Common data model for COVID-19 datasets.

MOTIVATION: A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling. RESULTS: To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability. AVAILABILITY AND IMPLEMENTATION: The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Early Mobilization After Stroke Is Not Associated With Cognitive Outcome.

Background and Purpose- We aimed to determine whether early mobilization after stroke affects subsequent cognitive function. Methods- AVERT (A Very Early Rehabilitation Trial) was an international, 56-site, phase 3 randomized controlled trial, conducted from 2006 to 2015. Participants were included if they were aged 18+, presented within 24 hours of stroke, and satisfied physiological limits for blood pressure, heart rate, and temperature. Participants were randomized to receive either usual stroke unit care or very early and more frequent mobilization in addition to usual stroke unit care. The Montreal Cognitive Assessment, scored 0 to 30, was introduced as a 3-month outcome during 2008. Results- Of the 2104 patients included in AVERT, 317 were assessed before the Montreal Cognitive Assessment's introduction. Of the remaining 1787, 1189 (66.5%) had complete Montreal Cognitive Assessment data, 456 (25.5%) had partially or completely missing data, 136 (7.6%) had died, and 6 (0.3%) were lost to follow-up. In surviving participants with complete data, adjusting for age and stroke severity, total Montreal Cognitive Assessment score was no different in the intervention (n=595; median, 23; interquartile range, 19-26; mean, 21.9; SD, 5.9) and usual care (n=594; median, 23; interquartile range, 19-26; mean, 21.8; SD, 5.9) groups ( P=0.68). Conclusions- Exposure to earlier and more frequent mobilization in the acute stage of stroke does not influence cognitive outcome at 3 months. This stands in contrast to the primary outcome from AVERT (modified Rankin Scale), where the intervention group had less favorable outcomes than controls. Clinical Trial Registration- URL: https://www.anzctr.org.au . Unique identifier: ACTRN12606000185561.

Positron Emission Tomographic Imaging in Stroke: Cross-Sectional and Follow-Up Assessment of Amyloid in Ischemic Stroke.

BACKGROUND AND PURPOSE: Cardiovascular risk factors significantly increase the risk of developing Alzheimer disease. A possible mechanism may be via ischemic infarction-driving amyloid deposition. We conducted a study to determine the presence of ß-amyloid in infarct, peri-infarct, and hemispheric areas after stroke. We hypothesized that an infarct would trigger ß-amyloid deposition, with deposition over time. METHODS: Patients were recruited within 40 days of acute ischemic stroke and imaged with computed tomographic or magnetic resonance imaging and Pittsburgh compound B (11C-PiB) positron emission tomographic scans. Follow-up positron emission tomographic scanning was performed in a subgroup ≤18 months after the stroke event. Standardized uptake value ratios for regions of interest were analyzed after coregistration. RESULTS: Forty-seven patients were imaged with (11)C-PiB positron emission tomography. There was an increase in (11)C-PiB accumulation in the stroke area compared with a reference region in the contralesional hemisphere, which was not statistically significant (median difference in standardized uptake value ratio, 0.07 [95% confidence interval, -0.06 to 0.123]; P=0.452). There was no significant increase in the accumulation of (11)C-PiB in the peri-infarct region or in the ipsilesional hemisphere (median difference in standardized uptake value ratio, 0.04 [95% confidence interval, -0.02 to 0.10]; P=0.095). We repeated (11)C-PiB positron emission tomography in 21 patients and found a significant reduction in accumulation of (11)C-PiB between regions of interest (median difference in standardized uptake value ratio, -0.08 [95% confidence interval, -0.23 to -0.03]; P=0.04). CONCLUSIONS: There was no significant increase in (11)C-PiB accumulation in or around the infarct. There was no increase in ipsilesional hemispheric (11)C-PiB accumulation over time. We found no evidence that infarction leads to sustained or increased ß-amyloid deposition ≤18 months after stroke.

