Conservative versus Liberal Oxygenation Targets in Intensive Care Unit Patients (ICONIC): A Randomized Clinical Trial.

Rationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear. Objectives: This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy. Methods: This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (PaO2, 55-80 mm Hg; or oxygen saturation as measured by pulse oximetry, 91-94%) or high-oxygenation (PaO2, 110-150 mm Hg; or oxygen saturation as measured by pulse oximetry, 96-100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included. Measurements and Main Results: Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved PaO2 was 75 mm Hg (interquartile range, 70-84) and 115 mm Hg (interquartile range, 100-129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9-1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively. Conclusions: Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy. Clinical trial registered with the National Trial Register and the International Clinical Trials Registry Platform (NTR7376).

Comparison of patient characteristics and long-term mortality between transferred and non-transferred COVID-19 patients in Dutch intensive care units: A national cohort study.

BACKGROUND: COVID-19 patients were often transferred to other intensive care units (ICUs) to prevent that ICUs would reach their maximum capacity. However, transferring ICU patients is not free of risk. We aim to compare the characteristics and outcomes of transferred versus non-transferred COVID-19 ICU patients in the Netherlands. METHODS: We included adult COVID-19 patients admitted to Dutch ICUs between March 1, 2020 and July 1, 2021. We compared the patient characteristics and outcomes of non-transferred and transferred patients and used a Directed Acyclic Graph to identify potential confounders in the relationship between transfer and mortality. We used these confounders in a Cox regression model with left truncation at the day of transfer to analyze the effect of transfers on mortality during the 180 days after ICU admission. RESULTS: We included 10,209 patients: 7395 non-transferred and 2814 (27.6%) transferred patients. In both groups, the median age was 64 years. Transferred patients were mostly ventilated at ICU admission (83.7% vs. 56.2%) and included a larger proportion of low-risk patients (70.3% vs. 66.5% with mortality risk <30%). After adjusting for age, APACHE IV mortality probability, BMI, mechanical ventilation, and vasoactive medication use, the hazard of mortality during the first 180 days was similar for transferred patients compared to non-transferred patients (HR [95% CI] = 0.99 [0.91-1.08]). CONCLUSIONS: Transferred COVID-19 patients are more often mechanically ventilated and are less severely ill compared to non-transferred patients. Furthermore, transferring critically ill COVID-19 patients in the Netherlands is not associated with mortality during the first 180 days after ICU admission.

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

BACKGROUND: There 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. METHODS: We 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 C-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. RESULTS: Discrimination 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. CONCLUSION: Although 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.

ICU Survivors Have a Substantial Higher Risk of Developing New Chronic Conditions Compared to a Population-Based Control Group.

OBJECTIVES: To describe the types and prevalence of chronic conditions in an ICU population and a population-based control group during the year before ICU admission and to quantify the risk of developing new chronic conditions in ICU patients compared with the control group. DESIGN: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Claims data in the timeframe 2012-2014 were combined with clinical data of patients who had been admitted to an ICU during 2013. To assess the differences in risk of developing new chronic conditions, ICU patients were compared with a population-based control group using logistic regression modeling. SETTING: Eighty-one Dutch ICUs. PATIENTS: All patients admitted to an ICU during 2013. A population-based control group was created, and weighted on the age, gender, and socio-economic status of the ICU population. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: ICU patients (n = 56,760) have more chronic conditions compared with the control group (n = 75,232) during the year before ICU admission (p < 0.0001). After case-mix adjustment ICU patients had a higher risk of developing chronic conditions, with odds ratios ranging from 1.67 (CI, 1.29-2.17) for asthma to 24.35 (CI, 14.00-42.34) for epilepsy, compared with the control group. CONCLUSIONS: Due to the high prevalence of chronic conditions and the increased risk of developing new chronic conditions, ICU follow-up care is advised and may focus on the identification and treatment of the new developed chronic conditions.

Adjusting for Disease Severity Across ICUs in Multicenter Studies.

