Systematic review of prognostic models in patients with acute stroke

Development and Validation of a Postprocedural Model to Predict Outcome After Endovascular Treatment for Ischemic Stroke

Importance

Outcome prediction after endovascular treatment (EVT) for ischemic stroke is important to patients, family members, and physicians.

Objective

To develop and validate a model based on preprocedural and postprocedural characteristics to predict functional outcome for individual patients after EVT.

Design, Setting, and Participants

A prediction model was developed using individual patient data from 7 randomized clinical trials, performed between December 2010 and December 2014. The model was developed within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration and external validation in data from the Dutch Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry of patients treated in clinical practice between March 2014 and November 2017. Participants included patients from multiple centers throughout different countries in Europe, North America, East Asia, and Oceania (derivation cohort), and multiple centers in the Netherlands (validation cohort). Included were adult patients with a history of ischemic stroke from an intracranial large vessel occlusion in the anterior circulation who underwent EVT within 12 hours of symptom onset or last seen well. Data were last analyzed in July 2022.

Main Outcome(s) and Measure(s)

A total of 19 variables were assessed by multivariable ordinal regression to predict functional outcome (modified Rankin Scale [mRS] score) 90 days after EVT. Variables were routinely available 1 day after EVT. Akaike information criterion (AIC) was used to optimize model fit vs model complexity. Probabilities for functional independence (mRS 0-2) and survival (mRS 0-5) were derived from the ordinal model. Model performance was expressed with discrimination (C statistic) and calibration.

Results

A total of 781 patients (median [IQR] age, 67 [57-76] years; 414 men [53%]) constituted the derivation cohort, and 3260 patients (median [IQR] age, 72 [61-80] years; 1684 men [52%]) composed the validation cohort. Nine variables were included in the model: age, baseline National Institutes of Health Stroke Scale (NIHSS) score, prestroke mRS score, history of diabetes, occlusion location, collateral score, reperfusion grade, NIHSS score at 24 hours, and symptomatic intracranial hemorrhage 24 hours after EVT. External validation in the MR CLEAN Registry showed excellent discriminative ability for functional independence (C statistic, 0.91; 95% CI, 0.90-0.92) and survival (0.89; 95% CI, 0.88-0.90). The proportion of functional independence in the MR CLEAN Registry was systematically higher than predicted by the model (41% vs 34%), whereas observed and predicted survival were similar (72% vs 75%). The model was updated and implemented for clinical use.

Conclusion and relevance

The prognostic tool MR PREDICTS@24H can be applied 1 day after EVT to accurately predict functional outcome for individual patients at 90 days and to provide reliable outcome expectations and personalize follow-up and rehabilitation plans. It will need further validation and updating for contemporary patients.

Clinical Factors Affecting the Recovery of Sensory Impairment After Cerebral Infarction: A Retrospective Study

BACKGROUND

About 75% of patients with cerebral infarction suffer from sensory impairment in varying degrees. It prolongs the time for patients to resume normal life and work. The aim of this study was to retrospectively investigate the clinical characteristics affecting the recovery of sensory impairment.

MATERIALS AND METHODS

This was a retrospective case-control study. Data of inpatients at the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine were investigated. We collected information on the patients with sensory disturbances after cerebral infarction. Cases were defined according to whether the National Institutes of Health Stroke Scale (NIHSS) and visual analogue scale (VAS) scores improved. A total of 1078 inpatients from January 1, 2019, to December 31, 2021, were screened. Among those, 187 cases included in this study were divided into no improvement and improvement groups. We compared the clinical characteristics affecting the rehabilitation of these patients.

RESULTS

The number of patients aged between 63 and 73 years in the no improvement group were significantly higher ( P <0.05). The incidence of coronary heart disease and thalamus infarction was significantly higher in patients in the no improvement cohort ( P <0.05). Furthermore, coronary heart disease [odds ratio=0.466, 95% confidence interval (0.252, 0.863), P =0.015] and thalamic infarction [odds ratio=0.457, 95% confidence interval (0.230, 0.908), P =0.025] were the independent risk factors against the recovery of sensory disturbance after cerebral infarction.

CONCLUSIONS

Patients with thalamus infarction and coronary heart disease may be more inclined to recover poorly from somatosensory deficits.

Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review

Estimating pre-operative mortality risk may inform clinical decision-making for peri-operative care. However, pre-operative mortality risk prediction models are rarely implemented in routine clinical practice. High predictive accuracy and clinical usability are essential for acceptance and clinical implementation. In this systematic review, we identified and appraised prediction models for 30-day postoperative mortality in non-cardiac surgical cohorts. PubMed and Embase were searched up to December 2022 for studies investigating pre-operative prediction models for 30-day mortality. We assessed predictive performance in terms of discrimination and calibration. Risk of bias was evaluated using a tool to assess the risk of bias and applicability of prediction model studies. To further inform potential adoption, we also assessed clinical usability for selected models. In all, 15 studies evaluating 10 prediction models were included. Discrimination ranged from a c-statistic of 0.82 (MySurgeryRisk) to 0.96 (extreme gradient boosting machine learning model). Calibration was reported in only six studies. Model performance was highest for the surgical outcome risk tool (SORT) and its external validations. Clinical usability was highest for the surgical risk pre-operative assessment system. The SORT and risk quantification index also scored high on clinical usability. We found unclear or high risk of bias in the development of all models. The SORT showed the best combination of predictive performance and clinical usability and has been externally validated in several heterogeneous cohorts. To improve clinical uptake, full integration of reliable models with sufficient face validity within the electronic health record is imperative.

The Need to Prioritize Model-Updating Processes in Clinical Artificial Intelligence (AI) Models: Protocol for a Scoping Review

BACKGROUND

With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety.

OBJECTIVE

The objective of this scoping review was to evaluate and assess the model-updating practices of AI and ML clinical models that are used in direct patient-provider clinical decision-making.

METHODS

We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the PRISMA-P protocol guidance in addition to a modified CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist to conduct this scoping review. A comprehensive medical literature search of databases, including Embase, MEDLINE, PsycINFO, Cochrane, Scopus, and Web of Science, was conducted to identify AI and ML algorithms that would impact clinical decision-making at the level of direct patient care. Our primary end point is the rate at which model updating is recommended by published algorithms; we will also conduct an assessment of study quality and risk of bias in all publications reviewed. In addition, we will evaluate the rate at which published algorithms include ethnic and gender demographic distribution information in their training data as a secondary end point.

RESULTS

Our initial literature search yielded approximately 13,693 articles, with approximately 7810 articles to consider for full reviews among our team of 7 reviewers. We plan to complete the review process and disseminate the results by spring of 2023.

CONCLUSIONS

Although AI and ML applications in health care have the potential to improve patient care by reducing errors between measurement and model output, currently there exists more hype than hope because of the lack of proper external validation of these models. We expect to find that the AI and ML model-updating methods are proxies for model applicability and generalizability on implementation. Our findings will add to the field by determining the degree to which published models meet the criteria for clinical validity, real-life implementation, and best practices to optimize model development, and in so doing, reduce the overpromise and underachievement of the contemporary model development process.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37685.

Regression-based prognostic models for functional independence after postacute brain injury rehabilitation are not transportable: a systematic review

BACKGROUND AND OBJECTIVES

To identify and summarize validated multivariable prognostic models for the Functional Independence Measure® (FIM®) at discharge from post-acute inpatient rehabilitation in adults with acquired brain injury (ABI).

METHODS

This review was conducted based on the recommendations of the Cochrane Prognosis Methods Group and adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Three databases were systematically searched in May 2021 and updated in April 2022. Main inclusion criteria were: a) adult patients with ABI, b) validated multivariable prognostic model, c) time of prognostication within 1-week of admission to post-acute rehabilitation, and d) outcome was the FIM® at discharge from post-acute rehabilitation.

