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BackgroundThe 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.MethodsA 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.Results972 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.DiscussionInformed 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.

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.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.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 risk30%). 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]).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.

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.

Supplemental Digital Content is available in the text., 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.

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.

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.

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 Ĉ-statistic) were calculated using bootstrapping. The cohort consisted of 36,632 Patients from the Dutch National Intensive Care Evaluation (NICE) registry having had a cardiac surgery procedure for which ICU admission was necessary between January 1st, 2006 and June 31st, 2018. 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.

PURPOSE: To assess the association of clinical variables and the development of specified chronic conditions in ICU survivors.MATERIALS AND METHODS: A retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Claims data from 2012 to 2014 were combined with clinical data of patients admitted to an ICU during 2013. To assess the association of clinical variables (ICU length of stay, mechanical ventilation, acute physiology score, reason for ICU admission, mean arterial pressure score and glucose score) and the development of chronic conditions (i.e. heart diseases, COPD or asthma, Diabetes mellitus type II, depression and kidney diseases), logistic regression was used.RESULTS: 49,004 ICU patients were included. ICU length of stay was associated with the development of heart diseases, asthma or COPD and depression. The reason for ICU admission was an important risk factor for the development of all chronic conditions with adjusted ORs ranging from 2.05 (CI 1.56; 2.69) for kidney diseases to 5.14 (CI 3.99; 6.62) for depression.CONCLUSIONS: Clinical variables, especially the reason for ICU admission, are associated with the development of chronic conditions after ICU discharge. Therefore, these clinical variables should be considered when organizing follow-up care for ICU survivors.

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

BACKGROUND: General Practitioners (GPs) play a key role in the healthcare trajectory of patients. If the patient experiences problems that are typically non-life-threatening, such as the symptoms of post-intensive-care syndrome, the GP will be the first healthcare professional they consult. The primary aim of this study is to gain insight in the frequency of GP consultations during the year before hospital admission and the year after discharge for ICU survivors and a matched control group from the general population. The secondary aim of this study is to gain insight into differences between subgroups of the ICU population with respect to the frequency of GP consultations.METHODS: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Clinical data of patients admitted to an ICU in 2013 were enriched with claims data from the years 2012, 2013 and 2014. Poisson regression was used to assess the differences in frequency of GP consultations between the ICU population and the control group.RESULTS: ICU patients have more consultations with GPs during the year before and after admission than individuals in the control group. In the last four weeks before admission, ICU patients have 3.58 (CI 3.37; 3.80) times more GP consultations than the control group, and during the first four weeks after discharge they have 4.98 (CI 4.74; 5.23) times more GP consultations. In the year after hospital discharge ICU survivors have an increased GP consultation rate compared to the year before their hospital admission.CONCLUSIONS: Close to hospital admission and shortly after hospital discharge, the frequency of GP consultations substantially increases in the population of ICU survivors. Even a year after hospital discharge, ICU survivors have increased GP consultation rates. Therefore, GPs should be well informed about the problems ICU patients suffer after discharge, in order to provide suitable follow-up care.

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.

Introduction: The long-term outcome of “very old intensive care unit patients” (VOPs; ≥ 80 years) is often disappointing. Little is known about the healthcare costs of these VOPs in comparison to younger ICU patients and the very elderly in the general population not admitted to the ICU. Methods: Data from a national health insurance claims database and a national quality registry for ICUs were combined. Costs of VOPs admitted to the ICU in 2013 were compared with costs of younger ICU patients (two groups, respectively 18–65 and 65–80 years old) and a matched control group of very elderly subjects who were not admitted to the ICU. We compared median costs and median costs per day alive in the year before ICU admission (2012), the year of ICU admission (2013) and the year after ICU admission (2014). Results: A total of 9272 VOPs were included and compared to three equally sized study groups. Median costs for VOPs in 2012, 2013 and 2014 (€5944, €35,653 and €12,565) are higher compared to the ICU 18–65 population (€3022, €30,223 and €5052, all p < 0.001) and the very elderly control population (€3590, €4238 and €4723, all p < 0.001). Compared to the ICU 65–80 population, costs of VOPs are higher in the year before and after ICU admission (€4323 and €6750, both p < 0.001), but not in the year of ICU admission (€34,448, p = 0.950). The median healthcare costs per day alive in the year before, the year of and the year after ICU admission are all higher for VOPs than for the other groups (p < 0.001). Conclusions: VOPs required more healthcare resources in the year before, the year of and the year after ICU admission compared to younger ICU patients and the very elderly control population, except compared to the ICU 65–80 population in the year of ICU admission. Healthcare costs per day alive, however, are substantially higher for VOPs than for all other study groups in all three studied years.

