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Objective: Despite the wide use of blood glucose management guidelines in intensive care (IC), hyperglycemia is still common. The aim of this study was the discovery of possible hyperglycemia determinants by applying the Patient Rule Induction Method (PRIM) to routinely collected data within the first 24 hours of admission, and to relate them to the literature.Methods: PRIM was applied in two set-ups to data of 2001 IC patients including 50,021 records of blood glucose levels and other variables. The independent predictors of blood glucose measurements were variables whose value is known before the time of the corresponding measurement. Subgroups were validated using a random split design, and time-sensitivity of performance was analyzed.Results: PRIM was able to identify relatively large subgroups having markedly high mean glucose values. PRIM also discovered possible determinants of which less is known about their relationship to hyperglycemia. Some possible determinants reported in the literature were not found by PRIM.Conclusions: We demonstrated for the first time the utility of using subgroup discovery to uncover possible determinants for non-responsiveness to treatment. This implies the possible use of this technology to scrutinize the effects of various guidelines in clinical medicine on patient outcomes without requiring the development of a global predictive model. We hypothesize that by focusing on the identified subgroups, clinical guidelines may be improved. Further research is required to test this hypothesis. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To measure the impact of a computerized guideline for glucose regulation in an ICU. DESIGN: A randomized, controlled trial with an off-on-off design. METHODS: We implemented a glucose regulation guideline in an intensive care unit in paper form during the first study period. During the second period, the guideline was randomly applied in either paper or computerized form. In the third period, the guideline was available only in paper form. MEASUREMENTS AND RESULTS: We analyzed data for 484 patients. During the intervention period, the control group included 54 patients and the computerized intervention group included 66 patients. The two guideline-related outcome measures consisted of compliance with: (a) glucose measurement timing recommendations and (b) insulin dose advice. We measured clinical impact as the proportion of time that glucose levels fell within target range. In the first (paper-based) study period, 29.0% of samples occurred with optimal timing; during the second period, this increased to 35.5% for paper-based and to 40.2% for computerized protocols. The third study period timeliness scores reverted to the first period rates. Late (suboptimal) sampling occurred for 66% of glucose measurements in the first study period, for 42% of paper-based and 28% of computer-based protocol samples in the second period, and for 50.0% of samples in the third study period. In the first study period, insulin-dosing guideline compliance was 56.3%; in the second period, it was 64.2% for paper-based and 77.3% for computer-based protocols, and it fell to 42.4% in the third period. For the second study period, the time that a patient's glucose values fell within target range improved for both the control (52.9%) and the computerized groups (54.2%) compared with the first study period (44.3%) and the third period (42.3%). CONCLUSION: Implementing a computerized version of a guideline significantly improved timeliness of measurements and glucose level regulation for critically ill patients compared with implementing a paper-based version of the guideline. [ABSTRACT FROM AUTHOR]

Introduction: Tobramycin is one of the components used for selective decontamination of the digestive tract (SDD), applied to prevent colonization and subsequent infections in critically ill patients. Tobramycin is administered in the oropharynx and gastrointestinal tract and is normally not absorbed. However, critical illness may convey gut barrier failure. The aim of the study was to assess the prevalence and amount of tobramycin leakage from the gut into the blood, to quantify tobramycin excretion in urine, and to determine the association of tobramycin leakage with markers of circulation, kidney function and other organ failure.Methods: This was a prospective observational cohort study. The setting was the 20-bed closed format-mixed ICU of a teaching hospital. The study population was critically ill patients with an expected stay of more than two days, receiving SDD with tobramycin, polymyxin-E and amphotericin-B four times daily in the oropharynx and stomach. Tobramycin concentration was measured in serum (sensitive high performance liquid chromatography - mass spectrometry/mass spectrometry (HLPC-MS/MS) assay) and 24-hour urine (conventional immunoassay), in 34 patients, 24 hours after ICU admission, and in 71 patients, once daily for 7 days. Tobramycin leakage was defined as tobramycin detected in serum at least once (> 0.05 mg/L). Ototoxicity was not monitored.Results: Of the 100 patients with available blood samples, 83 had tobramycin leakage. Median highest serum concentration for each patient was 0.12 mg/L; 99% of the patients had at least one positive urinary sample (> 0.5 mg/L), 49% had a urinary concentration ≥ 1 mg/L. The highest tobramycin serum concentration was significantly associated with vasopressor support, renal and hepatic dysfunction, and C-reactive protein. At binary logistic regression analysis, high dopamine dose and low urinary output on Day 1 were the significant predictors of tobramycin leakage. Nephrotoxicity could not be shown.Conclusions: The majority of acute critically ill patients treated with enteral tobramycin as a component of SDD had traces of tobramycin in the blood, especially those with severe shock, inflammation and subsequent acute kidney injury, suggesting loss of gut barrier and decreased renal removal. Unexpectedly, urinary tobramycin was above the therapeutic trough level in half of the patients. Nephrotoxicity could not be demonstrated. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: Knowledge about adverse drug events caused by drug-drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR + ) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential., Methods: We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019. We used computerized triggers (e-triggers) to preselect patients with potential DDI-ADEs. Between September 2020 and October 2021, clinical experts conducted a retrospective manual patient chart review on a subset of preselected patients, and assessed causality, severity, preventability, and contribution to ICU length of stay of DDI-ADEs using internationally prevailing standards., Results: In total 85 422 patients with ≥1 pDDI were included. Of these patients, 32 820 (38.4%) have been exposed to one of the three pDDIs. In the exposed group, 1141 (3.5%) patients were preselected using e-triggers. Of 237 patients (21%) assessed, 155 (65.4%) experienced an actual DDI-ADE; 52.9% had severity level of serious or higher, 75.5% were preventable, and 19.3% contributed to a longer ICU length of stay. The positive predictive value was the highest for DDI-INR + e-trigger (0.76), followed by DDI-AKI e-trigger (0.57)., Conclusion: The highly preventable nature and severity of DDI-ADEs, calls for action to optimize ICU patient safety. Use of e-triggers proved to be a promising preselection strategy., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Abstract: Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 by the Shock Society.)

Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data., Methods: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors., Results: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors., Conclusion: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources., Methods: From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO 2 /FiO 2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO 2 /FiO 2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking., Results: The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO 2 /FiO 2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode., Conclusions: In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning., (© 2022. The Author(s).)

Background: Adequate antibiotic dosing may improve outcomes in critically ill patients but is challenging due to altered and variable pharmacokinetics. To address this challenge, AutoKinetics was developed, a decision support system for bedside, real-time, data-driven and personalised antibiotic dosing. This study evaluates the feasibility, safety and efficacy of its clinical implementation., Methods: In this two-centre randomised clinical trial, critically ill patients with sepsis or septic shock were randomised to AutoKinetics dosing or standard dosing for four antibiotics: vancomycin, ciprofloxacin, meropenem, and ceftriaxone. Adult patients with a confirmed or suspected infection and either lactate > 2 mmol/L or vasopressor requirement were eligible for inclusion. The primary outcome was pharmacokinetic target attainment in the first 24 h after randomisation. Clinical endpoints included mortality, ICU length of stay and incidence of acute kidney injury., Results: After inclusion of 252 patients, the study was stopped early due to the COVID-19 pandemic. In the ciprofloxacin intervention group, the primary outcome was obtained in 69% compared to 3% in the control group (OR 62.5, CI 11.4-1173.78, p < 0.001). Furthermore, target attainment was faster (26 h, CI 18-42 h, p < 0.001) and better (65% increase, CI 49-84%, p < 0.001). For the other antibiotics, AutoKinetics dosing did not improve target attainment. Clinical endpoints were not significantly different. Importantly, higher dosing did not lead to increased mortality or renal failure., Conclusions: In critically ill patients, personalised dosing was feasible, safe and significantly improved target attainment for ciprofloxacin., Trial Registration: The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017., (© 2022. The Author(s).)

Background: Selective Decontamination of the Digestive tract (SDD) aims to prevent nosocomial infections, by eradication of potentially pathogenic micro-organisms from the digestive tract., Objectives: To estimate the rate of and the time to eradication of resistant vs. susceptible facultative aerobic gram-negative bacteria (AGNB) in patients treated with SDD., Methods: This observational and retrospective study included patients admitted to the ICU between January 2001 and August 2017. Patients were included when treated with SDD (tobramycin, polymyxin B, and amphotericin B) and colonized in the upper or lower gastro-intestinal (GI) tract with at least one AGNB present on admission. Decontamination was determined after the first negative set of cultures (rectal and throat). An additional analysis was performed of two consecutive negative cultures., Results: Of the 281 susceptible AGNB in the throat and 1,087 in the rectum on admission, 97.9 and 93.7%, respectively, of these microorganisms were successfully eradicated. In the upper GI-tract no differences in eradication rates were found between susceptible and resistant microorganisms. However, the median duration until eradication was significantly longer for aminoglycosides resistant vs. susceptible microorganisms (5 vs. 4 days, p < 0.01). In the lower GI-tract, differences in eradication rates between susceptible and resistant microorganisms were found for cephalosporins (90.0 vs. 95.6%), aminoglycosides (84.4 vs. 95.5%) and ciprofloxacin (90.0 vs. 95.2%). Differences in median duration until eradication between susceptible and resistant microorganisms were found for aminoglycosides and ciprofloxacin (both 5 days vs. 6 days, p = 0.001). Decontamination defined as two negative cultures was achieved in a lower rate (77-98% for the upper GI tract and 64-77% for the lower GI tract) and a median of 1 day later., Conclusion: The vast majority of both susceptible and resistant microorganisms are effectively eradicated from the upper and lower GI tract. In the lower GI tract decontamination rates of susceptible microorganisms are significantly higher and achieved in a shorter time period compared to resistant strains., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Buitinck, Jansen, Bosman, van der Meer and van der Voort.)