High throughput chromatography strategies for potential use in the formal process characterization of a monoclonal antibody.

High throughput experimental strategies are central to the rapid optimization of biologics purification processes. In this work, we extend common high throughput technologies towards the characterization of a multi-column chromatography process for a monoclonal antibody (mAb). Scale-down strategies were first evaluated by comparing breakthrough, retention, and performance (yields and clearance of aggregates and host cell protein) across miniature and lab scale columns. The process operating space was then evaluated using several integrated formats, with batch experimentation to define process testing ranges, miniature columns to evaluate the operating space, and comparison to traditional scale columns to establish scale-up correlations and verify the determined operating space. When compared to an independent characterization study at traditional lab column scale, the high throughput approach identified the same control parameters and similar process sensitivity. Importantly, the high throughput approach significantly decreased time and material needs while improving prediction robustness. Miniature columns and manufacturing scale centerpoint data comparisons support the validity of this approach, making the high throughput strategy an attractive and appropriate scale-down tool for the formal characterization of biotherapeutic processes in the future if regulatory acceptance of the miniature column data can be achieved. Biotechnol. Bioeng. 2016;113: 1273-1283. © 2015 Wiley Periodicals, Inc.

The High Prevalence of Anxiety Disorders After Stroke.

OBJECTIVES: Previous studies indicate that post-stroke anxiety is common and persistent. We aimed to determine whether point prevalence of anxiety after stroke is higher than in the population at large, and whether the profile of anxiety symptoms is different. METHODS: This case-control study was conducted in Göteborg, Sweden, with stroke patients recruited from the Sahlgrenska University Hospital and a comparison group selected from local population health studies. We included 149 stroke survivors (assessed at 20 months post-stroke) and 745 participants from the general population matched for age and sex. A comprehensive psychiatric interview was conducted, with anxiety and depressive disorders diagnosed according to DSM-III-R criteria. RESULTS: Those in the stroke group were significantly more likely than those in the comparison group to have generalized anxiety disorder (GAD) (27% versus 8%), phobic disorder (24% versus 8%) and obsessive-compulsive disorder (9% versus 2%). Multivariate regression indicated that being in the stroke group, female sex, and having depression were all significant independent associates of having an anxiety disorder. In terms of symptom profile, stroke survivors with GAD were significantly more likely to report vegetative disturbance than those in the comparison group with GAD but less likely to have observable muscle tension or reduced sleep. CONCLUSIONS: Point prevalence of anxiety disorders is markedly higher after stroke than in the general population, and this cannot be attributed to higher rates of comorbid depression.

Poststroke Physical Activity Levels No Higher in Rehabilitation than in the Acute Hospital.

BACKGROUND: Returning to physical activity is a common goal for stroke survivors undergoing rehabilitation, and higher levels of activity have been linked to better gait and greater independence in activities of daily living. Our aim was to determine if inpatient rehabilitation settings promoted higher levels of physical activity in stroke survivors than an acute stroke unit setting. METHODS: Stroke survivors were recruited from the inpatient rehabilitation wards of 4 different hospitals in southern Sweden and from the acute stroke unit at Karolinska University Hospital in Stockholm. Participants were observed for 1 minute every 10 minutes from 8:00 a.m. to 5:00 p.m. At each observation, the person's highest level of physical activity, location, and other people present were recorded. RESULTS: We collected data from 190 stroke survivors (104 rehabilitation, 86 acute). Contrary to our hypothesis, there was no significant difference between the groups in the amount of time spent in moderate-to-high physical activity (rehabilitation median 24%, acute median 23%; adjusted P = .74). Compared to those in the acute setting, participants in the rehabilitation setting spent less time lying in bed, more time sitting supported out of bed, less time in their bedroom, and more time with a therapist (all adjusted P < .001). CONCLUSION: In the context of stroke, the inpatient rehabilitation environment does not appear to promote higher levels of physical activity than the acute hospital environment.

