Improving Decision Making in Intensive Care

Many decisions are made during a day’s work in critical care. Should this octogenarian with pneumonia and cancer be admitted to the ICU or left on the ward with palliative care? And if admitted to the ICU, will she benefit from being ventilated or should she only be treated with antibiotics and inotropes? How long should we continue administrating antibiotics in a patient with peritonitis due to anastomotic leakage after low anterior resection? Will antibiotics do the job or does he have to go back to the operating theatre? Should we give more fluids in a patient with shock, should we start vasoconstrictors or vasodilators or should we accept this low blood pressure? Continue treatment with a curative intent or accept the inevitable? Act on a laboratory result or stop ‘just treating the numbers’

In Critically Ill Patients, Serum Procalcitonin Is More Useful in Differentiating between Sepsis and SIRS than CRP, Il-6, or LBP

We studied the usefulness of serum procalcitonin (PCT), interleukin-6 (IL-6), lipopolysaccharide binding protein (LBP) levels and C-reactive protein (CRP) levels, in differentiating between systemic inflammatory response syndrome (SIRS) and sepsis in critically ill patients. Methods. In this single centre prospective observational study we included all consecutive patients admitted with SIRS or sepsis to the ICU. Blood samples for measuring CRP, PCT, IL-6 and LBP were taken every day until ICU discharge. Results. A total of 76 patients were included, 32 with sepsis and 44 with SIRS. Patients with sepsis were sicker on admission and had a higher mortality. CRP, PCT, IL-6 and LBP levels were significantly higher in patients with sepsis as compared to SIRS. With PCT levels in the first 24 hours after ICU admission <2 ng/mL, sepsis was virtually excluded (negative predictive value 97%). With PCT >10 ng/mL, sepsis with bacterial infection was very likely (positive predictive value 88%). PCT was best at discriminating between SIRS and sepsis with the highest area under the ROC curve (0.95, 95% CI 0.90–0.99). Discussion. This study showed that PCT is more useful than LBP, CRP and IL-6 in differentiating sepsis from SIRS

Off hour admission to an intensivist-led ICU is not associated with increased mortality

Introduction: Caring for the critically ill is a 24-hour-a-day responsibility, but not all resources and staff are available during off hours. We evaluated whether intensive care unit (ICU) admission during off hours affects hospital mortality. Methods: This retrospective multicentre cohort study was carried out in three non-academic teaching hospitals in the Netherlands. All consecutive patients admitted to the three ICUs between 2004 and 2007 were included in the study, except for patients who did not fulfil APACHE II criteria (readmissions, burns, cardiac surgery, younger than 16 years, length of stay less than 8 hours). Data were collected prospectively in the ICU databases. Hospital mortality was the primary endpoint of the study. Off hours was defined as the interval between 10 pm and 8 am during weekdays and between 6 pm and 9 am during weekends. Intensivists, with no responsibilities outside the ICU, were present in the ICU during daytime and available for either consultation or assistance on site during off hours. Residents were available 24 hours a day 7 days a week in two and fellows in one of the ICUs. Results: A total of 6725 patients were included in the study, 4553 (67.7%) admitted during daytime and 2172 (32.3%) admitted during off hours. Baseline characteristics of patients admitted during daytime were significantly different from those of patients admitted during off hours. Hospital mortality was 767 (16.8%) in patients admitted during daytime and 469 (21.6%) in patients admitted during off hours (P < 0.001, unadjusted odds ratio 1.36, 95%CI 1.20-1.55). Standardized mortality ratios were similar for patients admitted during off hours and patients admitted during daytime. In a logistic regression model APACHE II expected mortality, age and admission type were all significant confounders but off-hours admission was not significantly associated with a higher mortality (P = 0.121, adjusted odds ratio 1.125, 95%CI 0.969-1.306). Conclusions: The increased mortality after ICU admission during off hours is explained by a higher illness severity in patients admitted during off hours

