National Intensive Care Evaluation Registry Research Articles

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.

Age Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study.

A high body mass index (BMI) is associated with an unfavorable disease course in COVID-19, but not among those who require admission to the ICU. This has not been examined across different age groups. We examined whether age modifies the association between BMI and mortality among critically ill COVID-19 patients. An observational cohort study. A nationwide registry analysis of critically ill patients with COVID-19 registered in the National Intensive Care Evaluation registry. We included 15,701 critically ill patients with COVID-19 (10,768 males [68.6%] with median [interquartile range] age 64 yr [55-71 yr]), of whom 1,402 (8.9%) patients were less than 45 years. None. In the total sample and after adjustment for age, gender, Acute Physiology and Chronic Health Evaluation IV, mechanical ventilation, and use of vasoactive drugs, we found that a BMI greater than or equal to 30 kg/m2 does not affect hospital mortality (adjusted odds ratio [ORadj] = 0.98; 95% CI, 0.90-1.06; p = 0.62). For patients less than 45 years old, but not for those greater than or equal to 45 years old, a BMI greater than or equal to 30 kg/m2 was associated with a lower hospital mortality (ORadj = 0.59; 95% CI, 0.36-0.96; p = 0.03). A higher BMI may be favorably associated with a lower mortality among those less than 45 years old. This is in line with the so-called "obesity paradox" that was established for other groups of critically ill patients in broad age ranges. Further research is needed to understand this favorable association in young critically ill patients with COVID-19.

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

PurposeTo assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. MethodsObservational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. ResultsA total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. ConclusionIn our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.

ICU respiratory admissions data for influenza severity surveillance?

ObjectiveIntensive Care Unit (ICU) data are registered for quality monitoring in the Netherlands with near 100% coverage. They are a ‘big data’ type source that may be useful for infectious disease surveillance. We explored their potential to enhance the surveillance of influenza which is currently based on the milder end of the disease spectrum. We ultimately aim to set up a real-surveillance system of severe acute respiratory infections.IntroductionWhile influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, little data is available on more severe cases. With retrospective data from ICU’s we aim to fill this current knowledge gap and to explore its worth for prospective surveillance. Using multiple parameters proposed by the World Health Organization we estimated the burden of severe acute respiratory infections (SARI) to ICU and how this varies between influenza epidemics.MethodsWe analyzed weekly ICU admissions of adults in the Netherlands (2007-2016) from the national intensive care evaluation (NICE) quality registry (100% coverage of adult ICU in 2016; population size 14 million adults. A SARI syndrome was defined as admission diagnosis being any of 6 pneumonia or pulmonary sepsis codes in the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. In recent years NICE has explored and promoted increased timeliness and automation of data transfer.ResultsAnnually, 11-14% of medical admissions to adult ICUs were for a SARI (5-25% weekly). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU-SARI. Between influenza epidemics, severity indicators varied: ICU-SARI incidence (between 558-2,400 cumulated admissions nation-wide, rate: 0.40-1.71/10,000 inhabitants), average APACHE score (between 71-78), ICU-SARI mortality (between 13-20%), ICU-SARI/ILI ratio (between 8-17 SARI ICU cases per 1,000 expected medically attended influenza-like-illness in primary care), peak incidence (between 101-188 ICU-SARI admissions nationally in the highest week, rate: between 0.07-0.13/10,000 population).ICUs use different types of electronic health records (EHRs). Data submitted to the NICE registry is mainly based on routinely collected data extracted from these EHRs. The timeliness of data submission varies between a few weeks and three months. Together with ICUs, the NICE registry has recently undertaken actions to increase timeliness of ICU data submission.ConclusionsIn ICU data, great variation can be seen between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU-SARI surveillance, as proposed by the World Health Organization would provide insight into severity of ongoing influenza epidemics which differ from season to season.Currently a subset of hospitals provide data with a 6-week delay. This can be a worthwhile addition to current influenza surveillance, which, while timelier, is based on milder cases seen by general practitioners (primary care). Future increases in data timeliness will remain an aim.

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.