RESULTS

The search yielded 3,169 unique articles. Three articles fulfilled the inclusion criteria, accounting for n = 6 internally and n = 2 externally validated prognostic models. Discrimination was estimated as an area under the curve between 0.76 and 0.89. Calibration was deemed to be assessed insufficiently. The included models were judged to be of high risk of bias.

CONCLUSION

Current prognostic models for the FIM® in post-acute rehabilitation for patients with ABI lack the methodological rigor to support clinical use outside the development setting. Future studies addressing functional independence should ensure appropriate model validation and conform to uniform reporting standards for prognosis research.

Stroke mortality prediction using machine learning: systematic review

BACKGROUND AND AIMS

Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality.

MATERIALS AND METHODS

We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool.

RESULTS

Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability.

CONCLUSION

Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review

BACKGROUND

Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends in personalised medical therapies, is expected to change clinical practice dramatically. The emerging field of Rehabilomics is only possible if methodologies are based on biomedical data collection and analysis. In this framework, the objective of this work is to develop a systematic review of machine learning algorithms as solutions to predict motor functional recovery of post-stroke patients after treatment.

METHODS

We conducted a comprehensive search of five electronic databases using the Patient, Intervention, Comparison and Outcome (PICO) format. We extracted health conditions, population characteristics, outcome assessed, the method for feature extraction and selection, the algorithm used, and the validation approach. The methodological quality of included studies was assessed using the prediction model risk of bias assessment tool (PROBAST). A qualitative description of the characteristics of the included studies as well as a narrative data synthesis was performed.

RESULTS

A total of 19 primary studies were included. The predictors most frequently used belonged to the areas of demographic characteristics and stroke assessment through clinical examination. Regarding the methods, linear and logistic regressions were the most frequently used and cross-validation was the preferred validation approach.

CONCLUSIONS

We identified several methodological limitations: small sample sizes, a limited number of external validation approaches, and high heterogeneity among input and output variables. Although these elements prevented a quantitative comparison across models, we defined the most frequently used models given a specific outcome, providing useful indications for the application of more complex machine learning algorithms in rehabilitation medicine.

Predicting 10-year stroke mortality: development and validation of a nomogram

Predicting long-term stroke mortality is a clinically important and unmet need. We aimed to develop and internally validate a 10-year ischaemic stroke mortality prediction score. In this UK cohort study, 10,366 patients with first-ever ischaemic stroke between January 2003 and December 2016 were followed up for a median (interquartile range) of 5.47 (2.96-9.15) years. A Cox proportional-hazards model was used to predict 10-year post-admission mortality. The predictors associated with 10-year mortality included age, sex, Oxfordshire Community Stroke Project classification, estimated glomerular filtration rate (eGFR), pre-stroke modified Rankin Score, admission haemoglobin, sodium, white blood cell count and comorbidities (atrial fibrillation, coronary heart disease, heart failure, cancer, hypertension, chronic obstructive pulmonary disease, liver disease and peripheral vascular disease). The model was internally validated using bootstrap resampling to assess optimism in discrimination and calibration. A nomogram was created to facilitate application of the score at the point of care. Mean age (SD) was 78.5 ± 10.9 years, 52% female. Most strokes were partial anterior circulation syndromes (38%). 10-year mortality predictors were: total anterior circulation stroke (hazard ratio, 95% confidence intervals) (2.87, 2.62-3.14), eGFR < 15 (1.97, 1.55-2.52), 1-year increment in age (1.04, 1.04-1.05), liver disease (1.50, 1.20-1.87), peripheral vascular disease (1.39, 1.23-1.57), cancers (1.37, 1.27-1.47), heart failure (1.24, 1.15-1.34), 1-point increment in pre-stroke mRS (1.20, 1.17-1.22), atrial fibrillation (1.17, 1.10-1.24), coronary heart disease (1.09, 1.02-1.16), chronic obstructive pulmonary disease (1.13, 1.03-1.25) and hypertension (0.77, 0.72-0.82). Upon internal validation, the optimism-adjusted c-statistic was 0.76 and calibration slope was 0.98. Our 10-year mortality model uses routinely collected point-of-care information. It is the first 10-year mortality score in stroke. While the model was internally validated, further external validation is also warranted.

Infarct volume and outcome of cerebral ischaemia, a systematic review and meta-analysis

BACKGROUND

Multiple studies have evaluated the accuracy of infarct volume (IV) as a predictor of outcome in patients with ischaemic stroke; however, no study has systematically reviewed the results of these studies.

AIM

This systematic review and meta-analysis aim to sum up the results of the studies evaluating IV as the prognostic criteria for patients with cerebral ischaemia.

METHODS

Human studies that reported the infarction volume and any prognostic outcome in patients with ischaemic stroke were collected from PubMed, Scopus, Embase and Cochrane library databases. Newcastle-Ottawa Quality Assessment Checklist was applied to evaluate the quality of the included articles. 90-day modified Rankin Scale (mRS) score was used as a meta-analysis outcome. The area under the curve, sensitivity and specificity among included studies was evaluated. The heterogeneity of the studies was assessed by Cochran test Egger and Begg test was used for assessing publication bias.

RESULTS

Among the included studies, nine studies assessed the association between IV and outcome (90-day mRS score). The results of the meta-analysis revealed a significant association between IV with the unfavourable functional outcome (mRS score of 3-6) (OR = 0.80; 95% CI: 0.74-0.86 per 10 mL, P < .001; I²  = 98.1%, P < .001). The infarction volume cut of point between 20 and 50 mL showed the best sensitivity and specificity for the prediction of poor clinical outcomes in patients with ischaemic stroke.

CONCLUSION

The results of the meta-analysis revealed a significant association between IV and unfavourable functional outcomes in patients with ischaemic stroke.

Predicting short and long-term mortality after acute ischemic stroke using EHR

OBJECTIVE

Despite improvements in treatment, stroke remains a leading cause of mortality and long-term disability. In this study, we leveraged administrative data to build predictive models of short- and long-term post-stroke all-cause-mortality.

METHODS

The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. We used patient-level data from electronic health records, three algorithms, and six prediction windows to develop models for post-stroke mortality.

RESULTS

We included 7144 patients from which 5347 had survived their ischemic stroke after two years. The proportion of mortality was between 8%(605/7144) within 1-month, to 25%(1797/7144) for the 2-years window. The three most common comorbidities were hypertension, dyslipidemia, and diabetes. The best Area Under the ROC curve(AUROC) was reached with the Random Forest model at 0.82 for the 1-month prediction window. The negative predictive value (NPV) was highest for the shorter prediction windows - 0.91 for the 1-month - and the best positive predictive value (PPV) was reached for the 6-months prediction window at 0.92. Age, hemoglobin levels, and body mass index were the top associated factors. Laboratory variables had higher importance when compared to past medical history and comorbidities. Hypercoagulation state, smoking, and end-stage renal disease were more strongly associated with long-term mortality.

CONCLUSION

All the selected algorithms could be trained to predict the short and long-term mortality after stroke. The factors associated with mortality differed depending on the prediction window. Our classifier highlighted the importance of controlling risk factors, as indicated by laboratory measures.

Risk factors among stroke subtypes and its impact on the clinical outcome of patients of Northern Portugal under previous aspirin therapy

BACKGROUND

In Western European countries, acute ischemic stroke (AIS) remains the third leading cause of death. Among the risk factors for cerebrovascular disease, some have more influence than others in certain stroke subtypes. The aim of this study was to evaluate the impact of risk factors among Stroke Subtypes on the clinical outcome of Portuguese patients under previous aspirin therapy.

MATERIALS AND METHODS

We studied a cohort of 371 patients diagnosed with AIS and a clinical follow-up protocol was set up.The patients were admitted in a Department of Internal Medicine of a major hospital. Standardized data assessment and stroke subtype classification (Oxfordshire Community Stroke Project) were used.

