Safety and Outcome of High-Flow Nasal Oxygen Therapy Outside ICU Setting in Hypoxemic Patients With COVID-19

OBJECTIVE

High-flow nasal oxygen (HFNO) therapy is frequently applied outside ICU setting in hypoxemic patients with COVID-19. However, safety concerns limit more widespread use. We aimed to assess the safety and clinical outcomes of initiation of HFNO therapy in COVID-19 on non-ICU wards.

DESIGN

Prospective observational multicenter pragmatic study.

SETTING

Respiratory wards and ICUs of 10 hospitals in The Netherlands.

PATIENTS

Adult patients treated with HFNO for COVID-19-associated hypoxemia between December 2020 and July 2021 were included. Patients with treatment limitations were excluded from this analysis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes included intubation and mortality rate, duration of hospital and ICU stay, severity of respiratory failure, and complications. Using propensity-matched analysis, we compared patients who initiated HFNO on the wards versus those in ICU. Six hundred eight patients were included, of whom 379 started HFNO on the ward and 229 in the ICU. The intubation rate in the matched cohort ( n = 214 patients) was 53% and 60% in ward and ICU starters, respectively ( p = 0.41). Mortality rates were comparable between groups (28-d [8% vs 13%], p = 0.28). ICU-free days were significantly higher in ward starters (21 vs 17 d, p < 0.001). No patient died before endotracheal intubation, and the severity of respiratory failure surrounding invasive ventilation and clinical outcomes did not differ between intubated ward and ICU starters (respiratory rate-oxygenation index 3.20 vs 3.38; Pa o2 :F io2 ratio 65 vs 64 mm Hg; prone positioning after intubation 81 vs 78%; mortality rate 17 vs 25% and ventilator-free days at 28 d 15 vs 13 d, all p values > 0.05).

CONCLUSIONS

In this large cohort of hypoxemic patients with COVID-19, initiation of HFNO outside the ICU was safe, and clinical outcomes were similar to initiation in the ICU. Furthermore, the initiation of HFNO on wards saved time in ICU without excess mortality or complicated course. Our results indicate that HFNO initiation outside ICU should be further explored in other hypoxemic diseases and clinical settings aiming to preserve ICU capacity and healthcare costs.

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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-01070-0.

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.

Effect of intensive care unit-specific virtual reality (ICU-VR) to improve psychological well-being in ICU survivors: study protocol for an international, multicentre, randomised controlled trial-the HORIZON-IC study

INTRODUCTION

A substantial proportion of intensive care unit (ICU) survivors develop psychological impairments after ICU treatment, part of the postintensive care syndrome, resulting in a decreased quality of life. Recent data suggest that an ICU-specific virtual reality intervention (ICU-VR) for post-ICU patients is feasible and safe, improves satisfaction with ICU aftercare, and might improve psychological sequelae. In the present trial, we firstly aim to determine whether ICU-VR is effective in mitigating post-traumatic stress disorder (PTSD)-related symptoms and secondly to determine the optimal timing for initiation with ICU-VR.

METHODS AND ANALYSIS

This international, multicentre, randomised controlled trial will be conducted in 10 hospitals. Between December 2021 and April 2023, we aim to include 300 patients who have been admitted to the ICU ≥72 hours and were mechanically ventilated ≥24 hours. Patients will be followed for 12 consecutive months. Patients will be randomised in a 1:1:1 ratio to the early ICU-VR group, the late ICU-VR group, or the usual care group. All patients will receive usual care, including a mandatory ICU follow-up clinic visit 3 months after ICU discharge. Patients in the early ICU-VR group will receive ICU-VR within 2 weeks after ICU discharge. Patients in the late VR group will receive ICU-VR during the post-ICU follow-up visit. The primary objective is to assess the effect of ICU-VR on PTSD-related symptoms. Secondary objectives are to determine optimal timing for ICU-VR, to assess the effects on anxiety-related and depression-related symptoms and health-related quality of life, and to assess patient satisfaction with ICU aftercare and perspectives on ICU-VR.

ETHICS AND DISSEMINATION

The Medical Ethics Committee United, Nieuwegein, the Netherlands, approved this study and local approval was obtained from each participating centre (NL78555.100.21). Our findings will be disseminated by presentation of the results at (inter)national conferences and publication in scientific, peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NL9812.

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.

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

Background

The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU.

Methods

We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure.

Results

The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model.

Discussion

We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

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.

