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Objectives: Prolonged emergency department to ICU waiting time may delay intensive care treatment, which could negatively affect patient outcomes. The aim of this study was to investigate whether emergency department to ICU time is associated with hospital mortality. Design, Setting, and Patients: We conducted a retrospective observational cohort study using data from the Dutch quality registry National Intensive Care Evaluation. Adult patients admitted to the ICU directly from the emergency department in six university hospitals, between 2009 and 2016, were included. Using a logistic regression model, we investigated the crude and adjusted (for disease severity; Acute Physiology and Chronic Health Evaluation IV probability) odds ratios of emergency department to ICU time on mortality. In addition, we assessed whether the Acute Physiology and Chronic Health Evaluation IV probability modified the effect of emergency department to ICU time on mortality. Secondary outcomes were ICU, 30-day, and 90-day mortality. Interventions: None. Measurements and Main Results: A total of 14,788 patients were included. The median emergency department to ICU time was 2.0 hours (interquartile range, 1.3-3.3 hr). Emergency department to ICU time was correlated to adjusted hospital mortality (p 3.7 hr), both compared with the reference category ( 2.4 hr) is associated with increased hospital mortality after ICU admission, mainly driven by patients who had a higher Acute Physiology and Chronic Health Evaluation IV probability. We hereby provide evidence that rapid admission of the most critically ill patients to the ICU might reduce hospital mortality.

The performance of ICUs can be compared by ranking them into a league table according to their risk-adjusted mortality rate. The statistical quality of a league table can be expressed as its rankability, the percentage of variation between ICUs attributable to unexplained differences. We examine whether we can improve the rankability of our league table by using data from a longer period or by grouping ICUs with similar performance constructing a league table of clusters rather than individual ICUs. We developed a league table for risk-adjusted mortality rate with its rankability. The effect of assessment period was determined using a resampling procedure. Hierarchical clustering was used to obtain clusters of similar ICUs. We used data from ICUs participating in the Dutch National Intensive Care Evaluation registry between 2011 and 2013. We constructed league tables using 157,394 admissions from 78 ICUs with risk-adjusted mortality rate between 5.9% and 13.9% per ICU over the inclusion period. The rankability was 73% for 2013 and 89% for the whole period 2011-2013. Rankability over the year 2013 increased till 98% when clustering ICUs, reaching an optimum at a league table of seven clusters. We conclude that, when using data from a single year, the rankability of a league table of Dutch ICUs based on risk-adjusted mortality rate was unacceptably low. We could improve the rankability of this league table by increasing the period of data collection or by grouping similar ICUs into clusters and constructing a league table of clusters of ICUs rather than individual ICUs. Ranking clusters of ICUs could be useful for identifying possible differences in performance between clusters of ICUs

OBJECTIVE Knowledge on characteristics and outcome of ICU patients with AIDS is highly limited. We aimed to determine the main reasons for admission and outcome in ICU patients with AIDS and trends over time therein. DESIGN A retrospective study within the Dutch National Intensive Care Evaluation registry. SETTING Dutch ICUs. PATIENTS We used data collected between 1997 and 2014. Characteristics of patients with AIDS were compared with ICU patients without AIDS, matched for age, sex, admission type, and admission year. Joinpoint regression analysis was applied to study trends over time. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We included 1,127 patients with AIDS and 4,479 matched controls. The main admission diagnoses of patients with AIDS were respiratory infection (28.6%) and sepsis (16.9%), which were less common in controls (7.7% and 7.5%, respectively; both p < 0.0001). Patients with AIDS had increased severity of illness and in-hospital mortality (28.2% vs 17.8%; p < 0.0001) compared with controls, which was associated with a higher rate of infections at admission in patients with AIDS (58.4% vs 25.5%). Over time, the proportion of patients with AIDS admitted with an infection decreased (75% in 1999 to 56% in 2013). Mortality declined in patients with AIDS (39% in 1999 to 16% in 2013), both in patients with or without an infection. Mortality also declined in matched controls without AIDS, but to a lesser extent. CONCLUSION Infections are still the main reason for ICU admission in patients with AIDS, but their prevalence is declining. Outcome of patients with AIDS continued to improve during a time of widespread availability of combination antiretroviral therapy, and mortality is reaching levels similar to ICU patients without AIDS.