RESULTS

Arterial hypertension (80.4 %), overweight (72.6 %) and dyslipidemia (62.0 %) were the most prevalent risk factors with no statistical differences among the group's subtypes. Current smoking was more prevalent in POCI(62.9 %) with differences among subtypes (p = 0.002). Atrial fibrillation was more commonly reported in TACI (39.3 %) and less common in POCI (8.1 %) (p < 0.001).Comparing TACI vs Non TACI Stroke Subtypes demonstrated major differences in cumulative survival,among the cases with no previous aspirin treatment, after 3 years (51.9 % vs 88.8 %).The increased risk of mortality at 12 months is consistently observed for the presence of a previous atrial fibrillation (OR 3.01 95 %CI 1.69-5.39), TACI subtype (OR 10.4 95 %CI 4.83-22.6) and NIHSS over 10 (OR 9.33 95 % CI 4,49-19.4). When we analyze the impact of previous aspirin treatment in the risk for a new stroke event, it seems to have a protective effect in a time frame of 12 months, but this protection is lost extending at 24 months (p = 0.094 vs p = 0.005).

DISCUSSION

Our results indicate that smoking, atrial fibrillation and age have different relevance in their distribution among ischemic stroke subtypes at the time of diagnosis. Concerning the influence of the main stroke risk factors on the clinical outcome, our results present a strong influence of atrial fibrillation and of age. Severity of disease at diagnosis, represented by TACI subtype is clearly associated to decreased survival among patients with no record of previous aspirin therapy. Our results reinforce the relevance cohort studies of different populations, to achieve a more comprehensive knowledge of the impact of risk factors on stroke subtypes and on its clinical outcome.

Blood Biomarkers to Predict Long-Term Mortality after Ischemic Stroke

Stroke is a major cause of disability and death globally, and prediction of mortality represents a crucial challenge. We aimed to identify blood biomarkers measured during acute ischemic stroke that could predict long-term mortality. Nine hundred and forty-one ischemic stroke patients were prospectively recruited in the Stroke-Chip study. Post-stroke mortality was evaluated during a median 4.8-year follow-up. A 14-biomarker panel was analyzed by immunoassays in blood samples obtained at hospital admission. Biomarkers were normalized and standardized using Z-scores. Multiple Cox regression models were used to identify clinical variables and biomarkers independently associated with long-term mortality and mortality due to stroke. In the multivariate analysis, the independent predictors of long-term mortality were age, female sex, hypertension, glycemia, and baseline National Institutes of Health Stroke Scale (NIHSS) score. Independent blood biomarkers predictive of long-term mortality were endostatin > quartile 2, tumor necrosis factor receptor-1 (TNF-R1) > quartile 2, and interleukin (IL)-6 > quartile 2. The risk of mortality when these three biomarkers were combined increased up to 69%. The addition of the biomarkers to clinical predictors improved the discrimination (integrative discriminative improvement (IDI) 0.022 (0.007–0.048), p < 0.001). Moreover, endostatin > quartile 3 was an independent predictor of mortality due to stroke. Altogether, endostatin, TNF-R1, and IL-6 circulating levels may aid in long-term mortality prediction after stroke.

Predicting specific abilities after disabling stroke: Development and validation of prognostic models

Background

Predicting specific abilities (e.g. walk and talk) to provide a functional profile six months after disabling stroke could help patients/families prepare for the consequences of stroke and facilitate involvement in treatment decision-making.

Aim

To develop new statistical models to predict specific abilities six months after stroke and test their performance in an independent cohort of patients with disabling stroke.

Methods

We developed models to predict six specific abilities (to be independent, walk, talk, eat normally, live without major anxiety/depression, and to live at home) using data from seven large multicenter stroke trials with multivariable logistic regression. We included 13,117 participants recruited within three days of hospital admission. We assessed model discrimination and derived optimal cut-off values using four statistical methods. We validated the models in an independent single-center cohort of patients (n = 403) with disabling stroke. We assessed model discrimination and calibration and reported the performance of our models at the statistically derived cut-off values.

Results

All six models had good discrimination in external validation (AUC 0.78–0.84). Four models (predicting to walk, eat normally, live without major anxiety/depression, live at home) calibrated well. Models had sensitivities between 45.0 and 97.9% and specificities between 21.6 and 96.5%.

Conclusions

We have developed statistical models to predict specific abilities and demonstrated that these models perform reasonably well in an independent cohort of disabling stroke patients. To aid decision-making regarding treatments, further evaluation of our models is required.

New Index for Multiple Chronic Conditions Predicts Functional Outcome in Ischemic Stroke

OBJECTIVE

To determine whether a new index for multiple chronic conditions (MCCs) predicts poststroke functional outcome (FO), we developed and internally validated the new MCC index in patients with ischemic stroke.

METHODS

A prospective cohort of patients with ischemic stroke (2008-2017) was interviewed at baseline and 90 days in the Brain Attack Surveillance in Corpus Christi Project. An average of 22 activities of daily living (ADL)/instrumental ADL (IADL) items measured the FO score (range 1-4) at 90 days. A FO score >3 (representing a lot of difficulty with ADL/IADLs) was considered unfavorable FO. A new index was developed using machine learning techniques to select and weight conditions and prestroke impairments.

RESULTS

Prestroke modified Rankin Scale (mRS) score, age, congestive heart failure (CHF), weight loss, diabetes, other neurologic disorders, and synergistic effects (dementia × age, CHF × renal failure, and prestroke mRS × prior stroke/TIA) were identified as important predictors in the MCC index. In the validation dataset, the index alone explained 31% of the variability in the FO score, was well-calibrated (p = 0.41), predicted unfavorable FO well (area under the receiver operating characteristic curve 0.81), and outperformed the modified Charlson Comorbidity Index in predicting the FO score and poststroke mRS.

CONCLUSIONS

A new MCC index was developed and internally validated to improve the prediction of poststroke FO. Novel predictors and synergistic interactions were identified.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with ischemic stroke, an index for MCC predicts FO at 90 days.

Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

OBJECTIVES

To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).

DESIGN

Systematic review of peer-reviewed journals.

DATA SOURCES

MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.

ELIGIBILITY CRITERIA

We included model development studies predicting in-hospital paediatric mortality in LMIC.

DATA EXTRACTION AND SYNTHESIS

This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.

RESULTS

Our search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.

CONCLUSION

This review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.

PROSPERO REGISTRATION NUMBER

CRD42018088599.

Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke

INTRODUCTION

Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitating deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predicting which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-h. We determined the incremental value of incorporating imaging-derived features from serial CTs to enhance prediction of malignant edema.

METHODS

We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-h clinical and CT imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-h data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sensitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC).

RESULTS

Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66).

CONCLUSION

Incorporating quantitative CT-based imaging features from baseline and 24-h CT enhances identification of patients with malignant edema needing DHC. Further refinements and external validation of such imaging-based machine-learning models are required.

A parsimonious score with a free web tool for predicting disability after an ischemic stroke: the Parsifal Score

BACKGROUND

Most of the models to predict prognosis after an ischemic stroke include complex mathematical equations or too many variables, making them difficult to use in the daily clinic. We want to predict disability 3 months after an ischemic stroke in an independent patient not receiving recanalization treatment within the first 24 h, using a minimum set of variables and an easy tool to facilitate its implementation. As a secondary aim, we calculated the capacity of the score to predict an excellent/devastating outcome and mortality.

METHODS

Eight hundred and forty-four patients were evaluated. A multivariable ordinal logistic regression was used to obtain the score. The Modified Rankin Scale (mRS) was used to estimate disability at the third month. The results were replicated in another independent cohort (378 patients). The "polr" function of R was used to perform the regression, stratifying the sample into seven groups with different cutoffs (from mRS 0 to 6).