Experimental verification of principal losses in a regulatory particulate matter emissions sampling system for aircraft turbine engines

A sampling system for measuring emissions of nonvolatile particulate matter (nvPM) from aircraft gas turbine engines has been developed to replace the use of smoke number and is used for international regulatory purposes. This sampling system can be up to 35 m in length. The sampling system length in addition to the volatile particle remover (VPR) and other sampling system components lead to substantial particle losses, which are a function of the particle size distribution, ranging from 50 to 90% for particle number concentrations and 10-50% for particle mass concentrations. The particle size distribution is dependent on engine technology, operating point, and fuel composition. Any nvPM emissions measurement bias caused by the sampling system will lead to unrepresentative emissions measurements which limit the method as a universal metric. Hence, a method to estimate size dependent sampling system losses using the system parameters and the measured mass and number concentrations was also developed (SAE 2017; SAE 2019). An assessment of the particle losses in two principal components used in ARP6481 (SAE 2019) was conducted during the VAriable Response In Aircraft nvPM Testing (VARIAnT) 2 campaign. Measurements were made on the 25-meter sample line portion of the system using multiple, well characterized particle sizing instruments to obtain the penetration efficiencies. An agreement of ± 15% was obtained between the measured and the ARP6481 method penetrations for the 25-meter sample line portion of the system. Measurements of VPR penetration efficiency were also made to verify its performance for aviation nvPM number. The research also demonstrated the difficulty of making system loss measurements and substantiates the E-31 decision to predict rather than measure system losses.

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.

No Additional Prognostic Value of Genetic Information in the Prediction of Vascular Events after Cerebral Ischemia of Arterial Origin: The PROMISe Study

Background

Patients who have suffered from cerebral ischemia have a high risk of recurrent vascular events. Predictive models based on classical risk factors typically have limited prognostic value. Given that cerebral ischemia has a heritable component, genetic information might improve performance of these risk models. Our aim was to develop and compare two models: one containing traditional vascular risk factors, the other also including genetic information.

Methods and Results

We studied 1020 patients with cerebral ischemia and genotyped them with the Illumina Immunochip. Median follow-up time was 6.5 years; the annual incidence of new ischemic events (primary outcome, n=198) was 3.0%. The prognostic model based on classical vascular risk factors had an area under the receiver operating characteristics curve (AUC-ROC) of 0.65 (95% confidence interval 0.61-0.69). When we added a genetic risk score based on prioritized SNPs from a genome-wide association study of ischemic stroke (using summary statistics from the METASTROKE study which included 12389 cases and 62004 controls), the AUC-ROC remained the same. Similar results were found for the secondary outcome ischemic stroke.

Conclusions

We found no additional value of genetic information in a prognostic model for the risk of ischemic events in patients with cerebral ischemia of arterial origin. This is consistent with a complex, polygenic architecture, where many genes of weak effect likely act in concert to influence the heritable risk of an individual to develop (recurrent) vascular events. At present, genetic information cannot help clinicians to distinguish patients at high risk for recurrent vascular events.

Meta-analysis in more than 17,900 cases of ischemic stroke reveals a novel association at 12q24.12

OBJECTIVES

To perform a genome-wide association study (GWAS) using the Immunochip array in 3,420 cases of ischemic stroke and 6,821 controls, followed by a meta-analysis with data from more than 14,000 additional ischemic stroke cases.

METHODS

Using the Immunochip, we genotyped 3,420 ischemic stroke cases and 6,821 controls. After imputation we meta-analyzed the results with imputed GWAS data from 3,548 cases and 5,972 controls recruited from the ischemic stroke WTCCC2 study, and with summary statistics from a further 8,480 cases and 56,032 controls in the METASTROKE consortium. A final in silico "look-up" of 2 single nucleotide polymorphisms in 2,522 cases and 1,899 controls was performed. Associations were also examined in 1,088 cases with intracerebral hemorrhage and 1,102 controls.

RESULTS

In an overall analysis of 17,970 cases of ischemic stroke and 70,764 controls, we identified a novel association on chromosome 12q24 (rs10744777, odds ratio [OR] 1.10 [1.07-1.13], p = 7.12 × 10(-11)) with ischemic stroke. The association was with all ischemic stroke rather than an individual stroke subtype, with similar effect sizes seen in different stroke subtypes. There was no association with intracerebral hemorrhage (OR 1.03 [0.90-1.17], p = 0.695).

CONCLUSION

Our results show, for the first time, a genetic risk locus associated with ischemic stroke as a whole, rather than in a subtype-specific manner. This finding was not associated with intracerebral hemorrhage.