Mobilization after thrombolysis (rtPA) within 24 hours of acute stroke: what factors influence inclusion of patients in A Very Early Rehabilitation Trial (AVERT)?

BACKGROUND: A key treatment for acute ischaemic stroke is thrombolysis (rtPA). However, treatment is not devoid of side effects and patients are carefully selected. AVERT (A Very Early Rehabilitation Trial), a large, ongoing international phase III trial, tests whether starting out of bed activity within 24 hours of stroke onset improves outcome. Patients treated with rtPA can be recruited if the physician allows (447 included to date). This study aimed to identify factors that might influence the inclusion of rtPA treated patients in AVERT. METHODS: Data from all patients thrombolysed at Austin Health, Australia, between September 2007 and December 2011 were retrospectively extracted from medical records. Factors of interest included: demographic and stroke characteristics, 24 hour clinical response to rtPA treatment, cerebral imaging and process factors (day and time of admission). RESULTS: 211 patients received rtPA at Austin Health and 50 (24%) were recruited to AVERT (AVERT). Of the 161 patients not recruited, 105 (65%) were eligible, and could potentially have been included (pot-AVERT). There were no significant differences in demographics, Oxfordshire classification or stroke severity (NIHSS) on admission between groups. Size and localization of stroke on imaging and symptomatic intracerebral heamorrhage rate did not differ. Patients included in AVERT showed less change in NIHSS 24 hours post rtPA (median change = 1, IQR (-1,4)) than those in the pot-AVERT group (median change = 3, IQR (0,6)) by the median difference of 2 points (95%CI:0.3; p = 0.03). A higher proportion of rtPA treated AVERT patients were admitted on weekdays (p = 0.04). CONCLUSION: Excluding a possible clinical instability, no significant clinical differences were identified between thrombolysed patients included in AVERT and those who were not. Over 500 AVERT patients will be treated with rtPA at trial end. These results suggest we may be able to generalize findings to other rtPA treated patients beyond the trial population.

Description of fecal shedding of Cryptosporidium parvum oocysts in experimentally challenged dairy calves.

The objective was to describe the probability of Cryptosporidium parvum fecal oocyst shedding at different magnitudes of exposure, the pattern of fecal shedding over time, and factors affecting fecal shedding in dairy calves. Within the first 24 h of life, 36 calves were experimentally challenged with C. parvum oocysts at one of four possible magnitudes of oral exposure (1 × 10(3), 1 × 10(4), 1 × 10(5), and 1 × 10(6) oocysts), and 7 control calves were sham dosed. Fecal shedding occurred in 33 (91.7 %) experimentally challenged calves and in none of the control calves. There was a difference in the log-total number of oocysts counted per gram of feces dry weight among the four exposure groups; calves with the lowest magnitude of exposure (1 × 10(3) oocysts) shed less than the other three groups. At higher magnitudes of exposure, there was more variability in the range of fecal oocyst shedding. There was an inverse relationship between the log-total amount of oocysts counted per gram of feces dry weight and the number of days to the onset of fecal shedding per calf, i.e., the more time that elapsed to the onset of fecal shedding, the fewer oocysts that were shed. The pattern of fecal shedding over time for all calves shedding oocysts was curvilinear; the number of oocysts increased with time, reached a peak, and declined. Therefore, the dynamics of oocyst shedding can be influenced in part by limiting exposure among calves and delaying the onset of fecal oocyst shedding.

A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression.

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine.

Mortality in hospitalized COVID-19 patients was associated with the COVID-19 admission rate during the first year of the pandemic in Sweden.

