Adverse drug events caused by three high-risk drug-drug interactions in patients admitted to intensive care units: A multicentre retrospective observational study.

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.

Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock-a two-centre randomised clinical trial.

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.

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.

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.

Why we should sample sparsely and aim for a higher target: Lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients.

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, Cmin , Cmax + Cmin , Cmax + Cmid-interval + Cmin , 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: AUC24 = 400, 500, and 600 mg·h/L and Cmin = 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 ≤ AUC24 ≤ 600 mg·h/L and Cmin ≤ 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 AUC24 = 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 AUC24 = 500 mg·h/L lead to the best POT.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

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.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

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.

Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.

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.

External Evaluation of Population Pharmacokinetic Models of Vancomycin in Large Cohorts of Intensive Care Unit Patients.

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.

Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient: a cross-sectional study.

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.

The ecological effects of selective decontamination of the digestive tract (SDD) on antimicrobial resistance: a 21-year longitudinal single-centre study.

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.

Clinical Effects of Perioperative Selective Decontamination of the Digestive Tract (SDD) in Cardiac Surgery: A Propensity Score Matched Cohort Analysis.

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.

The association between lactate, mean arterial pressure, central venous oxygen saturation and peripheral temperature and mortality in severe sepsis: a retrospective cohort analysis.

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.

Testing a conceptual model on early opening of the microcirculation in severe sepsis and septic shock: a randomised controlled pilot study.

BACKGROUND: Organ failure in severe sepsis and septic shock may be caused by microcirculatory failure. OBJECTIVE: The objective of this study is to test a conceptual model of microcirculatory failure by using a resuscitation strategy targeting early opening of the constricted microcirculation with active vasodilatation. DESIGN: A randomised controlled pilot study. SETTING: Single-centre mixed medical and surgical tertiary ICU. PATIENTS: Ninety severe sepsis and septic shock patients randomised to early opening microcirculation resuscitation group or standard resuscitation group. INTERVENTIONS: Standard resuscitation group: fluids, noradrenaline, dobutamine and hydrocortisone were given to achieve a mean arterial pressure (MAP) of more than 60 mmHg, cardiac index more than 2.5 l min m and ScvO2 more than 70%. Microcirculation resuscitation group: nitroglycerin, enoximone, dopamine and dexamethasone targeting a microvascular flow index (MFI), measured by sublingual side-stream dark field imaging, more than 2.5. MAIN OUTCOME MEASURE: A decrease in organ failure score (SOFA) on day four of ICU treatment. RESULTS: Data from 37 microcirculation resuscitation and 28 standard resuscitation patients were analysed. In the microcirculation resuscitation group, MFI of more than 2.5 was achieved after a mean ±â€ŠSD of 7.0 ±â€Š4.6 h. The microcirculation resuscitation group received more fluids, and noradrenaline was equally prescribed in both groups. Per protocol, the decrease in SOFA score at day 4 was not different between groups (P = 0.64). There was a significant reduction in SOFA score in both groups compared with admission (1.2 and 1.6 in microcirculation resuscitation and standard resuscitation groups, respectively; P = 0.028 and P = 0.045). CONCLUSION: Early opening of the microcirculation in patients with severe sepsis and septic shock using nitroglycerin, enoximone, dopamine and corticosteroids did not result in a faster reduction in organ failure than standard resuscitation. TRIAL REGISTRATION: Clinicaltrials.gov identifier NCT00484133.

Insulin treatment guided by subcutaneous continuous glucose monitoring compared to frequent point-of-care measurement in critically ill patients: a randomized controlled trial.

INTRODUCTION: Glucose measurement in intensive care medicine is performed intermittently with the risk of undetected hypoglycemia. The workload for the ICU nursing staff is substantial. Subcutaneous continuous glucose monitoring (CGM) systems are available and may be able to solve some of these issues in critically ill patients. METHODS: In a randomized controlled design in a mixed ICU in a teaching hospital we compared the use of subcutaneous CGM with frequent point of care (POC) to guide insulin treatment. Adult critically ill patients with an expected stay of more than 24 hours and in need of insulin therapy were included. All patients received subcutaneous CGM. CGM data were blinded in the control group, whereas in the intervention group these data were used to feed a computerized glucose regulation algorithm. The same algorithm was used in the control group fed by intermittent POC glucose measurements. Safety was assessed with the incidence of severe hypoglycemia (<2.2 mmol/L), efficacy with the percentage time in target range (5.0 to 9.0 mmol/L). In addition, we assessed nursing workload and costs. RESULTS: In this study, 87 patients were randomized to the intervention and 90 to the control group. CGM device failure resulted in 78 and 78 patients for analysis. The incidence of severe glycemia and percentage of time within target range was similar in both groups. A significant reduction in daily nursing workload for glucose control was found in the intervention group (17 versus 36 minutes; P <0.001). Mean daily costs per patient were significantly reduced with EUR 12 (95% CI -32 to -18, P = 0.02) in the intervention group. CONCLUSIONS: Subcutaneous CGM to guide insulin treatment in critically ill patients is as safe and effective as intermittent point-of-care measurements and reduces nursing workload and daily costs. A new algorithm designed for frequent measurements may lead to improved performance and should precede clinical implementation. TRIAL REGISTRATION: Clinicaltrials.gov, NCT01526044. Registered 1 February 2012.