Hospital mortality is associated with ICU admission time

Previous studies have shown that patients admitted to the intensive care unit (ICU) after "office hours" are more likely to die. However these results have been challenged by numerous other studies. We therefore analysed this possible relationship between ICU admission time and in-hospital mortality in The Netherlands. This article relates time of ICU admission to hospital mortality for all patients who were included in the Dutch national ICU registry (National Intensive Care Evaluation, NICE) from 2002 to 2008. We defined office hours as 08:00-22:00 hours during weekdays and 09:00-18:00 hours during weekend days. The weekend was defined as from Saturday 00:00 hours until Sunday 24:00 hours. We corrected hospital mortality for illness severity at admission using Acute Physiology and Chronic Health Evaluation II (APACHE II) score, reason for admission, admission type, age and gender. A total of 149,894 patients were included in this analysis. The relative risk (RR) for mortality outside office hours was 1.059 (1.031-1.088). Mortality varied with time but was consistently higher than expected during "off hours" and lower during office hours. There was no significant difference in mortality between different weekdays of Monday to Thursday, but mortality increased slightly on Friday (RR 1.046; 1.001-1.092). During the weekend the RR was 1.103 (1.071-1.136) in comparison with the rest of the week. Hospital mortality in The Netherlands appears to be increased outside office hours and during the weekends, even when corrected for illness severity at admission. However, incomplete adjustment for certain confounders might still play an important role. Further research is needed to fully explain this differenc

Computer-assisted glucose control in critically ill patients

Objective: Intensive insulin therapy is associated with the risk of hypoglycemia and increased costs of material and personnel. We therefore evaluated the safety and efficiency of a computer-assisted glucose control protocol in a large population of critically ill patients. Design and setting: Observational cohort study in three intensive care units (32 beds) in a 1,300-bed university teaching hospital. Patients: All 2,800 patients admitted to the surgical, neurosurgical, and cardiothoracic units; the study period started at each ICU after implementation of Glucose Regulation for Intensive Care Patients (GRIP), a freely available computer-assisted glucose control protocol. Measurements and results: We analysed compliance in relation to recommended insulin pump rates and glucose measurement frequency. Patients were on GRIP-ordered pump rates 97% of time. Median measurement time was 5 min late (IQR 20 min early to 34 min late). Hypoglycemia was uncommon (7% of patients for mild hypoglycemia, <3.5 mmol/l; 0.86% for severe hypoglycemia, <2.2 mmol/l). Our predefined target range (4.0 - 7.5 mmol/l) was reached after a median of 5.6h (IQR 0.2 - 11.8) and maintained for 89% (70 - 100%) of the remaining stay at the ICU. The number of measurements needed was 5.9 (4.8 - 7.3) per patient per day. In-hospital mortality was 10.1%. Conclusions: Our computer-assisted glucose control protocol provides safe and efficient glucose regulation in routine intensive care practice. A low rate of hypoglycemic episodes was achieved with a considerably lower number of glucose measurements than used in most other schemes

Blood glucose amplitude variability as predictor for mortality in surgical and medical intensive care unit patients: a multicenter cohort study

Purpose: The aim of this study was to test the hypothesis that blood glucose amplitude variability (BGAV) is associated with mortality in critically ill patients. Method: A prospectively collected multicenter data set including all glucose measurements during intensive care unit (ICU) treatment and outcome was analyzed. We used logistic regression to assess the association between hospital mortality and standard deviation (SD), mean amplitude of glycemic excursions (MAGE), mean absolute glucose change per hour (MAG), and glycemic lability index (GLI). The analysis was adjusted for ICU, Acute Physiology And Chronic Health Evaluation IV-expected mortality, the presence of severe hypoglycemia, mean glucose, mean glucose measurement interval, and interaction between the latter 2. Results: There were 855 032 glucose measurements included of 20 375 patients admitted to 37 Dutch ICUs in 2008 and 2009. Median Acute Physiology And Chronic Health Evaluation IV-predicted mortality was 14%, and median glucose was 7.3 mmol/L. In all patients combined, adjusted hospital mortality was associated with SD and MAGE, but not with MAG and GLI. In surgical patients, adjusted hospital mortality was associated with SD, MAGE, and MAG, but not GLI. In medical patients, adjusted mortality was associated with SD but not with other BGAV measures. Conclusion: Not all BGAV measures were associated with mortality. Blood glucose amplitude variability as quantified by SD was consistently independently associated with hospital mortality. (C) 2012 Elsevier Inc. All rights reserve