Dutch National Intensive Care Evaluation Research Articles

Emergency Department Triage, Transfer Times, and Hospital Mortality of Patients Admitted to the ICU: A Retrospective Replication and Continuation Study.

This study aimed to provide new insights into the impact of emergency department (ED) to ICU time on hospital mortality, stratifying patients by academic and nonacademic teaching (NACT) hospitals, and considering Acute Physiology and Chronic Health Evaluation (APACHE)-IV probability and ED-triage scores. We conducted a retrospective cohort study (2009-2020) using data from the Dutch National Intensive Care Evaluation registry. Patients directly admitted from the ED to the ICU were included from four academic and eight NACT hospitals. Odds ratios (ORs) for mortality associated with ED-to-ICU time were estimated using multivariable regression, both crude and after adjusting for and stratifying by APACHE-IV probability and ED-triage scores. None. A total of 28,455 patients were included. The median ED-to-ICU time was 1.9 hours (interquartile range, 1.2-3.1 hr). No overall association was observed between ED-to-ICU time and hospital mortality after adjusting for APACHE-IV probability (p = 0.36). For patients with an APACHE-IV probability greater than 55.4% (highest quintile) and an ED-to-ICU time greater than 3.4 hours the adjusted OR (ORsadjApache) was 1.24 (95% CI, 1.00-1.54; p < 0.05) as compared with the reference category (< 1.1 hr). In the academic hospitals, the ORsadjApache for ED-to-ICU times of 1.6-2.3, 2.3-3.4, and greater than 3.4 hours were 1.21 (1.01-1.46), 1.21 (1.00-1.46), and 1.34 (1.10-1.64), respectively. In NACT hospitals, no association was observed (p = 0.07). Subsequently, ORs were adjusted for ED-triage score (ORsadjED). In the academic hospitals the ORsadjED for ED-to-ICU times greater than 3.4 hours was 0.98 (0.81-1.19), no overall association was observed (p = 0.08). In NACT hospitals, all time-ascending quintiles had ORsadjED values of less than 1.0 (p < 0.01). In patients with the highest APACHE-IV probability at academic hospitals, a prolonged ED-to-ICU time was associated with increased hospital mortality. We found no significant or consistent unfavorable association in lower APACHE-IV probability groups and NACT hospitals. The association between longer ED-to-ICU time and higher mortality was not found after adjustment and stratification for ED-triage score.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Quality improvement of Dutch ICUs from 2009 to 2021: A registry based observational study

PurposeTo investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry. Materials and methodsWe included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry.We determined median and interquartile range for eight quality indicators. To evaluate changes over time on the indicators, we performed multilevel regression analyses, once without and once with the COVID-19 years 2020 and 2021 included. Additionally we explored between-ICU heterogeneity by calculating intraclass correlation coefficients (ICC). Results705,822 ICU admissions from 55 (65%) ICUs were included in the analyses. ICU length of stay (LOS), duration of mechanical ventilation (MV), readmissions, in-hospital mortality, hypoglycemia, and pressure ulcers decreased significantly between 2009 and 2019 (OR <1). After including the COVID-19 pandemic years, the significant change in MV duration, ICU LOS, and pressure ulcers disappeared. We found an ICC ≤0.07 on the quality indicators for all years, except for pressure ulcers with an ICC of 0.27 for 2009 to 2021. ConclusionsQuality of Dutch ICU care based on seven indicators significantly improved from 2009 to 2019 and between-ICU heterogeneity is medium to small, except for pressure ulcers. The COVID-19 pandemic disturbed the trend in quality improvement, but unaltered the between-ICU heterogeneity.

Trends in Early and Late Mortality in Patients With Severe Acute Pancreatitis Admitted to ICUs: A Nationwide Cohort Study.