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.

Trends in hospital and intensive care admissions in the Netherlands attributable to the very elderly in an ageing population

IntroductionThe Dutch population is ageing and it is unknown how this is affecting trends in the percentage of hospital and intensive care unit (ICU) admissions attributable to patients aged 80 years or older, the very elderly.MethodsWe present data on the percentage of the very elderly in the general population and the percentage of hospital admissions attributable to the very elderly. We subsequently performed a longitudinal cross-sectional study on ICU admissions from hospitals participating in the National Intensive Care Evaluation registry for the period 2005 to 2014. We modeled the percentage of adult ICU admissions and treatment days attributable to the very elderly separately for ICU admissions following cardiac surgery and other reasons.ResultsThe percentage of Dutch adults aged 80 years and older, increased from 4.5 % in 2005 to 5.4 % in 2014 (p-value < 0.0001) and with this ageing of the population, the percentage of hospital admissions attributable to very elderly increased from 9.0 % in 2005 to 10.6 % in 2014 (p-value < 0.0001). The percentage of ICU admissions following cardiac surgery attributable to the very elderly increased from 6.7 % in 2005 to 11.0 % in 2014 in nine hospitals (p-value < 0.0001), while the percentage of treatment days attributable to this group rose from 8.6 % in 2005 to 11.7 % in 2014 (p-value = 0.0157). In contrast, the percentage of very elderly patients admitted to the ICU for other reasons than following cardiac surgery remained stable at 13.8 % between 2005 and 2014 in 33 hospitals (p-value = 0.1315). The number of treatment days attributable to the very elderly rose from 11,810 in 2005 to 15,234 in 2014 (p-value = 0.0002), but the percentage of ICU treatment days attributable to this group remained stable at 12.0 % (p-value = 0.1429).ConclusionsAs in many European countries the Dutch population is ageing and the percentage of hospital admissions attributable to the very elderly rose between 2005 and 2014. However, the percentage of ICU admissions and treatment days attributable to very elderly remained stable. The percentage of ICU admissions following cardiac surgery attributable to this group increased between 2005 and 2014.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-1061-z) contains supplementary material, which is available to authorized users.

Intensive care admission of cancer patients: a comparative analysis.

The aim of this study was to obtain insight into which proportion of cancer patients is admitted to an Intensive Care Unit (ICU) and how their survival, demographic, and clinical characteristics relate to cancer patients not admitted to the ICU. Data from patients registered with cancer between 2006 and 2011 in four hospitals in the Netherlands were linked to the Dutch National Intensive Care Evaluation registry. About 36,860 patients with cancer were identified, of whom 2,374 (6.4%) were admitted to the ICU. Fifty-six percent of ICU admissions were after surgery, whereas 44% were for medical reasons. The risk for ICU admission was highest among cancer patients treated with surgery either alone or combined with chemotherapy and/or radiation therapy. Only 80 of 1,073 medical ICU admissions (3.3%) were for cancer-specific reasons. Although more women (54.0%) than men were registered with cancer, the proportion of male cancer patients admitted to an ICU was much higher (9.3 vs. 4.0%, P < 0.001). Five-year survival of cancer patients admitted to the ICU was substantial (41%) although median survival was much lower (1,104 days) than in patients not admitted to the ICU (median survival time not reached, P < 0.001). These results show that one out of 16 cancer patients was admitted to an ICU and that ICU support for this group should not be considered futile.