Can serum L-lactate, D-lactate, creatine kinase and I-FABP be used as diagnostic markers in critically ill patients suspected for bowel ischemia.

BACKGROUND: The prognostic value of biochemical tests in critically ill patients with multiple organ failure and suspected bowel ischemia is unknown. METHODS: In a prospective observational cohort study intensive care patients were included when the attending intensivist considered intestinal ischemia in the diagnostic workup at any time during intensive care stay. Patients were only included once. When enrolment was ended each patient was classified as 'proven intestinal ischemia', 'ischemia likely', 'ischemia unlikely' or 'no intestinal ischemia'. Proven intestinal ischemia was defined as the gross disturbance of blood flow in the bowel, regardless of extent and grade. Classification was based on reports from the operating surgeon, pathology department, endoscopy reports and CT-scan. Lactate dehydrogenase (LDH), creatine kinase (CK), alanine aminotransferase (ALAT), L-lactate were available for the attending physician. D-lactate and intestinal fatty acid binding protein (I-FABP) were analysed later in a batch. I-FABP was only measured in patients with proven ischemia or no ischemia. RESULTS: For 44 of the 120 included patients definite diagnostic studies were available. 23/44 patients (52%) had proven intestinal ischemia as confirmed by surgery, colonoscopy, autopsy and/or histopathological findings. LDH in these patients was 285 U/l (217-785) vs 287 U/l (189-836) in no-ischemia; p = 0.72. CK was 226 U/l in patients with proven ischemia (126-2145) vs 347 U/l (50-1427), p = 0.88. ALAT was 53 U/l (18-300) vs 34 U/l (14-34), p-0,56. D-lactate 0.41 mmol/l (0.11-0.75) vs 0.56 mmol/l (0.27-0.77), p = 0.46. L-lactate 3.5 mmol/l (2.2-8.4) vs 2.6 mmol/l (1.7-3.9), p = 0.09. I-FABP 2872 pg/ml (229-4340) vs 1020 pg/ml (239-5324), p = 0.98. Patient groups proven and likely ischemia together compared to unlikely and no-ischemia together showed significant higher L-lactate (p = 0.001) and higher D-lactate (p = 0.003). CONCLUSIONS: Measurement of LDH, CK, and ALAT did not discriminate critically ill patients with proven intestinal ischemia from those with definite diagnosis no-ischemia. However, L-lactate and D-lactate levels were higher in patients with proven or likely ischemia and need further study just as I-FABP.

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

PURPOSE: To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry. MATERIALS AND METHODS: We included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry. We determined median and interquartile range for eight quality indicators. To evaluate changes over time on the indicators, we performed multilevel regression analyses, once without and once with the COVID-19 years 2020 and 2021 included. Additionally we explored between-ICU heterogeneity by calculating intraclass correlation coefficients (ICC). RESULTS: 705,822 ICU admissions from 55 (65%) ICUs were included in the analyses. ICU length of stay (LOS), duration of mechanical ventilation (MV), readmissions, in-hospital mortality, hypoglycemia, and pressure ulcers decreased significantly between 2009 and 2019 (OR <1). After including the COVID-19 pandemic years, the significant change in MV duration, ICU LOS, and pressure ulcers disappeared. We found an ICC ≤0.07 on the quality indicators for all years, except for pressure ulcers with an ICC of 0.27 for 2009 to 2021. CONCLUSIONS: Quality of Dutch ICU care based on seven indicators significantly improved from 2009 to 2019 and between-ICU heterogeneity is medium to small, except for pressure ulcers. The COVID-19 pandemic disturbed the trend in quality improvement, but unaltered the between-ICU heterogeneity.

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

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 PaO2/FiO2 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 PaO2/FiO2 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 PaO2/FiO2 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.

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

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.

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.

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.