Publications by authors named "Paul W Elbers"

67 Publications

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

Crit Care Explor 2021 Oct 14;3(10):e0555. Epub 2021 Oct 14.

Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.

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, 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/Fio ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant ( ≤ 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCE.0000000000000555DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522869PMC
October 2021

Targeted Temperature Management in Out-of-Hospital Cardiac Arrest With Shockable Rhythm: A Post Hoc Analysis of the Coronary Angiography After Cardiac Arrest Trial.

Crit Care Med 2021 Sep 22. Epub 2021 Sep 22.

Department of Cardiology, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands. Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands. Department of Intensive Care Medicine, Erasmus Medical Center, Rotterdam, The Netherlands. Department of Cardiology, Amphia Hospital, Breda, The Netherlands. Department of Intensive Care Medicine, Amphia Hospital, Breda, The Netherlands. Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands. Department of Intensive Care Medicine, Rijnstate Hospital, Arnhem, The Netherlands. Department of Cardiology, HAGA Hospital, Den Haag, The Netherlands. Department of Intensive Care Medicine, HAGA Hospital, Den Haag, The Netherlands. Department of Cardiology, Maasstad Hospital, Rotterdam, The Netherlands. Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Maasstad Hospital, Rotterdam, The Netherlands. Department of Intensive Care Medicine, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands. Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Department of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Maastricht University Medical Center, University Maastricht, Maastricht, The Netherlands. Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht, The Netherlands. Department of Intensive Care Medicine, Medisch Spectrum Twente, Enschede, The Netherlands. Department of Cardiology, Medisch Spectrum Twente, Enschede, The Netherlands. Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands. Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. Department of Cardiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands. Department of Intensive Care Medicine, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands. Department of Cardiology, OLVG, Amsterdam, The Netherlands. Department of Intensive Care Medicine, OLVG, Amsterdam, The Netherlands. Department of Cardiology, Noord West Ziekenhuisgroep, Alkmaar, The Netherlands. Department of Intensive Care Medicine, Noord West Ziekenhuisgroep, Alkmaar, The Netherlands. Department of Cardiology, Maastricht University Medical Center, Maastricht, The Netherlands. Department of Cardiology, Scheper Hospital, Emmen, The Netherlands. Department of Cardiology, Haaglanden Medical Center, Den Haag, The Netherlands. Department of Cardiology, Isala Hospital, Zwolle, The Netherlands. Department of Cardiology, Tergooi Hospital, Blaricum, The Netherlands. Department of Cardiology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands. Department of Epidemiology and Data Science, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands.

Objectives: The optimal targeted temperature in patients with shockable rhythm is unclear, and current guidelines recommend targeted temperature management with a correspondingly wide range between 32°C and 36°C. Our aim was to study survival and neurologic outcome associated with targeted temperature management strategy in postarrest patients with initial shockable rhythm.

Design: Observational substudy of the Coronary Angiography after Cardiac Arrest without ST-segment Elevation trial.

Setting: Nineteen hospitals in The Netherlands.

Patients: The Coronary Angiography after Cardiac Arrest trial randomized successfully resuscitated patients with shockable rhythm and absence of ST-segment elevation to a strategy of immediate or delayed coronary angiography. In this substudy, 459 patients treated with mild therapeutic hypothermia (32.0-34.0°C) or targeted normothermia (36.0-37.0°C) were included. Allocation to targeted temperature management strategy was at the discretion of the physician.

Interventions: None.

Measurements And Main Results: After 90 days, 171 patients (63.6%) in the mild therapeutic hypothermia group and 129 (67.9%) in the targeted normothermia group were alive (hazard ratio, 0.86 [95% CI, 0.62-1.18]; log-rank p = 0.35; adjusted odds ratio, 0.89; 95% CI, 0.45-1.72). Patients in the mild therapeutic hypothermia group had longer ICU stay (4 d [3-7 d] vs 3 d [2-5 d]; ratio of geometric means, 1.32; 95% CI, 1.15-1.51), lower blood pressures, higher lactate levels, and increased need for inotropic support. Cerebral Performance Category scores at ICU discharge and 90-day follow-up and patient-reported Mental and Physical Health Scores at 1 year were similar in the two groups.