A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach

Genome-wide association studies (GWAS) have begun to identify the common genetic component to ischaemic stroke (IS). However, IS has considerable phenotypic heterogeneity. Where clinical covariates explain a large fraction of disease risk, covariate informed designs can increase power to detect associations. As prevalence rates in IS are markedly affected by age, and younger onset cases may have higher genetic predisposition, we investigated whether an age-at-onset informed approach could detect novel associations with IS and its subtypes; cardioembolic (CE), large artery atherosclerosis (LAA) and small vessel disease (SVD) in 6,778 cases of European ancestry and 12,095 ancestry-matched controls. Regression analysis to identify SNP associations was performed on posterior liabilities after conditioning on age-at-onset and affection status. We sought further evidence of an association with LAA in 1,881 cases and 50,817 controls, and examined mRNA expression levels of the nearby genes in atherosclerotic carotid artery plaques. Secondly, we performed permutation analyses to evaluate the extent to which age-at-onset informed analysis improves significance for novel loci. We identified a novel association with an MMP12 locus in LAA (rs660599; p = 2.5×10⁻⁷), with independent replication in a second population (p = 0.0048, OR(95% CI) = 1.18(1.05–1.32); meta-analysis p = 2.6×10⁻⁸). The nearby gene, MMP12, was significantly overexpressed in carotid plaques compared to atherosclerosis-free control arteries (p = 1.2×10⁻¹⁵; fold change = 335.6). Permutation analyses demonstrated improved significance for associations when accounting for age-at-onset in all four stroke phenotypes (p<0.001). Our results show that a covariate-informed design, by adjusting for age-at-onset of stroke, can detect variants not identified by conventional GWAS.

Ischaemic stroke places an enormous burden on global healthcare. However, the disease processes that lead to stroke are not fully understood. Genome-wide association studies have recently established that common genetic variants can increase risk of ischaemic stroke and its subtypes. In this study, we aimed to identify novel genetic associations with ischaemic stroke and its subtypes by addressing the fact that younger onset cases may have a stronger genetic component, and using this information in our analyses. We identify a novel genetic variant on chromosome 11 (rs660599), which is associated with increased risk of large artery stroke. We also show that mRNA expression of the nearest gene (MMP12) is higher in arteries with the disease process underlying large artery stroke (atherosclerosis). Finally, we evaluate our novel analysis approach, and show that our method is likely to identify further associations with ischaemic stroke.

Safety and FeasibiLity of Metformin in Patients with Impaired Glucose Tolerance and a Recent TIA or Minor Ischemic Stroke (LIMIT) Trial – A Multicenter, Randomized, Open-Label Phase II Trial

Background and purpose

We aimed to assess the safety, feasibility, and effects on glucose metabolism of treatment with metformin in patients with TIA or minor ischemic stroke and impaired glucose tolerance.

Methods

We performed a multicenter, randomized, controlled, open-label phase II trial with blinded outcome assessment. Patients with TIA or minor ischemic stroke in the previous six months and impaired glucose tolerance (2-hour post-load glucose levels of 7.8–11.0 mmol/l) were randomized to metformin, in a daily dose of 2 g, or no metformin, for three months. Primary outcome measures were safety and feasibility of metformin, and the adjusted difference in 2-hour post-load glucose levels at three months. This trial is registered as an International Standard Randomized Controlled Trial Number 54960762.

Results

Forty patients were enrolled; 19 patients were randomly assigned metformin. Nine patients in the metformin group had side effects, mostly gastrointestinal, leading to permanent discontinuation in four patients after 3–10 weeks. Treatment with metformin was associated with a significant reduction in 2-hour post-load glucose levels of 0·97 mmol/l (95% CI 0·11–1·83) in the on-treatment analysis, but not in the intention-to-treat analysis (0·71 mmol/l; 95% CI −0·36 to 1·78).

Conclusions

Treatment with metformin in patients with TIA or minor ischemic stroke and impaired glucose tolerance is safe, but leads to minor side effects. If tolerated, it may lead to a significant reduction in post-load glucose levels. This suggests that the role of metformin as potential therapeutic agent for secondary stroke prevention should be further explored.

Asymptomatic carotid artery stenosis and the risk of ischemic stroke according to subtype in patients with clinical manifest arterial disease

BACKGROUND AND PURPOSE

Because best medical treatment is improving, the risk of stroke in asymptomatic carotid artery stenosis (ACAS) may decline. We evaluated the risk of ischemic stroke and stratified it according to stroke subtype in patients with ACAS during long-term follow-up.