INTRODUCTION: Studies from the first pandemic wave found associations between COVID-19 hospital load and mortality. Here, we aimed to study if mortality of hospitalized COVID-19 patients was associated with the COVID-19 admission rate during a full year of the pandemic in Sweden. METHOD: Observational review of all patients admitted to hospital with COVID-19 in Sweden between March 2020 and February 2021 (n = 42,017). Primary outcome was 60-day all-cause mortality related to number of COVID-19 hospital admissions per month/100,000 inhabitants. Poisson regression was used to estimate the relative risk for death by month of admission, adjusting for pre-existing factors. RESULTS: The overall mortality was 17.4%. Excluding March 2020, mortality was clearly correlated to the number of COVID-19 admissions per month (coefficient of correlation ρ=.96; p<.0001). After adjustment for pre-existing factors, the correlation remained significant (ρ=.75, p=.02). Patients admitted in December (high admission rate and high mortality) had more comorbidities and longer hospital stays, and patients treated in intensive care units (ICU) had longer pre-ICU hospital stays and worse respiratory status on ICU admission than those admitted in July to September (low admission rate and low mortality). CONCLUSION: Mortality in hospitalized COVID-19 patients was clearly associated with the COVID-19 admission rate. Admission of healthier patients between pandemic waves and delayed ICU care during wave peaks could contribute to this pattern. The study supports measures to flatten-the-curve to reduce the number of COVID-19 patients admitted to hospital.

Do P2Y12 receptor inhibitors prescribed poststroke modify the risk of cognitive disorder or dementia? Protocol for a target trial using multiple national Swedish registries.

INTRODUCTION: The target of a class of antiplatelet medicines, P2Y12R inhibitors, exists both on platelets and on brain immune cells (microglia). This protocol aims to describe a causal (based on a counterfactual model) approach for analysing whether P2Y12R inhibitors prescribed for secondary prevention poststroke may increase the risk of cognitive disorder or dementia via their actions on microglia, using real-world evidence. METHODS AND ANALYSIS: This will be a cohort study nested within the Swedish National Health and Medical Registers, including all people with incident stroke from 2006 to 2016. We developed directed acyclic graphs to operationalise the causal research question considering potential time-independent and time-dependent confounding, using input from several experts. We developed a study protocol following the components of the target trial approach described by Hernan et al and describe the data structure that would be required in order to make a causal inference. We also describe the statistical approach required to derive the causal estimand associated with this important clinical question; that is, a time-to-event analysis for the development of cognitive disorder or dementia at 1, 2 and 5-year follow-up, based on approaches for competing events to account for the risk of all-cause mortality. Causal effect estimates and the precision in these estimates will be quantified. ETHICS AND DISSEMINATION: This study has been approved by the Ethics Committee of the University of Gothenburg and Confidentiality Clearance at Statistics Sweden with Dnr 937-18, and an approved addendum with Dnr 2019-0157. The analysis and interpretation of the results will be heavily reliant on the structure, quality and potential for bias of the databases used. When we implement the protocol, we will consider and document any biases specific to the dataset and conduct appropriate sensitivity analyses. Findings will be disseminated to local stakeholders via conferences, and published in appropriate scientific journals.

Corrigendum to "Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases"[Artificial Intelligence in Life Sciences] 1(2021), 100020.

[This corrects the article DOI: 10.1016/j.ailsci.2021.100020.].

The montreal cognitive assessment: short cognitive evaluation in a large stroke trial.

BACKGROUND AND PURPOSE: Cognitive function is often ignored in stroke research trials. The brief Montreal Cognitive Assessment (MoCA) may be sensitive to stroke-related cognitive deficits. METHODS: We evaluated the feasibility of administering the MoCA at 3 months in a large stroke trial (A Very Early Rehabilitation Trial [AVERT]). RESULTS: Data (blinded to intervention group) are presented for 294 patients with mean age of 70.6 years (SD, 12.8); 220 (75%) completed the MoCA, 54 (18%) had missing data, and 20 (7%) had died. Of those surviving to 3 months, the MoCA was completed by 87% with mild stroke, 79% with moderate stroke, and 67% with severe stroke on admission. Mean MoCA score was 21.1 (SD 7.5) out of 30; only 78 of 220 (35%) patients attained the "normal" cutoff (≥26). CONCLUSIONS: The MoCA is a feasible global cognitive screening tool in stroke trials. Clinical Trial Registration- URL: www.anzctr.org.au/trial_view.aspx?ID=1266. Unique identifier: ACTRN12606000185561.

