Effects of dalteparin on anti-Xa activities cannot be predicted in critically ill COVID-19 patients.

Critically ill COVID-19 patients are at high risk of thromboembolic events despite routine-dosed low-molecular-weight heparin thromboprophylaxis. However, in recent randomized trials increased-intensity thromboprophylaxis seemed futile and possibly even harmful. In this explorative pharmacokinetic (PK) study we measured anti-Xa activities on frequent timepoints in 15 critically ill COVID-19 patients receiving dalteparin and performed PK analysis by nonlinear mixed-effect modelling. A linear one-compartment model with first-order kinetics provided a good fit. However, wide interindividual variation in dalteparin absorption (variance 78%) and clearance (variance 34%) was observed, unexplained by routine clinical covariates. Using the final PK model for Monte Carlo simulations, we predicted increased-intensity dalteparin to result in anti-Xa activities well over prophylactic targets (0.2-0.4 IU/mL) in the majority of patients. Therapeutic-intensity dalteparin results in supratherapeutic anti-Xa levels (target 0.6-1.0 IU/mL) in 19% of patients and subtherapeutic levels in 22%. Therefore, anti-Xa measurements should guide high-intensity dalteparin in critically ill COVID-19 patients.

Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis.

BACKGROUND: This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors. METHODS: The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R2  = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128). RESULTS: The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2  = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors. CONCLUSION: Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.

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.

Dysregulated Innate and Adaptive Immune Responses Discriminate Disease Severity in COVID-19.

The clinical spectrum of COVID-19 varies and the differences in host response characterizing this variation have not been fully elucidated. COVID-19 disease severity correlates with an excessive proinflammatory immune response and profound lymphopenia. Inflammatory responses according to disease severity were explored by plasma cytokine measurements and proteomics analysis in 147 COVID-19 patients. Furthermore, peripheral blood mononuclear cell cytokine production assays and whole blood flow cytometry were performed. Results confirm a hyperinflammatory innate immune state, while highlighting hepatocyte growth factor and stem cell factor as potential biomarkers for disease severity. Clustering analysis revealed no specific inflammatory endotypes in COVID-19 patients. Functional assays revealed abrogated adaptive cytokine production (interferon-γ, interleukin-17, and interleukin-22) and prominent T-cell exhaustion in critically ill patients, whereas innate immune responses were intact or hyperresponsive. Collectively, this extensive analysis provides a comprehensive insight into the pathobiology of severe to critical COVID-19 and highlights potential biomarkers of disease severity.

High unbound flucloxacillin fraction in critically ill patients.

OBJECTIVES: To describe the unbound and total flucloxacillin pharmacokinetics in critically ill patients and to define optimal dosing strategies. PATIENTS AND METHODS: Observational multicentre study including a total of 33 adult ICU patients receiving flucloxacillin, given as intermittent or continuous infusion. Pharmacokinetic sampling was performed on two occasions on two different days. Total and unbound flucloxacillin concentrations were measured and analysed using non-linear mixed-effects modelling. Serum albumin was added as covariate on the maximum binding capacity and endogenous creatinine clearance (CLCR) as covariate for renal function. Monte Carlo simulations were performed to predict the unbound flucloxacillin concentrations for different dosing strategies and different categories of endogenous CLCR. RESULTS: The measured unbound concentrations ranged from 0.2 to 110 mg/L and the observed unbound fraction varied between 7.0% and 71.7%. An integral two-compartmental linear pharmacokinetic model based on total and unbound concentrations was developed. A dose of 12 g/24 h was sufficient for 99.9% of the population to achieve a concentration of >2.5 mg/L (100% fT>5×MIC, MIC = 0.5 mg/L). CONCLUSIONS: Critically ill patients show higher unbound flucloxacillin fractions and concentrations than previously thought. Consequently, the risk of subtherapeutic exposure is low.

Dexamethasone and tocilizumab treatment considerably reduces the value of C-reactive protein and procalcitonin to detect secondary bacterial infections in COVID-19 patients.