Conclusions: In the context of out-of-hospital cardiac arrest with shockable rhythm and no ST-elevation, treatment with mild therapeutic hypothermia was not associated with improved 90-day survival compared with targeted normothermia. Neurologic outcomes at 90 days as well as patient-reported Mental and Physical Health Scores at 1 year did not differ between the groups.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCM.0000000000005271DOI Listing
September 2021

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

Acta Anaesthesiol Scand 2021 Oct 8. Epub 2021 Oct 8.

Intensive Care, Reinier de Graaf Gasthuis, Delft, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/aas.13991DOI Listing
October 2021

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists.

Crit Care Explor 2021 Sep 10;3(9):e0529. Epub 2021 Sep 10.

Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCCI), Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning-based real-time bedside decision support tool.

Derivation Cohort: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016.

Validation Cohort: Data from 2016 to 2019 from the same center.

Prediction Model: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance.

Results: Our final derivation dataset included 14,105 admissions. The combined readmission/mortality rate within 7 days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.75-0.81) and an area under the precision-recall curve of 0.19 on the validation cohort ( = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%.

Conclusions: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCE.0000000000000529DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8437217PMC
September 2021

Lung ultrasound in a tertiary intensive care unit population: a diagnostic accuracy study.

Crit Care 2021 09 17;25(1):339. Epub 2021 Sep 17.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Location VU University Medical Center, de Boelelaan 11171007MB, Postbox 7505, Amsterdam, The Netherlands.

Background: Evidence from previous studies comparing lung ultrasound to thoracic computed tomography (CT) in intensive care unit (ICU) patients is limited due to multiple methodologic weaknesses. While addressing methodologic weaknesses of previous studies, the primary aim of this study is to investigate the diagnostic accuracy of lung ultrasound in a tertiary ICU population.

Methods: This is a single-center, prospective diagnostic accuracy study conducted at a tertiary ICU in the Netherlands. Critically ill patients undergoing thoracic CT for any clinical indication were included. Patients were excluded if time between the index and reference test was over eight hours. Index test and reference test consisted of 6-zone lung ultrasound and thoracic CT, respectively. Hemithoraces were classified by the index and reference test as follows: consolidation, interstitial syndrome, pneumothorax and pleural effusion. Sensitivity, specificity, positive and negative likelihood ratio were estimated.

Results: In total, 87 patients were included of which eight exceeded the time limit and were subsequently excluded. In total, there were 147 respiratory conditions in 79 patients. The estimated sensitivity and specificity to detect consolidation were 0.76 (95%CI: 0.68 to 0.82) and 0.92 (0.87 to 0.96), respectively. For interstitial syndrome they were 0.60 (95%CI: 0.48 to 0.71) and 0.69 (95%CI: 0.58 to 0.79). For pneumothorax they were 0.59 (95%CI: 0.33 to 0.82) and 0.97 (95%CI: 0.93 to 0.99). For pleural effusion they were 0.85 (95%CI: 0.77 to 0.91) and 0.77 (95%CI: 0.62 to 0.88).

Conclusions: In conclusion, lung ultrasound is an adequate diagnostic modality in a tertiary ICU population to detect consolidations, interstitial syndrome, pneumothorax and pleural effusion. Moreover, one should be careful not to interpret lung ultrasound results in deterministic fashion as multiple respiratory conditions can be present in one patient. Trial registration This study was retrospectively registered at Netherlands Trial Register on March 17, 2021, with registration number NL9344.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13054-021-03759-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447620PMC
September 2021

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

Crit Care 2021 08 23;25(1):304. Epub 2021 Aug 23.

Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13054-021-03733-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381710PMC
August 2021

Early high-dose vitamin C in post-cardiac arrest syndrome (VITaCCA): study protocol for a randomized, double-blind, multi-center, placebo-controlled trial.

Trials 2021 Aug 18;22(1):546. Epub 2021 Aug 18.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam Medical Data Science (AMDS), Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13063-021-05483-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371424PMC
August 2021

Rapid screening of critically ill patients for low plasma vitamin C concentrations using a point-of-care oxidation-reduction potential measurement.

Intensive Care Med Exp 2021 Aug 9;9(1):40. Epub 2021 Aug 9.

Department of Intensive Care Medicine, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Background: Hypovitaminosis C and vitamin C deficiency are common in critically ill patients and associated with organ dysfunction. Low vitamin C status often goes unnoticed because determination is challenging. The static oxidation reduction potential (sORP) reflects the amount of oxidative stress in the blood and is a potential suitable surrogate marker for vitamin C. sORP can be measured rapidly using the RedoxSYS system, a point-of-care device. This study aims to validate a model that estimates plasma vitamin C concentration and to determine the diagnostic accuracy of sORP to discriminate between decreased and higher plasma vitamin C concentrations.

Methods: Plasma vitamin C concentrations and sORP were measured in a mixed intensive care (IC) population. Our model estimating vitamin C from sORP was validated by assessing its accuracy in two datasets. Receiver operating characteristic (ROC) curves with areas under the curve (AUC) were constructed to show the diagnostic accuracy of sORP to identify and rule out hypovitaminosis C and vitamin C deficiency. Different cut-off values are provided.

Results: Plasma vitamin C concentration and sORP were measured in 117 samples in dataset 1 and 43 samples in dataset 2. Bias and precision (SD) were 1.3 ± 10.0 µmol/L and 3.9 ± 10.1 µmol/L in dataset 1 and 2, respectively. In patients with low plasma vitamin C concentrations, bias and precision were - 2.6 ± 5.1 µmol/L and - 1.1 ± 5.4 µmol in dataset 1 (n = 40) and 2 (n = 20), respectively. Optimal sORP cut-off values to differentiate hypovitaminosis C and vitamin C deficiency from higher plasma concentrations were found at 114.6 mV (AUC 0.91) and 124.7 mV (AUC 0.93), respectively.

Conclusion: sORP accurately estimates low plasma vitamin C concentrations and can be used to screen for hypovitaminosis C and vitamin C deficiency in critically ill patients. A validated model and multiple sORP cut-off values are presented for subgroup analysis in clinical trials or usage in clinical practice.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40635-021-00403-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8349944PMC
August 2021

Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort.

BMJ Open 2021 07 19;11(7):e047347. Epub 2021 Jul 19.

Department of Internal Medicine, Flevoziekenhuis, Almere, Flevoland, The Netherlands.

Objective: Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.

Design: Retrospective cohort study.

Setting: A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.

Participants: SARS-CoV-2 positive patients (age ≥18) admitted to the hospital.

Main Outcome Measures: 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.

Results: 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).

Conclusion: Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/bmjopen-2020-047347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290951PMC
July 2021

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

Intensive Care Med Exp 2021 Jun 28;9(1):32. Epub 2021 Jun 28.

ICU, Maasstad Ziekenhuis Rotterdam, Rotterdam, The Netherlands.

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 cmHO.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s40635-021-00397-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236316PMC
June 2021

Population Pharmacokinetics and Probability of Target Attainment of Different Dosing Regimens of Ceftazidime in Critically Ill Patients with a Proven or Suspected Infection.

Antibiotics (Basel) 2021 May 21;10(6). Epub 2021 May 21.

Hospital Pharmacy and Clinical Pharmacology, Amsterdam University Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.