Objective: To describe the effect of implementation of a Rapid Response System (RRS) on the composite endpoint of cardiopulmonary arrest, unplanned ICU admission, or death. Design: Pragmatic prospective Dutch multi-center before-after trial, Cost and Outcomes analysis of Medical Emergency Teams (COMET) trial. Setting: Twelve hospitals participated, each including two surgical and two non-surgical wards between April 2009 and November 2011. The Modified Early Warning Score (MEWS) and Situation-Background-Assessment-Recommendation (SBAR) instruments were implemented over seven months. The Rapid Response Team (RRT) was then implemented during the following 17 months. The effects of implementing the RRT were measured in the last 5 months of this period. Patients: All patients 18 years and older admitted to the study wards were included. Measurements and main results: In total 166,569 patients were included in the study representing 1,031,172 hospital admission days. No differences were observed in patient demographics between periods. The composite endpoint of cardiopulmonary arrest, unplanned ICU admission, or death per 1000 admissions was significantly reduced in the RRT versus the before phase, adjusted odds ratio (OR) 0.847 (95% CI 0.725-0.989, p=0.036). Cardiopulmonary arrests and in hospital mortality were also significantly reduced, OR 0.607 (95 CI 0.393-0.937, p=0.018) and OR 0.802 (95% CI 0.644-1.0, p=0.05) respectively. Unplanned ICU admissions showed a declining trend, OR 0.878 (95% CI 0.755-1.021, p=0.092) whereas severity of illness at the moment of ICU admission was not different between periods. Conclusions: In this study, introduction of nationwide implementation of RRSs was associated with a decrease in the composite endpoint of cardiopulmonary arrests, unplanned ICU admissions and mortality in patients on general hospital wards. These findings support the implementation of RRSs in hospitals to reduce severe adverse events.

OBJECTIVES\nTo assess the mortality risk of ICU patients after hospital discharge and compare it to mortality of the general Dutch population.\nDESIGN\nCohort study of ICU admissions from a national ICU registry linked to administrative records from an insurance claims database.\nSETTING\nEighty-one Dutch ICUs.\nPATIENTS\nICU patients (n = 91,203) who were discharged alive from the hospital between January 1, 2007, and October 1, 2010.\nINTERVENTIONS\nNone.\nMEASUREMENTS AND MAIN RESULTS\nThe unadjusted observed survival was inspected by Kaplan-Meier curves. Mortality risk at 1, 2, and 3 years after hospital discharge was 12.5%, 19.3%, and 27.5%, respectively. The 3-year mortality after hospital discharge in ICU patients was higher than the weighted average of the gender and age-specific death risks of the general Dutch population (27.5% versus 8.2%). The 1-year mortality after hospital discharge was adjusted for case-mix differences by a set of determinants which showed a statistically significant influence on the outcome in a 10-fold cross validation. The elective and cardiac surgical patients have statistically significantly better mortality outcomes (adjusted hazard ratio, 0.73 and 0.28, respectively), whereas medical patients and patients admitted for cancer have statistically significantly worse mortality outcomes (adjusted hazard ratio, 1.41, 1.94, respectively) compared with other ICU patients. Urgent surgery patients and patients with a subarachnoid hemorrhage, trauma, acute renal failure, or severe community-acquired pneumonia did not differ statistically from the other ICU patients after adjustment for case-mix differences.\nCONCLUSIONS\nIn-hospital mortality underestimates the true mortality of ICU patients as the mortality in the first months after hospital discharge is substantial. Most ICU patients still have an increased mortality risk in the subsequent years after hospital discharge compared with the general Dutch population. The mortality after hospital discharge differs widely between ICU subgroups. Future studies should focus on the analysis of mortality after hospital discharge that is attributable to the former ICU admission.