BACKGROUND: Procalcitonin (PCT) and C-reactive protein (CRP) were previously shown to have value for the detection of secondary infections in critically ill COVID-19 patients. However, since the introduction of immunomodulatory therapy, the value of these biomarkers is unclear. We investigated PCT and CRP kinetics in critically ill COVID-19 patients treated with dexamethasone with or without tocilizumab, and assessed the value of these biomarkers to detect secondary bacterial infections. METHODS: In this prospective study, 190 critically ill COVID-19 patients were divided into three treatment groups: no dexamethasone, no tocilizumab (D-T-), dexamethasone, no tocilizumab (D+T-), and dexamethasone and tocilizumab (D+T+). Serial data of PCT and CRP were aligned on the last day of dexamethasone treatment, and kinetics of these biomarkers were analyzed between 6 days prior to cessation of dexamethasone and 10 days afterwards. Furthermore, the D+T- and D+T+ groups were subdivided into secondary infection and no-secondary infection groups to analyze differences in PCT and CRP kinetics and calculate detection accuracy of these biomarkers for the occurrence of a secondary infection. RESULTS: Following cessation of dexamethasone, there was a rebound in PCT and CRP levels, most pronounced in the D+T- group. Upon occurrence of a secondary infection, no significant increase in PCT and CRP levels was observed in the D+T- group (p = 0.052 and p = 0.08, respectively). Although PCT levels increased significantly in patients of the D+T+ group who developed a secondary infection (p = 0.0003), this rise was only apparent from day 2 post-infection onwards. CRP levels remained suppressed in the D+T+ group. Receiver operating curve analysis of PCT and CRP levels yielded area under the curves of 0.52 and 0.55, respectively, which are both markedly lower than those found in the group of COVID-19 patients not treated with immunomodulatory drugs (0.80 and 0.76, respectively, with p values for differences between groups of 0.001 and 0.02, respectively). CONCLUSIONS: Cessation of dexamethasone in critically ill COVID-19 patients results in a rebound increase in PCT and CRP levels unrelated to the occurrence of secondary bacterial infections. Furthermore, immunomodulatory treatment with dexamethasone and tocilizumab considerably reduces the value of PCT and CRP for detection of secondary infections in COVID-19 patients.

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

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

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

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

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.

Breath-synchronized electrical stimulation of the expiratory muscles in mechanically ventilated patients: a randomized controlled feasibility study and pooled analysis.

BACKGROUND: Expiratory muscle weakness leads to difficult ventilator weaning. Maintaining their activity with functional electrical stimulation (FES) may improve outcome. We studied feasibility of breath-synchronized expiratory population muscle FES in a mixed ICU population ("Holland study") and pooled data with our previous work ("Australian study") to estimate potential clinical effects in a larger group. METHODS: Holland: Patients with a contractile response to FES received active or sham expiratory muscle FES (30 min, twice daily, 5 days/week until weaned). Main endpoints were feasibility (e.g., patient recruitment, treatment compliance, stimulation intensity) and safety. Pooled: Data on respiratory muscle thickness and ventilation duration from the Holland and Australian studies were combined (N = 40) in order to estimate potential effect size. Plasma cytokines (day 0, 3) were analyzed to study the effects of FES on systemic inflammation. RESULTS: Holland: A total of 272 sessions were performed (active/sham: 169/103) in 20 patients (N = active/sham: 10/10) with a total treatment compliance rate of 91.1%. No FES-related serious adverse events were reported. Pooled: On day 3, there was a between-group difference (N = active/sham: 7/12) in total abdominal expiratory muscle thickness favoring the active group [treatment difference (95% confidence interval); 2.25 (0.34, 4.16) mm, P = 0.02] but not on day 5. Plasma cytokine levels indicated that early FES did not induce systemic inflammation. Using a survival analysis approach for the total study population, median ventilation duration and ICU length of stay were 10 versus 52 (P = 0.07), and 12 versus 54 (P = 0.03) days for the active versus sham group. Median ventilation duration of patients that were successfully extubated was 8.5 [5.6-12.2] versus 10.5 [5.3-25.6] days (P = 0.60) for the active (N = 16) versus sham (N = 10) group, and median ICU length of stay was 10.5 [8.0-14.5] versus 14.0 [9.0-19.5] days (P = 0.36) for those active (N = 16) versus sham (N = 8) patients that were extubated and discharged alive from the ICU. During ICU stay, 3/20 patients died in the active group versus 8/20 in the sham group (P = 0.16). CONCLUSION: Expiratory muscle FES is feasible in selected ICU patients and might be a promising technique within a respiratory muscle-protective ventilation strategy. The next step is to study the effects on weaning and ventilator liberation outcome. TRIAL REGISTRATION: ClinicalTrials.gov, ID NCT03453944. Registered 05 March 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03453944 .