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. Electronic supplementary material The online version of this article (doi:10.1186/s12913-017-2234-z) contains supplementary material, which is available to authorized users.

Purpose To identify subgroups of ICU patients with high healthcare utilization for healthcare expenditure management purposes such as prevention and targeted care. Materials and methods We conducted a descriptive cohort study, combining a national health insurance claims database and a national quality registry database for ICUs. Claims data in the timeframe 2012–2014 were combined with the clinical data of ICU patients admitted to an ICU during 2013. A population based control group was created based on the ICU population. Results 56,760 ICU patients and 75,232 controls from the general population were included. Median healthcare costs per day alive for the ICU population were significantly higher during the year before (€8.9 (IQR €2.4; €32.1)) and the year after ICU admission (€15.4 (IQR €5.4; €51.2)) compared to the control group ((€2.8 (IQR €0.7; €8.8) and €3.1 (IQR €0.8; €10.1)). ICU patients with more chronic conditions had significantly higher healthcare costs before and after ICU admission compared to ICU patients with less chronic conditions. Conclusions ICU patients have three to five times higher healthcare costs per day alive compared to a control population. Our findings can be used to optimize the healthcare trajectories of ICU patients with high healthcare utilization after discharge.

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.

Objective: To evaluate the usability of a large compositional interface terminology based on SNOMED CT and the terminology application for registration of the reasons for intensive care admission in a Patient Data Management System. Design: Observational study with user-based usability evaluations before and 3 months after the system was implemented and routinely used. Measurements: Usability was defined by five aspects: effectiveness, efficiency, learnability, overall user satisfaction, and experienced usability problems. Qualitative (the Think-Aloud user testing method) and quantitative (the System Usability Scale questionnaire and Time-on-Task analyses) methods were used to examine these usability aspects. Results: The results of the evaluation study revealed that the usability of the interface terminology fell short (SUS scores before and after implementation of 47.2 out of 100 and 37.5 respectively out of 100). The qualitative measurements revealed a high number (n = 35) of distinct usability problems, leading to ineffective and inefficient registration of reasons for admission. The effectiveness and efficiency of the system did not change over time. About 14% (n = 5) of the revealed usability problems were related to the terminology content based on SNOMED CT, while the remaining 86% (n = 30) was related to the terminology application. The problems related to the terminology content were more severe than the problems related to the terminology application. Conclusions: This study provides a detailed insight into how clinicians interact with a controlled compositional terminology through a terminology application. The extensiveness, complexity of the hierarchy, and the language usage of an interface terminology are defining for its usability. Carefully crafted domain-specific subsets and a well-designed terminology application are needed to facilitate the use of a complex compositional interface terminology based on SNOMED CT. (C) 2011 Elsevier Ireland Ltd. All rights reserved

Background: Re-use of patient data from an electronic patient record heavily relies on structured, coded data. Terminological systems (TSs) are meant to support this. TSs are more commonly implemented nowadays, especially those that support post-coordination. The effect of using a compositional TS on correctness and specificity of captured patient data is unknown. Objective: To evaluate the agreement between free-text reasons for admission to intensive care captured in a patient data management system (PDMS) and reasons for admission that were recorded using a compositional TS embedded in the PDMS. Methods: Observational study comparing pairs of free-text reasons for admission to intensive care with reasons for admission that were recorded using a compositional TS. Both reasons for admission were captured in the PDMS by clinicians during regular care practice. Each pair was judged as exact match, partial match or mismatch by two independent raters. Partial matches were further analyzed to investigate whether free-text or TS-based reasons for admissions included more detail and whether these differences could be explained by the content, the interface of the TS or by user or usability characteristics. Results: Eleven percent of the pairs matched exactly, 79% of the pairs matched partially and 10% of the pairs did not match. Compared to free-text registration TS-based registration resulted in more detail for 21% of the partial matches, in less detail for 43% of the partial matches and in 36% of the partial matches some detail was added while at the same time other detail was lacking. In 65% of the cases in which the TS-based registration lacked some detail, this detail was available in the content of the TS. Physicians who used the TS occasionally had a significantly higher percentage of mismatches. Conclusion: In practice, post-coordination leads to information with different detail but a level of detail comparable to free-text registration of reasons for admission. Details missing in the TS-based reasons for admission were most often available in the TS, indicating that user interaction with the system is more of an impediment than the contents of the TS. (C) 2008 Elsevier Ireland Ltd. All rights reserved

Background: Postoperative care for major elective cancer surgery is frequently provided on the Intensive Care Unit (ICU). Objective: To analyze the characteristics and outcome of patients after ICU admission following elective surgery for different cancer diagnoses. Methods: 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. Results: 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-cholangiocarcimoma 2.0%). Conclusion: Elective cancer surgery represents a significant part of all ICU admissions, with a short length of stay and low mortality. (c) 2013 Elsevier Ltd. All rights reserved

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.