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU., Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure., Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model., Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction., Methods: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores., Results: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively)., Conclusions: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far., (© 2021 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)

Introduction: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19., Methods: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots., Results: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure., Conclusion: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records., (© 2021. The Author(s).)

Objectives: In critically ill patients, dysnatremia is common, and in these patients, in-hospital mortality is higher. It remains unknown whether changes of serum sodium after ICU admission affect mortality, especially whether normalization of mild hyponatremia improves survival., Design: Retrospective cohort study., Setting: Ten Dutch ICUs between January 2011 and April 2017., Patients: Adult patients were included if at least one serum sodium measurement within 24 hours of ICU admission and at least one serum sodium measurement 24-48 hours after ICU admission were available., Interventions: None., Measurements and Main Results: A logistic regression model adjusted for age, sex, and Acute Physiology and Chronic Health Evaluation-IV-predicted mortality was used to assess the difference between mean of sodium measurements 24-48 hours after ICU admission and first serum sodium measurement at ICU admission (Δ48 hr-[Na]) and in-hospital mortality. In total, 36,660 patients were included for analysis. An increase in serum sodium was independently associated with a higher risk of in-hospital mortality in patients admitted with normonatremia (Δ48 hr-[Na] 5-10 mmol/L odds ratio: 1.61 [1.44-1.79], Δ48 hr-[Na] > 10 mmol/L odds ratio: 4.10 [3.20-5.24]) and hypernatremia (Δ48 hr-[Na] 5-10 mmol/L odds ratio: 1.47 [1.02-2.14], Δ48 hr-[Na] > 10 mmol/L odds ratio: 8.46 [3.31-21.64]). In patients admitted with mild hyponatremia and Δ48 hr-[Na] greater than 5 mmol/L, no significant difference in hospital mortality was found (odds ratio, 1.11 [0.99-1.25])., Conclusions: An increase in serum sodium in the first 48 hours of ICU admission was associated with higher in-hospital mortality in patients admitted with normonatremia and in patients admitted with hypernatremia., Competing Interests: Drs. Termorshuizen’s and de Keizer’s institutions received funding from the National Intensive Care Evaluation Foundation. Dr. Termorshuizen received funding from the Mental Health Care Institute, Rivierduinen, Leiden. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Objectives: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients., Design: Multicenter retrospective cohort study., Setting: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020., Patients: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded., Measurements and Main Results: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio 2 , set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao 2 /Fio 2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant ( p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves., Conclusions: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Introduction: The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in the number of patients per nurse they had to take care of., Objective: The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups., Methods: We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data., Results: Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care., Conclusion: With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse., Competing Interests: Declaration of Competing Interest The author M.E. Hoogendoorn and co-authors S. Brinkman, J.J. Spijkstra, R.J. Bosman, and N.F. de Keizer are members of the board of NICE. The funding by the NICE foundation does not alter the authors' adherence to the policies of Critical Care Nursing on sharing data and materials., (Copyright © 2021. Published by Elsevier Ltd.)

Background: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients., Methods: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers., Results: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive., Conclusions: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine., (© 2021. The Author(s).)

Background: High-dose intravenous vitamin C directly scavenges and decreases the production of harmful reactive oxygen species (ROS) generated during ischemia/reperfusion after a cardiac arrest. The aim of this study is to investigate whether short-term treatment with a supplementary or very high-dose intravenous vitamin C reduces organ failure in post-cardiac arrest patients., Methods: This is a double-blind, multi-center, randomized placebo-controlled trial conducted in 7 intensive care units (ICUs) in The Netherlands. A total of 270 patients with cardiac arrest and return of spontaneous circulation will be randomly assigned to three groups of 90 patients (1:1:1 ratio, stratified by site and age). Patients will intravenously receive a placebo, a supplementation dose of 3 g of vitamin C or a pharmacological dose of 10 g of vitamin C per day for 96 h. The primary endpoint is organ failure at 96 h as measured by the Resuscitation-Sequential Organ Failure Assessment (R-SOFA) score at 96 h minus the baseline score (delta R-SOFA). Secondary endpoints are a neurological outcome, mortality, length of ICU and hospital stay, myocardial injury, vasopressor support, lung injury score, ventilator-free days, renal function, ICU-acquired weakness, delirium, oxidative stress parameters, and plasma vitamin C concentrations., Discussion: Vitamin C supplementation is safe and preclinical studies have shown beneficial effects of high-dose IV vitamin C in cardiac arrest models. This is the first RCT to assess the clinical effect of intravenous vitamin C on organ dysfunction in critically ill patients after cardiac arrest., Trial Registration: ClinicalTrials.gov NCT03509662. Registered on April 26, 2018. https://clinicaltrials.gov/ct2/show/NCT03509662 European Clinical Trials Database (EudraCT): 2017-004318-25. Registered on June 8, 2018. https://www.clinicaltrialsregister.eu/ctr-search/trial/2017-004318-25/NL., (© 2021. The Author(s).)