Body Mass Index Is Associated With Hospital Mortality in Critically Ill Patients

Obesity is associated with a variety of diseases, which results in a decreased overall life expectancy. Nevertheless, some studies suggest that being overweight may reduce hospital mortality of certain patient groups, referred to as obesity paradox. Conflicting results for critically ill patients are reported. Therefore, we wished to investigate the association of body mass index and hospital mortality in critically ill patients. Observational cohort study in Dutch critically ill patients. A dataset from the Dutch National Intensive Care Evaluation registry that includes patients admitted to Dutch ICUs was used. One hundred fifty-four thousand three hundred and eight ICU patients of teaching and nonteaching units in urban and nonurban hospitals. None. We used logistic regression analysis, correcting for case mix (Simplified Acute Physiology Score II, age, gender, admission type, neoplasm, AIDS, hematologic malignancy, immunologic insufficiency, mechanical ventilation, and calendar year), to determine the relationship between body mass index and hospital mortality. Body mass index was included in the model as a continuous nonlinear covariate in a restricted regression spline transformation. To facilitate interpretation, adjusted odds ratios were calculated for the World Health Organization-based body mass index classes. Body mass index was found to be significantly associated with hospital mortality, with risks quickly increasing for underweight patients (body mass index < 18.5 kg/m). Obese and seriously obese patients, with a body mass index of 30-39.9 kg/m, had the lowest risk of death with an adjusted odds ratio of 0.86 (0.83-0.90). This large observational database shows an inverse association between obesity and hospital mortality in critically ill patients that could not be explained by a variety of known confounders.

A strategy to enhance the safety and efficiency of handovers of ICU patients: study protocol of the pICUp study

BackgroundTo use intensive care unit (ICU) facilities efficiently and ensure high quality of care, an optimal patient flow is necessary. Discharging patients relieves the pressure on ICU beds but the risk of premature discharge must be managed carefully. Suboptimal patient discharge may result in ICU readmissions and in patients’ death.The aim of this study is to obtain insight into the safety and efficiency of current ICU discharge practices and into barriers and facilitators to the implementation of effective ICU discharge interventions, and to develop an implementation strategy tailored to the barriers and facilitators identified.Methods/designThis study exists of five phases. Phase A: analysis of routinely registered data on variation in ICU readmissions and hospital mortality after ICU discharge of all ICUs participating in the Dutch National Intensive Care Evaluation registry (n = 83). Phase B: systematic review of effective interventions aiming to improve the efficiency and safety of the ICU discharge process. Phase C: assessing the intervention adherence with a questionnaire survey among all Dutch ICUs (n = 90). Phase D: assessing barriers and facilitators to the implementation of effective ICU discharge interventions with a questionnaire survey among all Dutch intensivists (n = 700). The questionnaire will be based on barriers and facilitators identified by focus groups (n = 4) and individual interviews with professionals of ICUs and general wards and adult discharged ICU patients (n = 25 to 30). Phase E: systematic development of an implementation strategy based on the sampled data in phase A to D, and effective implementation strategies from the literature using the intervention mapping method.DiscussionUsing theory and empirical data, an implementation strategy will be developed to improve the safety and efficiency of the ICU discharge process. The developed strategy will be evaluated in a subsequent study. The knowledge obtained in this study should be used for further implementation of ICU discharge interventions, and can be used for implementation of handover interventions in other healthcare transition settings.

Outcomes of Intensive Care Unit admissions after elective cancer surgery

Postoperative care for major elective cancer surgery is frequently provided on the Intensive Care Unit (ICU). To analyze the characteristics and outcome of patients after ICU admission following elective surgery for different cancer diagnoses. We analyzed all ICU admissions following elective cancer surgery in the Netherlands collected in the National Intensive Care Evaluation registry between January 2007 and January 2012. 28,973 patients (9.0% of all ICU admissions; 40% female) were admitted to the ICU after elective cancer surgery. Of these admissions 77% were planned; in 23% of cases the decision for ICU admission was made during or directly after surgery. The most frequent malignancies were colorectal cancer (25.6%), lung cancer (18.5%) and tumors of the central nervous system (14.3%). Mechanical ventilation was necessary in 24.8% of all patients, most frequently after surgery for esophageal (62.5%) and head and neck cancer (50.2%); 20.7% of patients were treated with vasopressors in the acute postoperative phase, in particular after surgery for esophageal cancer (41.8%). The median length of stay on the ICU was 0.9 days (interquartile ranges [IQR] 0.8-1.5); surgery for esophageal cancer was associated with the longest ICU length of stay (median 2.0 days) with the largest variation (IQR 1.0-4.8 days). ICU mortality was 1.4%; surgery for gastrointestinal cancer was associated with the highest ICU mortality (colorectal cancer 2.2%, pancreatico-cholangiocarcinoma 2.0%). Elective cancer surgery represents a significant part of all ICU admissions, with a short length of stay and low mortality.