RESULTS

The Parsifal score was generated with: age, previous mRS, initial NIHSS, glycemia on admission, and dyslipidemia. This score predicts disability with an accuracy of 80-76% (discovery-replication cohorts). It has an AUC of 0.86 in the discovery and replication cohort. The specificity was 90-80% (discovery-replication cohorts); while, the sensitivity was 64-74% (discovery-replication cohorts). The prediction of an excellent or devastating outcome, as well as mortality, obtained good discrimination with AUC > 0.80.

CONCLUSIONS

The Parsifal Score is a model that predicts disability at the third month, with only five variables, with good discrimination and calibration, and being replicated in an independent cohort.

Modelling the prognostic effect of glucose and lipid profiles on stroke recurrence in Malaysia: an event-history analysis

Background

Diabetes and dyslipidemia are significantly associated with stroke recurrence, yet the evidence for this relationship is conflicting. Consequently, the parameters in the glucose and lipid profiles may inform us regarding their relationship with stroke recurrence, thus enhancing the physicians' clinical decision-making.

Aim

This study sought to investigate whether glucose and lipid profiles could prognosticate stroke recurrence in Malaysia.

Methods

We conducted a retrospective hospital-based study where we analyzed the first-ever stroke cases regarding about which the Malaysia National Stroke Registry was informed between 2009 and 2017, that fulfilled this study's criteria, and that were followed for stroke recurrence. Using the Cox proportional hazard regression analysis, we estimated the adjusted hazard ratios (HRs), which reflected the prognostic effect of the primary variables (i.e., glucose and lipid profiles on the first-stroke admission) on stroke recurrence.

Results

Among the 8,576 first-ever stroke patients, 394 (4.6%) experienced a subsequent first stroke recurrence event. The prognostic effect measured by univariable Cox regression showed that, when unadjusted, ten variables have prognostic value with regards to stroke recurrence. A multivariable regression analysis revealed that glucose was not a significant prognostic factor (adjusted HR 1.28; 95% CI [1.00-1.65]), while triglyceride level was the only parameter in the lipid profile found to have an independent prognostication concerning stroke recurrence (adjusted HR: 1.28 to 1.36).

Conclusions

Triglyceride could independently prognosticate stroke recurrence, which suggests the role of physicians in intervening hypertriglyceridemia. In line with previous recommendations, we call for further investigations in first-ever stroke patients with impaired glucose and lipid profiles and suggest a need for interventions in these patients.

Experiences and needs of patients on the endovascular therapy pathway after acute ischaemic stroke: Being helpless and next to yourself

Aims

To explore the experiences and needs of patients on the endovascular therapy pathway.

Design

A qualitative design using a phenomenological-hermeneutic approach.

Methods

Semi-structured interviews and participant observations were carried out. Data were collected from April 2016-January 2017. Data were analysed using Ricoeur's theory of interpretation, capturing meaning and ensuring comprehensive understanding. The Consolidated Criteria for Reporting Qualitative Research checklist was used as a guideline.

Results

The findings of this study show that the impact of stroke goes far beyond physical disability. During the structural analysis, four themes were identified: (1) Acute admission to a stroke unit - an overwhelming and blurred experience. (2) Being helpless and next to yourself. (3) The important care when you worry about dying. (4) Poststroke feelings of loneliness and uncertainty.

Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods- In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain's prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient's diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R2). Results- Of 41 patients (mean age [range]: 70 [45-89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions- In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

Associations Between Diffusion Dynamics and Functional Outcome in Acute and Early Subacute Ischemic Stroke

PURPOSE

The current study aimed to investigate the associations between diffusion dynamics of ischemic lesions and clinical functional outcome of acute and early subacute stroke.

MATERIAL AND METHODS

A total of 80 patients with first ever infarcts in the territory of the middle cerebral artery underwent multi-b-values diffusion-weighted imaging and diffusion kurtosis imaging. Multiple diffusion parameters were generated in postprocessing using different diffusion models. Long-term functional outcome was evaluated with modified Rankin scale (mRS) at 6 months post-stroke. Good functional outcome was defined as mRS score ≤ 2 and poor functional outcome was defined as mRS score ≥ 3. Univariate analysis was used to compare the diffusion parameters and clinical features between patients with poor and good functional outcome. Significant parameters were further analyzed for correlations with functional outcome using partial correlation.

RESULTS

In univariate analyses, standard-b-values apparent diffusion coefficient (ADC_st) ratio and fractional anisotropy (FA) ratio of acute stroke, ADC_st ratio and mean kurtosis (MK) ratio of early subacute stroke were statistically different between patients with poor outcome and good outcome (P < 0.05). When the potential confounding factor of lesion volume was controlled, only FA ratio of acute stroke, ADC_st ratio and MK ratio of early subacute stroke remained correlated with the functional outcome (P < 0.05).

CONCLUSION

Diffusion dynamics are correlated with the clinical functional outcome of ischemic stroke. This correlation is independent of the effect of lesion volume and is specific to the time period between symptom onset and imaging. More effort is needed to further investigate the predictive value of diffusion-weighted imaging.

Selection of anterior circulation acute stroke patients for mechanical thrombectomy

BACKGROUND

The use of mechanical thrombectomy (MT) for acute ischemic stroke (AIS) patients has increased with a parallel burden in procedural costs. We tested whether a new prognostic score could identify patients who are unlikely to benefit from MT.

METHODS

Patients from our endovascular stroke registry were assessed for imaging and clinical outcome measures and randomly divided into two subsets for derivation and validation. We created a new prognostic score based on clinical and radiological prognostic factors of poor outcome (mRS score ≥ 3) from the derivation cohort. Receiver operating characteristics curve analysis was used to assess the discrimination ability of the score. The score was then validated and compared to the MR PREDICTS score.

RESULTS

The derivation/validation included 270/116 patients, respectively. After multivariate logistic regression analysis, pre stroke mRS, age, admission glycaemia, admission NIHSS, collateral flow, Clot Burden Score, Alberta Stroke Program Early CT score were used to create a new prognostic scoring system called Tor Vergata Stroke Score (TVSS). TVSS revealed a good prognostic accuracy with an AUC of 0.825 [95% CI 0.77-0.88] in the derivation cohort and an AUC of 0.820 [95% CI 0.74-0.90] in the validation cohort. When compared to the MR PREDICTS in the validation cohort, TVSS demonstrated higher prediction ability which was, however, not statistically significant (0.80 vs 0.78; P = 0.26).

CONCLUSIONS

TVSS is a reliable tool for selection of AIS candidates for MT and optimization of transfer to comprehensive stroke centers.

Who is driving and who is prone to have traffic accidents? A systematic review and meta-analysis among people with seizures

INTRODUCTION

Epilepsy influences the ability to drive. We aimed to systematically summarize factors associated with driving, holding a driver's license, and traffic accidents among people with seizures.

MATERIAL AND METHODS

Eight databases were searched (from their inception to 27 June 2018). We included all published observational studies, except for case reports and studies with fewer than 50 participants. Pooled mean differences and pooled risk ratios (pRRs) with corresponding confidence intervals (CIs) were calculated using random effects.

RESULTS

Data were available from 18 studies, reporting a wide range of factors. There were frequent biases associated with cross-sectional study designs, selection bias, poor statistical quality, small samples, and lack of validation of models. The following six variables were consistently associated with driving: male gender (pRR: 1.42; 95% CI: 1.23 to 1.64), being in paid work (pRR: 1.72; 95% CI: 1.46 to 2.03), married (pRR: 1.26; 95% CI: 1.01 to 1.57), older age at seizure onset or diagnosis (pooled mean difference: 4.83; 95% CI: 0.48 to 9.18 years), less frequent seizures (fewer than monthly, pRR: 1.32; 95% CI: 1.12 to 1.56), and taking one or no antiepileptic drug (pRR: 1.34; 95% CI: 1.09 to 1.63). Lower seizure frequency was also protective for avoiding traffic accidents (pRR: 0.26; 95% CI: 0.10 to 0.66).

