Validation of an acute ischemic stroke model: does diffusion-weighted imaging lesion volume offer a clinically significant improvement in prediction of outcome?

Prognostic value of combining 24-hour ASPECTS and hemoglobin to red cell distribution width ratio to the THRIVE score in predicting in-hospital mortality among ischemic stroke patients treated with intravenous thrombolysis

BACKGROUND

Acute ischemic stroke (AIS) is a significant global health issue, directly impacting mortality and disability. The Totaled Health Risks in Vascular Events (THRIVE) score is appreciated for its simplicity and ease of use to predict stroke clinical outcomes; however, it lacks laboratory and neuroimaging data, which limits its ability to predict outcomes precisely. Our study evaluates the impact of integrating the 24-hour Alberta Stroke Program Early CT Score (ASPECTS) and hemoglobin-to-red cell distribution width (HB/RDW) ratio into the THRIVE score using the multivariable fractional polynomial (MFP) method (combined THRIVE-MFP model) compared to the THRIVE-c model. We aim to assess their added value in predicting in-hospital mortality (IHM) prognosis.

MATERIALS AND METHODS

A retrospective study from January 2015 to July 2022 examined consecutive AIS patients receiving intravenous thrombolysis. Data on THRIVE scores, 24-hour ASPECTS, and HB/RDW levels were collected upon admission. The model was constructed using logistic regression and the MFP method. The prognostic value was determined using the area under the receiver operating characteristic curve (AuROC). Ischemic cerebral lesions within the middle cerebral artery territory were evaluated with non-contrast computed tomography (NCCT) after completing 24 hours of intravenous thrombolysis (24-hour ASPECTS).

RESULTS

Among a cohort of 345 patients diagnosed with AIS who received intravenous thrombolysis, 65 individuals (18.8%) experienced IHM. The combined THRIVE-MFP model was significantly superior to the THRIVE-c model in predicting IHM (AuROC 0.980 vs. 0.876, p<0.001), 3-month mortality (AuROC 0.947 vs. 0.892, p<0.001), and 3-month poor functional outcome (AuROC 0.910 vs. 0.853, p<0.001).

CONCLUSION

The combined THRIVE-MFP model showed excellent predictive performance, enhancing physicians' ability to stratify patient selection for intensive neurological monitoring and guiding treatment decisions. Incorporating 24-hour ASPECTS on NCCT and HB/RDW proved valuable in mortality prediction, particularly for hospitals with limited access to advanced neuroimaging resources.

Factors influencing the prognosis of patients with acute cerebral infarction who received usual care: a multicenter prospective cohort study

[Purpose] The prognostic factors for patients with acute stroke who received usual care (mobilization ≥48 h after admission) remain unclear. This study aimed to investigate the prognostic factors that predict functional outcomes using evaluations performed immediately after onset in patients with acute cerebral infarction who received usual care from admission until discharge. [Participants and Methods] Participants with acute cerebral infarction admitted to five acute care hospitals in Tokyo and Saitama, Japan and prescribed physical therapy were included. Participants information, functional evaluations, and progress were recorded during the first physical therapy session, mobilization, and discharge. Participants who received usual care were assigned to either the good- or poor-outcome group based on the Modified Rankin Scale at discharge. [Results] In total, 161 Participants receiving usual care (mobilization ≥48 h after admission) were included. Reinfarction and the First National Institutes of Health Stroke Scale score were identified as independent predictors of functional outcome at hospital discharge in participants who received usual care (median, 22.0 d). The cutoff NIHSS score was 4. [Conclusion] Our results provided evidence that the National Institutes of Health Stroke Scale score and reinfarction are useful predictors of functional outcomes in participants who received usual care.

Cerebral Small Vessel Disease MRI Features Do Not Improve the Prediction of Stroke Outcome

OBJECTIVE

To determine whether the total small vessel disease (SVD) score adds information to the prediction of stroke outcome compared to validated predictors, we tested different predictive models of outcome in patients with stroke.

METHODS

White matter hyperintensity, lacunes, perivascular spaces, microbleeds, and atrophy were quantified in 2 prospective datasets of 428 and 197 patients with first-ever stroke, using MRI collected 24 to 72 hours after stroke onset. Functional, cognitive, and psychological status were assessed at the 3- to 6-month follow-up. The predictive accuracy (in terms of calibration and discrimination) of age, baseline NIH Stroke Scale score (NIHSS), and infarct volume was quantified (model 1) on dataset 1, the total SVD score was added (model 2), and the improvement in predictive accuracy was evaluated. These 2 models were also developed in dataset 2 for replication. Finally, in model 3, the MRI features of cerebral SVD were included rather than the total SVD score.

RESULTS

Model 1 showed excellent performance for discriminating poor vs good functional outcomes (area under the curve [AUC] 0.915), and fair performance for identifying cognitively impaired and depressed patients (AUCs 0.750 and 0.688, respectively). A higher SVD score was associated with a poorer outcome (odds ratio 1.30 [1.07-1.58], p = 0.0090 at best for functional outcome). However, adding the total SVD score (model 2) or individual MRI features (model 3) did not improve the prediction over model 1. Results for dataset 2 were similar.

CONCLUSIONS

Cerebral SVD was independently associated with functional, cognitive, and psychological outcomes, but had no clinically relevant added value to predict the individual outcomes of patients when compared to the usual predictors, such as age and baseline NIHSS.

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.

Temporal evolution and spatial distribution of quantitative T2 MRI following acute ischemia reperfusion injury

Background

Determining mechanisms of secondary stroke injury related to cerebral blood flow and the severity of microvascular injury contributing to edema and blood-brain barrier breakdown will be critical for the development of adjuvant therapies for revascularization treatment.

Aim

To characterize the heterogeneity of the ischemic lesion using quantitative T2 imaging along with diffusion-weighted magnetic resonance imaging (DWI) within five hours of treatment.

Methods

Quantitative T2 magnetic resonance imaging was acquired within 5 h (baseline) and at 24 h (follow-up) of stroke treatment in 29 patients. Dynamic contrast enhanced permeability imaging was performed at baseline in a subgroup of patients. Absolute volume change and lesion percent change was determined for the quantitative T2, DWI, and absolute volume change sequences. A Gaussian process with RRELIEFF feature selection algorithm was used for prediction of relative quantitative T2 and DWI lesion growth, baseline and follow-up quantitative T2/DWI lesion ratios, and also NIHSS at 24 h and change in NIHSS from admission to 24 h.