Altered pharmacokinetics (PK) of hydrophilic antibiotics in critically ill patients is common, with possible consequences for efficacy and resistance. We aimed to describe ceftazidime population PK in critically ill patients with a proven or suspected infection and to establish optimal dosing. Blood samples were collected for ceftazidime concentration measurement. A population PK model was constructed, and probability of target attainment (PTA) was assessed for targets 100% T > MIC and 100% T > 4 × MIC in the first 24 h. Ninety-six patients yielded 368 ceftazidime concentrations. In a one-compartment model, variability in ceftazidime clearance (CL) showed association with CVVH. For patients not receiving CVVH, variability in ceftazidime CL was 103.4% and showed positive associations with creatinine clearance and with the comorbidities hematologic malignancy, trauma or head injury, explaining 65.2% of variability. For patients treated for at least 24 h and assuming a worst-case MIC of 8 mg/L, PTA was 77% for 100% T > MIC and 14% for 100% T > 4 × MIC. Patients receiving loading doses before continuous infusion demonstrated higher PTA than patients who did not (100% T > MIC: 95% ( = 65) vs. 13% ( = 15); < 0.001 and 100% T > 4 × MIC: 20% vs. 0%; = 0.058). The considerable IIV in ceftazidime PK in ICU patients could largely be explained by renal function, CVVH use and several comorbidities. Critically ill patients are at risk for underexposure to ceftazidime when empirically aiming for the breakpoint MIC for . A loading dose is recommended.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/antibiotics10060612DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224000PMC
May 2021

The effect of immediate coronary angiography after cardiac arrest without ST-segment elevation on left ventricular function. A sub-study of the COACT randomised trial.

Resuscitation 2021 07 28;164:93-100. Epub 2021 Apr 28.

Department of Intensive care medicine, Noord West Ziekenhuisgroep, Alkmaar, The Netherlands.

Background: The effect of immediate coronary angiography and percutaneous coronary intervention (PCI) in patients who are successfully resuscitated after cardiac arrest in the absence of ST-segment elevation myocardial infarction (STEMI) on left ventricular function is currently unknown.

Methods: This prespecified sub-study of a multicentre trial evaluated 552 patients, successfully resuscitated from out-of-hospital cardiac arrest without signs of STEMI. Patients were randomized to either undergo immediate coronary angiography or delayed coronary angiography, after neurologic recovery. All patients underwent PCI if indicated. The main outcomes of this analysis were left ventricular ejection fraction and end-diastolic and systolic volumes assessed by cardiac magnetic resonance imaging or echocardiography.

Results: Data on left ventricular function was available for 397 patients. The mean (± standard deviation) left ventricular ejection fraction was 45.2% (±12.8) in the immediate angiography group and 48.4% (±13.2) in the delayed angiography group (mean difference: -3.19; 95% confidence interval [CI], -6.75 to 0.37). Median left ventricular end-diastolic volume was 177 ml in the immediate angiography group compared to 169 ml in the delayed angiography group (ratio of geometric means: 1.06; 95% CI, 0.95-1.19). In addition, mean left ventricular end-systolic volume was 90 ml in the immediate angiography group compared to 78 ml in the delayed angiography group (ratio of geometric means: 1.13; 95% CI 0.97-1.32).

Conclusion: In patients successfully resuscitated after out-of-hospital cardiac arrest and without signs of STEMI, immediate coronary angiography was not found to improve left ventricular dimensions or function compared with a delayed angiography strategy.

Clinical Trial Registration: Netherlands Trial Register number, NTR4973.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.resuscitation.2021.04.020DOI Listing
July 2021

Acute pancreatitis in COVID-19 patients: true risk?

Scand J Gastroenterol 2021 05 14;56(5):585-587. Epub 2021 Mar 14.

Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.

Background: A relation between coronavirus disease 2019 (COVID-19) and acute pancreatitis has been suggested. However, the incidence and clinical relevance of this relation remain unclear.

Objective: We aimed to investigate the incidence, severity and clinical impact of acute pancreatitis in patients with COVID-19.