OBJECTIVE: We systematically reviewed models to predict adult ICU length of stay.DATA SOURCES: We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models.STUDY SELECTION: We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models.DATA EXTRACTION: Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.DATA SYNTHESIS: The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R was 0.05-0.28 across patients and 0.01-0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22.CONCLUSION: No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation.

Objectives: Increases in case-mix adjusted mortality may be indications of decreasing quality of care. Risk-adjusted control charts can be used for in-hospital mortality monitoring in intensive care units by issuing a warning signal when there are more deaths than expected. The aim of this study was to systematically assess and compare, by computer simulation, expected delay before a warning signal was given for an upward shift in mortality rate in intensive care mortality data by different risk-adjusted control charts. Design: We compared the efficiency of the risk-adjusted P-chart, risk-adjusted Additive P-chart, risk-adjusted Multiplicative P-chart, monthly Standardized Mortality Ratio, risk-adjusted Cumulative Sum, risk-adjusted Resetting Sequential Probability Ratio Test, and risk-adjusted Exponentially Weighted Moving Average control chart to detect an upward shift in mortality rate in eight different scenarios that varied by mortality increase factor and monthly patient volume. Setting: Adult intensive care units in The Netherlands. Patients: Patients admitted to 73 intensive care units from the Dutch National Intensive Care Evaluation quality registry from the year 2009. Interventions: None. Measurements: We compared the performance of the different risk-adjusted control charts by the median time-to-signal and the 6-month detection rate. Main results:In all eight scenarios,the risk-adjusted Exponentially Weighted Moving Average control chart had the shortest median time-to-signal, and in four, the highest 6-month detection rate. The median time-to-signal for an average volume intensive care unit (i.e., 50 admissions per month) with an increase in mortality rate of R = 1.50 on the odds scale was 9 months for the risk-adjusted Exponentially Weighted Moving Average control chart. Conclusions: The risk-adjusted Exponentially Weighted Moving Average control chart signaled the fastest in most of the simulated scenarios and is therefore superior in detecting increases in in-hospital mortality of intensive care patients compared to the other types of risk-adjusted control charts. (Crit Care Med 2012; 40:1799-1807)

Objective: Disseminated intravascular coagulation (DIC) is a syndrome characterized by systemic intravascular activation of coagulation, leading to widespread deposition of fibrin in the circulation. We addressed the issue of whether there is evidence that this fibrin deposition contributes to multiple organ failure. We also explored the current knowledge on the pathogenesis of DIC and reviewed current and future treatment for DIC. Data Sources: We searched and reviewed published articles on experimental studies of DIC models in animals and clinical studies in patients with DIC. Data Synthesis: There is ample experimental and clinical evidence that DIC contributes to morbidity and mortality. Recent knowledge on important pathogenetic mechanisms that may lead to DIC has resulted in novel preventive and therapeutic approaches to patients with DIC. Although the trigger for the activation of the coagulation system may vary depending on the underlying condition, it is usually mediated by several cytokines. Thrombin generation proceeds via the (extrinsic) tissue factor/ factor VIIa route and simultaneously occurring depression of inhibitory mechanisms, such as antithrombin III and the protein C-protein S system. Also, impaired fibrin degradation, because of high circulating levels of plasminogen activator inhibitor, type 1, contributes to enhanced intravascular fibrin deposition. Conclusions: Although the cornerstone of DIC management is the specific and vigorous treatment of the underlying disorder, strategies aimed at inhibiting coagulation activation may theoretically be justified. Such strategies have been found to be beneficial in experimental and initial clinical studies. These strategies, which follow from our current understanding of the pathophysiology of DIC, involve inhibition of tissue factor-mediated activation of coagulation or restoration of physiologic anticoagulant pathways by means of the administration of antithrombin concentrate or (activated) protein C concentrate. Although no complete evidence from controlled clinical trials is available for most of the proposed therapeutic interventions, these novel strategies are being studied.