Results

In n = 27 patients, median (interquartile range) lesion percent change was 114.8% (48.9%, 259.1%) for quantitative T2, 48.2% (−12.6%, 179.6%) for absolute volume change, and 62.7% (26.3%, 230.9%) for DWI, respectively. Our model, consisting of baseline NIHSS, CT ASPECTS, and systolic blood pressure, showed a strong correlation with quantitative T2 percent change (cross correlation R2 = 0.80). There was a strong predictive ability for quantitative T2/DWI lesion ratio at 24 h using baseline NIHSS and last seen normal to 24 h magnetic resonance imaging time (cross correlation R2 = 0.93). Baseline dynamic contrast enhanced permeability was moderately correlated to the baseline quantitative T2 values (rho = 0.38).

Conclusion

Quantitative T2 imaging provides critical information for development of therapeutic approaches that could ameliorate microvascular damage during ischemia reperfusion.

Qualitative Posttreatment Diffusion-Weighted Imaging as a Predictor of 90-day Outcome in Stroke Intervention

PURPOSE

The aim was to assess the ability of post-treatment diffusion-weighted imaging (DWI) to predict 90-day functional outcome in patients with endovascular therapy (EVT) for large vessel occlusion in acute ischemic stroke (AIS).

METHODS

We examined a retrospective cohort from March 2016 to January 2018, of consecutive patients with AIS who received EVT. Planimetric DWI was obtained and infarct volume calculated. Four blinded readers were asked to predict modified Rankin Score (mRS) at 90 days post-thrombectomy.

RESULTS

Fifty-one patients received endovascular treatment (mean age 65.1 years, median National Institutes of Health Stroke Scale (NIHSS) 18). Mean infarct volume was 43.7 mL. The baseline NIHSS, 24-hour NIHSS, and the DWI volume were lower for the mRS 0-2 group. Also, the thrombolysis in cerebral infarction (TICI) 2b/3 rate was higher in the mRS 0-2 group. No differences were found in terms of the occlusion level, reperfusion technique, or recombinant tissue plasminogen activator use. There was a significant association noted between average infarct volume and mRS at 90 days. On multivariable analysis, higher infarct volume was significantly associated with 90-day mRS 3-5 when adjusted to TICI scores and occlusion location (OR 1.01; CI 95% 1.001-1.03; p = 0.008). Area under curve analysis showed poor performance of DWI volume reader ability to qualitatively predict 90-day mRS.

CONCLUSION

The subjective impression of DWI as a predictor of clinical outcome is poorly correlated when controlling for premorbid status and other confounders. Qualitative DWI by experienced readers both overestimated the severity of stroke for patients who achieved good recovery and underestimated the mRS for poor outcome patients. Infarct core quantitation was reliable.

Comparison of free breathing and respiratory triggered diffusion-weighted imaging sequences for liver imaging

BACKGROUND

Diffusion-weighted imaging (DWI) has become a useful tool in the detection, characterization, and evaluation of response to treatment of many cancers, including malignant liver lesions. DWI offers higher image contrast between lesions and normal liver tissue than other sequences. DWI images acquired at two or more b-values can be used to derive an apparent diffusion coefficient (ADC). DWI in the body has several technical challenges. This include ghosting artifacts, mis-registration and susceptibility artifacts. New DWI sequences have been developed to overcome some of these challenges. Our goal is to evaluate 3 new DWI sequences for liver imaging.

AIM

To qualitatively and quantitatively compare 3 DWI sequences for liver imaging: free-breathing (FB), simultaneous multislice (SMS), and prospective acquisition correction (PACE).

METHODS

Magnetic resonance imaging (MRI) was performed in 20 patients in this prospective study. The MR study included 3 separate DWI sequences: FB-DWI, SMS-DWI, and PACE-DWI. The image quality, mean ADC, standard deviations (SD) of ADC, and ADC histogram were compared. Wilcoxon signed-rank tests were used to compare qualitative image quality. A linear mixed model was used to compare the mean ADC and the SDs of the ADC values. All tests were 2-sided and P values of < 0.05 were considered statistically significant.

RESULTS

There were 56 lesions (50 malignant) evaluated in this study. The mean qualitative image quality score of PACE-DWI was 4.48. This was significantly better than that of SMS-DWI (4.22) and FB-DWI (3.15) (P < 0.05). Quantitatively, the mean ADC values from the 3 different sequences did not significantly differ for each liver lesion. FB-DWI had a markedly higher variation in the SD of the ADC values than did SMS-DWI and PACE-DWI. We found statistically significant differences in the SDs of the ADC values for FB-DWI vs PACE-DWI (P < 0.0001) and for FB-DWI vs SMS-DWI (P = 0.03). The SD of the ADC values was not statistically significant for PACE-DWI and SMS-DWI (P = 0.18). The quality of the PACE-DWI ADC histograms were considered better than the SMS-DWI and FB-DWI.

CONCLUSION

Compared to FB-DWI, both PACE-DWI and SMS-DWI provide better image quality and decreased quantitative variability in the measurement of ADC values of liver lesions.

Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes

INTRODUCTION

Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction. Therefore, the quality of the measurements is crucial for training and validation of these models. The objective of this study was to apply and evaluate density-based outlier detection methods for identifying potentially incorrect measurements in multiple large stroke datasets to assess the measurement quality.

METHOD

We applied three density-based outlier detection methods including density-based spatial clustering of applications (DBSCAN), hierarchical DBSCAN (HDBSCAN) and local outlier factor (LOF) based on a large dataset obtained from a nationwide prospective stroke registry in Taiwan. The testing of each method was done by using four different NINDS funded stroke datasets.

RESULT

The DBSCAN achieved a high performance across all mRS values where the highest average accuracy was 99.2 ± 0.7 at mRS of 4 and the lowest average accuracy was 92.0 ± 4.6 at mRS of 3. The LOF also achieved similar performance, however, the HDBSCAN with default parameters setting required further tuning improvement.