Methods: This is a cross-sectional study of a prospective, observational cohort concerning all COVID-19 patients admitted to two Dutch university hospitals between 4 March 2020 and 26 May 2020. Primary outcome was acute pancreatitis potentially related to COVD-19 infection. Acute pancreatitis was defined according to the revised Atlanta Classification. Potential relation with COVID-19 was defined as the absence of a clear aetiology of acute pancreatitis.

Results: Among 433 patients with COVID-19, five (1.2%) had potentially related acute pancreatitis according to the revised Atlanta Classification. These five patients suffered from severe COVID-19 infection; all had (multiple) organ failure and 60% died. None of the patients developed necrotizing pancreatitis. Moreover, development of acute pancreatitis did not lead to major treatment consequences.

Conclusions: In contrast with previous research, our study demonstrated that COVID-19 related acute pancreatitis is rare and of little clinical impact. It is therefore debatable if acute pancreatitis in COVID-19 patients requires specific screening. We hypothesize that acute pancreatitis occurs in patients with severe illness due to COVID-19 infection as a result of transient hypoperfusion and pancreatic ischemia, not as a direct result of the virus.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/00365521.2021.1896776DOI Listing
May 2021

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave].

Ned Tijdschr Geneeskd 2021 01 11;165. Epub 2021 Jan 11.

Maastricht UMC.

Objective: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.

Design: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.

Methods: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.

Results: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.

Conclusion: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.
View Article and Find Full Text PDF

Download full-text PDF

Source
January 2021

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

Crit Care Med 2021 06;49(6):e563-e577

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Vrije Universiteit, Universiteit van Amsterdam, Amsterdam, The Netherlands.

Objectives: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data.

Setting: University hospital ICU.

Subjects: Data from ICU patients admitted between 2003 and 2016.

Interventions: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database.

Measurements And Main Results: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous.

Conclusions: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CCM.0000000000004916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132908PMC
June 2021

Cardiovascular risk factors and COVID-19 outcomes in hospitalised patients: a prospective cohort study.

BMJ Open 2021 02 22;11(2):e045482. Epub 2021 Feb 22.

Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands

Objectives: Recent reports suggest a high prevalence of hypertension and diabetes in COVID-19 patients, but the role of cardiovascular disease (CVD) risk factors in the clinical course of COVID-19 is unknown. We evaluated the time-to-event relationship between hypertension, dyslipidaemia, diabetes and COVID-19 outcomes.

Design: We analysed data from the prospective Dutch CovidPredict cohort, an ongoing prospective study of patients admitted for COVID-19 infection.

Setting: Patients from eight participating hospitals, including two university hospitals from the CovidPredict cohort were included.

Participants: Admitted, adult patients with a positive COVID-19 PCR or high suspicion based on CT-imaging of the thorax. Patients were followed for major outcomes during the hospitalisation. CVD risk factors were established via home medication lists and divided in antihypertensives, lipid-lowering therapy and antidiabetics.

Primary And Secondary Outcomes Measures: The primary outcome was mortality during the first 21 days following admission, secondary outcomes consisted of intensive care unit (ICU) admission and ICU mortality. Kaplan-Meier and Cox regression analyses were used to determine the association with CVD risk factors.

Results: We included 1604 patients with a mean age of 66±15 of whom 60.5% were men. Antihypertensives, lipid-lowering therapy and antidiabetics were used by 45%, 34.7% and 22.1% of patients. After 21-days of follow-up; 19.2% of the patients had died or were discharged for palliative care. Cox regression analysis after adjustment for age and sex showed that the presence of ≥2 risk factors was associated with increased mortality risk (HR 1.52, 95% CI 1.15 to 2.02), but not with ICU admission. Moreover, the use of ≥2 antidiabetics and ≥2 antihypertensives was associated with mortality independent of age and sex with HRs of, respectively, 2.09 (95% CI 1.55 to 2.80) and 1.46 (95% CI 1.11 to 1.91).