OBJECTIVES: To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers. DESIGN: In silico simulation study using national registry data. SETTING: Twenty mixed ICUs in The Netherlands. SUBJECTS: Fifty-five-thousand six-hundred fifty-five ICU admissions between January 1, 2011, and January 1, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: To mimic an intervention study with confounding, a fictitious treatment variable was simulated whose effect on the outcome was confounded by Acute Physiology and Chronic Health Evaluation IV predicted mortality (a common measure for disease severity). Diverse, realistic scenarios were investigated where the availability of disease severity measures (i.e., Acute Physiology and Chronic Health Evaluation IV, Acute Physiology and Chronic Health Evaluation II, and Simplified Acute Physiology Score II scores) varied across centers. For each scenario, eight different methods to adjust for confounding were used to obtain an estimate of the (fictitious) treatment effect. These were compared in terms of relative (%) and absolute (odds ratio) bias to a reference scenario where the treatment effect was estimated following correction for the Acute Physiology and Chronic Health Evaluation IV scores from all centers. Complete neglect of differences in disease severity measures across centers resulted in bias ranging from 10.2% to 173.6% across scenarios, and no commonly used methodology-such as two-stage modeling or score standardization-was able to effectively eliminate bias. In scenarios where some of the included centers had (only) Acute Physiology and Chronic Health Evaluation II or Simplified Acute Physiology Score II available (and not Acute Physiology and Chronic Health Evaluation IV), either restriction of the analysis to Acute Physiology and Chronic Health Evaluation IV centers alone or multiple imputation of Acute Physiology and Chronic Health Evaluation IV scores resulted in the least amount of relative bias (0.0% and 5.1% for Acute Physiology and Chronic Health Evaluation II, respectively, and 0.0% and 4.6% for Simplified Acute Physiology Score II, respectively). In scenarios where some centers used Acute Physiology and Chronic Health Evaluation II, regression calibration yielded low relative bias too (relative bias, 12.4%); this was not true if these same centers only had Simplified Acute Physiology Score II available (relative bias, 54.8%). CONCLUSIONS: When different disease severity measures are available across centers, the performance of various methods to control for confounding by disease severity may show important differences. When planning multicenter studies, researchers should make contingency plans to limit the use of or properly incorporate different disease measures across centers in the statistical analysis.

The healthcare costs of intoxicated patients who survive ICU admission are higher than non-intoxicated ICU patients: a retrospective study combining healthcare insurance data and data from a Dutch national quality registry.

BACKGROUND: The aim of this study was to describe the healthcare costs of intoxicated ICU patients in the year before and the year after ICU admission, and to compare their healthcare costs with non-intoxicated ICU patients and a population based control group. METHODS: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry database for ICUs. Claims data in the timeframe 2012 until 2014 were combined with the clinical data of patients who had been admitted to an ICU during 2013. Three study populations were compared and matched according to socioeconomic status, type of admission, age and gender: an "ICU population", an "intoxication population" and a "control population" (who had never been on the ICU). RESULTS: 2591 individual "intoxicated ICU patients" were compared to 2577 general "ICU patients" and 2591 patients from the "control population". The median and interquartile ranges (IQR) healthcare costs per day alive for the "intoxicated ICU patients" were higher during the year before ICU admission (€20.3 (IQR €3.6-€76.4)) and the year after ICU admission (€23.9 (IQR €5.1-€82.4)) compared to the ICU population (€6.1 (IQR €0.9-€29.3) and €13.6 (IQR €3.3-€54.9) respectively) and a general control population (€1.1 (IQR €0.3-€4.6) and €1.1 (IQR €0.4-€4.9) respectively). The healthcare associated costs in intoxicated ICU patients were correlated with the number of chronic conditions present prior ICU admission (p < 0.0001). CONCLUSIONS: Intoxicated patients admitted to the ICU had in the year before and after ICU admission much higher median healthcare costs per day alive compared to other ICU patients and a general population control group. Healthcare costs are greatly influenced by the number of psychiatric and other chronic conditions of these intoxicated patients.

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

BACKGROUND: Variation 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: We 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.

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

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

Reported burden on informal caregivers of ICU survivors: a literature review.