CONCLUSION

The density-based outlier detection methods were proven to be promising for validation of stroke outcome measures. The outlier detection algorithm developed from a large prospective registry dataset was effectively applied in four different NINDS stroke datasets with high performance results. The tool developed from this detection algorithm can be further applied to real world datasets to increase the data quality in stroke outcome measures.

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.

GWTG Risk Model for All Stroke Types Predicts In-Hospital and 3-Month Mortality in Chinese Patients with Acute Stroke

BACKGROUND

We aimed to externally validate the Get With the Guidelines (GWTG) risk model for all stroke types to predict in-hospital stroke mortality in Chinese patients and moreover to explore its prognostic value in predicting 3-month mortality after stroke.

METHODS

The prognostic model was applied to patients with acute stroke from China National Stroke Registry II (CNSR II) to predict in-hospital and 3-month mortality. Model discrimination was estimated by calculating c-statistic and 95% confidence intervals (CIs). Calibration was assessed by Pearson correlation coefficient and Hosmer-Lemeshow test.

RESULTS

Date from 21,684 stroke patients with complete data for in-hospital mortality prediction and 20,348 stroke patients with complete data for 3-month mortality prediction in the CNSR II were abstracted. The in-hospital and 3-month mortality were 1.4% and 5.6%, respectively. The c-statistics in the CNSR II were .86 (95% CI, .84-.88) and .83 (95% CI, .81-.84) for in-hospital and 3-month mortality, respectively. Calibration plot presented high correlation between the observed and predicted mortality rates (Pearson correlation coefficient, .996 for in-hospital and .998 for 3-month mortality; both P < .001). The Hosmer-Lemeshow statistics for the prediction of in-hospital and 3-month mortality were 0.21 and less than .001, respectively. The model performed nearly as well in each stroke type as in the overall model including all types.

CONCLUSIONS

The GWTG risk model for all stroke types is a valid clinical tool to predict in-hospital and 3-month mortality in Chinese patients with acute stroke of any type.

Predicting the pattern and severity of chronic post-stroke language deficits from functionally-partitioned structural lesions

There is an ever-increasing wealth of knowledge arising from basic cognitive and clinical neuroscience on how speech and language capabilities are organised in the brain. It is, therefore, timely to use this accumulated knowledge and expertise to address critical research challenges, including the ability to predict the pattern and level of language deficits found in aphasic patients (a third of all stroke cases). Previous studies have mainly focused on discriminating between broad aphasia dichotomies from purely anatomically-defined lesion information. In the current study, we developed and assessed a novel approach in which core language areas were mapped using principal component analysis in combination with correlational lesion mapping and the resultant ‘functionally-partitioned’ lesion maps were used to predict a battery of 21 individual test scores as well as aphasia subtype for 70 patients with chronic post-stroke aphasia. Specifically, we used lesion information to predict behavioural scores in regression models (cross-validated using 5-folds). The winning model was identified through the adjusted R² (model fit to data) and performance in predicting holdout folds (generalisation to new cases). We also used logistic regression to predict fluent/non-fluent status and aphasia subtype. Functionally-partitioned models generally outperformed other models at predicting individual tests, fluency status and aphasia subtype.

•

Predict the pattern and level of language deficits found in chronic aphasic patients

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Use principal component analysis to identify functional lesion maps

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Functionally-partitioned lesion maps used as predictor variables instead of lesion volume

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Functionally-partitioned lesion model plus age produced the best regression model

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Model can successfully predict fluent/non-fluent types and aphasia classification

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.

Contribution of Neuro-Imaging for Prediction of Functional Recovery after Ischemic Stroke

Prediction measures of recovery and outcome after stroke perform with only modest levels of accuracy if based only on clinical data. Prediction scores can be improved by including morphologic imaging data, where size, location, and development of the ischemic lesion is best documented by magnetic resonance imaging. In addition to the primary lesion, the involvement of fiber tracts contributes to prognosis, and consequently the use of diffusion tensor imaging (DTI) to assess primary and secondary pathways improves the prediction of outcome and of therapeutic effects. The recovery of ischemic tissue and the progression of damage are dependent on the quality of blood supply. Therefore, the status of the supplying arteries and of the collateral flow is not only crucial for determining eligibility for acute interventions, but also has an impact on the potential to integrate areas surrounding the lesion that are not typically part of a functional network into the recovery process. The changes in these functional networks after a localized lesion are assessed by functional imaging methods, which additionally show altered pathways and activated secondary centers related to residual functions and demonstrate changes in activation patterns within these networks with improved performance. These strategies in some instances record activation in secondary centers of a network, for example, also in homolog contralateral areas, which might be inhibitory to the recovery of primary centers. Such findings might have therapeutic consequences, for example, image-guided inhibitory stimulation of these areas. In the future, a combination of morphological imaging including DTI of fiber tracts and activation studies during specific tasks might yield the best information on residual function, reserve capacity, and prospects for recovery after ischemic stroke.

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.

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.

Prediction of Walking and Arm Recovery after Stroke: A Critical Review

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

Early Fiber Number Ratio Is a Surrogate of Corticospinal Tract Integrity and Predicts Motor Recovery After Stroke

BACKGROUND AND PURPOSE

The contribution of imaging metrics to predict poststroke motor recovery needs to be clarified. We tested the added value of early diffusion tensor imaging (DTI) of the corticospinal tract toward predicting long-term motor recovery.

METHODS

One hundred seventeen patients were prospectively assessed at 24 to 72 hours and 1 year after ischemic stroke with diffusion tensor imaging and motor scores (Fugl-Meyer). The initial fiber number ratio (iFNr) and final fiber number ratio were computed as the number of streamlines along the affected corticospinal tract normalized to the unaffected side and were compared with each other. The prediction of motor recovery (ΔFugl-Meyer) was first modeled using initial Fugl-Meyer and iFNr. Multivariate ordinal logistic regression models were also used to study the association of iFNr, initial Fugl-Meyer, age, and stroke volume with Fugl-Meyer at 1 year.