The present study evaluated whether LLInC (Leerkracht-Leerling Interactie Coaching in Dutch, or Teacher-Student Interaction Coaching), a teacher-based coaching-intervention, yielded improvements in dyadic affective teacher-child relationships in elementary school (Grades 2-6). Based on attachment theory, LLInC aims to foster more flexible and differentiated mental representations of teachers' relationships with individual children with whom they experience relationship difficulties. Using a quasi-experimental design, we compared an intervention group of teachers (n = 46 teachers and 92 children) receiving LLInC with a control group receiving no form of intervention (n = 32 teachers and 88 children). To investigate possible transfer effects, we asked teachers from the intervention group to report on their relationships and self-efficacy beliefs regarding two other children with whom they experienced relationship difficulties as well (n = 46 teachers and 81 children). Multilevel models were used to examine intervention effects on teachers' perceptions of relationship quality (i.e., Closeness, Conflict, Dependency), and teachers' student-specific self-efficacy beliefs for Behavior Management and Emotional Support. Teachers receiving LLInC reported short-term improvements in Closeness and self-efficacy beliefs for Emotional Support and decreases in Conflict as compared to control teachers. Similar improvements in Closeness and self-efficacy for Emotional Support were found for the intervention-transfer group as compared to control teachers. Also, teachers receiving LLInC had short-term and longer-term improvements in self-efficacy beliefs for Behavior Management as compared to control teachers. These improvements regarding Behavior Management were not found for the intervention-transfer group., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values., Methods: Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG)., Results: The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions., Conclusion: In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2021. Published by Elsevier B.V.)

Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients., Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split., Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH 2 O., Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

Purpose: Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting., Materials & Methods: In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting., Results: The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when considering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs., Conclusions: Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model-based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients., Methods: We simulated concentration data for 1 day following four sampling schemes, C min , C max + C min , C max + C mid-interval + C min , and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC 24 = 400, 500, and 600 mg·h/L and C min = 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against "true" PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 ≤ AUC 24 ≤ 600 mg·h/L and C min ≤ 20 mg/L., Results: PK parameters were unbiasedly estimated in all investigated scenarios and the 6-day average NRMSE were 32.5%/38.5% (CL/V, where CL is clearance and V is volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC 24 = 500 mg·h/L., Conclusions: For model-based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC 24 = 500 mg·h/L lead to the best POT., (© 2020 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Background: A range of classification systems are in use for the measurement of nursing workload in Intensive Care Units. However, it is unknown to what extent the measured (objective) nursing workload, usually in terms of the amount of nursing activities, is related to the workload actually experienced (perceived) by nurses., Objectives: The aim of this study was to assess the association between the objective nursing workload and the perceived nursing workload and to identify other factors associated with the perceived nursing workload., Methods: We measured the objective nursing workload with the Nursing Activities Score and the perceived nursing workload with the NASA-Task Load Index during 228 shifts in eight different Intensive Care Units. We used linear mixed-effect regression models to analyze the association between the objective and perceived nursing workload. Furthermore, we investigated the association of patient characteristics (severity of illness, comorbidities, age, body mass index, and planned or unplanned admission), education level of the nurse, and contextual factors (numbers of patients per nurse, the type of shift (day, evening, night) and day of admission or discharge) with perceived nursing workload. We adjusted for confounders., Results: We did not find a significant association between the observed workload per nurse and perceived nursing workload (p=0.06). The APACHE-IV Acute Physiology Score of a patient was significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.02). None of the other patient characteristics was significantly associated with perceived nursing workload. Being a certified nurse or a student nurse was the only nursing or contextual factor significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.03)., Conclusion: Workload is perceived differently by nurses compared to the objectively measured workload by the Nursing Activities Score. Both the severity of illness of the patient and being a student nurse are factors that increase the perceived nursing workload. To keep the workload of nurses in balance, planning nursing capacity should be based on the Nursing Activities Score, on the severity of patient illness and the graduation level of the nurse., (Copyright © 2020. Published by Elsevier Ltd.)