In vivo fluorescence-mediated tomography imaging demonstrates atorvastatin-mediated reduction of lesion macrophages in ApoE-/- mice.

BACKGROUND: Macrophage recruitment into atherosclerotic plaques drives lesion progression, destabilization, and rupture. Chronic statin treatment reduces macrophage plaque content. Information on dynamics of macrophage recruitment would help assessing plaque vulnerability and guiding therapy. Techniques to image macrophage homing to vulnerable plaques in vivo are scarcely available. The authors tested if noninvasive fluorescence-mediated tomography (FMT) can assess plaque-stabilizing effects of short-term high-dosage atorvastatin. METHODS: Macrophages from green-fluorescent-protein-transgenic mice were labeled with a near-infrared fluorescent dye and were injected IV in apolipoprotein E-deficient mice (n=9) on Western diet 7 days after guidewire-injury of the carotid artery. FMT-scans, 2 and 7 days thereafter, quantified macrophage recruitment into carotid artery plaques. Atorvastatin was tested for macrophage adhesion, proliferation, and viability (n=5 to 6) in vitro. Fourteen mice received atorvastatin or vehicle for 4 days after 16 weeks on Western diet. FMT assessed macrophage recruitment into aortic and innominate artery lesions. Means (±SD)% are reported. RESULTS: Double-labeled macrophages were recruited into carotid artery lesions. FMT resolved fluorescence projecting on the injured carotid artery and detected a signal increase to 300% (±191) after guidewire injury. Atorvastatin reduced macrophage adhesion to activated endothelial cells by 36% (±19). In a clinically relevant proof-of-concept intervention, FMT-imaging detected that 4 days atorvastatin treatment reduced macrophage recruitment by 57% (±8) indicating plaque stabilization. Immunohistochemistry confirmed reduced macrophage infiltration. CONCLUSIONS: FMT optical imaging proved its high potential for clinical applicability for tracking recruitment of near-infrared fluorescent-labeled macrophages to vulnerable plaques in vivo. FMT-based quantification of macrophage recruitment demonstrated rapid plaque stabilization by 4-day atorvastatin treatment in apolipoprotein E-deficient mice.

Using long-term predicted Quality of Life in ICU clinical practice to prepare patients for life post-ICU: A feasibility study.

PURPOSE: To examine the feasibility of using the PREdicting PAtients' long-term outcome for Recovery (PREPARE) prediction model for Quality of Life (QoL) 1 year after ICU admission in ICU practice to prepare expected ICU survivors and their relatives for life post-ICU. MATERIALS AND METHODS: Between June 2020 and February 2021, the predicted change in QoL after 1 year was discussed in 25 family conferences in the ICU. 13 physicians, 10 nurses and 19 patients and/or family members were interviewed to evaluate intervention feasibility in ICU practice. Interviews were analysed qualitatively using thematic coding. RESULTS: Patients' median age was 68.0 years, five patients (20.0%) were female and seven patients (28.0%) died during ICU stay. Generally, study participants thought the intervention, which clarified the concept of QoL through visualization and served as a reminder to discuss QoL and expectations for life post-ICU, had merit. However, some participants, especially physicians, thought the prediction model needed more data on more severely ill ICU patients to curb uncertainty. CONCLUSIONS: Using predicted QoL scores in ICU practice to prepare patients and family members for life after ICU discharge is feasible. After optimising the model and implementation strategy, its effectiveness can be evaluated in a larger trial.

An Integral Pharmacokinetic Analysis of Piperacillin and Tazobactam in Plasma and Urine in Critically Ill Patients.