Introduction: Clinical decision support systems (CDSSs) should be seamlessly integrated with existing clinical information systems to enable automatic provision of advice at the time and place where decisions are made. It has been suggested that a lack of agreed data standards frequently hampers this integration. We performed a literature review to investigate whether CDSSs used standardized (i.e. coded or numerical) data and which terminological systems have been used to code data. We also investigated whether a lack of standardized data was considered an impediment for CDSS implementation. Methods: Articles reporting an evaluation of a CDSS that provided a computerized advice based on patient-specific data items were identified based on a former literature review on CDSS and on CDSS studies identified in AMIA's 'Year in Review'. Authors of these articles were contacted to check and complete the extracted data. A questionnaire among the authors of included studies was used to determine the obstacles in CDSS implementation. Results: We identified 77 articles published between 1995 and 2008. Twenty-two percent of the evaluated CDSSs used only numerical data. Fifty one percent of the CDSSs that used coded data applied an international terminology. The most frequently used international terminology were the ICD (International Classification of Diseases), used in 68% of the cases and LOINC (Logical Observation Identifiers Names and Codes) in 12% of the cases. More than half of the authors experienced barriers in CDSS implementation. In most cases these barriers were related to the lack of electronically available standardized data required to invoke or activate the CDSS. Conclusion: Many CDSSs applied different terminological systems to code data. This diversity hampers the possibility of sharing and reasoning with data within different systems. The results of the survey confirm the hypothesis that data standardization is a critical success factor for CDSS development. (c) 2010 Elsevier Ireland Ltd. All rights reserved

Objective: Terminological Systems (TSs) need to be maintained in order to sustain their utility. This paper describes a study aiming at the standardization of the maintenance processes of medical TSs by capturing the criteria for the management of the maintenance processes into a framework. Furthermore, this paper describes application of the framework, which sheds light on the current practice of TS maintenance. Design: Observational study. Measurements: By means of a literature study, criteria for the maintenance of TSs were obtained and categorized into a framework. The current practice of TS maintenance was explored by a survey among organizations that maintain a TS. Results were stratified by the size of the TS being maintained. Results: From Sixty-three relevant articles, criteria for the maintenance processes of TSs were extracted and organized into four components. The primary component “Execution” concerns the core activities of the maintenance process. The other three components “Process management,” “Change specifications,” and “Editing tools” support the core activities of the component “Execution.” The survey had a response rate of 40% (37 of 93). The answers reflect the large variation in the number of criteria that are satisfied for the participating organizations. Overall, maintenance of larger TSs seems to satisfy more criteria. Conclusions: The framework is an important step towards standardization of the maintenance of medical TSs and can be used to eliminate shortcomings in this process. Surveying the current practice showed that there is ample room to improve the maintenance processes of medical TSs, especially for the smaller TSs.

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

Purpose: The aim of this study was to validate and compare the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) IV in the Dutch intensive care unit (ICU) population to the APACHE II and Simplified Acute Physiology Score (SAPS) II. Materials and Methods: This is a prospective study based on data from a national quality registry between 2006 and 2009 from 59 Dutch ICUs. The validation set consisted of 62 737 patients; the 3 models were compared using 44 112 patients. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R-2, and C-statistic) were calculated using bootstrapping. In addition, the standardized mortality ratios were calculated. Results: The original APACHE IV showed good discrimination and accuracy (AUC = 0.87, Brier score = 0.10, R-2 = 0.29) but poor calibration (G-statistic = 822.67). Customization significantly improved the performance of the APACHE IV. The overall discrimination and accuracy of the customized APACHE IV were statistically better, and the overall G-statistic was inferior to those of the customized APACHE II and SAPS II, but these differences were small in perspective of clinical use. Conclusions: The 3 models have comparable capabilities for benchmarking purposes after customization. Main advantage of APACHE IV is the large number of diagnoses that enable subgroup analysis. The APACHE IV coronary artery bypass grafting (CABG) model has a good performance in the Dutch ICU population and can be used to complement the 3 models. (C) 2011 Elsevier Inc. All rights reserved