Reliability of In-hospital Mortality as a Quality Indicator in Clinical Quality Registries

Errors in the registration or extraction of patient outcome data, such as in-hospital mortality, may lower the reliability of the quality indicator that uses this (partly) incorrect data. Our aim was to measure the reliability of in-hospital mortality registration in the Dutch National Intensive Care Evaluation (NICE) registry. We linked data of the NICE registry with an insurance claims database, resulting in a list of discrepancies in in-hospital mortality. Eleven Intensive Care Units (ICUs) were visited where local data sources were investigated to find the true in-hospital mortality status of the discrepancies and to identify the causes of the data errors in the NICE registry. Original and corrected Standardized Mortality Ratios (SMRs) were calculated to determine if conclusions about quality of care changed compared to the national benchmark. In eleven ICUs, 23,855 records with 460 discrepancies were identified of which 255 discrepancies (1.1% of all linked records) were due to incorrect in-hospital mortality registration in the NICE registry. Two programming errors in computer software of six ICUs caused 78% of errors, the remainder was caused by manual transcription errors and failure to record patient outcomes. For one ICU the performance became concordant with the national benchmark after correction, instead of being better. The reliability of in-hospital mortality registration in the NICE registry was good. This was reflected by the low number of data errors and by the fact that conclusions about the quality of care were only affected for one ICU due to systematic data errors. We recommend that registries frequently verify the software used in the registration process, and compare mortality data with an external source to assure consistent quality of data.

Outcomes of cancer patients after unplanned admission to general intensive care units

Background. Acute admission to an intensive care unit (ICU) of cancer patients is considered with increasing frequency due to a better life expectancy and more aggressive therapies. The aim of this study was to determine the characteristics and outcomes of cancer patients with unplanned admissions to general ICUs, and to compare these with outcomes of critically ill patients without cancer. Material and methods. All unplanned ICU admissions in the Netherlands collected in the National Intensive Care Evaluation registry between January 2007 and January 2011 were analyzed. Results and conclusion. Of the 140,154 patients with unplanned ICU admission 10.9% had a malignancy. Medical cancer patients were more severely ill on ICU admission in comparison with medical non-cancer patients, as reflected by higher needs for mechanical ventilation (50.8% vs. 46.4%, p < 0.001) and vasopressors within 24 hours after admission (41.5% vs. 33.0%, p < 0.001), higher Acute Physiology and Chronic Health Evaluation (APACHE) IV scores (88.1 vs. 67.5, p < 0.001) and a longer ICU stay (5.1 vs. 4.6 days, p < 0.001). In contrast, surgical cancer patients only displayed a modestly higher APACHE IV score on admission when compared with non-cancer surgical patients, whereas the other afore mentioned parameters were lower in the surgical cancer patients group. In-hospital mortality was almost twice as high in medical cancer patients (40.6%) as in medical patients without cancer (23.7%). In-hospital mortality of surgical cancer patients (17.4%) was slightly higher than in patients without cancer (14.6%). These data indicate that unplanned ICU admission is associated with a high mortality in patients with cancer when admitted for medical reasons.

Evaluating the effectiveness of a tailored multifaceted performance feedback intervention to improve the quality of care: protocol for a cluster randomized trial in intensive care