BACKGROUND: Critical illness and the problems faced after ICU discharge do not only affect the patient, it also negatively impacts patients' informal caregivers. There is no review which summarizes all types of burden reported in informal caregivers of ICU survivors. It is important that the burdens these informal caregivers suffer are systematically assessed so the caregivers can receive the professional care they need. We aimed to provide a complete overview of the types of burdens reported in informal caregivers of adult ICU survivors, to make recommendations on which burdens should be assessed in this population, and which tools should be used to assess them. METHOD: We performed a systematic search in PubMed and CINAHL from database inception until June 2014. All articles reporting on burdens in informal caregivers of adult ICU survivors were included. Two independent reviewers used a standardized form to extract characteristics of informal caregivers, types of burdens and instruments used to assess these burdens. The quality of the included studies was assessed using the Newcastle-Ottawa and the PEDro scales. RESULTS: The search yielded 2704 articles, of which we included 28 in our review. The most commonly reported outcomes were psychosocial burden. Six months after ICU discharge, the prevalence of anxiety was between 15% and 24%, depression between 4.7% and 36.4% and post-traumatic stress disorder (PTSD) between 35% and 57.1%. Loss of employment, financial burden, lifestyle interference and low health-related quality of life (HRQoL) were also frequently reported. The most commonly used tools were the Hospital Anxiety and Depression Scale (HADS), Centre for Epidemiological Studies-Depression questionnaire, and Impact of Event Scale (IES). The quality of observational studies was low and of randomized studies moderate to fair. CONCLUSIONS: Informal caregivers of ICU survivors suffer a substantial variety of burdens. Although the quality of the included studies was poor, there is evidence that burden in the psychosocial field is most prevalent. We suggest screening informal caregivers of ICU survivors for anxiety, depression, PTSD, and HRQoL using respectively the HADS, IES and Short Form 36 and recommend a follow-up period of at least 6 months.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the consecutive stages of the COVID-19 pandemic in the Netherlands, an update.

BACKGROUND: Previously, we reported a decreased mortality rate among patients with COVID-19 who were admitted at the ICU during the final upsurge of the second wave (February-June 2021) in the Netherlands. We examined whether this decrease persisted during the third wave and the phases with decreasing incidence of COVID-19 thereafter and brought up to date the information on patient characteristics. METHODS: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and rates of in-hospital mortality (the primary outcome) during the consecutive periods after the first wave (periods 2-9, May 25, 2020-January 31, 2023) were compared with those during the first wave (period 1, February-May 24, 2020). RESULTS: After adjustment for patient characteristics and ICU occupancy rate, the mortality risk during the initial upsurge of the third wave (period 6, October 5, 2021-January, 31, 2022) was similar to that of the first wave (ORadj = 1.01, 95%-CI [0.88-1.16]). The mortality rates thereafter decreased again (e.g., period 9, October 5, 2022-January, 31, 2023: ORadj = 0.52, 95%-CI [0.41-0.66]). Among the SARS-CoV-2 positive patients, there was a huge drop in the proportion of patients with COVID-19 as main reason for ICU admission: from 88.2% during the initial upsurge of the third wave to 51.7%, 37.3%, and 41.9% for the periods thereafter. Restricting the analysis to these patients did not modify the results on mortality. CONCLUSIONS: The results show variation in mortality rates among critically ill COVID-19 patients across the calendar time periods that is not explained by differences in case-mix and ICU occupancy rates or by varying proportions of patients with COVID-19 as main reason for ICU admission. The consistent increase in mortality during the initial, rising phase of each separate wave might be caused by the increased virulence of the contemporary virus strain and lacking immunity to the new strain, besides unmeasured patient-, treatment- and healthcare system characteristics.

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

PURPOSE: To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry. MATERIALS AND METHODS: We 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). RESULTS: 705,822 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. CONCLUSIONS: Quality 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.

Determinants of mortality after hospital discharge in ICU patients: literature review and Dutch cohort study.

OBJECTIVES: First, to conduct a literature review on the long-term mortality of ICU patients and its determinants. Second, to assess the influence of the found determinants at 3, 6, and 12 months mortality after hospital discharge in the Dutch ICU population. DESIGN: Combination of a literature review to evaluate determinants of long-term mortality and a Dutch cohort study in which the found determinants are applied. SETTING: PubMed and EMBASE were searched on English written articles published between 1966 and 2011. The cohort study was conducted in ICU patients from 81 Dutch mixed ICUs. DATA: A total of 24 articles with a main focus on describing or predicting the case-mix adjusted long-term mortality of the general ICU population were identified. The cohort study consisted of 48,107 ICU patients who were discharged alive from the hospital between January 1, 2007, and October 1, 2010. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The included articles are summarized on patient and study characteristics, methods, results, and determinants used for case-mix adjustment. Additionally, the quality of the included articles was assessed using a checklist for studies on long-term survival. The median mortality rate of the general ICU population 1 year after ICU admission was 24% (range 16% to 44%). The determinants used for case-mix adjustment differed widely between the studies. In the cohort study, we found that age, reason for ICU admission, and comorbidities were associated with all long-term mortality endpoints. However, the magnitude and direction of the influence by these determinants differed for the different endpoints (i.e., 3, 6, and 12 mo after hospital discharge). CONCLUSIONS: The long-term mortality found in the included articles was difficult to compare due to low quality, variation in case-mix, study design, and differences in case-mix adjustment. The most commonly used determinants in the literature were comparable to the most important determinants found in the Dutch cohort study.

