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.

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.

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.

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

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, Fio2, 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 Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.

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.

SARS-CoV-2-Induced Multisystem Inflammatory Syndrome in a Young Adult: Case Report.

We describe a case of a previous healthy 20-year-old male athlete who presented with an atypical clinical profile with multiorgan involvement within five weeks after confirmed SARS-CoV-2 infection, suggestive for multisystem inflammatory syndrome (MIS); MIS is a rare, potentially life-threatening complication associated with SARS-CoV-2. MIS shares similar clinical features compatible with several overlapping lifethreatening hyperinflammatory syndromes, such as incomplete Kawasaki Disease (KD) and toxic shock syndrome (TSS) associated to a cytokine storm suggestive of a macrophage activation syndrome (MAS) without fulfilling the criteria for hemophagocytic lymphohistiocytosis (HLH), that may create a great challenge to distinguish between them. MIS should promptly be considered and treated, as uncontrolled MIS has a high mortality. In MIS cardiac involvement, heart failure may present as an additional problem, especially because volume loading is advised in accordance with proposed therapy. Carefully monitoring of the respiratory and cardiac status in response of resuscitation is therefore warranted.

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.

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 cmH2O. 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.

Discontinuation of Antihypertensive Medications on the Outcome of Hospitalized Patients With Severe Acute Respiratory Syndrome-Coronavirus 2.

[Figure: see text].

Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients.

AIMS: Vancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients. METHODS: We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error. RESULTS: We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%). CONCLUSION: Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.

The value of the neutrophil-lymphocyte count ratio in the diagnosis of sepsis in patients admitted to the Intensive Care Unit: A retrospective cohort study.

BACKGROUND: Early diagnosis and treatment has proven to be of utmost importance in the outcome of sepsis patients. We compared the accuracy of the neutrophil-lymphocyte count ratio (NLCR) to conventional inflammatory markers in patients admitted to the Intensive Care Unit (ICU). METHODS: We performed a retrospective cohort study consisting of 276 ICU patients with sepsis and 388 ICU patients without sepsis. We compared the NLCR as well as C-reactive protein (CRP) level, procalcitonin (PCT) level, white blood cell (WBC) count, neutrophil count and lymphocyte count on ICU admission between sepsis and non-sepsis ICU patients. To evaluate the sensitivity and specificity, we constructed receiver operating characteristics (ROC) curves. RESULTS: Significant differences in NLCR values were observed between sepsis and non-sepsis patients (15.3 [10.8-38.2] (median [interquartile range] vs. 9.3 [6.2-14.5]; P<0.001), as well as for CRP level, PCT level and lymphocyte count. The area under the ROC curve (AUROC) of the NLCR was 0.66 (95%CI = 0.62-0.71). AUROC was significantly higher for CRP and PCT level with AUROC's of 0.89 (95%CI 0.87-0.92) and 0.88 (95%CI 0.86-0.91) respectively. CONCLUSIONS: The NLCR is less suitable than conventional inflammatory markers CRP and PCT to detect the presence of sepsis in ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01274819.

The utility of peripheral blood leucocyte ratios as biomarkers in infectious diseases: A systematic review and meta-analysis.

OBJECTIVES: To assess the utility of the neutrophil:lymphocyte (NLR), lymphocyte:monocyte (LMR) and platelet:lymphocyte ratios (PLR) as infection biomarkers. METHODS: PubMed/MEDLINE, Embase and Cochrane databases were searched to identify eligible articles. Studies of diagnosis, severity or outcome were included. PROSPERO systematic review registration CRD42017075032. RESULTS: Forty studies were included, reporting on bacterial and viral infections, malaria, and critical illness due to sepsis. Ten studies reported an association of higher NLR with bacteraemia, supported by meta-analysis of patient-level data (five studies, n = 3320; AUC 0.72, p<0.0001) identifying a cut-off of >12.65. Two studies reported an association with lower LMR and diagnosis of influenza virus infection in patients with respiratory tract infection. Meta-analysis of patient-level data (n = 85; AUC 0.66, p = 0.01) identified a cut-off of ≤2.06. The directionality of associations between NLR and outcomes in heterogeneous cohorts of critically ill adults with sepsis varied. Potential clinical utility was also demonstrated in pneumonia (NLR), pertussis (NLR), urinary tract infection (NLR), diabetic foot infections (NLR) and Crimean Congo Haemorrhagic Fever (PLR). Longitudinal measurement of LMR during respiratory virus infection reflected symptoms and NLR during sepsis and bacteraemia predicted mortality. CONCLUSIONS: Peripheral blood leucocyte ratios are useful infection biomarkers, with the most evidence related to diagnosis of bacteraemia and influenza virus infection. In critical illness due to sepsis, a signal towards an association with NLR and outcomes exists, and NLR should be evaluated in future stratification models. Longitudinal measurement of ratios during infection could be informative. Overall, these biomarkers warrant further recognition and study in infectious diseases.