To investigate national mortality trends over a 12-year period for patients with severe acute pancreatitis (SAP) admitted to Dutch ICUs. Additionally, an assessment of outcome in SAP was undertaken to differentiate between early (< 14 d of ICU admission) and late (> 14 d of ICU admission) mortality. Data from the Dutch National Intensive Care Evaluation and health insurance companies' databases were extracted. Outcomes included 14-day, ICU, hospital, and 1-year mortality. Mortality before and after 2010 was compared using mixed logistic regression and mixed Cox proportional-hazards models. Sensitivity analyses, excluding early mortality, were performed to assess trends in late mortality. Not applicable. Consecutive adult patients with SAP admitted to all 81 Dutch ICUs between 2007 and 2018. Not applicable. Among 4,160 patients treated in 81 ICUs, 14-day mortality was 17%, ICU mortality 17%, hospital mortality 23%, and 1-year mortality 33%. After 2010 in-hospital mortality adjusted for age, sex, modified Marshall, and Acute Physiology and Chronic Health Evaluation III scores were lower (odds ratio [OR], 0.76; 95% CI, 0.61-0.94) than before 2010. There was no change in ICU and 1-year mortality. Sensitivity analyses excluding patients with early mortality demonstrated a decreased ICU mortality (OR, 0.45; 95% CI, 0.32-0.64), decreased in-hospital (OR, 0.48; 95% CI, 0.36-0.63), and decreased 1-year mortality (hazard ratio, 0.81; 95% CI, 0.68-0.96) after 2010 compared with 2007-2010. Over the 12-year period examined, mortality in patients with SAP admitted to Dutch ICUs did not change, although after 2010 late mortality decreased. Novel therapies should focus on preventing early mortality in SAP.

FAIRifying a Quality Registry Using OMOP CDM: Challenges and Solutions.

The need for health data to be internationally Findable, Accessible, Interoperable and Reusable (FAIR) and thereby support integrative analysis with other datasets has become crystal clear in the ongoing pandemic. The Dutch National Intensive Care Evaluation (NICE) quality registry adopted the Observational Medical Outcomes Partnership Common Database Model (OMOP CDM) to achieve a FAIR database. In the process of adopting the OMOP CDM, many modeling, technical, and communication challenges needed to be solved. Through communication with the OMOP CDM implementation community, previously done research and trial-and-error we found solutions that we believe can help other healthcare institutions, especially ICU quality registries, FAIRify their databases.

Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

BackgroundBuilding Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of building ML models (AutoML) in-hospital mortality prediction modelling of triage COVID-19 patients at ICU admission versus expert-based predictor pre-selection followed by logistic regression. MethodsWe conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. We included 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24h, respectively) of AutoML compared to the more traditional approach of predictor pre-selection and logistic regression. FindingsPredictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0·75-0·76 (sdev = 0·03), PPV = 0·70-0·76 (sdev = 0·1) at cut-off = 0·3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0·78 (sdev = 0·03) and PPV = 0·79 (sdev = 0·2)). Extending the models with variables that are available at 24h after admission resulted in models with higher predictive performance (average AUROC = 0·77-0·79 (sdev = 0·03) and PPV = 0·79-0·80 (sdev = 0·10-0·17)). ConclusionsAutoML delivers prediction models with fair discriminatory performance, and good calibration and accuracy, which is as good as regression models with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24h after admission showed small (but significantly) performance increase.

Body Mass Index and Mortality in Coronavirus Disease 2019 and Other Diseases: A Cohort Study in 35,506 ICU Patients.

Obesity is a risk factor for severe coronavirus disease 2019 and might play a role in its pathophysiology. It is unknown whether body mass index is related to clinical outcome following ICU admission, as observed in various other categories of critically ill patients. We investigated the relationship between body mass index and inhospital mortality in critically ill coronavirus disease 2019 patients and in cohorts of ICU patients with non-severe acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma. Multicenter observational cohort study. Eighty-two Dutch ICUs participating in the Dutch National Intensive Care Evaluation quality registry. Thirty-five-thousand five-hundred six critically ill patients. None. Patient characteristics and clinical outcomes were compared between four cohorts (coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma patients) and between body mass index categories within cohorts. Adjusted analyses of the relationship between body mass index and inhospital mortality within each cohort were performed using multivariable logistic regression. Coronavirus disease 2019 patients were more likely male, had a higher body mass index, lower Pao2/Fio2 ratio, and were more likely mechanically ventilated during the first 24 hours in the ICU compared with the other cohorts. Coronavirus disease 2019 patients had longer ICU and hospital length of stay, and higher inhospital mortality. Odds ratios for inhospital mortality for patients with body mass index greater than or equal to 35 kg/m2 compared with normal weight in the coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and trauma cohorts were 1.15 (0.79-1.67), 0.64 (0.43-0.95), 0.73 (0.61-0.87), and 0.81 (0.57-1.15), respectively. The obesity paradox, which is the inverse association between body mass index and mortality in critically ill patients, is not present in ICU patients with coronavirus disease 2019-related respiratory failure, in contrast to nonsevere acute respiratory syndrome coronavirus 2 viral and bacterial respiratory infections.