Objectives: To study regional perfusion during experimental endotoxemic and obstructive shock and compare the effect of initial cardiac output-targeted fluid resuscitation with optimal cardiac output-targeted resuscitation on different peripheral tissues. Design: Controlled experimental study. Setting: University-affiliated research laboratory. Subjects: Fourteen fasted anesthetized mechanically ventilated domestic pigs. Interventions: Domestic pigs were randomly assigned to the endotoxemic (n = 7) or obstructive shock (n = 7) model. Central and regional perfusion parameters were obtained at baseline, during greater than or equal to 50% reduction of cardiac output (T1), after initial resuscitation to baseline (T2), and after optimization of cardiac output (T3). Measurements and Main Results: Regional perfusion was assessed in the sublingual, intestinal, and muscle vascular beds at the different time points and included visualization of the microcirculation, measurement of tissue oxygenation, and indirect assessments of peripheral skin perfusion. Hypodynamic shock (T1) simultaneously decreased all regional perfusion variables in both models. In the obstructive model, these variables returned to baseline levels at T2 and remained in this range after T3, similar to cardiac output. In the endotoxemic model, however, the different regional perfusion variables were only normalized at T3 associated with the hyperdynamic state at this point. The magnitude of changes over time between the different vascular beds was similar in both models, but the endotoxemic model displayed greater heterogeneity between tissues. Conclusions: This study demonstrates that the relationship between the systemic and regional perfusion is dependent on the underlying cause of circulatory shock. Further research will have to demonstrate whether different microvascular perfusion variables can be used as additional resuscitation endpoints.

Objective: To describe how nurses and physicians judge their own quality of care for deteriorating patients on medical wards compared with the judgment of independent experts. Design: Cross-sectional study using interviews of care-providers regarding their perceived quality of care for clinically deteriorating patients compared with retrospective judgment by independent experts. Setting: Academic Medical Center of Amsterdam, the Netherlands. Patients: Between April and July 2009, all patients with cardiopulmonary arrests and unplanned intensive care unit admissions from six medical nursing wards were included. The care-providers (nurses and physicians) taking care of these patients in the previous 12 hrs were included. Measurements and Main Results: Forty-seven events and 198 interviews were analyzed. Skill and knowledge level regarding the recognition of a deteriorating patient were rated on a scale of 1-10 with means (SD) of 7.9 (0.8) and 7.7 (0.9), respectively. Nurses and residents attributed coordination of care largely to themselves (74% and 76%, respectively). Communication, cooperation, and coordination were graded in a positive manner (medians between 7.3 and 8), whereas the medical staff graded these factors higher compared to the grading by nurses and residents. Negative predictive values regarding the presence of a delay compared with an expert panel was 37% for nurses and 38% for residents and specialists. Conclusions: Care-providers mostly rate their care provided to patients in the hours preceding a life-threatening adverse event as good. In contrast, independent experts had a more critical appraisal of the provided care in regards to timely recognition. These findings may partly explain the reluctance of care-providers to implement patient safety initiatives. (Crit Care Med 2012; 40: 2982-2986)

Objectives: It has been postulated that prognostic models based on administrative data can provide valid adjusted mortality rates in specific patient populations. In this study we compared the performance and robustness of a model based on administrative data (customized hospital standardized mortality ratio) and a model based on clinical data (customized Simplified Acute Physiology Score II) in the Dutch intensive care unit population. Design: Cohort study of intensive care unit records from a national intensive care unit quality registry linked to administrative records from the Dutch National Medical Registration. The hospital standardized mortality ratio and Simplified Acute Physiology Score II models were first-level customized on the intensive care unit population. Setting: Fifty-five Dutch intensive care units. Patients: A total of 66,564 intensive care unit patients admitted from 2005 to 2008. Interventions: None. Measurements and Main Results: Performance expressed by measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve, Brier score, Hosmer-Lemeshow Ĉ-statistic, and calibration plots). Additionally, the robustness of the models was assessed by simulating changes in the population's severity of illness and analyzing the effect on the intensive care units' standardized mortality ratios. Measurements and Main Results: The area under the receiver operating characteristic curve and Brier score of the customized Simplified Acute Physiology Score II were significantly superior to that of the customized hospital standardized mortality ratio (0.85 and 0.11 vs. 0.77 and 0.13, respectively). Calibration plots showed good agreement between observed and predicted mortality for low-risk patients in both models, with more discrepancy in the high-risk patients when using the customized hospital standardized mortality ratio. Severity of illness had influence on the intensive care units' standardized mortality ratios in both models, but the customized Simplified Acute Physiology Score II showed more robustness. Conclusions: The customized Simplified Acute Physiology Score II outperforms the customized hospital standardized mortality ratio in the Dutch intensive care unit population. Comparing institutions based on standardized mortality ratios can be unfavorable for those with a more severely ill intensive care unit population, especially when using the customized hospital standardized mortality ratio.