Noise in the intensive care unit and its influence on sleep quality: a multicenter observational study in Dutch intensive care units.

BACKGROUND: High noise levels in the intensive care unit (ICU) are a well-known problem. Little is known about the effect of noise on sleep quality in ICU patients. The study aim is to determine the effect of noise on subjective sleep quality. METHODS: This was a multicenter observational study in six Dutch ICUs. Noise recording equipment was installed in 2-4 rooms per ICU. Adult patients were eligible for the study 48 h after ICU admission and were followed up to maximum of five nights in the ICU. Exclusion criteria were presence of delirium and/or inability to be assessed for sleep quality. Sleep was evaluated using the Richards Campbell Sleep Questionnaire (range 0-100 mm). Noise recordings were used for analysis of various auditory parameters, including the number and duration of restorative periods. Hierarchical mixed model regression analysis was used to determine associations between noise and sleep. RESULTS: In total, 64 patients (68% male), mean age 63.9 (± 11.7) years and mean Acute Physiology And Chronic Health Evaluation (APACHE) II score 21.1 (± 7.1) were included. Average sleep quality score was 56 ± 24 mm. The mean of the 24-h average sound pressure levels (LAeq, 24h) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (ß = - 0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (ß = 0.53, p < 0.01) and female sex (ß = 1.25, p < 0.01) were weakly but significantly correlated with sleep. CONCLUSIONS: Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients. TRIAL REGISTRATION: www.ClinicalTrials.gov, NCT01826799 . Registered on 9 April 2013.

Effect of Dynamic Light Application on Cognitive Performance and Well-being of Intensive Care Nurses.

BACKGROUND: Exposure to bright light has alerting effects. In nurses, alertness may be decreased because of shift work and high work pressure, potentially reducing work performance and increasing the risk for medical errors. OBJECTIVES: To determine whether high-intensity dynamic light improves cognitive performance, self-reported depressive signs and symptoms, fatigue, alertness, and well-being in intensive care unit nurses. METHODS: In a single-center crossover study in an intensive care unit of a teaching hospital in the Netherlands, 10 registered nurses were randomly divided into 2 groups. Each group worked alternately for 3 to 4 days in patients' rooms with dynamic light and 3 to 4 days in control lighting settings. High-intensity dynamic light was administered through ceiling-mounted fluorescent tubes that delivered bluish white light up to 1700 lux during the daytime, versus 300 lux in control settings. Cognitive performance, self-reported depressive signs and symptoms, fatigue, and well-being before and after each period were assessed by using validated cognitive tests and questionnaires. RESULTS: Cognitive performance, self-reported depressive signs and symptoms, and fatigue did not differ significantly between the 2 light settings. Scores of subjective well-being were significantly lower after a period of working in dynamic light. CONCLUSIONS: Daytime lighting conditions did not affect intensive care unit nurses' cognitive performance, perceived depressive signs and symptoms, or fatigue. Perceived quality of life, predominantly in the psychological and environmental domains, was lower for nurses working in dynamic light.

Temporal biomarker profiles and their association with ICU acquired delirium: a cohort study.

BACKGROUND: Neuroinflammation is thought to play an important role in the pathogenesis of ICU-acquired delirium, but the association between inflammatory and brain-specific proteins and ICU delirium is poor. We investigated whether or not serial determinations of markers may improve this association. METHODS: Critically ill patients with a high risk of ICU delirium and with an ICU length of stay of at least 6 days were included in the study. Blood was drawn on days 1, 2, 4 and 6 after ICU admission and analyzed for different markers of inflammation and several brain proteins. Differences in courses over time prior to and following the onset of delirium and absolute differences over time were analyzed in patients with and without delirium using repeated measurement analysis of variance. In addition, a cross-sectional analysis of levels of these markers before the first onset of delirium was performed. RESULTS: Fifty patients were included in this study. In the longitudinal analysis, there were no differences in the levels of any of the markers immediately prior to and following the onset of delirium, but overall, median levels of adiponectin (9019 (IQR 5776-15,442) vs. 6148 (IQR 4447-8742) ng/ml, p = 0.05) were significantly higher in patients with delirium compared to patients without delirium. In the cross-sectional analysis, median levels of the brain protein Tau (90 (IQR 46-224) vs. 31 (IQR 31-52) pg/ml, p = 0.009) and the ratio Tau/amyloid ß1-42 (1.42 ((IQR 0.9-2.57) vs. 0.68 (IQR 0.54-0.96), p = 0.003) were significantly higher in patients with hypoactive delirium compared to patients without. Levels of neopterin (111 (IQR 37-111) vs. 29 (IQR 16-64) mmol/l, p = 0.004) and IL-10 (28 (IQR 12-39) vs. 9 (IQR 4-12) pg/ml, p = 0.001) were significantly higher in patients with hypoactive delirium compared to patients with mixed-type delirium. CONCLUSIONS: While there are differences in markers (adiponectin and several brain proteins) between patients with and without delirium, the development of delirium is not preceded by a change in the biomarker profile of inflammatory markers or brain proteins. Patients with hypoactive delirium account for the observed differences in biomarkers. TRIAL REGISTRATION: ClinicalTrials.gov, NCT 01274819 . Registered on 12 January 2011.