RESULTS

The iFNr correlated with the final fiber number ratio at 1 year (r=0.70; P<0.0001). The initial Fugl-Meyer strongly predicted motor recovery (≈73% of initial impairment) for all patients except those with initial severe stroke (Fugl-Meyer<50). For these severe patients (n=26), initial Fugl-Meyer was not correlated with motor recovery (R(2)=0.13; p=ns), whereas iFNr showed strong correlation (R(2)=0.56; P<0.0001). In multivariate analysis, the iFNr was an independent predictor of motor outcome (β=2.601; 95% confidence interval=0.304-5.110; P=0.031), improving prediction compared with using only initial Fugl-Meyer, age, and stroke volume (P=0.026).

CONCLUSIONS

Early measurement of FNr at 24 to 72 hours poststroke is a surrogate marker of corticospinal tract integrity and provides independent prediction of motor outcome at 1 year especially for patients with severe initial impairment.

Derivation and Validation of a Novel Prognostic Scale (Modified–Stroke Subtype, Oxfordshire Community Stroke Project Classification, Age, and Prestroke Modified Rankin) to Predict Early Mortality in Acute Stroke

Background and Purpose—

The stroke subtype, Oxfordshire Community Stroke Project classification, age, and prestroke modified Rankin (SOAR) score is a prognostic scale proposed for early mortality prediction after acute stroke. We aimed to evaluate whether including a measure of initial stroke severity (National Institutes of Health Stroke Scale and modified-SOAR [mSOAR] scores) would improve the prognostic accuracy.

Methods—

Using Anglia Stroke and Heart Clinical Network data, 2008 to 2011, we assessed the performance of SOAR and mSOAR against in-hospital mortality using area under the receiver operating curve statistics. We externally validated the prognostic utility of SOAR and mSOAR using an independent cohort data set from Glasgow. We described calibration using Hosmer–Lemeshow goodness-of-fit test.

Results—

A total of 1002 patients were included in the derivation cohort, and 105 (10.5%) died as inpatients. The area under the receiver operating curves for outcome of early mortality derived from the SOAR and mSOAR scores were 0.79 (95% confidence interval, 0.75–0.84) and 0.83 (95% confidence interval, 0.79–0.86), respectively ( P =0.001). The external validation data set contained 1012 patients with stroke; of which, 121 (12.0%) patients died within 90 days. The mSOAR scores identified the risk of early mortality ranging from 3% to 42%. External validation of mSOAR score yielded an area under the receiver operating curve of 0.84 (95% confidence interval, 0.82–0.88) for outcome of early mortality. Calibration was good ( P =0.70 for the Hosmer–Lemeshow test).

Conclusions—

Adding National Institutes of Health Stroke Scale data to create a modified-SOAR score improved prognostic utility in both derivation and validation data sets. The mSOAR may have clinical utility by using easily available data to predict mortality.

Validation of the DRAGON Score in a Chinese Population to Predict Functional Outcome of Intravenous Thrombolysis-Treated Stroke Patients

BACKGROUND

The DRAGON score predicts functional outcome of ischemic stroke patients treated with intravenous thrombolysis. Our aim was to evaluate its utility in a Chinese stroke population.

METHODS

Patients with acute ischemic stroke treated with intravenous thrombolysis were prospectively registered in the Thrombolysis Implementation and Monitor of acute ischemic Stroke in China. We excluded patients with basilar artery occlusion and missing data, leaving 970 eligible patients. We calculated the DRAGON score, and the clinical outcome was measured by the modified Rankin Scale at 3 months. Model discrimination was quantified by calculating the C statistic. Calibration was assessed using Pearson correlation coefficient.

RESULTS

The C statistic was .73 (.70-.76) for good outcome and .75 (.70-.79) for miserable outcome. Proportions of patients with good outcome were 94%, 83%, 70%, and 0% for 0 to 1, 2, 3, and 8 to 10 score points, respectively. Proportions of patients with miserable outcome were 0%, 3%, 9%, and 50% for 0 to 1, 2, 3, and 8 to 10 points, respectively. There was high correlation between predicted and observed probability of 3-month favorable and miserable outcome in the external validation cohort (Pearson correlation coefficient, .98 and .98, respectively, both P < .0001).

CONCLUSIONS

The DRAGON score showed good performance to predict functional outcome after tissue-type plasminogen activator treatment in the Chinese population. This study demonstrated the accuracy and usability of the DRAGON score in the Chinese population in daily practice.

Hyperglycaemia, insulin therapy and critical penumbral regions for prognosis in acute stroke: further insights from the INSULINFARCT trial

BACKGROUND

Recently, the concept of 'clinically relevant penumbra' was defined as an area saved by arterial recanalization and correlated with stroke outcome. This clinically relevant penumbra was located in the subcortical structures, especially the periventricular white matter. Our aims were to confirm this hypothesis, to investigate the impact of admission hyperglycemia and of insulin treatment on the severity of ischemic damages in this area and to study the respective contributions of infarct volume and ischemic damage severity of the clinically relevant penumbra on 3-month outcome.

METHODS

We included 99 patients from the INSULINFARCT trial. Voxel-Based Analysis was carried on the Apparent Diffusion Coefficient (ADC) maps obtained at day one to localize the regions, which were more damaged in patients i) with poor clinical outcomes at three months and ii) without arterial recanalization. We determined the intersection of the detected areas, which represents the clinically relevant penumbra and investigated whether hyperglycemic status and insulin regimen affected the severity of ischemic damages in this area. We performed logistic regression to examine the contribution of infarct volume or early ADC decrease in this strategic area on 3-month outcome.

FINDINGS

Lower ADC values were found in the corona radiata in patients with poor prognosis (p< 0.0001) and in those without arterial recanalization (p< 0.0001). The tracking analysis showed that lesions in this area interrupted many important pathways. ADC values in this area were lower in hyperglycemic than in normoglycemic patients (average decrease of 41.6 ± 20.8 x10(-6) mm2/s) and unaffected by the insulin regimen (p: 0.10). ADC values in the clinically relevant penumbra, but not infarct volumes, were significant predictors of 3-month outcome.

CONCLUSION

These results confirm that the deep hemispheric white matter is part of the clinically relevant penumbra and show that hyperglycaemia exacerbates the apparition of irreversible ischemic damage within 24 hours in this area. However, early intensive insulin therapy fails to protect this area from infarction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00472381.