Conclusions: The accumulation of hypertension, dyslipidaemia and diabetes leads to a stepwise increased risk for short-term mortality in hospitalised COVID-19 patients independent of age and sex. Further studies investigating how these risk factors disproportionately affect COVID-19 patients are warranted.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1136/bmjopen-2020-045482DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902321PMC
February 2021

Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse.

Intensive Care Med 2021 04 17;47(4):478-481. Epub 2021 Feb 17.

Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00134-021-06361-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887418PMC
April 2021

Artificial intelligence in telemetry: what clinicians should know.

Intensive Care Med 2021 02 2;47(2):150-153. Epub 2021 Jan 2.

The Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00134-020-06295-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776290PMC
February 2021

The Value of Artificial Intelligence in Laboratory Medicine.

Am J Clin Pathol 2021 05;155(6):823-831

Section Acute Medicine, Department of Internal Medicine, Amsterdam UMC.

Objectives: As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evaluate it, what are its limitations, and how it can be implemented are not well understood. With a survey, we aimed to evaluate the thoughts of stakeholders in laboratory medicine on the value of AI in the diagnostics space and identify anticipated challenges and solutions to introducing AI.

Methods: We conducted a web-based survey on the use of AI with participants from Roche's Strategic Advisory Network that included key stakeholders in laboratory medicine.

Results: In total, 128 of 302 stakeholders responded to the survey. Most of the participants were medical practitioners (26%) or laboratory managers (22%). AI is currently used in the organizations of 15.6%, while 66.4% felt they might use it in the future. Most had an unsure attitude on what they would need to adopt AI in the diagnostics space. High investment costs, lack of proven clinical benefits, number of decision makers, and privacy concerns were identified as barriers to adoption. Education in the value of AI, streamlined implementation and integration into existing workflows, and research to prove clinical utility were identified as solutions needed to mainstream AI in laboratory medicine.

Conclusions: This survey demonstrates that specific knowledge of AI in the medical community is poor and that AI education is much needed. One strategy could be to implement new AI tools alongside existing tools.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/ajcp/aqaa170DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130876PMC
May 2021

Data on sex differences in one-year outcomes of out-of-hospital cardiac arrest patients without ST-segment elevation.

Data Brief 2020 Dec 12;33:106521. Epub 2020 Nov 12.

Department of Intensive care medicine, Maastricht University Medical Center, University Maastricht, Maastricht, the Netherlands.

Sex differences in out-of-hospital cardiac arrest (OHCA) patients are increasingly recognized. Although it has been found that post-resuscitated women are less likely to have significant coronary artery disease (CAD) than men, data on follow-up in these patients are limited. Data for this data in brief article was obtained as a part of the randomized controlled Coronary Angiography after Cardiac Arrest without ST-segment elevation (COACT) trial. The data supplements the manuscript "Sex differences in out-of-hospital cardiac arrest patients without ST-segment elevation: A COACT trial substudy" were it was found that women were less likely to have significant CAD including chronic total occlusions, and had worse survival when CAD was present. The dataset presented in this paper describes sex differences on interventions, implantable-cardioverter defibrillator (ICD) shocks and hospitalizations due to heart failure during one-year follow-up in patients successfully resuscitated after OHCA. Data was derived through a telephone interview at one year with the patient or general practitioner. Patients in this randomized dataset reflects a homogenous study population, which can be valuable to further build on research regarding long-term sex differences and to further improve cardiac care.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.dib.2020.106521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691722PMC
December 2020

Sex differences in patients with out-of-hospital cardiac arrest without ST-segment elevation: A COACT trial substudy.

Resuscitation 2021 01 12;158:14-22. Epub 2020 Nov 12.

Department of Intensive care medicine, Maastricht University Medical Centre, University Maastricht, Maastricht, the Netherlands.

Background: Whether sex is associated with outcomes of out-of-hospital cardiac arrest (OHCA) is unclear.