BackgroundFeedback is potentially effective in improving the quality of care. However, merely sending reports is no guarantee that performance data are used as input for systematic quality improvement (QI). Therefore, we developed a multifaceted intervention tailored to prospectively analyzed barriers to using indicators: the Information Feedback on Quality Indicators (InFoQI) program. This program aims to promote the use of performance indicator data as input for local systematic QI. We will conduct a study to assess the impact of the InFoQI program on patient outcome and organizational process measures of care, and to gain insight into barriers and success factors that affected the program's impact. The study will be executed in the context of intensive care. This paper presents the study's protocol.Methods/designWe will conduct a cluster randomized controlled trial with intensive care units (ICUs) in the Netherlands. We will include ICUs that submit indicator data to the Dutch National Intensive Care Evaluation (NICE) quality registry and that agree to allocate at least one intensivist and one ICU nurse for implementation of the intervention. Eligible ICUs (clusters) will be randomized to receive basic NICE registry feedback (control arm) or to participate in the InFoQI program (intervention arm). The InFoQI program consists of comprehensive feedback, establishing a local, multidisciplinary QI team, and educational outreach visits. The primary outcome measures will be length of ICU stay and the proportion of shifts with a bed occupancy rate above 80%. We will also conduct a process evaluation involving ICUs in the intervention arm to investigate their actual exposure to and experiences with the InFoQI program.DiscussionThe results of this study will inform those involved in providing ICU care on the feasibility of a tailored multifaceted performance feedback intervention and its ability to accelerate systematic and local quality improvement. Although our study will be conducted within the domain of intensive care, we believe our conclusions will be generalizable to other settings that have a quality registry including an indicator set available.Trial registrationCurrent Controlled Trials ISRCTN50542146

External validation of prognostic models for critically ill patients required substantial sample sizes

Objective To investigate the behavior of predictive performance measures that are commonly used in external validation of prognostic models for outcome at intensive care units (ICUs). Study Design and Setting Four prognostic models (Simplified Acute Physiology Score II, the Acute Physiology and Chronic Health Evaluation II, and the Mortality Probability Models II) were evaluated in the Dutch National Intensive Care Evaluation registry database. For each model discrimination (AUC), accuracy (Brier score), and two calibration measures were assessed on data from 41,239 ICU admissions. This validation procedure was repeated with smaller subsamples randomly drawn from the database, and the results were compared with those obtained on the entire data set. Results Differences in performance between the models were small. The AUC and Brier score showed large variation with small samples. Standard errors of AUC values were accurate but the power to detect differences in performance was low. Calibration tests were extremely sensitive to sample size. Direct comparison of performance, without statistical analysis, was unreliable with either measure. Conclusion Substantial sample sizes are required for performance assessment and model comparison in external validation. Calibration statistics and significance tests should not be used in these settings. Instead, a simple customization method to repair lack-of-fit problems is recommended.

The influence of volume and intensive care unit organization on hospital mortality in patients admitted with severe sepsis: a retrospective multicentre cohort study

IntroductionThe aim of the study was to assess the influence of annual volume and factors related to intensive care unit (ICU) organization on in-hospital mortality among patients admitted to the ICU with severe sepsis.MethodsA retrospective cohort study was conducted using the database of the Dutch National Intensive Care Evaluation (NICE) registry. Analyses were based on consecutive patients admitted between 1 January 2003 and 30 June 2005 who fulfilled criteria for severe sepsis within the first 24 hours of admission. A 13-item questionnaire was sent to all 32 ICUs across The Netherlands that participated in the NICE registry within this period in order to obtain information on ICU organization and staffing. The association between in-hospital mortality and factors related to ICU organization was investigated using logistic regression analysis, combined with generalized estimation equations to account for potential correlations of outcomes within ICUs. Correction for patient-related factors took place by including Simplified Acute Physiology Score II, age, sex and number of dysfunctioning organ systems in the analyses.ResultsAnalyses based on 4,605 patients from 28 ICUs (questionnaire response rate 90.6%) revealed that a higher annual volume of severe sepsis patients is associated with a lower in-hospital mortality (P = 0.029). The presence of a medium care unit (MCU) as a step-down facility with intermediate care is associated with a higher in-hospital mortality (P = 0.013). For other items regarding ICU organization, no independent significant relationships with in-hospital mortality were found.ConclusionA larger annual volume of patients with severe sepsis admitted to Dutch ICUs is associated with lower in-hospital mortality in this patient group. The presence of a MCU as a step-down facility is associated with greater in-hospital mortality. No other significant associations between in-hospital mortality and factors related to ICU organization were found.