DISCUSSION

Stable multivariate models to predict driving or traffic accidents among people with seizures have not yet been developed. Current evidence shows that the likelihood of driving is associated with demographic and epilepsy-related factors, while the risk of traffic accidents is associated with seizure frequency.

Clinical characteristics and risk score for poor clinical outcome of acute ischemic stroke patients treated with intravenous thrombolysis therapy

BACKGROUND

Tissue plasminogen activator (t-PA) is an effective therapy for acute ischemic stroke, but some patients still have poor clinical outcome. In this study, we investigated clinical characteristics of stroke patients and determined predictors for poor clinical outcome in response to t-PA treatment.

METHODS

Clinical data from 247 patients were retrospectively reviewed. Clinical parameters that were associated with survival of patients were analyzed. Areas under receiver operating characteristic curves (ROC) were used to determine the feasibility of using various combinations of the clinical parameters to predict poor clinical response. The clinical outcome was defined according to the changes in Modified Rankin Scale.

RESULTS

Overall, 145 patients had improved/complete recovery, 73 had no change, and 29 had worsening conditions or died during the in-clinic period. A univariate analysis showed that baseline characteristics including age, CRP, blood glucose level, systolic blood pressure, and admission NIHSS were significantly different (p < 0.05) among patients with different clinical outcome. A further multivariate analysis was then performed. Variables associated with poor clinical outcome (worsening/death) (p < 0.1) were included in the logistic regression model. Four parameters were retained in the model: Age, CRP, Blood glucose level, and Systolic blood pressure (ACBS). To allow a convenient usage of the ACBS classifier, the parameters were put into a scoring system, and the score at 7.7 was chosen as a cut-off. The ROC curve of this ACBS classifier has an area under the curve (AUC) of 0.7788, higher than other individual parameters. The ACBS classifier provided enhanced sensitivity of 69.2% and specificity of 74.3%.

CONCLUSION

The ACBS classifier provided a satisfactory power in estimating the patients' clinical outcome. After further validating, the classifier may provide important information to clinicians for making clinical decisions.

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

Background: There are many prognostic scales that aim to predict functional outcome following acute stroke. Despite considerable research interest, these scales have had limited impact in routine clinical practice. This may be due to perceived problems with internal validity (quality of research), as well as external validity (generalizability of results). We set out to collate information on exemplar stroke prognosis scales, giving particular attention to the scale content, derivation, and validation.

Methods: We performed a focused literature search, designed to return high profile scales that use baseline clinical data to predict mortality or disability. We described prognostic utility and collated information on the content, development and validation of the tools. We critically appraised chosen scales based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS).

Results: We chose 10 primary scales that met our inclusion criteria, six of which had revised/modified versions. Most primary scales used 5 input variables (range: 4–13), with substantial overlap in the variables included. All scales included age, eight included a measure of stroke severity, while five scales incorporated pre-stroke level of function (often using modified Rankin Scale), comorbidities and classification of stroke type. Through our critical appraisal, we found issues relating to excluding patients with missing data from derivation studies, and basing the selection of model variable on significance in univariable analysis (in both cases noted for six studies). We identified separate external validation studies for all primary scales but one, with a total of 60 validation studies.

Conclusions: Most acute stroke prognosis scales use similar variables to predict long-term outcomes and most have reasonable prognostic accuracy. While not all published scales followed best practice in development, most have been subsequently validated. Lack of clinical uptake may relate more to practical application of scales rather than validity. Impact studies are now necessary to investigate clinical usefulness of existing scales.

Prevalence of pre-stroke depression and its association with post-stroke depression: a systematic review and meta-analysis

BACKGROUND

Depression is a common post-stroke complication. Pre-stroke depression may be an important contributor, however the epidemiology of pre-stroke depression is poorly understood. Using systematic review and meta-analysis, we described the prevalence of pre-stroke depression and its association with post-stroke depression.

METHODS

We searched multiple cross-disciplinary databases from inception to July 2017 and extracted data on the prevalence of pre-stroke depression and its association with post-stroke depression. We assessed the risk of bias (RoB) using validated tools. We described summary estimates of prevalence and summary odds ratio (OR) for association with post-stroke depression, using random-effects models. We performed subgroup analysis describing the effect of depression assessment method. We used a funnel plot to describe potential publication bias. The strength of evidence presented in this review was summarised via 'GRADE'.

RESULTS

Of 11 884 studies identified, 29 were included (total participants n = 164 993). Pre-stroke depression pooled prevalence was 11.6% [95% confidence interval (CI) 9.2-14.7]; range: 0.4-24% (I2 95.8). Prevalence of pre-stroke depression varied by assessment method (p = 0.02) with clinical interview suggesting greater pre-stroke depression prevalence (~17%) than case-note review (9%) or self-report (11%). Pre-stroke depression was associated with increased odds of post-stroke depression; summary OR 3.0 (95% CI 2.3-4.0). All studies were judged to be at RoB: 59% of included studies had an uncertain RoB in stroke assessment; 83% had high or uncertain RoB for pre-stroke depression assessment. Funnel plot indicated no risk of publication bias. The strength of evidence based on GRADE was 'very low'.

CONCLUSIONS

One in six stroke patients have had pre-stroke depression. Reported rates may be routinely underestimated due to limitations around assessment. Pre-stroke depression significantly increases odds of post-stroke depression.Protocol identifierPROSPERO identifier: CRD42017065544.

Predicting Early Mortality of Acute Ischemic Stroke

Background and Purpose—

Several risk factors are known to increase mid- and long-term mortality of ischemic stroke patients. Information on predictors of early stroke mortality is scarce but often requested in clinical practice. We therefore aimed to develop a rapidly applicable tool for predicting early mortality at the stroke unit.

Methods—

We used data from the nationwide Austrian Stroke Unit Registry and multivariate regularized logistic regression analysis to identify demographic and clinical variables associated with early (≤7 days poststroke) mortality of patients admitted with ischemic stroke. These variables were then used to develop the Predicting Early Mortality of Ischemic Stroke score that was validated both by bootstrapping and temporal validation.

Results—

In total, 77 653 ischemic stroke patients were included in the analysis (median age: 74 years, 47% women). The mortality rate at the stroke unit was 2% and median stay of deceased patients was 3 days. Age, stroke severity measured by the National Institutes of Health Stroke Scale, prestroke functional disability (modified Rankin Scale >0), preexisting heart disease, diabetes mellitus, posterior circulation stroke syndrome, and nonlacunar stroke cause were associated with mortality and served to build the Predicting Early Mortality of Ischemic Stroke score ranging from 0 to 12 points. The area under the curve of the score was 0.879 (95% CI, 0.871–0.886) in the derivation cohort and 0.884 (95% CI, 0.863–0.905) in the validation sample. Patients with a score ≥10 had a 35% (95% CI, 28%–43%) risk to die within the first days at the stroke unit.

Conclusions—

We developed a simple score to estimate early mortality of ischemic stroke patients treated at a stroke unit. This score could help clinicians in short-term prognostication for management decisions and counseling.

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

Low self-reported sports activity before stroke predicts poor one-year-functional outcome after first-ever ischemic stroke in a population-based stroke register

Background

Physical activity (PA) is associated with lower risk of stroke. We tested the hypothesis that lack of pre-stroke PA is an independent predictor of poor outcome after first-ever ischemic stroke.

Methods

We assessed recent self-reported PA and other potential predictors for loss of functional independence - modified Rankin Scale (mRS) > 2 - one year after first-ever ischemic stroke in 1370 patients registered between 2006 and 2010 in the Ludwigshafen Stroke Study, a population-based stroke registry.