Background: Several instruments have been developed to measure nursing workload. The commonly used Nursing Activities Score (NAS) and Therapeutic Intervention Scoring System (TISS) are applied to all types of ICU patients. Former research showed that NAS explained 59 to 81% of actual nursing time, whereas the Therapeutic Intervention Scoring System (TISS) described only 43% of the actual nursing time. In both models the development was not based on time measurements., Objectives: The aim of this study was to develop a time-based model which can assess patient related nursing workload more accurately and to evaluate whether patient characteristics influence nursing time and therefore should be included in the model., Design: Observational study design., Setting: All 82 Dutch ICUs participate in the National Intensive Care Evaluation (NICE) quality registry. Fifteen of these ICUs are participating in the newly implemented voluntary nursing capacity module. Seven of these ICUs voluntarily participated in this study., Participants: The patient(s) that were under the responsibility of a chosen nurse were followed by the observer during the entire shift., Methods: Time spent per nursing activity per patient was measured in different shifts in seven Dutch ICUs. Nursing activities were measured using an in-house developed web application. Three different models of varying complexity (1. nursing activities only; 2. nursing activities and case-mix correction; 3. complex model with case-mix correction per nursing activity) were developed to explain the total amount of nursing time per patient. The performance of the three models was assessed in 1000 bootstrap samples using the squared Pearson correlation coefficient (R 2 ), Root Mean Squared Prediction Error (RMSPE), Mean Absolute Prediction Error (MAPE), and prediction bias., Results: In total 287 unique patients have been observed in 371 shifts. Model one's Pearson's R was 0.89 (95%CI 0.86-0.92), model two with case-mix correction 0.90 (95%CI 0.88-0.93), and the third complex model 0.64 (95%CI 0.56-0.72) compared with the actual patient related nursing workload., Conclusion: The newly developed Nurse Operation Workload (NOW) model outperforms existing models in measuring nursing workload, while it includes a lower number of activities and therewith lowers the registration burden. Case-mix correction does not further improve the performance of this model. The patient related nursing workload measured by the NOW gives insight in the actual nursing time needed by patients and can therefore be used to evaluate the average workload per patient per nurse., (Copyright © 2020. Published by Elsevier Ltd.)

Purpose: Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM., Methods: We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days., Results: The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were - 7.7%, -4.5% and - 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors., Conclusions: The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.

Introduction: Antibiotic dosing in critically ill patients is challenging because their pharmacokinetics (PK) are altered and may change rapidly with disease progression. Standard dosing frequently leads to inadequate PK exposure. Therapeutic drug monitoring (TDM) offers a potential solution but requires sampling and PK knowledge, which delays decision support. It is our philosophy that antibiotic dosing support should be directly available at the bedside through deep integration into the electronic health record (EHR) system. Therefore we developed AutoKinetics, a clinical decision support system (CDSS) for real time, model informed precision antibiotic dosing., Objective: To provide a detailed description of the design, development, validation, testing, and implementation of AutoKinetics., Methods: We created a development framework and used workflow analysis to facilitate integration into popular EHR systems. We used a development cycle to iteratively adjust and expand AutoKinetics functionalities. Furthermore, we performed a literature review to select and integrate pharmacokinetic models for five frequently prescribed antibiotics for sepsis. Finally, we tackled regulatory challenges, in particular those related to the Medical Device Regulation under the European regulatory framework., Results: We developed a SQL-based relational database as the backend of AutoKinetics. We developed a data loader to retrieve data in real time. We designed a clinical dosing algorithm to find a dose regimen to maintain antibiotic pharmacokinetic exposure within clinically relevant safety constraints. If needed, a loading dose is calculated to minimize the time until steady state is achieved. Finally, adaptive dosing using Bayesian estimation is applied if plasma levels are available. We implemented support for five extensively used antibiotics following model development, calibration, and validation. We integrated AutoKinetics into two popular EHRs (Metavision, Epic) and developed a user interface that provides textual and visual feedback to the physician., Conclusion: We successfully developed a CDSS for real time model informed precision antibiotic dosing at the bedside of the critically ill. This holds great promise for improving sepsis outcome. Therefore, we recently started the Right Dose Right Now multi-center randomized control trial to validate this concept in 420 patients with severe sepsis and septic shock., (Copyright © 2020 Roggeveen, Guo, Driessen, Fleuren, Thoral, van der Voort, Girbes, Bosman and Elbers.)

Background: Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines and clinicians continue to rely on standard dosing schemes. To address this challenge, we developed AutoKinetics, a clinical decision support system for antibiotic dosing. By feeding large amounts of electronic health record patient data into pharmacokinetic models, patient-specific predicted future plasma concentrations are displayed graphically. In addition, a tailored dosing advice is provided at the bedside in real time. To evaluate the effect of AutoKinetics on pharmacometric and clinical endpoints, we are conducting the Right Dose Right Now multicenter, randomized controlled, two-arm, parallel-group, non-blinded, superiority trial., Methods: All adult intensive care patients with a suspected or proven infection and having either lactatemia or receiving vasopressor support are eligible for inclusion. Randomization to the AutoKinetics or control group is initiated at the bedside when prescribing at least one of four commonly administered antibiotics: ceftriaxone, ciprofloxacin, meropenem and vancomycin. Dosing advice is available for patients in the AutoKinetics group, whereas patients in the control group receive standard dosing. The primary outcome of the study is pharmacometric target attainment during the first 24 h. Power analysis revealed the need for inclusion of 42 patients per group per antibiotic. Thus, a total of 336 patients will be included, 168 in each group. Secondary pharmacometric endpoints include time to target attainment and fraction of target attainment during an entire antibiotic course. Secondary clinical endpoints include mortality, clinical cure and days free from organ support. Several other exploratory and subgroup analyses are planned., Discussion: This is the first randomized controlled trial to assess the effectiveness and safety of bedside data-driven automated antibiotic dosing advice. This is important as adequate antibiotic exposure may be crucial to treat severe sepsis and septic shock. In addition, the trial could prove to be a significant contribution to clinical pharmacometrics and serve as a stepping stone for the use of big data and artificial intelligence in the field., Trial Registration: Netherlands Trial Register (NTR), NL6501/NTR6689. Registered on 25 August 2017. European Clinical Trials Database (EudraCT), 2017-002478-37. Registered on 6 November 2017.