Automated Clinical Prognostic Modelling for ICU Triage Prediction of In-Hospital Mortality of COVID-19 Patients: A Retrospective Cohort Study in the Netherlands

Background: ICU capacity has been under extreme pressure throughout the COVID-19 pandemic. Prediction models that estimate the risk of in-hospital mortality of COVID-19 patients could support decision making on ICU admittance and treatment. We aimed to assess the predictive performance of automated in-hospital mortality prediction modelling (AutoML) methods to triage COVID-19 patients at ICU admission versus 24 hours after ICU admission. Methods: We conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. Data was analyzed of 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24 hours, respectively) of AutoML versus the more traditional approach with expert-based predictor pre-selection followed by logistic regression. Findings: Predictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0&middot;75-0&middot;76 (sdev = 0&middot;03), PPV = 0&middot;70-0&middot;76 (sdev = 0&middot;1) at cut-off = 0&middot;3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0&middot;78 (sdev = 0&middot;03) and PPV = 0&middot;79 (sdev = 0&middot;2)). The obtained best AutoML model was a Linear Discriminant Analysis model. Extending the models with variables that are available at 24 hours after admission resulted in models with higher predictive performance (average AUROC = 0&middot;77-0&middot;79 (sdev = 0&middot;03) and PPV = 0&middot;79-0&middot;80 (sdev&amp;nbsp;=&amp;nbsp;0&middot;10-0&middot;17)). Interpretation: Automated clinical prognostic modelling delivers prediction models with fair discriminatory performance, and good calibration and accuracy. The performance of the automated models is shown to be as good as models that were developed using regression analysis with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24 hours after admission showed small (but significantly) increase in predictive performance. Funding Statement: This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) COVID-19 Programme in the bottom-up focus area 1 &ldquo;Predictive diagnostics and treatment&rdquo; for theme 3 &ldquo;Risk analysis and prognostics&rdquo; (project number 10430 01 201 0011: IRIS). The funder had no role in the design of the study or writing the manuscript. Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study protocol was reviewed by the Medical Ethics Committee of the Amsterdam Medical Center, the Netherlands. This committee provided a waiver from formal approval (W20_273 # 20.308) and informed consent since this trial does not fall within the scope of the Dutch Medical Research (Human Subjects) Act.

Long-Term Mortality Among ICU Patients With Stroke Compared With Other Critically Ill Patients.

Assessment of all-cause mortality of intracerebral hemorrhage and ischemic stroke patients admitted to the ICU and comparison to the mortality of other critically ill ICU patients classified into six other diagnostic subgroups and the general Dutch population. Observational cohort study. All ICUs participating in the Dutch National Intensive Care Evaluation database. All adult patients admitted to these ICUs between 2010 and 2015; patients were followed until February 2017. None. Of all 370,386 included ICU patients, 7,046 (1.9%) were stroke patients, 4,072 with ischemic stroke, and 2,974 with intracerebral hemorrhage. Short-term mortality in ICU-admitted stroke patients was high with 30 days mortality of 31% in ischemic stroke and 42% in intracerebral hemorrhage. In the longer term, the survival curve gradient among ischemic stroke and intracerebral hemorrhage patients stabilized. The gradual alteration of mortality risk after ICU admission was assessed using left-truncation with increasing minimum survival period. ICU-admitted stroke patients who survive the first 30 days after suffering from a stroke had a favorable subsequent survival compared with other diseases necessitating ICU admission such as patients admitted due to sepsis or severe community-acquired pneumonia. After having survived the first 3 months after ICU admission, multivariable Cox regression analyses showed that case-mix adjusted hazard ratios during the follow-up period of up to 3 years were lower in ischemic stroke compared with sepsis (adjusted hazard ratio, 1.21; 95% CI, 1.06-1.36) and severe community-acquired pneumonia (adjusted hazard ratio, 1.57; 95% CI, 1.39-1.77) and in intracerebral hemorrhage patients compared with these groups (adjusted hazard ratio, 1.14; 95% CI, 0.98-1.33 and adjusted hazard ratio, 1.49; 95% CI, 1.28-1.73). Stroke patients who need intensive care treatment have a high short-term mortality risk, but this alters favorably with increasing duration of survival time after ICU admission in patients with both ischemic stroke and intracerebral hemorrhage, especially compared with other populations of critically ill patients such as sepsis or severe community-acquired pneumonia patients.