OBJECTIVES: To evaluate the influence of choice of a prognostic model and the effect of customization of these models on league tables (i.e., rank-order listing) in which intensive care units (ICUs) are ranked by standardized mortality ratios using Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Model II (MPM24II). DESIGN: Retrospective analysis of prospectively collected data on ICU admissions. SETTING: Forty Dutch ICUs. PATIENTS: A data set from a national registry of 86,427 patients from January 2002 to October 2006. INTERVENTIONS: The league tables associated with the different models were compared to evaluate their agreement. Bootstrapping was used to quantify the uncertainty in the ranks for ICUs. First, for each ICU the median rank and its 95% confidence interval were identified for each model. Then, for a given pair of models, for each ICU the median difference in rank and its associated 95% confidence interval were computed. A difference in rank for an ICU for a given pair of models was considered relevant if it was statistically significant and if one of the models would categorize this ICU as a performance outlier (excellent performer or very poor performer) while the other did not. MEASUREMENTS AND MAIN RESULTS: For 20 ICUs, there was a significant difference in rank (2-19 positions) between one or more pairs of models. Three ICUs were rated as performance outliers by one of the models, while the other excluded this possibility with 95% certainty. Furthermore, for ten ICUs, one or more pairs of models classified these ICUs as performance outliers while the other model did not do so with certainty. Regarding the agreement between the original models and their customized versions, in all cases the median change in rank was three positions or less and the models fully agreed with respect to which ICUs should be classified as performance outliers. CONCLUSIONS: Institutional comparison based on case-mix adjusted league tables is sensitive to the choice of prognostic model but not to customization of these models. League tables should always display the uncertainty associated with institutional ranks

BACKGROUND: Introduction of strict glycemic control has increased the risk for hypoglycemia in the intensive care unit. Little is known about the consequences of hypoglycemia in this setting. We examined short-term consequences (seizures, coma, and death) of hypoglycemia in the intensive care unit. PATIENTS AND METHODS: All occurrences of hypoglycemia (glucose of

To compare the effects of different colloid plasma substitutes on blood coagulation and postoperative blood loss. Relevant studies were obtained from the medical literature. Articles were selected that provided data on the effects of colloids on hemostasis and postoperative blood loss in humans. Studies comparing different colloids were looked for using MEDLINE and by searching through the references of studies as they were collected. Articles were reviewed and relevant data were extracted and partly presented in comparative tables. Dextran, gelatin, and hydroxyethyl starch (HES) all can induce a specific decrease of von Willebrand factor and factor VIII:c. Blood coagulation is most impaired by dextran and high molecular weight HES, both associated with increased postoperative blood loss. The effects of HES on blood coagulation have been shown to depend on its molecular weight and rate of elimination. Detrimental effects have been shown for high molecular weight HES. Medium molecular weight (MMW)-HES with a high degree of substitution (HES 200/0.62) and MMW-HES with high C2/C6 hydroxyethylation ratio (HES 200/0.5/13) are slowly degradable and have been shown to impair blood coagulation after repeated administration. Rapidly degradable HES 200/0.5/6 and gelatin-based plasma expanders appear not to impair hemostasis. However, based on the reviewed literature, all artificial colloids could potentially induce increased bleeding tendency after infusion of very large volumes and especially when given to patients with even mild forms of von Willebrand disease. In those circumstances, crystalloid solutions or alternatives such as plasma or albumin, although associated with other serious complications, could be considered