Objectives: To determine the clinical effects of perioperative endotoxin reduction in the gut lumen in patients undergoing cardiac surgery with cardiopulmonary bypass., Design: Retrospective cohort analysis with propensity score matching according to treatment group., Setting: Tertiary center for cardiopulmonary diseases and intensive care medicine., Participants: Included were patients who underwent cardiac surgery with cardiopulmonary bypass between 2008 and 2017. Excluded were readmitted patients., Interventions: Endotoxin reduction in the gut lumen by ingestion of oral tobramycin 80 mg and polymyxin B 100 mg 4 times daily (TP) as part of selective digestive tract decontamination, which contains amphotericin B 500 mg as well., Measurements and Main Results: A total of 6,394 patients were included, of whom 2,044 patients were in the intervention group. A total of 835 patients received both pre- and postoperative TP (Pre+/Post+), and 1,165 patients received TP only postoperatively (Pre-/Post+). The control group, not treated with TP at any moment, consisted of 4,350 patients (Pre-/Post-). After matching, 652 Pre+/Post+ patients were compared with an equal number of controls (Pre-/Post-). Pre+/Post+ group did not do better for any clinical outcome. A total of 682 Pre+/Post+ patients matched with an equal number of Pre-/Post+ patients. The latter group had a 0.3 points higher mean Sequential Organ Failure Assessment score and in the regression analysis a significantly higher intensive care unit mortality but not hospital mortality. A significant reduction in length of stay and length of mechanical ventilation for the Pre+/Post+ group was shown compared with Pre-/Post+, but these differences can be explained by unbalanced differences in the severity of illness., Conclusion: Cardiosurgical patients who receive tobramycin and polymyxin orally preoperatively to reduce the gut endotoxin level do not expose convincing and relevant beneficial effects on clinical outcomes in this retrospective propensity score matching cohort study., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: The long-term ecological effects on the emergence of antimicrobial resistance at the ICU level during selective decontamination of the digestive tract (SDD) are unknown. We determined the incidence of newly acquired antimicrobial resistance of aerobic gram-negative potentially pathogenic bacteria (AGNB) during SDD., Methods: In a single-centre observational cohort study over a 21-year period, all consecutive patients, treated with or without SDD, admitted to the ICU were included. The antibiotic regime was unchanged over the study period. Incidence rates for ICU-acquired AGNB's resistance for third-generation cephalosporins, colistin/polymyxin B, tobramycin/gentamicin or ciprofloxacin were calculated per year. Changes over time were tested by negative binomial regression in a generalized linear model., Results: Eighty-six percent of 14,015 patients were treated with SDD. Most cultures were taken from the digestive tract (41.9%) and sputum (21.1%). A total of 20,593 isolates of AGNB were identified. The two most often found bacteria were Escherichia coli (N = 6409) and Pseudomonas (N = 5269). The incidence rate per 1000 patient-day for ICU-acquired resistance to cephalosporins was 2.03, for polymyxin B/colistin 0.51, for tobramycin 2.59 and for ciprofloxacin 2.2. The incidence rates for ICU-acquired resistant microbes per year ranged from 0 to 4.94 per 1000 patient-days, and no significant time-trend in incidence rates were found for any of the antimicrobials. The background prevalence rates of resistant strains measured on admission for cephalosporins, polymyxin B/colistin and ciprofloxacin rose over time with 7.9%, 3.5% and 8.0% respectively., Conclusions: During more than 21-year SDD, the incidence rates of resistant microbes at the ICU level did not significantly increase over time but the background resistance rates increased. An overall ecological effect of prolonged application of SDD by counting resistant microorganisms in the ICU was not shown in a country with relatively low rates of resistant microorganisms.

Background: Antibiotic exposure in intensive care patients with sepsis is frequently inadequate and is associated with poorer outcomes. Antibiotic dosing is challenging in the intensive care, as critically ill patients have altered and fluctuating antibiotic pharmacokinetics that make current one-size-fits-all regimens unsatisfactory. Real-time bedside dosing software is not available yet, and therapeutic drug monitoring is typically used for few antibiotic classes and only allows for delayed dosing adaptation. Thus, adequate and timely antibiotic dosing continues to rely largely on the level of pharmacokinetic expertise in the ICU. Therefore, we set out to assess the level of knowledge on antibiotic pharmacokinetics among these intensive care professionals., Methods: In May 2018, we carried out a cross-sectional study by sending out an online survey on antibiotic dosing to more than 20,000 intensive care professionals. Questions were designed to cover relevant topics in pharmacokinetics related to intensive care antibiotic dosing. The preliminary pass mark was set by members of the examination committee for the European Diploma of Intensive Care using a modified Angoff approach. The final pass mark was corrected for clinical relevance as assessed for each question by international experts on pharmacokinetics., Results: A total of 1448 respondents completed the survey. Most of the respondents were intensivists (927 respondents, 64%) from 97 countries. Nearly all questions were considered clinically relevant by pharmacokinetic experts. The pass mark corrected for clinical relevance was 52.8 out of 93.7 points. Pass rates were 42.5% for intensivists, 36.1% for fellows, 24.8% for residents, and 5.8% for nurses. Scores without correction for clinical relevance were worse, indicating that respondents perform better on more relevant topics. Correct answers and concise clinical background are provided., Conclusions: Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient. This should be addressed given the importance of adequate antibiotic exposure in critically ill patients with sepsis. Solutions include improved education, intensified pharmacy support, therapeutic drug monitoring, or the use of real-time bedside dosing software. Questions may provide useful for teaching purposes.

