute Physiology Score (SAPS) II, SAPS III, Mortality Probability Model (MPM) II₀ and Sequential Organ Failure Assessment (SOFA) score in critically ill oncology patients.AIMTo compare the efficacy of seven commonly employed scoring systems to predict outcomes of critically ill cancer patients.METHODSWe conducted a retrospective analysis of 400 consecutive cancer patients admitted in the medical intensive care unit over a two-year period. Primary outcome was hospital mortality and the secondary outcome measure was comparison of various scoring systems in predicting hospital mortality. RESULTSIn our study, the overall intensive care unit and hospital mortality was 43.5% and 57.8%, respectively. All of the seven tested scores underestimated mortality. The mortality as predicted by MPM II₀ predicted death rate (PDR) was nearest to the actual mortality followed by that predicted by APACHE II, with a standardized mortality rate (SMR) of 1.305 and 1.547, respectively. The best calibration was shown by the APACHE III score (χ² = 4.704, P = 0.788). On the other hand, SOFA score (χ² = 15.966, P = 0.025) had the worst calibration, although the difference was not statistically significant. All of the seven scores had acceptable discrimination with good efficacy however, SAPS III PDR and MPM II₀ PDR (AUROC = 0.762), had a better performance as compared to others. The correlation between the different scoring systems was significant (P < 0.001).CONCLUSIONAll the severity scores were tested under-predicted mortality in the present study. As the difference in efficacy and performance was not statistically significant, the choice of scoring system used may depend on the ease of use and local preferences.     

Transfusion trigger in critically ill patients: has the puzzle been completed?

In stable critically ill children, the adoption of a restrictive transfusion strategy based on a predefined hemoglobin threshold of 7 g/dl significantly decreased transfusion requirements without affecting outcome. These results strengthen previous observations made in volume resuscitated adults when a similar blood transfusion strategy was used. It also indirectly corroborates studies reporting the beneficial effects of leukoreduction of red blood cell (RBC) transfusion units on patient outcome. This study indicated that the maintenance of a higher hemoglobin concentration with RBC transfusion in an attempt to increase tissue oxygen delivery is not associated with a clinical benefit. This may be related to the storage process, which could affect the ability of RBCs to transport and deliver oxygen to the tissues. This point, however, remains controversial. It should also be remembered that increasing hemoglobin concentration will not always result in a greater oxygen delivery, as transfusion related increased blood viscosity could be associated with a reduction in blood flow. Further research should compare a symptomatic transfusion strategy to a hemoglobin-based strategy on the outcome of high risk patients.     

Adrenal function in critically ill patients: how to test? When to treat?

Although the true incidence of adrenal insufficiency in critically ill patients is unknown, there is evidence that even partial adrenal insufficiency in such patients is associated with increased mortality. But exactly how should adrenal insufficiency be defined and diagnosed, and who should receive treatment?