Objectives: This study examined sex differences in survival in patients with OHCA without ST-segment elevation myocardial infarction (STEMI).

Methods: Using data from the randomized controlled Coronary Angiography after Cardiac Arrest (COACT) trial, the primary point of interest was sex differences in OHCA-related one-year survival. Secondary points of interest included the benefit of immediate coronary angiography compared to delayed angiography until after neurologic recovery, angiographic and clinical outcomes.

Results: In total, 522 patients (79.1% men) were included. Overall one-year survival was 59.6% in women and 63.4% in men (HR 1.18; 95% CI: 0.76-1.81;p = 0.47). No cardiovascular risk factors were found that modified survival. Women less often had significant coronary artery disease (CAD) (37.0% vs. 71.3%;p < 0.001), but when present, they had a worse prognosis than women without CAD (HR 3.06; 95% CI 1.31-7.19;p = 0.01). This was not the case for men (HR 1.05; 95% CI 0.67-1.65;p = 0.83). In both sexes, immediate coronary angiography did not improve one-year survival compared to delayed angiography (women, odds ratio (OR) 0.87; 95% CI 0.58-1.30;p = 0.49; vs. men, OR 0.97; 95% CI 0.45-2.09;p = 0.93).

Conclusion: In OHCA patients without STEMI, we found no sex differences in overall one-year survival. Women less often had significant CAD, but when CAD was present they had worse survival than women without CAD. This was not the case for men. Both sexes did not benefit from a strategy of immediate coronary angiography as compared to delayed strategy with respect to one-year survival.

Clinical Trial Registration Number: Netherlands trial register (NTR) 4973.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.resuscitation.2020.10.026DOI Listing
January 2021

Coronary Angiography After Cardiac Arrest Without ST Segment Elevation: One-Year Outcomes of the COACT Randomized Clinical Trial.

JAMA Cardiol 2020 Dec;5(12):1358-1365

Department of Cardiology, Scheper Hospital, Emmen, the Netherlands.

Importance: Ischemic heart disease is a common cause of cardiac arrest. However, randomized data on long-term clinical outcomes of immediate coronary angiography and percutaneous coronary intervention (PCI) in patients successfully resuscitated from cardiac arrest in the absence of ST segment elevation myocardial infarction (STEMI) are lacking.

Objective: To determine whether immediate coronary angiography improves clinical outcomes at 1 year in patients after cardiac arrest without signs of STEMI, compared with a delayed coronary angiography strategy.

Design, Setting, And Participants: A prespecified analysis of a multicenter, open-label, randomized clinical trial evaluated 552 patients who were enrolled in 19 Dutch centers between January 8, 2015, and July 17, 2018. The study included patients who experienced out-of-hospital cardiac arrest with a shockable rhythm who were successfully resuscitated without signs of STEMI. Follow-up was performed at 1 year. Data were analyzed, using the intention-to-treat principle, between August 29 and October 10, 2019.

Interventions: Immediate coronary angiography and PCI if indicated or coronary angiography and PCI if indicated, delayed until after neurologic recovery.

Main Outcomes And Measures: Survival, myocardial infarction, revascularization, implantable cardiac defibrillator shock, quality of life, hospitalization for heart failure, and the composite of death or myocardial infarction or revascularization after 1 year.

Results: At 1 year, data on 522 of 552 patients (94.6%) were available for analysis. Of these patients, 413 were men (79.1%); mean (SD) age was 65.4 (12.3) years. A total of 162 of 264 patients (61.4%) in the immediate angiography group and 165 of 258 patients (64.0%) in the delayed angiography group were alive (odds ratio, 0.90; 95% CI, 0.63-1.28). The composite end point of death, myocardial infarction, or repeated revascularization since the index hospitalization was met in 112 patients (42.9%) in the immediate group and 104 patients (40.6%) in the delayed group (odds ratio, 1.10; 95% CI, 0.77-1.56). No significant differences between the groups were observed for the other outcomes at 1-year follow-up. For example, the rate of ICD shocks was 20.4% in the immediate group and 16.2% in the delayed group (odds ratio, 1.32; 95% CI, 0.66-2.64).