An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities (e.g., anxiety, migraine, and stroke). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient-specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for individual epilepsy patients. In this work, we use inpatient and outpatient administrative health claims data of around 19,500 U.S. epilepsy patients. We suggest a dedicated multimodal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model-DeepLORI) to predict the time-dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods. Moreover, we show that DeepLORI-based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.

Mortality trends among hospitalised COVID-19 patients in Sweden: A nationwide observational cohort study.

BACKGROUND: It is important to know if mortality among hospitalised COVID-19 patients has changed as the pandemic has progressed. The aim of this study was to describe the dynamics over time of mortality among patients hospitalised for COVID-19 in Sweden, using nationwide data compiled by the Swedish National Board of Health and Welfare. METHODS: Observational cohort study where all patients hospitalised in Sweden between March 1 and September 30, 2020, with SARS-CoV-2 RNA positivity 14 days before to 5 days after admission and a discharge code for COVID-19 were included. Outcome was 60-day all-cause mortality. Patients were categorised according to month of hospital admission. Poisson regression was used to estimate the relative risk of death by month of admission, adjusting for, age, sex, comorbidities, care dependency, country of birth, healthcare region, and Simplified Acute Physiology, version 3 (patients in intensive care units; ICU). FINDINGS: A total of 17,140 patients were included, of which 2943 died within 60 days of admission. The overall 60-day mortality was thus 17·2% (95% CI, 16·6%-17·7%), and it decreased from 24·7% (95% CI, 23·0%-26·5%) in March to 10·4% (95% CI, 8·9%-12·1%) post-wave (July-September). Adjusted relative risk (RR) of death was 0·46 (95% CI, 0·39-0·54) post-wave, using March as reference. Corresponding RR for patients not admitted to ICU and those admitted to ICU were 0·49 (95% CI, 0·42-0·59) and 0·49 (95% CI, 0·33-0·72), respectively. The proportion of patients admitted to ICU decreased from 19·4% (95% CI, 17·9%-21·1%) in the March cohort to 8·9% (95% CI, 7·5%-10·6%) post-wave. INTERPRETATION: There was a gradual decline in mortality during the spring of 2020 in Swedish hospitalised COVID-19 patients, independent of baseline patient characteristics. Future research is needed to explain the reasons for this decline. The changing COVID-19 mortality should be taken into account when management and results of studies from the first pandemic wave are evaluated. FUNDING: This study was funded by Sweden's National Board of Health and Welfare.

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

The NIH stroke scale can establish cognitive function after stroke.

BACKGROUND: Cognitive impairment is an important but underrecognised consequence of stroke. We investigated whether a subset of items from the NIH Stroke Scale (NIHSS) could yield valid information on cognitive status in a group of stroke patients. METHODS: 149 stroke patients from the Göteborg 70+ Stroke Study were investigated after 18 months. We extracted 4 items corresponding to the NIHSS items on orientation, executive function, language and inattention. Scores on this subset of 4 NIHSS items (Cog-4) and the Mini-Mental State Examination (MMSE) were evaluated against a reference diagnosis of severe cognitive impairment. RESULTS: The area under the receiver-operator curve (AUC) plotted for the Cog-4 scale against the diagnosis of severe cognitive impairment was 0.78; the MMSE had a slightly better diagnostic precision, with an AUC of 0.84. Making the executive task more difficult increased the precision of the Cog-4, raising the AUC to 0.81. CONCLUSIONS: A composite score based on 4 NIHSS items is almost as good as the MMSE in detecting severe cognitive impairment. Ideally, dedicated measures of cognition should be employed as a matter of course after stroke, but in their absence, the Cog-4 subscale provides an indication of cognitive functioning.