Assessing the FAIRness of databases on the EHDEN portal: A case study on two Dutch ICU databases.

OBJECTIVE: To address the growing need for effective data reuse in health research, healthcare institutions need to make their data Findable, Accessible, Interoperable, and Reusable (FAIR). A prevailing method to model databases for interoperability is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative. A European repository for OMOP CDM-converted databases called the "European Health Data & Evidence Network (EHDEN) portal" was developed, aiming to make these databases Findable and Accessible. This paper aims to assess the FAIRness of databases on the EHDEN portal. MATERIALS AND METHODS: Two researchers involved in the OMOP CDM conversion of separate Dutch Intensive Care Unit (ICU) research databases each manually assessed their own database using seventeen metrics. These were defined by the FAIRsFAIR project as a list of minimum requirements for a database to be FAIR. Each metric is given a score from zero to four based on how well the database adheres to the metric. The maximum score for each metric varies from one to four based on the importance of the metric. RESULTS: Fourteen out of the seventeen metrics were unanimously rated: seven were rated the highest score, one was rated half of the highest score, and five were rated the lowest score. The remaining three metrics were assessed differently for the two use cases. The total scores achieved were 15.5 and 12 out of a maximum of 25. CONCLUSION: The main omissions in supporting FAIRness were the lack of globally unique identifiers such as Uniform Resource Identifiers (URIs) in the OMOP CDM and the lack of metadata standardization and linkage in the EHDEN portal. By implementing these in future updates, the EHDEN portal can be more FAIR.

Negative calcium balance despite normal plasma ionized calcium concentrations during citrate anticoagulated continuous venovenous hemofiltration (CVVH) in ICU patients.

BACKGROUND: Supplementation of calcium during continuous venovenous hemofiltration (CVVH) with citrate anticoagulation is usually titrated using a target blood ionized calcium concentration. Plasma calcium concentrations may be normal despite substantial calcium loss, by mobilization of calcium from the skeleton. Aim of our study is to develop an equation to calculate CVVH calcium and to retrospectively calculate CVVH calcium balance in a cohort of ICU-patients. METHODS: This is a single-center retrospective observational cohort study. In a subcohort of patients, all calcium excretion measurements in patients treated with citrate CVVH were randomly divided into a development set (n = 324 in 42 patients) and a validation set (n = 441 in 42 different patients). Using mixed linear models, we developed an equation to calculate calcium excretion from routinely available parameters. We retrospectively calculated calcium balance in 788 patients treated with citrate CVVH between 2014 and 2021. RESULTS: Calcium excretion (mmol/24 h) was - 1.2877 + 0.646*[Ca]blood,total * ultrafiltrate (l/24 h) + 0.107*blood flow (ml/h). The mean error of the estimation was - 1.0 ± 6.7 mmol/24 h, the mean absolute error was 4.8 ± 4.8 mmol/24 h. Calculated calcium excretion was 105.8 ± 19.3 mmol/24 h. Mean daily CVVH calcium balance was - 12.0 ± 20.0 mmol/24 h. Mean cumulative calcium balance ranged from - 3687 to 448 mmol. CONCLUSION: During citrate CVVH, calcium balance was negative in most patients, despite supplementation of calcium based on plasma ionized calcium levels. This may contribute to demineralization of the skeleton. We propose that calcium supplementation should be based on both plasma ionized calcium and a simple calculation of calcium excretion by CVVH.

Structural under-reporting of informed consent, data handling and sharing, ethical approval, and application of Open Science principles as proxies for study quality conduct in COVID-19 research: a systematic scoping review.