Preexisting Serum Autoantibodies Against the NMDAR Subunit NR1 Modulate Evolution of Lesion Size in Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Recently, we reported high seroprevalence (age-dependent up to >19%) of N-methyl-d-aspartate-receptor subunit NR1 (NMDAR1) autoantibodies in both healthy and neuropsychiatrically ill subjects (N=4236). Neuropsychiatric syndrome relevance was restricted to individuals with compromised blood-brain barrier, for example, apolipoprotein E4 (APOE4) carrier status, both clinically and experimentally. We now hypothesized that these autoantibodies may upon stroke be protective in individuals with hitherto intact blood-brain barrier, but harmful for subjects with chronically compromised blood-brain barrier.

METHODS

Of 464 patients admitted with acute ischemic stroke in the middle cerebral artery territory, blood for NMDAR1 autoantibody measurements and APOE4 carrier status as indicator of a preexisting leaky blood-brain barrier was collected within 3 to 5 hours after stroke. Evolution of lesion size (delta day 7-1) in diffusion-weighted magnetic resonance imaging was primary outcome parameter. In subgroups, NMDAR1 autoantibody measurements were repeated on days 2 and 7.

RESULTS

Of all 464 patients, 21.6% were NMDAR1 autoantibody-positive (immunoglobulin M, A, or G) and 21% were APOE4 carriers. Patients with magnetic resonance imaging data available on days 1 and 7 (N=384) were divided into 4 groups according to NMDAR1 autoantibody and APOE4 status. Groups were comparable in all stroke-relevant presenting characteristics. The autoantibody+/APOE4- group had a smaller mean delta lesion size compared with the autoantibody-/APOE4- group, suggesting a protective effect of circulating NMDAR1 autoantibodies. In contrast, the autoantibody+/APOE4+ group had the largest mean delta lesion area. NMDAR1 autoantibody serum titers dropped on day 2 and remounted by day 7.

CONCLUSIONS

Dependent on blood-brain barrier integrity before an acute ischemic brain injury, preexisting NMDAR1 autoantibodies seem to be beneficial or detrimental.

Magnetic resonance imaging in acute ischemic stroke treatment

Although intravenous administration of tissue plasminogen activator is the only proven treatment after acute ischemic stroke, there is always a concern of hemorrhagic risk after thrombolysis. Therefore, selection of patients with potential benefits in overcoming potential harms of thrombolysis is of great importance. Despite the practical issues in using magnetic resonance imaging (MRI) for acute stroke treatment, multimodal MRI can provide useful information for accurate diagnosis of stroke, evaluation of the risks and benefits of thrombolysis, and prediction of outcomes. For example, the high sensitivity and specificity of diffusion-weighted image (DWI) can help distinguish acute ischemic stroke from stroke-mimics. Additionally, the lesion mismatch between perfusion-weighted image (PWI) and DWI is thought to represent potential salvageable tissue by reperfusion therapy. However, the optimal threshold to discriminate between benign oligemic areas and the penumbra is still debatable. Signal changes of fluid-attenuated inversion recovery image within DWI lesions may be a surrogate marker for ischemic lesion age and might indicate risks of hemorrhage after thrombolysis. Clot sign on gradient echo image may reflect the nature of clot, and their location, length and morphology may provide predictive information on recanalization by reperfusion therapy. However, previous clinical trials which solely or mainly relied on perfusion-diffusion mismatch for patient selection, failed to show benefits of MRI-based thrombolysis. Therefore, understanding the clinical implication of various useful MRI findings and comprehensively incorporating those variables into therapeutic decision-making may be a more reasonable approach for expanding the indication of acute stroke thrombolysis.

Correlation of ADC value with pathologic indexes in colorectal tumor homografts in Balb/c mouse

OBJECTIVE

Noninvasive diffusion-weighted magnetic resonance imaging (DWI) is a well-studied MR imaging technique for quantifying water diffusion especially in tumor area. The correlation between apparent diffusion coefficient (ADC) value and apoptosis or proliferation is not clear by now. This study aimed to investigate whether DWI-ADC value could be used as an imaging marker related with pathologic indexes of tumors.

METHODS

A total of 30 Balb/c mice with HT29 colorectal carcinoma were subjected to DWI and histologic analysis. The percentage of ADC changes and the apoptotic and proliferating indexes were calculated at predefined time points. Kolmogorov-Smirnov distances were considered to determine whether the percentage of ADC changes, and the apoptotic and proliferating indexes were normally distributed. An independent-samples t-test was used to analyze the difference between apoptotic and proliferating indexes in the two groups.

RESULTS

THERE WAS A STATISTICALLY SIGNIFICANT DIFFERENCE IN PROLIFERATING INDEX BETWEEN THE RADIOTHERAPY AND CONTROL GROUPS (MEAN PROLIFERATING INDEX: 49.27% vs. 83.09%), and there was a statistically significant difference in apoptotic index between the two groups (mean apoptotic index: 37.7% vs. 2.71%). A significant positive correlation was found between the percentage of ADC changes of the viable tissue and apoptotic index. Pearson's correlation coefficient was 0.655 (P=0.015). A significant negative correlation was found between the percentage of ADC changes of the viable tissue and ki-67 proliferation index. Pearson's correlation coefficient was 0.734 (P<0.001).

CONCLUSIONS

Our results suggest that ADC value may be used in measurement of cell apoptotic and proliferating indexes in colorectal carcinoma.

Aortic arch atherosclerosis in ischaemic stroke of unknown origin affects prognosis

Background

Cerebral infarction of unknown origin at admission accounts for half of all cerebral infarction cases in some institutions. However, the factors associated with cerebral infarction prognosis have not been sufficiently examined. Here, we investigated whether aortic arch plaques (AAPs) on transoesophageal echocardiography (TOE) were associated with the prognosis of cerebral infarction of unknown origin at admission.

Methods

Of 571 patients who were hospitalised between June 2009 and September 2011, 149 (age: 67 ± 14 years; 95 men) with cerebral infarctions of unknown origin at admission underwent TOE and were enrolled in this study. We examined their clinical characteristics, the incidence of intermittent atrial fibrillation detected on 24-hour electrocardiography, and the echographic findings of the carotid artery in the hospital. A poor prognostic outcome was defined as a modified Rankin Scale score of ≥3 after 90 days.