The AVERT MoCA Data: Scoring Reliability in a Large Multicenter Trial.

The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool in stroke. As scoring the visuospatial/executive MoCA items involves subjective judgement, reliability is important. Analyzing data on these items from A Very Early Rehabilitation Trial (AVERT), we compared the original scoring of assessors (n = 102) to blind scoring by a single, independent rater. In a sample of scoresheets from 1,119 participants, we found variable interrater reliability. The match between original assessors and the independent rater was the following: trail-making 97% (κ = 0.94), cube copy 90% (κ = 0.80), clock contour 92% (κ = 0.49), clock numbers 89% (κ = 0.67), and clock hands 72% (κ = 0.46). For all items except clock contour, the independent rater was "stricter" than the original assessors. Discrepancies were typically errors in original scoring, rather than borderline differences in subjective judgement. In trials that include the MoCA, researchers should emphasize scoring rules to assessors and implement independent data checking, especially for clock hands, to maximize accuracy.

Hemispatial neglect and rehabilitation in acute stroke.

OBJECTIVES: To compare 2 methods for determining neglect in patients within 2 days of stroke, and to investigate whether early neglect was related to rehabilitation practice, and whether this relationship was affected by an early, intensive mobilization intervention. DESIGN: Data were collected from patients participating in a phase II randomized controlled trial of early rehabilitation after stroke. SETTING: Acute hospital stroke unit. PARTICIPANTS: Stroke patients (N=71). INTERVENTION: The 2 arms of the trial were very early mobilization (VEM) and standard care (SC). MAIN OUTCOME MEASURES: Neglect was assessed using the Star Cancellation Test and the National Institutes of Health Stroke Scale (NIHSS) inattention item within 48 hours of stroke onset, and therapy details were recorded during the hospital stay. RESULTS: Assessing neglect so acutely after stroke was difficult: 29 of the 71 patients were unable to complete the Star Cancellation Test, and agreement between this test and the NIHSS measure was only .42. Presence of neglect did not preclude early mobilization. SC group patients with neglect had longer hospital stays (median, 11d) than those without neglect (median, 4d); there was no difference in length of stay between patients with and without neglect in the VEM group (median, 6d in both). CONCLUSION: Early mobilization of patients with neglect was feasible and may contribute to a shorter acute hospital stay.

Serum cholesterol, body mass index and smoking status do not predict long-term cognitive impairment in elderly stroke patients.

OBJECTIVES: Older stroke survivors are at risk of long-term cognitive impairment, which is associated with a number of modifiable and non-modifiable factors. We aimed to assess the association between the modifiable risk factors, serum cholesterol, low density lipoprotein, high density lipoprotein, serum triglycerides, body mass index (BMI) and smoking status on cognitive function, while controlling for the non-modifiable factors, acute functional impairment, diabetes status and age. METHODS: A cross-sectional study from a metropolitan university hospital in Sweden involving older adults (n = 149). Assessments occurred at 20 months post-stroke, using the Mini Mental State Examination and serum blood levels of cholesterol, low density lipoprotein, high density lipoprotein and serum triglycerides. RESULTS: Hierarchical linear regression showed that only acute functional impairment significantly contributed to long-term cognitive impairment in stroke survivors. Only 12% of the sample showed healthy cholesterol levels while the remaining patients showed borderline or high cholesterol levels. In terms of BMI, only 2% of the sample were underweight, 38% were within healthy range and 26% were overweight/obese. Only eight women and four men were smokers, therefore our sample of smokers was likely too small to detect any differences between smokers and non-smokers in regard to cognitive outcomes. CONCLUSION: Serum cholesterol, low density lipoprotein, high density lipoprotein, serum triglycerides, BMI or smoking status did not influence cognitive outcomes in older stroke surviving individuals. These findings suggest that modification of these factors may not influence cognitive outcomes in stroke-surviving individuals however should be interpreted as preliminary given limitations in the current study.