Dutch National Intensive Care Evaluation Research Articles

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.

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.

Performance of risk-adjusted control charts to monitor in-hospital mortality of intensive care unit patients

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. 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. Adult intensive care units in The Netherlands. Patients admitted to 73 intensive care units from the Dutch National Intensive Care Evaluation quality registry from the year 2009. None. We compared the performance of the different risk-adjusted control charts by the median time-to-signal and the 6-month detection rate. 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. 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.

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

Influence of mild therapeutic hypothermia after cardiac arrest on hospital mortality*

Following two randomized controlled trials that demonstrated reduced mortality and better neurological outcome in cardiac arrest patients, mild therapeutic hypothermia was implemented in many intensive care units. Up to now, no large observational studies have confirmed the beneficial effects of mild therapeutic hypothermia. Internet-based survey combined with a retrospective, observational study. All patients admitted to an intensive care unit in The Netherlands after cardiac arrest from January 1, 1999 until January 1, 2009. Dutch National Intensive Care Evaluation database. The moment of implementation of mild therapeutic hypothermia for each hospital participating in the Dutch National Intensive Care Evaluation database was determined with an Internet survey. To compare mortality before and after implementation of mild therapeutic hypothermia, the odds ratio adjusted for Simplified Acute Physiology Score II score, age, gender, propensity score, and in- or out-of-hospital cardiac arrest was calculated. Patients were excluded if 1) they were admitted to an intensive care unit that did not respond to the survey, 2) they were admitted within 3 months after implementation of mild therapeutic hypothermia, 3) they had a Glasgow Coma Scale score of >8, or 4) they did not satisfy the Simplified Acute Physiology Score II inclusion criteria. None. A total of 13,962 patients were admitted to an intensive care unit following cardiac arrest. In total 8,645 patients were excluded, 5,544 because of a Glasgow Coma Scale score of >8. Of the resultant 5,317 patients, 1,547 patients were treated before and 3,770 patients after implementation of mild therapeutic hypothermia. Patients admitted after implementation of mild therapeutic hypothermia had lower minimal and maximal temperatures (p < .0001) during the first 24 hrs on the intensive care unit compared to patients admitted before implementation of mild therapeutic hypothermia. The adjusted odds ratio of the hospital mortality of patients treated after implementation of mild therapeutic hypothermia was 0.80 (95% confidence interval of 0.65-0.98, p = .029). The results of this retrospective, observational survey suggest that implementation of mild therapeutic hypothermia in Dutch intensive care units is associated with a 20% relative reduction of hospital mortality in cardiac arrest patients.

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.

Training in data definitions improves quality of intensive care data.

Our aim was to assess the contribution of training in data definitions and data extraction guidelines to improving quality of data for use in intensive care scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II in the Dutch National Intensive Care Evaluation (NICE) registry. Before and after attending a central training programme, a training group of 31 intensive care physicians from Dutch hospitals who were newly participating in the NICE registry extracted data from three sample patient records. The 5-hour training programme provided participants with guidelines for data extraction and strict data definitions. A control group of 10 intensive care physicians, who were trained according the to train-the-trainer principle at least 6 months before the study, extracted the data twice, without specific training in between. In the training group the mean percentage of accurate data increased significantly after training for all NICE variables (+7%, 95% confidence interval 5%-10%), for APACHE II variables (+6%, 95% confidence interval 4%-9%) and for SAPS II variables (+4%, 95% confidence interval 1%-6%). The percentage data error due to nonadherence to data definitions decreased by 3.5% after training. Deviations from 'gold standard' SAPS II scores and predicted mortalities decreased significantly after training. Data accuracy in the control group did not change between the two data extractions and was equal to post-training data accuracy in the training group. Training in data definitions and data extraction guidelines is an effective way to improve quality of intensive care scoring data.