BACKGROUND AND OBJECTIVES: Although dose optimization studies have been performed for piperacillin and tazobactam separately, a combined integral analysis is not yet reported. As piperacillin and tazobactam pharmacokinetics are likely to show correlation, a combined pharmacokinetic model should be preferred to account for this correlation when predicting the exposure. Therefore, the aim of this study was to describe the pharmacokinetics and evaluate different dosing regimens of piperacillin and tazobactam in critically ill patients using an integral population pharmacokinetic model in plasma and urine. METHODS: In this observational study, a total of 39 adult intensive care unit patients receiving piperacillin-tazobactam as part of routine clinical care were included. Piperacillin and tazobactam concentrations in plasma and urine were measured and analyzed using non-linear mixed-effects modeling. Monte Carlo simulations were performed to predict the concentrations for different dosing strategies and different categories of renal function. RESULTS: A combined two-compartment linear pharmacokinetic model for both piperacillin and tazobactam was developed, with an output compartment for the renally excreted fraction. The addition of 24-h urine creatinine clearance significantly improved the model fit. A dose of 12/1.5 g/24 h as a continuous infusion is sufficient to reach a tazobactam concentration above the target (2.89 mg/L) and a piperacillin concentration above the target of 100% f T>1×MIC (minimum inhibitory concentration [MIC] ≤ 16 mg/L). To reach a target of 100% f T>5×MIC with an MIC of 16 mg/L, piperacillin doses of up to 20 g/24 h are inadequate. Potential toxic piperacillin levels were reached in 19.6% and 47.8% of the population with a dose of 12 g/24 h and 20 g/24 h, respectively. CONCLUSIONS: A regular dose of 12/1.5 g/24 h is sufficient in > 90% of the critically ill population to treat infections caused by Escherichia coli and Klebsiella pneumoniae with MICs ≤ 8 mg/L. In case of infections caused by Pseudomonas aeruginosa with an MIC of 16 mg/L, there is a fine line between therapeutic and toxic exposure. Dosing guided by renal function and therapeutic drug monitoring could enhance target attainment in such cases. GOV IDENTIFIER: NCT03738683.

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.

Endothelial progenitor cell transplantation improves long-term stroke outcome in mice.

OBJECTIVE: Endothelial progenitor cells (EPCs) play an important role in tissue repairing and regeneration in ischemic organs, including the brain. However, the cause of EPC migration and the function of EPCs after ischemia are unclear. In this study, we demonstrated the effects of EPCs on ischemic brain injury in a mouse model of transient middle cerebral artery occlusion (tMCAO). METHODS: Circulating human EPCs were characterized with immunofluorescent staining and flow cytometry. EPCs (1 x 10(6)) were injected into nude mice after 1 hour of tMCAO. Histological analysis and behavioral tests were performed from day 0 to 28 days after tMCAO. RESULTS: EPCs were detected in ischemic brain regions 24 hours after tMCAO. EPC transplantation significantly reduced ischemic infarct volume at 3 days after tMCAO compared with control animals (p < 0.05). CXCR4 was expressed in the majority of EPCs, and stromal-derived factor-1 (SDF-1) induced EPC migration, which was blocked by pretreated EPCs with AMD3100 in vitro. SDF-1 was upregulated in ischemic brain. Compared with control animals, injecting AMD3100-pretreated EPCs resulted in a larger infarct volume 3 days after tMCAO, suggesting that SDF-1-mediated signaling was involved in EPC-mediated neuroprotection. In addition, EPC transplantation reduced mouse cortex atrophy 4 weeks after tMCAO and improved neurobehavioral outcomes (p < 0.05). EPC injection potently increased angiogenesis in the peri-infarction area (p < 0.05). INTERPRETATION: We conclude that systemic delivery of EPCs protects the brain against ischemic injury, promotes neurovascular repair, and improves long-term neurobehavioral outcomes. Our data suggest that SDF-1-mediated signaling plays a critical role in EPC-mediated neuroprotection.

Development of a practically usable prediction model for quality of life of ICU survivors: A sub-analysis of the MONITOR-IC prospective cohort study.

PURPOSE: As the goal of ICU treatment is survival in good health, we aimed to develop a prediction model for ICU survivors' change in quality of life (QoL) one year after ICU admission. MATERIALS & METHODS: This is a sub-study of the prospective cohort MONITOR-IC study. Adults admitted ≥12 h to the ICU of a university hospital between July 2016-January 2019 were included. Moribund patients were excluded. Change in QoL one year after ICU admission was quantified using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and Short-Form 36 (SF-36). Multivariable linear regression analysis and best subsets regression analysis (SRA) were used. Models were internally validated by bootstrapping. RESULTS: The PREdicting PAtients' long-term outcome for Recovery (PREPARE) model was developed (n = 1308 ICU survivors). The EQ-5D-models had better predictive performance than the SF-36-models. Explained variance (adjusted R2) of the best model (33 predictors) was 58.0%. SRA reduced the number of predictors to 5 (adjusted R2 = 55.3%, SE = 0.3), including QoL, diagnosis of a Cardiovascular Incident and frailty before admission, sex, and ICU-admission following planned surgery. CONCLUSIONS: Though more long-term data are needed to ascertain model accuracy, in future, the PREPARE model may be used to better inform and prepare patients and their families for ICU recovery.