Dynamic light application therapy to reduce the incidence and duration of delirium in intensive-care patients: a randomised controlled trial.

BACKGROUND: Disturbed circadian rhythm is a potentially modifiable cause of delirium among patients in intensive-care units (ICUs). Bright-light therapy in the daytime can realign circadian rhythm and reduce the incidence of delirium. We investigated whether a high-intensity dynamic light application (DLA) would reduce ICU-acquired delirium. METHODS: This was a randomised, controlled, single-centre trial of medical and surgical patients admitted to the ICU of a teaching hospital in the Netherlands. Patients older than 18 years, expected to stay in the ICU longer than 24 h and who could be assessed for delirium were randomised to DLA or normal lighting (control), according to a computer-generated schedule. The DLA was administered through ceiling-mounted fluorescent tubes that delivered bluish-white light up to 1700 lux between 0900 h and 1600 h, except for 1130-1330 h, when the light was dimmed to 300 lux. The light could only be turned off centrally by investigators. Control light levels were 300 lux and lights could be turned on and off from inside the room. The primary endpoint was the cumulative incidence of ICU-acquired delirium. Analyses were by intention to treat and per protocol. The study was terminated prematurely after an interim analysis for futility. This study is registered with Clinicaltrials.gov, number NCT01274819. FINDINGS: Between July 1, 2011, and Sept 9, 2013, 734 patients were enrolled, 361 in the DLA group and 373 in the control group. Delirium occurred in 137 (38%) of 361 DLA patients and 123 (33%) of 373 control patients (odds ratio 1·24, 95% CI 0·92-1·68, p=0·16). No adverse events were noted in patients or staff. INTERPRETATION: DLA as a single intervention does not reduce the cumulative incidence of delirium. Bright-light therapy should be assessed as part of a multicomponent strategy. FUNDING: None.

Biomarkers and molecular analysis to improve bloodstream infection diagnostics in an emergency care unit.

Molecular pathogen detection from blood is still expensive and the exact clinical value remains to be determined. The use of biomarkers may assist in preselecting patients for immediate molecular testing besides blood culture. In this study, 140 patients with ≥ 2 SIRS criteria and clinical signs of infection presenting at the emergency department of our hospital were included. C-reactive protein (CRP), neutrophil-lymphocyte count ratio (NLCR), procalcitonin (PCT) and soluble urokinase plasminogen activator receptor (suPAR) levels were determined. One ml EDTA blood was obtained and selective pathogen DNA isolation was performed with MolYsis (Molzym). DNA samples were analysed for the presence of pathogens, using both the MagicPlex Sepsis Test (Seegene) and SepsiTest (Molzym), and results were compared to blood cultures. Fifteen patients had to be excluded from the study, leaving 125 patients for further analysis. Of the 125 patient samples analysed, 27 presented with positive blood cultures of which 7 were considered to be contaminants. suPAR, PCT, and NLCR values were significantly higher in patients with positive blood cultures compared to patients without (p < 0.001). Receiver operating characteristic curves of the 4 biomarkers for differentiating bacteremia from non-bacteremia showed the highest area under the curve (AUC) for PCT (0.806 (95% confidence interval 0.699-0.913)). NLCR, suPAR and CRP resulted in an AUC of 0.770, 0.793, and 0.485, respectively. When compared to blood cultures, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for SepsiTest and MagicPlex Sepsis Test were 11%, 96%, 43%, 80%, and 37%, 77%, 30%, 82%, respectively. In conclusion, both molecular assays perform poorly when one ml whole blood is used from emergency care unit patients. NLCR is a cheap, fast, easy to determine, and rapidly available biomarker, and therefore seems most promising in differentiating BSI from non-BSI patients for subsequent pathogen identification using molecular diagnostics.