METHODS

In total, 4319 consecutive patients in the Second Manifestations of Arterial disease study with clinically manifest arterial disease or specific risk factors, but without a history of cerebrovascular disease, were included. Degree of stenosis was evaluated with duplex ultrasound scanning. Strokes during follow-up were classified according to subtype. Cox-proportional hazard-regression models were used to evaluate the relationship between ACAS and future stroke.

RESULTS

We identified 293 (6.8%) patients with ACAS 50% to 99%, of whom 193 had 70% to 99% stenosis. In these subgroups, mean follow-up was 6.2 and 6.0 years, respectively. In total, 94 ischemic strokes occurred, of which 8 in ACAS 50% to 99% patients. The any territory annual ischemic stroke risk was 0.4% in 50% to 99% ACAS and 0.5% per year for 70% to 99% ACAS patients. The risk of ischemic stroke was not significantly increased in patients with ACAS 70% to 99% (hazard ratio, 1.5; 95% confidence interval, 0.7-3.5). Patients with ACAS 50% to 99% and ACAS 70% to 99% tended to have nonsignificantly more large vessel disease strokes (hazard ratio, 1.5; 95% confidence interval, 0.5-4.2 and hazard ratio, 1.7; 95% confidence interval, 0.5-5.6).

CONCLUSIONS

Patients with clinically manifest arterial disease or type 2 diabetes mellitus have a low risk of developing ischemic stroke, irrespective of its subtype and independent of the degree of ACAS stenosis.

Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies

BACKGROUND

Various genome-wide association studies (GWAS) have been done in ischaemic stroke, identifying a few loci associated with the disease, but sample sizes have been 3500 cases or less. We established the METASTROKE collaboration with the aim of validating associations from previous GWAS and identifying novel genetic associations through meta-analysis of GWAS datasets for ischaemic stroke and its subtypes.

METHODS

We meta-analysed data from 15 ischaemic stroke cohorts with a total of 12 389 individuals with ischaemic stroke and 62 004 controls, all of European ancestry. For the associations reaching genome-wide significance in METASTROKE, we did a further analysis, conditioning on the lead single nucleotide polymorphism in every associated region. Replication of novel suggestive signals was done in 13 347 cases and 29 083 controls.

FINDINGS

We verified previous associations for cardioembolic stroke near PITX2 (p=2·8×10(-16)) and ZFHX3 (p=2·28×10(-8)), and for large-vessel stroke at a 9p21 locus (p=3·32×10(-5)) and HDAC9 (p=2·03×10(-12)). Additionally, we verified that all associations were subtype specific. Conditional analysis in the three regions for which the associations reached genome-wide significance (PITX2, ZFHX3, and HDAC9) indicated that all the signal in each region could be attributed to one risk haplotype. We also identified 12 potentially novel loci at p<5×10(-6). However, we were unable to replicate any of these novel associations in the replication cohort.

INTERPRETATION

Our results show that, although genetic variants can be detected in patients with ischaemic stroke when compared with controls, all associations we were able to confirm are specific to a stroke subtype. This finding has two implications. First, to maximise success of genetic studies in ischaemic stroke, detailed stroke subtyping is required. Second, different genetic pathophysiological mechanisms seem to be associated with different stroke subtypes.

FUNDING

Wellcome Trust, UK Medical Research Council (MRC), Australian National and Medical Health Research Council, National Institutes of Health (NIH) including National Heart, Lung and Blood Institute (NHLBI), the National Institute on Aging (NIA), the National Human Genome Research Institute (NHGRI), and the National Institute of Neurological Disorders and Stroke (NINDS).

Differential propensity for major hemorrhagic events in patients with different types of arterial disease

AIMS

Atherosclerosis is the most frequent cause of coronary artery disease (CAD), cerebrovascular disease (CVD), and peripheral arterial obstructive disease (PAD). We previously found that patients with CVD or PAD had a two-fold higher risk of major hemorrhagic complications than patients with CAD. We investigated whether this difference was attributable to baseline risk factors or genetic variants involved in hemostasis.

METHODS AND RESULTS

We included 2622 consecutive patients from a single university hospital who presented with non-disabling CAD, CVD, or PAD. All patients were followed for the occurrence of major hemorrhagic complications for a mean of 6.6 years. Major hemorrhagic events included intracranial hemorrhagic events, fatal hemorrhagic events, and any hemorrhagic complications requiring hospitalization, irrespective of interventions. Major hemorrhagic complications occurred in 122 patients (annual event rate of 0.77%). Patients with CVD or PAD had more hemorrhagic complications than patients with CAD (hazard ratio [HR] 2.05, 95% confidence interval [CI] 1.39-3.01). Hypertension, diabetes, renal failure and use of oral anticoagulants or antiplatelet therapy did not explain the difference (HR adjusted for all characteristics 1.74; 95% CI 1.14-2.61). Additional adjustment for genetic variants did not further change the HR.