Mortality prediction by SOFA score in ICU-patients after cardiac surgery; comparison with traditional prognostic\u2013models

BackgroundThere are many prognostic models and scoring systems in use to predict mortality in ICU patients. The only general ICU scoring system developed and validated for patients after cardiac surgery is the APACHE-IV model. This is, however, a labor-intensive scoring system requiring a lot of data and could therefore be prone to error. The SOFA score on the other hand is a simpler system, has been widely used in ICUs and could be a good alternative.The goal of the study was to compare the SOFA score with the APACHE-IV and other ICU prediction models.MethodsWe investigated, in a large cohort of cardiac surgery patients admitted to Dutch ICUs, how well the SOFA score from the first 24 h after admission, predict hospital and ICU mortality in comparison with other recalibrated general ICU scoring systems. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R2, and Ĉ-statistic) were calculated using bootstrapping. The cohort consisted of 36,632 Patients from the Dutch National Intensive Care Evaluation (NICE) registry having had a cardiac surgery procedure for which ICU admission was necessary between January 1st, 2006 and June 31st, 2018.ResultsDiscrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict hospital mortality was good with an AUC of respectively: 0.809, 0.851, 0.830, 0.850, 0.801. Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict ICU mortality was slightly better with AUCs of respectively: 0.809, 0.906, 0.892, 0.919, 0.862. Calibration of the models was generally poor.ConclusionAlthough the SOFA score had a good discriminatory power for hospital- and ICU mortality the discriminatory power of the APACHE-IV and SAPS-II was better. The SOFA score should not be preferred as mortality prediction model above traditional prognostic ICU-models.

Diabetes Is Not Associated With Increased 90-Day Mortality Risk in Critically Ill Patients With Sepsis.

To determine the association of pre-existing diabetes, hyperglycemia, and hypoglycemia during the first 24 hours of ICU admissions with 90-day mortality in patients with sepsis admitted to the ICU. We used mixed effects logistic regression to analyze the association of diabetes, hyperglycemia, and hypoglycemia with 90-day mortality (n = 128,222). All ICUs in the Netherlands between January 2009 and 2014 that participated in the Dutch National Intensive Care Evaluation registry. All unplanned ICU admissions in patients with sepsis. The association between 90-day mortality and pre-existing diabetes, hyperglycemia, and hypoglycemia, corrected for other factors, was analyzed using a generalized linear mixed effect model. In a multivariable analysis, diabetes was not associated with increased 90-day mortality. In diabetes patients, only severe hypoglycemia in the absence of hyperglycemia was associated with increased 90-day mortality (odds ratio, 2.95; 95% CI, 1.19-7.32), whereas in patients without pre-existing diabetes, several combinations of abnormal glucose levels were associated with increased 90-day mortality. In the current retrospective large database review, diabetes was not associated with adjusted 90-day mortality risk in critically ill patients admitted with sepsis.

Is patient length of stay associated with intensive care unit characteristics?

PurposeWe described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics. Materials and methodsWe included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics. ResultsWe found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R2 was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics. ConclusionAfter correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction.

Electronic audit and feedback intervention with action implementation toolbox to improve pain management in intensive care: protocol for a laboratory experiment and cluster randomised trial

BackgroundAudit and feedback is often used as a strategy to improve quality of care, however, its effects are variable and often marginal. In order to learn how to design and deliver effective feedback, we need to understand their mechanisms of action. This theory-informed study will investigate how electronic audit and feedback affects improvement intentions (i.e. information–intention gap), and whether an action implementation toolbox with suggested actions and materials helps translating those intentions into action (i.e. intention–behaviour gap). The study will be executed in Dutch intensive care units (ICUs) and will be focused on pain management.Methods and designWe will conduct a laboratory experiment with individual ICU professionals to assess the impact of feedback on their intentions to improve practice. Next, we will conduct a cluster randomised controlled trial with ICUs allocated to feedback without or feedback with action implementation toolbox group. Participants will not be told explicitly what aspect of the intervention is randomised; they will only be aware that there are two variations of providing feedback. ICUs are eligible for participation if they submit indicator data to the Dutch National Intensive Care Evaluation (NICE) quality registry and agree to allocate a quality improvement team that spends 4 h per month on the intervention. All participating ICUs will receive access to an online quality dashboard that provides two functionalities: gaining insight into clinical performance on pain management indicators and developing action plans. ICUs with access to the toolbox can develop their action plans guided by a list of potential barriers in the care process, associated suggested actions, and supporting materials to facilitate implementation of the actions. The primary outcome measure for the laboratory experiment is the proportion of improvement intentions set by participants that are consistent with recommendations based on peer comparisons; for the randomised trial it is the proportion of patient shifts during which pain has been adequately managed. We will also conduct a process evaluation to understand how the intervention is implemented and used in clinical practice, and how implementation and use affect the intervention’s impact.DiscussionThe results of this study will inform care providers and managers in ICU and other clinical settings how to use indicator-based performance feedback in conjunction with an action implementation toolbox to improve quality of care. Within the ICU context, this study will produce concrete and directly applicable knowledge with respect to what is or is not effective for improving pain management, and under which circumstances. The results will further guide future research that aims to understand the mechanisms behind audit and feedback and contribute to identifying the active ingredients of successful interventions.Trial registrationClinicalTrials.gov NCT02922101. Registered 26 September 2016.