Results

In all, 110 patients (74%) showed good prognoses and 39 patients (26%) showed poor outcomes. A National Institutes of Health Stroke Scale score of >6 on admission [odds ratio (OR) = 6.77; 95% confidence interval (CI): 2.59-18.8; p < 0.001] and AAPs of ≥4 mm (OR = 2.75; 95% CI: 1.19-6.91; p = 0.024) showed significant associations with a poor prognosis of cerebral infarction of unknown origin at admission.

Conclusions

Thick AAPs could be a factor in the prediction of a poor prognosis of cerebral infarction of unknown origin at admission. The establishment of international standards for aortogenic brain embolisms is required. Future prospective studies should examine cerebral infarctions of unknown origin.

Activated protein C analog protects from ischemic stroke and extends the therapeutic window of tissue-type plasminogen activator in aged female mice and hypertensive rats

BACKGROUND AND PURPOSE

3K3A-activated protein C (APC) protects young, healthy male rodents after ischemic stroke. 3K3A-APC is currently under development as a neuroprotectant for acute ischemic stroke in humans. Stroke Therapy Academic Industry Roundtable recommends that after initial studies in young, healthy male animals, further studies should be performed in females, aged animals, and animals with comorbid conditions. Here, we studied the effects of delayed 3KA-APC therapy alone and with tissue-type plasminogen activator (tPA) in aged female mice and spontaneously hypertensive rats.

METHODS

We used Stroke Therapy Academic Industry Roundtable recommendations for ensuring good scientific inquiry. Murine recombinant 3K3A-APC (0.2 mg/kg) alone or with recombinant tPA (10 mg/kg) was given intravenously 4 hours after transient middle cerebral artery occlusion in aged female mice and rats and after embolic stroke in spontaneously hypertensive rat. 3K3A-APC was additionally administered within 3 to 7 days after stroke. The neuropathological analysis and neurological scores, foot-fault, forelimb asymmetry, and adhesive removal tests were performed within 7 and 28 days of stroke.

RESULTS

In all models, tPA alone had no effects on the infarct volume or behavior. 3K3A-APC alone or with tPA reduced the infarct volume 7 days after the middle cerebral artery occlusion in aged female mice and embolic stroke in spontaneously hypertensive rat by 62% to 66% and 50% to 53%, respectively, significantly improved (P<0.05) behavior, and eliminated tPA-induced intracerebral microhemorrhages. In aged female mice, 3K3A-APC was protective within 4 weeks of stroke.

CONCLUSIONS

3K3A-APC protects from ischemic stroke and extends the therapeutic window of tPA in aged female mice and in spontaneously hypertensive rat with a comorbid condition.

Models containing age and NIHSS predict recovery of ambulation and upper limb function six months after stroke: an observational study

QUESTIONS

What is the incidence of recovery of ambulation and upper limb function six months after stroke? Can measures such as age and the National Institutes of Health Stroke Scale (NIHSS) be used to develop models to predict the recovery of ambulation and upper limb function?

DESIGN

Prospective cohort study.

PARTICIPANTS

Consecutive sample of 200 people with stroke admitted to a Sydney Hospital.

OUTCOME MEASURES

Ambulation was measured with item 5 of the Motor Assessment Scale (MAS); patients scoring ≥3 could ambulate independently. Upper limb function was measured with items 7 and 8 of the MAS; patients scoring ≥5 could move a cup across the table and feed themselves with a spoonful of liquid with the hemiplegic arm.

RESULTS

Of the 114 stroke survivors who were unable to ambulate initially, 80 (70%) achieved independent ambulation at six months. Of the 51 stroke survivors who could not move a cup across the table initially, 21 (41%) achieved the upper limb task at six months. Of the 56 stroke survivors who were unable to feed themselves initially, 25 (45%) could feed themselves at six months. Models containing age and severity of stroke (measured with NIHSS) predicted recovery of ambulation and ability to move a cup across the table, whilst a model containing severity of stroke predicted ability to feed oneself. All prediction models showed good discrimination (AUC 0.73 to 0.84).

CONCLUSION

More than two-thirds of people after stroke recovered independent ambulation and less than half recovered upper limb function at six months. Models using age and NIHSS can predict independent ambulation and upper limb function but these prediction models now require external validation before use in clinical practice.

The SOAR (Stroke subtype, Oxford Community Stroke Project classification, Age, prestroke modified Rankin) score strongly predicts early outcomes in acute stroke

BACKGROUND

Previous prognostic scoring systems in predicting stroke mortality are complex, require multiple measures that vary with time and failed to produce a simple scoring system.

AIMS/HYPOTHESIS

The study aims to derive and internally validate a stroke prognostic scoring system to predict early mortality and hospital length of stay.

METHODS

Data from a U.K. multicenter stroke register were examined (1997-2010). Using a prior hypothesis based on our and others observations, we selected five patient-related factors (age, gender, stroke subtype, clinical classification, and prestroke disability) as candidate prognostic indicators. An 8-point score was derived based on multiple logistic regression model using four out of five variables. Performance of the model was assessed by plotting the estimated probability of in-hospital death against the actual probability by testing for overfitting (calibration) and area under the curve methods (discrimination).

RESULTS

The total sample consisted of 12,355 acute stroke patients (ischemic stroke 91.0%). The score predicted both in-patient and seven-day mortality. The crude in-patient mortality were 1.57%, 4.02%, 10.65%, 21.41%, 46.60%, 62.72%, and 75.81% for those who scored 0, 1, 2, 3, 4, 5, and 6, respectively. The calibration of the model revealed no evidence of overfitting (estimated overfitting 0.001). The area under the curve values for both in-hospital and seven-day mortality were 0.79. The score predicted length of stay with a higher score was associated with longer median length of stay in those discharged alive and shorter median length of stay in those who died (P for both <0.001).

CONCLUSIONS

A simple 8-point clinical score is highly predictive of acute stroke mortality and length of hospital stay. It could be used as prognostic tool in service planning and also to risk-stratify patients to use these outcomes as markers of stroke care quality across institutions.

A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke

BACKGROUND

We aimed to derive and validate a single risk score for predicting death from ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH).