BACKGROUND: The COVID-19 pandemic required science to provide answers rapidly to combat the outbreak. Hence, the reproducibility and quality of conducting research may have been threatened, particularly regarding privacy and data protection, in varying ways around the globe. The objective was to investigate aspects of reporting informed consent and data handling as proxies for study quality conduct. METHODS: A systematic scoping review was performed by searching PubMed and Embase. The search was performed on November 8th, 2020. Studies with hospitalised patients diagnosed with COVID-19 over 18 years old were eligible for inclusion. With a focus on informed consent, data were extracted on the study design, prestudy protocol registration, ethical approval, data anonymisation, data sharing and data transfer as proxies for study quality. For reasons of comparison, data regarding country income level, study location and journal impact factor were also collected. RESULTS: 972 studies were included. 21.3% of studies reported informed consent, 42.6% reported waivers of consent, 31.4% did not report consent information and 4.7% mentioned other types of consent. Informed consent reporting was highest in clinical trials (94.6%) and lowest in retrospective cohort studies (15.0%). The reporting of consent versus no consent did not differ significantly by journal impact factor (p=0.159). 16.8% of studies reported a prestudy protocol registration or design. Ethical approval was described in 90.9% of studies. Information on anonymisation was provided in 17.0% of studies. In 257 multicentre studies, 1.2% reported on data sharing agreements, and none reported on Findable, Accessible, Interoperable and Reusable data principles. 1.2% reported on open data. Consent was most often reported in the Middle East (42.4%) and least often in North America (4.7%). Only one report originated from a low-income country. DISCUSSION: Informed consent and aspects of data handling and sharing were under-reported in publications concerning COVID-19 and differed between countries, which strains study quality conduct when in dire need of answers.

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.

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.

Comparing continuous versus categorical measures to assess and benchmark intensive care unit performance.

PURPOSE: To compare categorical and continuous combinations of the standardized mortality ratio (SMR) and the standardized resource use (SRU) to evaluate ICU performance. MATERIALS AND METHODS: We analysed data from adult patients admitted to 128 ICUs in Brazil and Uruguay (BR/UY) and 83 ICUs in The Netherlands between 2016 and 2018. SMR and SRU were calculated using SAPS-3 (BR/UY) or APACHE-IV (The Netherlands). Performance was defined as a combination of metrics. The categorical combination was the efficiency matrix, whereas the continuous combination was the average SMR and SRU (average standardized ratio, ASER). Association among metrics in each dataset was evaluated using Spearman's rho and R2. RESULTS: We included 277,459 BR/UY and 164,399 Dutch admissions. Median [interquartile range] ASER = 0.99[0.83-1.21] in BR/UY and 0.99[0.92-1.09] in Dutch datasets. The SMR and SRU were more correlated in BR/UY ICUs than in Dutch ICUs (Spearman's Rho: 0.54vs.0.24). The highest and lowest ASER values were concentrated in the least and most efficient groups. An expert focus group listed potential advantages and limitations of both combinations. CONCLUSIONS: The categorical combination of metrics is easy to interpret but limits statistical inference for benchmarking. The continuous combination offers appropriate statistical properties for evaluating performance when metrics are positively correlated.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands.

BACKGROUND: To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. METHODS: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020-January 31, 2021) and the final upsurge of the second wave, called the 'third wave' (N = 4602: February 1, 2021-June 30, 2021) were compared with those during the first wave (N = 2733, February-May 24, 2020). RESULTS: During the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71-0.90]). CONCLUSIONS: These data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19.

Number of intensivists per bed is associated with efficiency of Dutch intensive care units.

PURPOSE: To measure efficiency in Intensive Care Units (ICUs) and to determine which organizational factors are associated with ICU efficiency, taking confounding factors into account. MATERIALS AND METHODS: We used data of all consecutive admissions to Dutch ICUs between January 1, 2016 and January 1, 2019 and recorded ICU organizational factors. We calculated efficiency for each ICU by averaging the Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU) and examined the relationship between various organizational factors and ICU efficiency. We thereby compared the results of linear regression models before and after covariate adjustment using propensity scores. RESULTS: We included 164,399 admissions from 83 ICUs. ICU efficiency ranged from 0.51-1.42 (average 0.99, 0.15 SD). The unadjusted model as well as the propensity score adjusted model showed a significant association between the ratio of employed intensivists per ICU bed and ICU efficiency. Other organizational factors had no statistically significant association with ICU efficiency after adjustment. CONCLUSIONS: We found marked variability in efficiency in Dutch ICUs. After applying covariate adjustment using propensity scores, we identified one organizational factor, ratio intensivists per bed, having an association with ICU efficiency.