Results

After 1 year, 717 (52.3%) of patients lost their independence including 251 patients (18.3%) who had died. In multivariate logistic regression analysis lack of regular PA prior to stroke (Odds Ratio (OR) 1.7, Confidence Interval (CI) 1.1–2.5), independently predicted poor outcome together with higher age (65–74: OR 1.7; CI 1.1–2.8, 75–84 years: OR 3.3; CI 2.1–5.3; ≥85 years OR 14.5; CI 7.4–28.5), female sex (OR 1.5; CI 1.1–2.1), diabetes mellitus (OR 1.8; CI 1.3–2.5), stroke severity (OR 1.2; CI 1.1–1.2), probable atherothrombotic stroke etiology (OR 1.8; CI 1.1–2.8) and high leukocyte count (> 9.000/mm³; OR 1.4; CI 1.0–1.9) at admission. Subclassifying unknown stroke etiology, embolic stroke of unknown source (ESUS; n = 40, OR 2.2; CI 0.9–5.5) tended to be associated with loss of independence.

Conclusion

In addition to previously reported factors, lack of PA prior to stroke as potential indicator of worse physical condition, high leukocyte count at admission as indicator of the inflammatory response and probable atherothrombotic stroke etiology might be independent predictors for non-functional independence in first-ever ischemic stroke.

Non-cardioembolic TIA and ischemic stroke: Implications of severity

OBJECTIVES

Our purpose was to explore major vascular and bleeding outcomes in relation to risk and severity scores (ABCD2 or NIHSS) in patients with transient ischemic attack (TIA) or acute ischemic stroke (AIS).

METHODS

This nationwide observational study was based on data from 4 national registries. Outcomes were assessed by Kaplan-Meier and Cox regression analyses.

RESULTS

The total cohort comprised 21 268 patients (median age 73 years, 47.6% females). Based on ABCD2-score, the TIA-population (n = 10 174) was divided into low-risk (0-3 p, n = 3463) and high-risk (4-7 p, n = 6711). Based on NIHSS-score, the AIS-population (n = 11 454) was divided into minor (0-5 p, n = 8596), moderate (6-10 p, n = 1630) and severe (≥11 p, n = 1228). During follow-up (mean 1.7 years), the composite endpoint of stroke, myocardial infarction or death occurred in 3572 (16.5%) of all the patients, and major bleeding in 668 (3.1%) patients. Using low-risk TIA as reference, the adjusted hazard ratios (HR, 95% CI) of the composite endpoint were 1.41 (1.23-1.62) for high-risk TIA, 1.94 (1.70-2.22) for minor, 2.86 (2.45-3.34) for moderate and 4.18 (3.57-4.90) for severe stroke. When analyzed separately, the association with increased risk remained significant for stroke and death, but not for myocardial infarction. The HR of major bleeding were 1.31 (0.99-1.73) for high-risk TIA, 1.49 (1.13-1.95) for minor, 1.54 (1.08-2.21) for moderate and 2.10 (1.44-3.05) for severe stroke.

CONCLUSIONS

This study confirms the association between severity of the index ischemic stroke and risk of future major vascular and bleeding events, and highlights the increased risk also for patients with high-risk TIA.

Effective Reserve: A Latent Variable to Improve Outcome Prediction in Stroke

Prediction of functional outcome after stroke based on initial presentation remains an open challenge, suggesting that an important aspect is missing from these prediction models. There exists the notion of a protective mechanism called brain reserve, which may be utilized to understand variations in disease outcome. In this work, we expand the concept of brain reserve (effective reserve) to improve prediction models of functional outcome after acute ischemic stroke (AIS). Consecutive AIS patients with acute brain magnetic resonance imaging (<48 hours) were eligible for this study. White matter hyperintensity and acute infarct volume were determined on T2 fluid attenuated inversion recovery and diffusion weighted images, respectively. Modified Rankin Scale scores were obtained at 90days poststroke. Effective reserve was defined as a latent variable using structural equation modeling by including age, systolic blood pressure, and intracranial volume measurements. Of 453 AIS patients (mean age 66.6 ± 14.7 years), 36% were male and 311 hypertensive. There was inverse association between effective reserve and 90-day modified Rankin Scale scores (path coefficient -0.18 ± 0.01, P < .01). Compared to a model without effective reserve, correlation between predicted and observed modified Rankin Scale scores improved in the effective-reserve-based model (Spearman's ρ 0.29 ± 0.18 versus 0.15 ± 0.17, P < .001). Furthermore, hypertensive patients exhibited lower effective reserve (P < 10^-6). Using effective reserve in prediction models of stroke outcome is feasible and leads to better model performance. Furthermore, higher effective reserve is associated with more favorable functional poststoke outcome and might correspond to an overall better vascular health.

The relationship between the severity of disability and serum IL-8 in acute ischemic stroke patients

Background

Stroke is the third leading cause of death and leading cause of adult disability worldwide. Long-term disability is a significant problem among survivors; post-stroke inflammation is well known to contribute to the expansion of the ischemic lesion resulting in significant morbidity and disability. To study the impact of serum level of IL-8 on severity of disability in patients with acute ischemic stroke in the first 48 h post stroke.

Methods

A cross-sectional case control study was conducted on 44 patients with acute ischemic stroke (in the first 48 h). The patients were subjected to full neurological examination, computed tomography (CT) and magnetic resonance imaging (MRI) of the brain, and assessment of stroke disability using the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS). Measurement of the serum levels of IL-8, erythrocyte sedimentation rate, and C-reactive protein (CRP) was done. Forty-four matched control subjects for their age and sex were included for comparison of serum level of IL-8.

Results

The level of IL-8 was significantly higher in the patients than in the control subjects (p < 0.001).There was a statistically significant positive correlation between serum level of IL-8 and disability assessed by NIHSS (r = 0.42, p = 0.004). The patients with moderate disability showed significant higher IL-8 levels than those with minor disability (p = 0.02).

Conclusion

The severity of disability in early acute ischemic stroke is highly correlated to the serum level of IL-8.

Simplifying the use of prognostic information in traumatic brain injury. Part 2: Graphical presentation of probabilities

OBJECTIVE Clinical features such as those included in the Glasgow Coma Scale (GCS) score, pupil reactivity, and patient age, as well as CT findings, have clear established relationships with patient outcomes due to neurotrauma. Nevertheless, predictions made from combining these features in probabilistic models have not found a role in clinical practice. In this study, the authors aimed to develop a method of displaying probabilities graphically that would be simple and easy to use, thus improving the usefulness of prognostic information in neurotrauma. This work builds on a companion paper describing the GCS-Pupils score (GCS-P) as a tool for assessing the clinical severity of neurotrauma. METHODS Information about early GCS score, pupil response, patient age, CT findings, late outcome according to the Glasgow Outcome Scale, and mortality were obtained at the individual adult patient level from the CRASH (Corticosteroid Randomisation After Significant Head Injury; n = 9045) and IMPACT (International Mission for Prognosis and Clinical Trials in TBI; n = 6855) databases. These data were combined into a pooled data set for the main analysis. Logistic regression was first used to model the combined association between the GCS-P and patient age and outcome, following which CT findings were added to the models. The proportion of variability in outcomes "explained" by each model was assessed using Nagelkerke's R². RESULTS The authors observed that patient age and GCS-P have an additive effect on outcome. The probability of mortality 6 months after neurotrauma is greater with increasing age, and for all age groups the probability of death is greater with decreasing GCS-P. Conversely, the probability of favorable recovery becomes lower with increasing age and lessens with decreasing GCS-P. The effect of combining the GCS-P with patient age was substantially more informative than the GCS-P, age, GCS score, or pupil reactivity alone. Two-dimensional charts were produced displaying outcome probabilities, as percentages, for 5-year increments in age between 15 and 85 years, and for GCS-Ps ranging from 1 to 15; it is readily seen that the movement toward combinations at the top right of the charts reflects a decreasing likelihood of mortality and an increasing likelihood of favorable outcome. Analysis of CT findings showed that differences in outcome are very similar between patients with or without a hematoma, absent cisterns, or subarachnoid hemorrhage. Taken in combination, there is a gradation in risk that aligns with increasing numbers of any of these abnormalities. This information provides added value over age and GCS-P alone, supporting a simple extension of the earlier prognostic charts by stratifying the original charts in the following 3 CT groupings: none, only 1, and 2 or more CT abnormalities. CONCLUSIONS The important prognostic features in neurotrauma can be brought together to display graphically their combined effects on risks of death or on prospects for independent recovery. This approach can support decision making and improve communication of risk among health care professionals, patients, and their relatives. These charts will not replace clinical judgment, but they will reduce the risk of influences from biases.

Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis

Background

Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke.

Methods

We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values.

Results

We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85).

Conclusions

The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

Electronic supplementary material

The online version of this article (10.1186/s12883-018-1032-5) contains supplementary material, which is available to authorized users.

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

Objective

We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke.

Methods

Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers. The discrimination of the prediction models was measured by the area under the curve receiver operating characteristic curve or c-statistic in random effects meta-analysis. Heterogeneity was measured using I2. Appropriate appraisal tools and reporting guidelines were used in this review.

Results

31395 references were screened, of which 109 articles were included in the review. These articles described 66 different predictive risk models. Appraisal identified poor methodological quality and a high risk of bias for most models. However, all models precede the development of reporting guidelines for prediction modelling studies. Generalisability of models could be improved, less than half of the included models have been externally validated(n = 27/66). 152 predictors of mortality and 192 predictors and functional outcome were identified. No studies assessing ability to improve patient outcome (model impact studies) were identified.

Conclusions

Further external validation and model impact studies to confirm the utility of existing models in supporting decision-making is required. Existing models have much potential. Those wishing to predict stroke outcome are advised to build on previous work, to update and adapt validated models to their specific contexts opposed to designing new ones.

Development and validation of the Dutch Stroke Score for predicting disability and functional outcome after ischemic stroke: A tool to support efficient discharge planning

Introduction

We aimed to develop and validate a prognostic score for disability at discharge and functional outcome at three months in patients with acute ischemic stroke based on clinical information available on admission.

Patients and methods

The Dutch Stroke Score (DSS) was developed in 1227 patients with ischemic stroke included in the Paracetamol (Acetaminophen) In Stroke study. Predictors for Barthel Index (BI) at discharge (‘DSS-discharge’) and modified Rankin Scale (mRS) at three months (‘DSS-3 months’) were identified in multivariable ordinal regression. The models were internally validated with bootstrapping techniques. The DSS-3 months was externally validated in the PRomoting ACute Thrombolysis in Ischemic StrokE study (1589 patients) and the Preventive Antibiotics in Stroke Study (2107 patients). Model performance was assessed in terms of discrimination, expressed by the area under the receiver operating characteristic curve (AUC), and calibration.

Results

At model development, the strongest predictors of Barthel Index at discharge were age per decade over 60 (odds ratio = 1.55, 95% confidence interval (CI) 1.41–1.68), National Institutes of Health Stroke Scale (odds ratio = 1.24 per point, 95% CI 1.22–1.26) and diabetes (odds ratio = 1.62, 95% CI 1.32–1.91). The internally validated AUC was 0.76 (95% CI 0.75–0.79). The DSS-3 months, additionally consisting of previous stroke and atrial fibrillation, performed similarly at internal (AUC 0.75, 95% CI 0.74–0.77) and external validation (AUC 0.74 in PRomoting ACute Thrombolysis in Ischemic StrokE (95% CI 0.72–0.76) and 0.69 in Preventive Antibiotics in Stroke Study (95% CI 0.69–0.72)). Observed outcome was slightly better than predicted.

Discussion: The DSS had satisfactory performance in predicting BI at discharge and mRS at three months in ischemic stroke patients.

Conclusion

If further validated, the DSS may contribute to efficient stroke unit discharge planning alongside patients' contextual factors and therapeutic needs.

Validation and comparison of two stroke prognostic models for in hospital, 30-day and 90-day mortality

INTRODUCTION

We aimed to validate and compare two clinical prognostic models for mortality which include the National Institutes of Health Stroke Scale (NIHSS); the Age and NIHSS Score (ANS) and case mix model (CMM) of the Sentinel Stroke National Audit Program (SSNAP). The NIHSS on admission was also tested as a prognostic score.

PATIENTS AND METHODS

Prospectively collected data from the SSNAP register for a cohort of patients (ischaemic and haemorrhagic stroke) admitted over 1 year to Gloucestershire Royal Hospital, England were accessed. The ANS and CMM were calculated and tested for in hospital, 30-day and 90-day mortality using calibration plots with Hosmer-Lemeshow tests, receiver operating characteristics curves and other measures of prognostic accuracy.

RESULTS

Of 848 patients, 110 (12.9%) died in hospital, 112 (13.2%) at 30 days and 164 (19.2%) at 90 days. Calibration for all three scores was good, although Hosmer-Lemeshow test p values were <0.05 with the NIHSS alone for in hospital and 30-day deaths, suggesting deviation from good fit. The c-statistics for in hospital, 30-day and 90-day mortality were ANS (0.783, 0.782, 0.779) and CMM (0.783, 0.774, 0.758), respectively. The NIHSS alone showed fair discrimination but performed less well. A NIHSS score ≥6 was associated with significant mortality (p < 0.0001) in comparison to a score <6.

CONCLUSION

A simple prognostic model containing age and admission NIHSS only, performed as well as a more complex score at predicting in hospital, 30-day and 90-day mortality. Admission NIHSS recording should be encouraged for stroke registries.

Development and validation of clinical prediction models for mortality, functional outcome and cognitive impairment after stroke: a study protocol

INTRODUCTION

Stroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management. Furthermore, prediction models of this kind can be useful for clinical research, health economics, policymaking and clinical decision support.

METHODS

2869 patients with first-ever stroke from South London Stroke Register (SLSR) (1995-2004) will be included in the development cohort. We will use information captured after baseline to construct multilevel models and a Cox proportional hazard model to predict cognitive impairment, functional outcome and mortality up to 5 years after stroke. Repeated random subsampling validation (Monte Carlo cross-validation) will be evaluated in model development. Data from participants recruited to the stroke register (2005-2014) will be used for temporal validation of the models. Data from participants recruited to the Dijon Stroke Register (1985-2015) will be used for external validation. Discrimination, calibration and clinical utility of the models will be presented.

ETHICS

Patients, or for patients who cannot consent their relatives, gave written informed consent to participate in stroke-related studies within the SLSR. The SLSR design was approved by the ethics committees of Guy's and St Thomas' NHS Foundation Trust, Kings College Hospital, Queens Square and Westminster Hospitals (London). The Dijon Stroke Registry was approved by the Comité National des Registres and the InVS and has authorisation of the Commission Nationale de l'Informatique et des Libertés.

Validating and comparing stroke prognosis scales

Objective:

To compare the prognostic accuracy of various acute stroke prognostic scales using a large, independent, clinical trials dataset.

Methods:

We directly compared 8 stroke prognostic scales, chosen based on focused literature review (Acute Stroke Registry and Analysis of Lausanne [ASTRAL]; iSCORE; iSCORE-revised; preadmission comorbidities, level of consciousness, age, and neurologic deficit [PLAN]; stroke subtype, Oxfordshire Community Stroke Project, age, and prestroke modified Rankin Scale [mRS] [SOAR]; modified SOAR; Stroke Prognosis Instrument 2 [SPI2]; and Totaled Health Risks in Vascular Events [THRIVE]) using individual patient-level data from a clinical trials archive (Virtual International Stroke Trials Archive [VISTA]). We calculated area under receiver operating characteristic curves (AUROC) for each scale against 90-day outcomes of mRS (dichotomized at mRS >2), Barthel Index (>85), and mortality. We performed 2 complementary analyses: the first limited to patients with complete data for all components of all scales (simultaneous) and the second using as many patients as possible for each individual scale (separate). We compared AUROCs and performed sensitivity analyses substituting extreme outcome values for missing data.