Variation in rates of ICU readmissions and post-ICU in-hospital mortality and their association with ICU discharge practices

BackgroundVariation in intensive care unit (ICU) readmissions and in-hospital mortality after ICU discharge may indicate potential for improvement and could be explained by ICU discharge practices. Our objective was threefold: (1) describe variation in rates of ICU readmissions within 48 h and post-ICU in-hospital mortality, (2) describe ICU discharge practices in Dutch hospitals, and (3) study the association between rates of ICU readmissions within 48 h and post-ICU in-hospital mortality and ICU discharge practices.MethodsWe analysed data on 42,040 admissions to 82 (91.1%) Dutch ICUs in 2011 from the Dutch National Intensive Care Evaluation (NICE) registry to describe variation in standardized ICU readmission and post-ICU mortality rates using funnel-plots. We send a questionnaire to all Dutch ICUs. 75 ICUs responded and their questionnaire data could be linked to 38,498 admissions in the NICE registry. Generalized estimation equations analyses were used to study the association between ICU readmissions and post-ICU mortality rates and the identified discharge practices, i.e. (1) ICU discharge criteria; (2) bed managers; (3) early discharge planning; (4) step-down facilities; (5) medication reconciliation; (6) verbal and written handover; (7) monitoring of post-ICU patients; and (8) consulting ICU nurses. In all analyses, the outcomes were corrected for patient-related confounding factors.ResultsThe standardized rate of ICU readmissions varied between 0.14 and 2.67 and 20.8% of the hospitals fell outside the 95% control limits and 3.6% outside the 99.8% control limits. The standardized rate of post-ICU mortality varied between 0.07 and 2.07 and 17.1% of the hospitals fell outside the 95% control limits and 4.9% outside the 99.8% control limits. We could not demonstrate an association between the eight ICU discharge practices and rates of ICU readmissions or post-ICU in-hospital mortality. Implementing a higher number of ICU discharge practices was also not associated with better patient outcomes.ConclusionsWe found both variation in patient outcomes and variation in ICU discharge practices between ICUs. However, we found no association between discharge practices and rates of ICU readmissions or post-ICU mortality. Further research is necessary to find factors, which may influence these patient outcomes, in order to improve quality of care.

Guidelines on constructing funnel plots for quality indicators: A case study on mortality in intensive care unit patients.

Funnel plots are graphical tools to assess and compare clinical performance of a group of care professionals or care institutions on a quality indicator against a benchmark. Incorrect construction of funnel plots may lead to erroneous assessment and incorrect decisions potentially with severe consequences. We provide workflow-based guidance for data analysts on constructing funnel plots for the evaluation of binary quality indicators, expressed as proportions, risk-adjusted rates or standardised rates. Our guidelines assume the following steps: (1) defining policy level input; (2) checking the quality of models used for case-mix correction; (3) examining whether the number of observations per hospital is sufficient; (4) testing for overdispersion of the values of the quality indicator; (5) testing whether the values of quality indicators are associated with institutional characteristics; and (6) specifying how the funnel plot should be constructed. We illustrate our guidelines using data from the Dutch National Intensive Care Evaluation registry. We expect that our guidelines will be useful to data analysts preparing funnel plots and to registries, or other organisations publishing quality indicators. This is particularly true if these people and organisations wish to use standard operating procedures when constructing funnel plots, perhaps to comply with the demands of certification.