Conclusions And Relevance: In this trial of patients successfully resuscitated after out-of-hospital cardiac arrest and without signs of STEMI, a strategy of immediate angiography was not found to be superior to a strategy of delayed angiography with respect to clinical outcomes at 1 year. Coronary angiography in this patient group can therefore be delayed until after neurologic recovery without affecting outcomes.

Trial Registration: trialregister.nl Identifier: NTR4973.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1001/jamacardio.2020.3670DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489423PMC
December 2020

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

Pharm Res 2020 Aug 23;37(9):171. Epub 2020 Aug 23.

Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s11095-020-02908-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443423PMC
August 2020

The effect of small versus large clog size on emergency response time: A randomized controlled trial.

J Crit Care 2020 12 8;60:116-119. Epub 2020 Aug 8.

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.

Objectives: To assess the effect on healthcare professional emergency response time and safety of small compared to large clog size.

Design: Randomized controlled trial.

Setting: The intensive care unit of a single university medical centre in The Netherlands.

Participants: Intensive care medicine professionals.

Interventions: Participants were randomized to wear European size 38 clogs (US male size 6½, US female size 7½) or European size 47 clogs (US male size 13½, US female size 14½) clogs and were required to run a 125 m course from the coffee break room to the elevator providing access to the emergency department.

Main Outcome Measures: The primary outcome was the time to complete the running course. Height, shoe size, self-described fitness, age and staff category were investigated as possible effect modifiers. Secondary endpoints were reported clog comfort and suspected unexpected clog-related adverse events (SUCRAEs).

Results: 50 participants were randomized (25 to European size 38 clogs and 25 to size 47 clogs). Mean age was 37 years (SD 12) and 29 participants (58%) were female. The primary outcome was 4.4 s (95% CI -7.1; -1.6) faster in the size 5 clogs group compared to the size 12 clogs group. This effect was not modified by any of the predefined participant characteristics. No differences were found in reported clog comfort or SUCRAEs.

Conclusions: European size 38 clogs lead to faster emergency response times than size 47 clogs.

Trial Registration: NCT04406220.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jcrc.2020.07.028DOI Listing
December 2020

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

Br J Clin Pharmacol 2021 03 17;87(3):1234-1242. Epub 2020 Aug 17.

Department of Intensive Care Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Medical Data Science, Research VUmc Intensive Care, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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 , C + C , C + C + C , 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 = 400, 500, and 600 mg·h/L and C = 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 ≤ 600 mg·h/L and C ≤ 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 = 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 = 500 mg·h/L lead to the best POT.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/bcp.14498DOI Listing
March 2021

Machine learning in intensive care medicine: ready for take-off?

Intensive Care Med 2020 07 12;46(7):1486-1488. Epub 2020 May 12.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Location VUmc, VU Amsterdam, Amsterdam, The Netherlands.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00134-020-06045-yDOI Listing
July 2020

Vitamin C for Sepsis and Acute Respiratory Failure.

JAMA 2020 02;323(8):792

Department of Intensive Care, Erasmus Hospital, Brussels, Belgium.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1001/jama.2019.21981DOI Listing
February 2020

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.

Intensive Care Med 2020 03 21;46(3):383-400. Epub 2020 Jan 21.

Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam, Amsterdam, The Netherlands.

Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.

Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.

Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.

Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00134-019-05872-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067741PMC
March 2020

Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock - rationale and design of a multicenter randomized controlled superiority trial.

Trials 2019 Dec 18;20(1):745. Epub 2019 Dec 18.

Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Research VUmc Intensive Care (REVIVE), Amsterdam Cardiovascular Science (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Location VUmc, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13063-019-3911-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921499PMC
December 2019
-->