METHODS AND RESULTS

Data from 333 865 stroke patients (IS, 82.4%; ICH, 11.2%; SAH, 2.6%; uncertain type, 3.8%) in the Get With The Guidelines-Stroke database were used. In-hospital mortality varied greatly according to stroke type (IS, 5.5%; ICH, 27.2%; SAH, 25.1%; unknown type, 6.0%; P<0.001). The patients were randomly divided into derivation (60%) and validation (40%) samples. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model in the overall population and in the subset with the National Institutes of Health Stroke Scale (NIHSS) recorded (37.1%). The c statistic, a measure of how well the models discriminate the risk of death, was 0.78 in the overall validation sample and 0.86 in the model including NIHSS. The model with NIHSS performed nearly as well in each stroke type as in the overall model including all types (c statistics for IS alone, 0.85; for ICH alone, 0.83; for SAH alone, 0.83; uncertain type alone, 0.86). The calibration of the model was excellent, as demonstrated by plots of observed versus predicted mortality.

CONCLUSIONS

A single prediction score for all stroke types can be used to predict risk of in-hospital death following stroke admission. Incorporation of NIHSS information substantially improves this predictive accuracy.

One-year clinical prediction in Chinese ischemic stroke patients using the CHADS2 and CHA2DS2-VASc scores: the China National Stroke Registry

AIMS

We investigated whether CHADS2 or CHA2DS2-VASc scores could be used to predict 1-year prognosis in stroke recurrence, mortality, and mortality of ischemic stroke or transient ischemic attack (TIA) patients with nonvalvular atrial fibrillation (NVAF).

METHODS

Patients were selected from a national prospective registry in China. The clinical prediction of the scores was examined using the C statistic. Univariate and multivariate logistic regressions were performed to analyze the relevant risk factors.

RESULTS

Thousand two hundred and ninety-seven of 22,216 patients were enrolled in the study. For stroke recurrence rate, the C statistic value was 0.53 (odds ratio [OR] 1.15, 95% confidence interval [CI]: 1.01 to 1.32) for CHADS2 and 0.55 (OR 1.14, 95% CI: 1.05 to 1.24) for CHA2DS2-VASc; adding baseline National Institutes of Health Stroke Scale (NIHSS) score to these two scores, the value of C statistic was 0.58 (OR 1.25 95% CI: 1.14 to 1.37) and 0.58 (OR 1.19, 95% CI: 1.11 to 1.27), respectively.

CONCLUSIONS

Both CHADS2 and CHA2DS2-VASc scores have limitations in predicting the 1-year prognosis of stroke/TIA patients with NVAF in China. The predictive value of these two scores improved by adding the baseline NIHSS score.

Computed tomography findings for intracerebral hemorrhage have little incremental impact on post-stroke mortality prediction model performance

BACKGROUND

Stroke outcome studies often combine cases of intracerebral hemorrhage (ICH) and ischemic stroke (IS). These studies of mixed stroke typically ignore computed tomography (CT) findings for ICH cases, though the impact of omitting these traditional predictors of ICH mortality is unknown. We investigated the incremental impact of ICH CT findings on mortality prediction model performance.

METHODS

Cases of ICH and IS (2000-2003) were identified from the Brain Attack Surveillance in Corpus Christi (BASIC) project. Base models predicting 30-day mortality included demographics, stroke type, and clinical findings (National Institutes of Health Stroke Scale (NIHSS) +/- Glasgow Coma Scale (GCS)). The impact of adding CT data (volume, intraventricular hemorrhage, infratentorial location) was assessed with the area under the curve (AUC), unweighted sum of squared residuals (Ŝ), and integrated discrimination improvement (IDI). The model assessment was performed first for the mixed case of IS and ICH, and then repeated for ICH cases alone to determine whether any lack of improvement in model performance with CT data for mixed stroke type was due to IS cases naturally forming a larger proportion of the total sample than ICH.

RESULTS

A total of 1,256 cases were included (86% IS, 14% ICH). Thirty-day mortality was 16% overall (11% for IS; 43% for ICH). When both clinical scales (NIHSS and GCS) were included, none of the model performance measures showed improvement with the addition of CT findings whether considering IS and ICH together (ΔAUC: 0.002, 95% CI -0.01, 0.02; ΔŜ: -3.0, 95% CI -9.1, 2.6; IDI: 0.017, 95% CI -0.004, 0.05) or considering ICH cases alone (ΔAUC: 0.02, 95% CI -0.02, 0.08; ΔŜ: -2.0, 95% CI -9.7, 3.4; IDI 0.065, 95% CI -0.03, 0.21). If NIHSS was the only clinical scale included, there was still no improvement in AUC or Ŝ when CT findings were added for the sample with IS/ICH combined (ΔAUC: 0.005, 95% CI -0.01, 0.02; ΔŜ: -5.0, 95% CI -11.6, 1.0) or for ICH cases alone (ΔAUC: 0.05, 95% CI -0.002, 0.11; ΔŜ: -4.2, 95% CI -11.5, 2.3). However, IDI was improved when NIHSS was the only clinical scale for IS/ICH combined (IDI: 0.029, 95% CI 0.002, 0.065) and ICH alone (IDI: 0.12, 95% CI 0.005, 0.26).

CONCLUSIONS

Excluding ICH CT findings had only minimal impact on mortality prediction model performance whether examining ICH and IS together or ICH alone. These findings have important implications for the design of clinical studies involving ICH patients.

In vitro and in vivo repeatability of abdominal diffusion-weighted MRI

OBJECTIVE

To study the in vitro and in vivo (abdomen) variability of apparent diffusion coefficient (ADC) measurements at 1.5 T using a free-breathing multislice diffusion-weighted (DW) MRI sequence.

METHODS

DW MRI images were obtained using a multislice spin-echo echo-planar imaging sequence with b-values=0, 100, 200, 500, 750 and 1000 s mm(-2). A flood-field phantom was imaged at regular intervals over 100 days, and 10 times on the same day on 2 occasions. 10 healthy volunteers were imaged on two separate occasions. Mono-exponential ADC maps were fitted excluding b=0. Paired analysis was carried out on the liver, spleen, kidney and gallbladder using multiple regions of interest (ROIs) and volumes of interest (VOIs).

