Health Economic Evaluation of a Strict Glucose Control Guideline Implemented Using Point-of-Care Testing in Three Intensive Care Units in The Netherlands

Background Point-of-care testing of blood glucose (BG-POCT) is essential for safe and effective insulin titrations in critically ill patients under glucose control with insulin. The costs associated with this practice are considered substantial, especially when more frequent blood glucose (BG) testing is needed, as with more strict glucose control (SGC) aiming for lower BG levels. Objective The objective of this study was to estimate, from a hospital perspective, the incremental cost effectiveness of an SGC guideline, aiming for BG levels of 4.4–6.1 mmol/L, compared to the situation before implementation of that guideline (aiming for BG levels <8.3 mmol/L), both using BG–POCT. Methods This is a secondary analysis of a guideline implementation project aiming for implementation of a guideline of SGC in three intensive care units in The Netherlands. A Markov model including the four health states ‘target glucose’, ‘hyperglycaemia’ (defined as BG levels >6.1 mmol/L), ‘hypoglycaemia’ (defined as BG levels <4.4 mmol/L) and ‘in-hospital death’ was developed to compare expected costs, number of patients within target and number of life-years saved before and after implementation of the SGC guideline. The effectiveness estimates are based on empirical data from 3195 patients 12 and 24 months before and after implementation of the guideline, respectively. All costs have been converted to price year 2013, and are estimated based on hospital data, the literature and available price lists. Results The number of BG–POCT increased from 4.8 [interquartile range (IQR) 2.6–6.7] to 8.0 [IQR 4.1–11.2] per patient per day, accruing 58 % higher costs for BG–POCT (€13.56 vs. €8.57 per patient) in the SGC protocol versus the situation before implementation. When taking total hospital costs and clinical effects into account, implementation of the SGC guideline increased total hospital costs per patient by 1.8 %, i.e. €355 (from €20,617 to €20,972) during the inpatient stay, while the number of patients in target glucose levels increased by 1.4 % (i.e. from 881 to 895 per 1000 patients). This translates to an incremental cost-effectiveness ratio of €25 per additional patient within the target glucose level. The model outcomes are most sensitive to changes in ICU length of stay. Conclusion The increase in the number of patients and time within target glucose levels is achieved with a small increase in total direct hospital cost

Effect of cytomegalovirus reactivation on the time course of systemic host response biomarkers in previously immunocompetent critically ill patients with sepsis : A matched cohort study

Background: Cytomegalovirus (CMV) reactivation in previously immunocompetent critically ill patients is associated with increased mortality, which has been hypothesized to result from virus-induced immunomodulation. Therefore, we studied the effects of CMV reactivation on the temporal course of host response biomarkers in patients with sepsis. Methods: In this matched cohort study, each sepsis patient developing CMV reactivation between day 3 and 17 (CMV+) was compared with one CMV seropositive patient without reactivation (CMVs+) and one CMV seronegative patient (CMVs-). CMV serostatus and plasma loads were determined by enzyme-linked immunoassays and real-time polymerase chain reaction, respectively. Systemic interleukin-6 (IL-6), IL-8, IL-18, interferon-gamma-induced protein-10 (IP-10), neutrophilic elastase, IL-1 receptor antagonist (RA), and IL-10 were measured at five time points by multiplex immunoassay. The effects of CMV reactivation on sequential concentrations of these biomarkers were assessed in multivariable mixed models. Results: Among 64 CMV+ patients, 45 could be matched to CMVs+ or CMVs- controls or both. The two baseline characteristics and host response biomarker levels at viremia onset were similar between groups. CMV+ patients had increased IP-10 on day 7 after viremia onset (symmetric percentage difference +44% versus -15% when compared with CMVs+ and +37% versus +4% when compared with CMVs-) and decreased IL-1RA (-41% versus 0% and -49% versus +10%, respectively). However, multivariable analyses did not show an independent association between CMV reactivation and time trends of IL-6, IP-10, IL-10, or IL-1RA. Conclusion: CMV reactivation was not independently associated with changes in the temporal trends of host response biomarkers in comparison with non-reactivating patients. Therefore, these markers should not be used as surrogate clinical endpoints for interventional studies evaluating anti-CMV therapy

Effect of cytomegalovirus reactivation on the time course of systemic host response biomarkers in previously immunocompetent critically ill patients with sepsis : A matched cohort study

Background: Cytomegalovirus (CMV) reactivation in previously immunocompetent critically ill patients is associated with increased mortality, which has been hypothesized to result from virus-induced immunomodulation. Therefore, we studied the effects of CMV reactivation on the temporal course of host response biomarkers in patients with sepsis. Methods: In this matched cohort study, each sepsis patient developing CMV reactivation between day 3 and 17 (CMV+) was compared with one CMV seropositive patient without reactivation (CMVs+) and one CMV seronegative patient (CMVs-). CMV serostatus and plasma loads were determined by enzyme-linked immunoassays and real-time polymerase chain reaction, respectively. Systemic interleukin-6 (IL-6), IL-8, IL-18, interferon-gamma-induced protein-10 (IP-10), neutrophilic elastase, IL-1 receptor antagonist (RA), and IL-10 were measured at five time points by multiplex immunoassay. The effects of CMV reactivation on sequential concentrations of these biomarkers were assessed in multivariable mixed models. Results: Among 64 CMV+ patients, 45 could be matched to CMVs+ or CMVs- controls or both. The two baseline characteristics and host response biomarker levels at viremia onset were similar between groups. CMV+ patients had increased IP-10 on day 7 after viremia onset (symmetric percentage difference +44% versus -15% when compared with CMVs+ and +37% versus +4% when compared with CMVs-) and decreased IL-1RA (-41% versus 0% and -49% versus +10%, respectively). However, multivariable analyses did not show an independent association between CMV reactivation and time trends of IL-6, IP-10, IL-10, or IL-1RA. Conclusion: CMV reactivation was not independently associated with changes in the temporal trends of host response biomarkers in comparison with non-reactivating patients. Therefore, these markers should not be used as surrogate clinical endpoints for interventional studies evaluating anti-CMV therapy

Iron metabolism in critically ill patients developing anemia of inflammation : a case control study

Background: Anemia occurring as a result of inflammatory processes (anemia of inflammation, AI) has a high prevalence in critically ill patients. Knowledge on changes in iron metabolism during the course of AI is limited, hampering the development of strategies to counteract AI. This case control study aimed to investigate iron metabolism during the development of AI in critically ill patients. Methods: Iron metabolism in 30 patients who developed AI during ICU stay was compared with 30 septic patients with a high Hb and 30 non-septic patients with a high Hb. Patients were matched on age and sex. Longitudinally collected plasma samples were analyzed for levels of parameters of iron metabolism. A linear mixed model was used to assess the predictive values of the parameters. Results: In patients with AI, levels of iron, transferrin and transferrin saturation showed an early decrease compared to controls with a high Hb, already prior to the development of anemia. Ferritin, hepcidin and IL-6 levels were increased in AI compared to controls. During AI development, erythroferrone decreased. Differences in iron metabolism between groups were not influenced by APACHE IV score. Conclusions: The results show that in critically ill patients with AI, iron metabolism is already altered prior to the development of anemia. Levels of iron regulators in AI differ from septic controls with a high Hb, irrespective of disease severity. AI is characterized by high levels of hepcidin, ferritin and IL-6 and low levels of iron, transferrin and erythroferrone