Metrics of Arterial Hyperoxia and Associated Outcomes in Critical Care.

Emerging evidence has shown the potential risks of arterial hyperoxia, but the lack of a clinical definition and methodologic limitations hamper the interpretation and clinical relevance of previous studies. Our purpose was to evaluate previously used and newly constructed metrics of arterial hyperoxia and systematically assess their association with clinical outcomes in different subgroups in the ICU. Observational cohort study. Three large tertiary care ICUs in the Netherlands. A total of 14,441 eligible ICU patients. None. In total, 295,079 arterial blood gas analyses, including the PaO2, between July 2011 and July 2014 were extracted from the patient data management system database. Data from all admissions with more than one PaO2 measurement were supplemented with anonymous demographic and admission and discharge data from the Dutch National Intensive Care Evaluation registry. Mild hyperoxia was defined as PaO2 between 120 and 200 mm Hg; severe hyperoxia as PaO2 greater than 200 mm Hg. Characteristics of existing and newly constructed metrics for arterial hyperoxia were examined, and the associations with hospital mortality (primary outcome), ICU mortality, and ventilator-free days and alive at day 28 were retrospectively analyzed using regression models in different subgroups of patients. Severe hyperoxia was associated with higher mortality rates and fewer ventilator-free days in comparison to both mild hyperoxia and normoxia for all metrics except for the worst PaO2. Adjusted effect estimates for conditional mortality were larger for severe hyperoxia than for mild hyperoxia. This association was found both within and beyond the first 24 hours of admission and was consistent for large subgroups. The largest point estimates were found for the exposure identified by the average PaO2, closely followed by the median PaO2, and these estimates differed substantially between subsets. Time spent in hyperoxia showed a linear and positive relationship with hospital mortality. Our results suggest that we should limit the PaO2 levels of critically ill patients within a safe range, as we do with other physiologic variables. Analytical metrics of arterial hyperoxia should be judiciously considered when interpreting and comparing study results and future studies are needed to validate our findings in a randomized fashion design.

Individual and Clustered Rankability of ICUs According to Case-Mix–Adjusted 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.

Characteristics and Outcome of Patients With AIDS in Dutch ICUs Between 1997 and 2014.

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. A retrospective study within the Dutch National Intensive Care Evaluation registry. Dutch ICUs. 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. None. 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. 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.

Associations of arterial carbon dioxide and arterial oxygen concentrations with hospital mortality after resuscitation from cardiac arrest.

IntroductionArterial concentrations of carbon dioxide (PaCO2) and oxygen (PaO2) during admission to the intensive care unit (ICU) may substantially affect organ perfusion and outcome after cardiac arrest. Our aim was to investigate the independent and synergistic effects of both parameters on hospital mortality.MethodsThis was a cohort study using data from mechanically ventilated cardiac arrest patients in the Dutch National Intensive Care Evaluation (NICE) registry between 2007 and 2012. PaCO2 and PaO2 levels from arterial blood gas analyses corresponding to the worst oxygenation in the first 24 h of ICU stay were retrieved for analyses. Logistic regression analyses were performed to assess the relationship between hospital mortality and both categorized groups and a spline-based transformation of the continuous values of PaCO2 and PaO2.ResultsIn total, 5,258 cardiac arrest patients admitted to 82 ICUs in the Netherlands were included. In the first 24 h of ICU admission, hypocapnia was encountered in 22 %, and hypercapnia in 35 % of included cases. Hypoxia and hyperoxia were observed in 8 % and 3 % of the patients, respectively. Both PaCO2 and PaO2 had an independent U-shaped relationship with hospital mortality and after adjustment for confounders, hypocapnia and hypoxia were significant predictors of hospital mortality: OR 1.37 (95 % CI 1.17–1.61) and OR 1.34 (95 % CI 1.08–1.66). A synergistic effect of concurrent derangements of PaCO2 and PaO2 was not observed (P = 0.75).ConclusionsThe effects of aberrant arterial carbon dioxide and arterial oxygen concentrations were independently but not synergistically associated with hospital mortality after cardiac arrest.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-1067-6) contains supplementary material, which is available to authorized users.

Data Resource Profile: the Dutch National Intensive Care Evaluation (NICE) Registry of Admissions to Adult Intensive Care Units.

Data Resource Profile: the Dutch National Intensive Care Evaluation (NICE) Registry of Admissions to Adult Intensive Care Units.