CONCLUSION

Patients with CVD or PAD are at higher risk for major hemorrhagic events than patients with CAD. This difference could not be explained by known risk factors, use of antithrombotic agents, or genetic variants involved in hemostasis. Further research to find the reason for this difference and possible differences in pathogenesis is warranted.

Prognostic value of the Rose questionnaire: a validation with future coronary events in the SMART study

AIM

The Rose questionnaire was developed in epidemiological studies to obtain a reproducible diagnosis of angina pectoris. We studied the prognostic value of this questionnaire with respect to the occurrence of future coronary events.

METHODS AND RESULTS

We studied 7916 consecutive patients (mean age 56 years; 67% men) with clinically manifest vascular disease or cardiovascular risk factors, enrolled in the Second Manifestations of ARTerial disease (SMART) study from 1996 to 2009. At inclusion, all patients completed the Rose questionnaire. We investigated the prognostic value of four definitions of angina pectoris that were based on the following elements of the questionnaire (1) the full questionnaire; (2) three key questions concerning chest pain; (3) one question about discomfort or pain in the chest; (4) two questions about complaints when slowing down or stopping activities (the definition that is used in the SMART study). All patients were followed for new coronary events and interventions for an average of 4.6 years. Analyses were with multivariable Cox regression models. Discriminatory ability of the four definitions as assessed with areas under the receiver-operator characteristics curves was similar (range 0.708-0.726) for coronary events in isolation as well as in combination with coronary interventions. The models were assessed for their ability to improve risk stratification compared with each other; differences between definitions are small.

CONCLUSION

Our data implicate that the use of a subset of questions of the Rose questionnaire performs equally well compared with the full Rose questionnaire regarding the prediction of coronary events.

Prognostic modelling in ischaemic stroke study, additional value of genetic characteristics. Rationale and design

BACKGROUND AND AIM

The prediction of prognosis after cerebral infarction might be improved by genetic information. The aim of the Prognostic Modelling in Ischaemic Stroke study is to develop 2 different prognostic models on the basis of traditional vascular risk factors and genetic information in patients who have suffered from cerebral ischaemia of arterial origin, 1 concerning new ischaemic and the other new haemorrhagic events.

METHODS

Polymorphisms and haplotypes describing the haemostatic system and those that influence antithrombotic drug activity will be identified in a cohort of 1,200 patients with cerebral ischaemia of arterial origin who will be followed up for a mean of 6.5 years. In total, 312 ischaemic and 78 haemorrhagic events are anticipated. With a prevalence of a genetic characteristic of 10% a relative risk of 1.4 (95% confidence interval = 1.1-1.8) for ischaemic events and of 1.8 (95% confidence interval = 1.0-3.2) for haemorrhagic events can be estimated with sufficient precision. To determine the additional prognostic value of genetic characteristics the area under the ROC curves of 2 separate models will be compared: 1 based on non-genetic risk factors only, the other also including genetic data.

Characterization of an imatinib-sensitive subset of high-grade human glioma cultures

High-grade gliomas, including glioblastomas, are malignant brain tumors for which improved treatment is urgently needed. Genetic studies have demonstrated the existence of biologically distinct subsets. Preliminary studies have indicated that platelet-derived growth factor (PDGF) receptor signaling contributes to the growth of some of these tumors. In this study, human high-grade glioma primary cultures were analysed for sensitivity to treatment with the PDGF receptor inhibitor imatinib/Glivec/Gleevec/STI571. Six out of 15 cultures displayed more than 40% growth inhibition after imatinib treatment, whereas seven cultures showed less than 20% growth inhibition. In the sensitive cultures, apoptosis contributed to growth inhibition. Platelet-derived growth factor receptor status correlated with imatinib sensitivity. Supervised analyses of gene expression profiles and real-time PCR analyses identified expression of the chemokine CXCL12/SDF-1 (stromal cell-derived factor 1) as a predictor of imatinib sensitivity. Exogenous addition of CXCL12 to imatinib-insensitive cultures conferred some imatinib sensitivity. Finally, coregulation of CXCL12 and PDGF alpha-receptor was observed in glioblastoma biopsies. We have thus defined the characteristics of a novel imatinib-sensitive subset of glioma cultures, and provided evidence for a functional relationship between imatinib sensitivity and chemokine signaling. These findings will assist in the design and evaluation of clinical trials exploring therapeutic effects of imatinib on malignant brain tumors.