Results:

In total, 10,777 patients contributed to the analyses. Our simultaneous analyses suggested that ASTRAL had greatest prognostic accuracy for mRS, AUROC 0.78 (95% confidence interval [CI] 0.75–0.82), and SPI2 had poorest AUROC, 0.61 (95% CI 0.57–0.66). Our separate analyses confirmed these results: ASTRAL AUROC 0.79 (95% CI 0.78–0.80 and SPI2 AUROC 0.60 (95% CI 0.59–0.61). On formal comparative testing, there was a significant difference in modified Rankin Scale AUROC between ASTRAL and all other scales. Sensitivity analysis identified no evidence of systematic bias from missing data.

Conclusions:

Our comparative analyses confirm differences in the prognostic accuracy of stroke scales. However, even the best performing scale had prognostic accuracy that may not be sufficient as a basis for clinical decision-making.

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

Why do authors derive new cardiovascular clinical prediction rules in the presence of existing rules? A mixed methods study

Background

Researchers should examine existing evidence to determine the need for a new study. It is unknown whether developers evaluate existing evidence to justify new cardiovascular clinical prediction rules (CPRs).

Objective

We aimed to assess whether authors of cardiovascular CPRs cited existing CPRs, why some authors did not cite existing CPRs, and why they thought existing CPRs were insufficient.

Method

Derivation studies of cardiovascular CPRs from the International Register of Clinical Prediction Rules for Primary Care were evaluated. We reviewed the introduction sections to determine whether existing CPRs were cited. Using thematic content analysis, the stated reasons for determining existing cardiovascular CPRs insufficient were explored. Study authors were surveyed via e-mail and post. We asked whether they were aware of any existing cardiovascular CPRs at the time of derivation, how they searched for existing CPRs, and whether they thought it was important to cite existing CPRs.

Results

Of 85 derivation studies included, 48 (56.5%) cited existing CPRs, 33 (38.8%) did not cite any CPR, and four (4.7%) declared there was none to cite. Content analysis identified five categories of existing CPRs insufficiency related to: (1) derivation (5 studies; 11.4% of 44), (2) construct (31 studies; 70.5%), (3) performance (10 studies; 22.7%), (4) transferability (13 studies; 29.5%), and (5) evidence (8 studies; 18.2%). Authors of 54 derivation studies (71.1% of 76 authors contacted) responded to the survey. Twenty-five authors (46.3%) reported they were aware of existing CPR at the time of derivation. Twenty-nine authors (53.7%) declared they conducted a systematic search to identify existing CPRs. Most authors (90.7%) indicated citing existing CPRs was important.

Conclusion

Cardiovascular CPRs are often developed without citing existing CPRs although most authors agree it is important. Common justifications for new CPRs concerned construct, including choice of predictor variables or relevance of outcomes. Developers should clearly justify why new CPRs are needed with reference to existing CPRs to avoid unnecessary duplication.

Mortality prediction models in the general trauma population: A systematic review

BACKGROUND

Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population.

METHODS

An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included.

RESULTS

A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance.

CONCLUSION

Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care.

Family discussions on life-sustaining interventions in neurocritical care

Approximately 20% of all deaths in the USA occur in the intensive care unit (ICU) and the majority of ICU deaths involves decision of de-escalation of life-sustaining interventions. Life-sustaining interventions may include intubation and mechanical ventilation, artificial nutrition and hydration, antibiotic treatment, brain surgery, or vasoactive support. Decision making about goals of care can be defined as an end-of-life communication and the decision-making process between a clinician and a patient (or a surrogate decision maker if the patient is incapable) in an institutional setting to establish a plan of care. This process includes deciding whether to use life-sustaining treatments. Therefore, family discussion is a critical element in the decision-making process throughout the patient's stay in the neurocritical care unit. A large part of care in the neurosciences intensive care unit is discussion of proportionality of care. This chapter provides a stepwise approach to hold these conferences and discusses ways to do it effectively.

Predictors of 30-day mortality in patients with spontaneous primary intracerebral hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is a life threatening entity, and an early outcome assessment is mandatory for optimizing therapeutic efforts.

METHODS

We retrospectively analyzed data from 342 patients with spontaneous primary ICH to evaluate possible predictors of 30-day mortality considering clinical, radiological, and therapeutical parameters. We also applied three widely accepted outcome grading scoring systems [(ICH score, FUNC score and intracerebral hemorrhage grading scale (ICH-GS)] on our population to evaluate the correlation of these scores with the 30-day mortality in our study. We also applied three widely accepted outcome grading scoring systems [(ICH score, FUNC score and intracerebral hemorrhage grading scale (ICH-GS)] on our population to evaluate the correlation of these scores with the 30-day mortality in our study.

RESULTS

From 342 patients (mean age: 67 years, mean Glasgow Coma Scale [GCS] on admission: 9, mean ICH volume: 62.19 ml, most common hematoma location: basal ganglia [43.9%]), 102 received surgical and 240 conservative treatment. The 30-day mortality was 25.15%. In a multivariate analysis, GCS (Odds ratio [OR] =0.726, 95% confidence interval [CI] =0.661-0.796, P < 0.001), bleeding volume (OR = 1.012 per ml, 95% CI = 1.007 - 1.017, P < 0.001), and infratentorial hematoma location (OR = 5.381, 95% CI = 2.166-13.356, P = 0.009) were significant predictors for the 30-day mortality. After receiver operating characteristics analysis, we defined a "high-risk group" for an unfavorable short-term outcome with GCS <11 and ICH volume >32 ml supratentorially or 21 ml infratentorially. Using Pearson correlation, we found a correlation of 0.986 between ICH score and 30-day mortality (P < 0.001), 0.853 between FUNC score and 30-day mortality (P = 0.001), and 0.924 between ICH-GS and 30-day mortality (P = 0.001).

CONCLUSIONS

GCS score on admission together with the baseline volume and localization of the hemorrhage are strong predictors for 30-day mortality in patients with spontaneous primary intracerebral hemorrhage, and by relying on them it is possible to identify high-risk patients with poor short-term outcome. The ICH score and the ICH-GS accurately predict the 30-day mortality.

Predicting spasticity after stroke in those surviving to 12 months

Objective: To measure muscle tone in a cohort of patients 12 months after stroke and develop a preliminary model, using data recorded routinely after stroke, to predict who will develop spasticity.

Design: A cohort study.

Setting: Initially hospitalized but subsequently community-dwelling stroke survivors in Liverpool, United Kingdom.

Subjects: One hundred and six consecutively presenting stroke patients surviving to 12 months.

Main outcome measures: Spasticity measured at a range of joints using the Tone Assessment Scale.

Results: The Tone Assessment Scale revealed spasticity in 38 (36%) patients and more severe spasticity in 21 (20%) of the 106 patients. Logistic regression analysis revealed that lower day 7 Barthel Index score and early arm or leg weakness were significant predictors of abnormal muscle tone; and lower day 7 Barthel Index score, left-sided weakness and ever smoked to be significant predictors of more severe muscle tone.

Conclusions: Using the models, it may be possible to predict whether or not spasticity will develop in patients 12 months after stroke. The utility of the models is aided by their use of predictor variables that are routinely collected as part of stroke care in hospital and which are easy to measure. The models need testing prospectively in a new cohort of patients in order to test their validity, reliability and utility and to determine if other data could improve their efficiency.