Dosing of vancomycin is often guided by therapeutic drug monitoring and population pharmacokinetic models in the intensive care unit (ICU). The validity of these models is crucial, as ICU patients have marked pharmacokinetic variability. Therefore, we set out to evaluate the predictive performance of published population pharmacokinetic models of vancomycin in ICU patients. The PubMed database was used to search for population pharmacokinetic models of vancomycin in adult ICU patients. The identified models were evaluated in two independent data sets which were collected from two large hospitals in the Netherlands (Amsterdam UMC, Location VUmc, and OLVG Oost). We also tested a one-compartment model with fixed values for clearance and volume of distribution, in which a clinical standard dosage regimen (SDR) was mimicked to assess its predictive performance. Prediction error was calculated to assess the predictive performance of the models. Six models plus the SDR model were evaluated. The model of Roberts et al. (J. A. Roberts, F. S. Taccone, A. A. Udy, J.-L. Vincent, F. Jacobs, and J. Lipman, Antimicrob Agents Chemother 55:2704-2709, 2011, https://doi.org/10.1128/AAC.01708-10) performed satisfactorily, with mean and median values of prediction error of 5.1% and -7.5%, respectively, for Amsterdam UMC, Location VUmc, patients, and -12.6% and -17.2% respectively, for OLVG Oost patients. The other models, including the SDR model, yielded high mean values (-49.7% to 87.7%) and median values (-56.1% to 66.1%) for both populations. In conclusion, only the model of Roberts et al. was able to validly predict the concentrations of vancomycin for our data, whereas other models and standard dosing were largely inadequate. Extensive evaluation should precede the adoption of any model in clinical practice for ICU patients., (Copyright © 2019 American Society for Microbiology.)

The present study used a person-centered approach to identify teacher-student relationship trajectories from kindergarten to sixth grade in a Dutch sample (N = 1300). Teachers reported about relationships with individual students (closeness, conflict, and dependency) in kindergarten, grade 3, and grade 6, and about externalizing behaviors in kindergarten. Students were tested for verbal ability in kindergarten, and completed math and reading tests and questionnaires about task motivation and self-efficacy in sixth grade. Latent class growth analyses revealed three trajectories for closeness: high-stable (normative), very high-decreasing, and moderate-increasing. For conflict, low-stable (normative), low-increasing, and high-decreasing trajectories were found. For dependency, a low-decreasing (normative) and a low-increasing trajectory were found. Boys, students with lower levels of verbal ability, and students with more externalizing behavior were overrepresented in non-normative trajectories. Furthermore, students with low-increasing levels of teacher-student dependency scored lower on achievement tests and reported lower motivation in sixth grade compared to students with a normative trajectory., (Copyright © 2018 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.)