RESULTS

The in vitro coefficient of variation was 1.3% over 100 days, and 0.5% and 1.0% for both the daily experiments. In vivo, there was no statistical difference in the group mean ADC value between visits for any organ. Using ROIs, the coefficient of reproducibility was 20.0% for the kidney, 21.0% for the gallbladder, 24.7% for the liver and 28.0% for the spleen. For VOIs, values fall to 7.7%, 6.4%, 8.6% and 9.6%, respectively.

CONCLUSION

Good in vitro repeatability of ADC measurements provided a sound basis for in vivo measurement. In vivo variability is higher and when considering single measurements in the abdomen as a whole, only changes in ADC value greater than 23.1% would be statistically significant using a two-dimensional ROI. This value is substantially lower (7.9%) if large three-dimensional VOIs are considered.

Optimal end points for acute stroke therapy trials: best ways to measure treatment effects of drugs and devices

BACKGROUND AND PURPOSE

Over the past decade, analysis of completed actual trials, model population studies, and theoretical work have improved approaches to selecting and analyzing end points in acute stroke treatment trials.

METHODS

Narrative review.

RESULTS

Because stroke affects persons in their biological, functional, social, and experiential dimensions, measures of impairment, disability, handicap, and quality of life are all desirable in pivotal trials, with disability being most important. Scales that are valid, reliable, responsive, and easy to administer are preferred; consequently, the modified Rankin Scale has become the most widely used single clinical efficacy measure. Because stroke cripples and kills, most outcome scales array patient outcome in ordered ranks, spread over the entire range from normal to disabled to dead. Generally, shift analysis, analyzing all health state transitions concurrently, is the most efficient analytic technique to detect treatment effects, with sliding dichotomy less efficient and fixed dichotomy least efficient, unless treatment effects strongly cluster at 1 or a few health state transitions that can be prespecified. Test statistics must also take into account interpretability, ie, how well they can be converted into metrics capturing all outcomes the intervention might alter in proportion to the degree they are valued by the patient; full ordinal analysis is most informative, sliding dichotomy is intermediately informative, and fixed dichotomy is least informative regarding this global outcome.

CONCLUSIONS

Stroke trial power and interpretation can be substantially enhanced by adherence to the principles delineated in this review. Full ordinal and sliding dichotomy analysis will most often be advantageous compared with fixed dichotomous approaches.

The ABCD and ABCD2 Scores and the Risk of Stroke following a TIA: A Narrative Review

The California, ABCD, and ABCD2 risk scores (ABCD system) were developed to help stratify short-term stroke risk in patients with TIA (transient ischemic attack). Beyond this scope, the ABCD system has been extensively used to study other prognostic information such as DWI (diffusion-weighted imaging) abnormalities, large artery stenosis, atrial fibrillation and its diagnostic accuracy in TIA patients, which are independent predictors of subsequent stroke in TIA patients. Our comprehensive paper suggested that all scores have and equivalent prognostic value in predicting short-term risk of stroke; however, the ABCD2 score is being predominantly used at most centers. The majority of studies have shown that more than half of the strokes in the first 90 days, occur in the first 7 days. The majority of patients studied were predominantly classified to have a higher ABCD/ABCD2 > 3 scores and were particularly at a higher short-term risk of stroke or TIA and other vascular events. However, patients with low risk ABCD2 score < 4 may have high-risk prognostic indicators, such as diffusion weighted imaging (DWI) abnormalities, large artery atherosclerosis (LAA), and atrial fibrillation (AF). The prognostic value of these scores improved if used in conjunction with clinical information, vascular imaging data, and brain imaging data. Before more data become available, the diagnostic value of these scores, its applicability in triaging patients, and its use in evaluating long-term prognosis are rather secondary; thus, indicating that the primary significance of these scores is for short-term prognostic purposes.

Predictors of good clinical outcome in acute stroke patients treated with intravenous thrombolysis

OBJECTIVES

Intravenous thrombolysis (IVT) is considered an effective treatment for acute ischemic stroke (IS). However, not all treated patients may achieve good outcome. The aim was to evaluate whether the initial NIHSS and DWI infarct volume could be the predictors for good outcome after IVT.

PATIENTS AND METHODS

The set of 125 patients with consecutive hemispheric IS (78 men; mean age 66.0 ± 12.1 years) treated with IVT within 3 h was analyzed. DWI volume was measured on admission. Good outcome was defined as a score 0-2 in modified Rankin Scale.

RESULTS

Multivariate logistic regression analysis showed initial NIHSS as an independent predictor of good outcome (P = 0.001). ROC curves showed baseline NIHSS ≤13.5 points and DWI volume ≤13.7 ml as cut-offs related to good outcome.

CONCLUSIONS

The initial NIHSS and DWI volume might be the predictors for good clinical outcome in acute stroke patients treated with IVT. The initial NIHSS score seems to be more accurate.

Early prediction of outcome of activities of daily living after stroke: a systematic review

BACKGROUND AND PURPOSE

Knowledge about robust and unbiased factors that predict outcome of activities of daily living (ADL) is paramount in stroke management. This review investigates the methodological quality of prognostic studies in the early poststroke phase for final ADL to identify variables that are predictive or not predictive for outcome of ADL after stroke.

METHODS

PubMed, Ebsco/Cinahl and Embase were systematically searched for prognostic studies in which stroke patients were included ≤2 weeks after onset and final outcome of ADL was determined ≥3 months poststroke. Risk of bias scores were used to distinguish high- and low-quality studies and a qualitative synthesis was performed.

RESULTS

Forty-eight of 8425 identified citations were included. The median risk of bias score was 17 out of 27 (range, 6-22) points. Most studies failed to report medical treatment applied, management of missing data, rationale for candidate determinants and outcome cut-offs, results of univariable analysis, and validation and performance of the model, making the predictive value of most determinants indistinct. Six high-quality studies showed strong evidence for baseline neurological status, upper limb paresis, and age as predictors for outcome of ADL. Gender and risk factors such as atrial fibrillation were unrelated to this outcome.

CONCLUSIONS

Because of insufficient methodological quality of most prognostic studies, the predictive value of many clinical determinants for outcome of ADL remains unclear. Future cohort studies should focus on early prediction using simple models with good clinical performance to enhance application in stroke management and research.