Objectives: The Behavioral Pain Scale (BPS) and Critical-Care Pain Observation Tool (CPOT) are behavioral pain assessment tools for sedated and unconscious critically ill patients. The aim of this study was to compare the reliability, internal consistency, and discriminant validation of the BPS and the CPOT simultaneously in mechanically ventilated patients after cardiac surgery., Design: A prospective, observational cohort study., Setting: A 20-bed closed-format intensive care unit with mixed medical, surgical, and cardiac surgery patients in a teaching hospital in Amsterdam, The Netherlands., Participants: The study comprised 72 consecutive intubated and mechanically ventilated patients after cardiac surgery who were not able to self-report pain., Measurements and Main Results: Two nurses assessed the BPS and CPOT simultaneously and independently at the following 4 moments: rest, a nonpainful procedure (oral care), rest, and a painful procedure (turning). Both scores showed a significant increase of 2 points between rest and turning. The median BPS score of nurse 1 showed a significant increase of 1 point between rest and the nonpainful procedure (oral care), whereas both median CPOT scores did not change. The interrater reliability of the BPS and CPOT showed fair-to-good agreement of 0.74 overall. During the periods of rest 1 and rest 2, values ranged from 0.24 to 0.46. Cronbach's alpha values for the BPS were 0.62 (nurse 1) and 0.59 (nurse 2) compared with 0.65 and 0.58, respectively, for the CPOT., Conclusions: The BPS and CPOT are reliable and valid pain assessment tools in a daily clinical setting. However, the discriminant validation of both scores seems less satisfactory in sedated or agitated patients and this topic requires further investigation., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Purpose: Although the course of disease of type 1 and type 2 diabetes differs, the distinction is rarely made when patients are admitted to the intensive care unit (ICU). Here, we report patient- and admission-related characteristics in relation to glycemic measures of patients with type 1 and type 2 diabetes admitted to the ICU., Materials and Methods: A retrospective chart review was performed of 1574 patients with diabetes admitted between 2004 and 2011 to our ICU. Glycemic measures included mean glucose, the incidence of hypoglycemia and hyperglycemia, percentage of glucose values in/below/above target, and glucose variability. The ICU and hospital mortality were secondary outcomes., Results: We classified 2% (n=27) of patients as having type 1 diabetes and 98% (n=1547) as having type 2 diabetes. Patients with type 1 diabetes were significantly younger, had a lower body mass index, and were more frequently admitted to the ICU for medical diagnoses. No differences in glycemic measures were found, apart from a 20% higher glucose variability in the type 1 diabetes group., Conclusions: Patients with type 1 diabetes showed a higher glucose variability, but overall glycemic control was not different between patients with type 1 and type 2 diabetes. Very few diabetes patients admitted to the ICU have type 1 diabetes., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Readmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate., Material and Methods: In a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores., Results: In all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70., Conclusions: Readmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Introduction: Tele-intensive care (teleIC) can be used to increase the level of care in the intensive care unit (ICU) with the tele-intensivist being at a remote site. In this study we describe the implementation and outcomes, including patient and family satisfaction, of the first Dutch teleIC., Methods: In a retrospective analysis the patient characteristics are described with a focus on patient outcomes and patient and family satisfaction. The teleIC started on 6 December 2010 in an ICU with three beds. Data for all admitted patients were collected from 1 January 2011 through 31 December 2013 from available systems. Severity of disease (APACHE IV) and standardized mortality ratios (SMR) were available over the complete calendar years 2012 and 2013. A prospective self-made survey of patient and family satisfaction was performed over a 14-month period after the implementation of teleIC., Results: In 2012 and 2013, 556 patients were admitted. The number of operating room recovery patients in the ICU declined over time. One hundred and seven patients were transferred to a higher-level ICU (19%). The SMR for non-transferred patients was 0.73 (95% CI (confidence interval) 0.54-0.98) and 0.66 (95% CI (confidence interval) 0.40-1.02) for transferred patients. These numbers are in line with national data. Thirty-six percent of family and 38% of the patients responded to the survey and showed a positive response concerning communication within the tele IC setting., Conclusion: Implementation of teleIC in a Dutch situation was successful and showed favourable results in patient outcomes and the satisfaction of patients and family members., (© The Author(s) 2015.)

Background: During resuscitation in severe sepsis and septic shock, several goals are set. However, usually not all goals are equally met. The aim of this study is to determine the relative importance of the different goals, such as mean arterial pressure (MAP), lactate, central venous oxygen saturation (ScvO2) and central to forefoot temperature (delta-T), and how they relate to intensive care unit (ICU) and hospital mortality., Methods: In a retrospective cohort study in a 20-bed mixed medical and surgical ICU of a teaching hospital we studied consecutive critically ill patients who were admitted for confirmed infection and severe sepsis or septic shock between 2008 and 2014. All validated MAP, lactate levels, ScvO2 and delta-T for the first 24 hours of ICU treatment were extracted from a clinical database. Logistic regression analyses were performed on validated measurements in the first hour after admission and on mean values over 24 hours. Patients were categorized by MAP (24-hour mean below or above 65 mmHg) and lactate (24-hour mean below or above 2 mmol/l) for Cox regression analysis., Results: From 837 patients, 821 were eligible for analysis. All had MAP and lactate measurements. The delta-T was available in 812 (99%) and ScvO2 was available for 193 out of these patients (23.5%). Admission lactate (p < 0.001) and admission MAP (p < 0.001) were independent predictors of ICU and hospital mortality. The 24-hour mean values for lactate, MAP and delta-T were all independent predictors of ICU mortality. Hospital mortality was independently predicted by the 24-hour mean lactate (odds ratio (OR) 1.34, 95% confidence interval (CI) 1.30-1.40, p = 0.001) mean MAP (OR 0.96, 95% CI 0.95-0.97, p = 0.001) and mean delta-T (OR 1.09, 95% CI 1.06-1.12, p = 0.001). Patients with a 24-hour mean lactate below 2 mmol/l and a 24-hour mean MAP above 65 mmHg had the best survival, followed by patients with a low lactate and a low MAP., Conclusions: Admission MAP and lactate independently predicted ICU and hospital mortality. The 24-hour mean lactate, mean MAP and mean delta-T independently predicted hospital mortality. A Cox regression analysis showed that 24-hour mean lactate above 2 mmol/l is the strongest predictor for ICU mortality.