Stroke Severity and Comorbidity Index for Prediction of Mortality after Ischemic Stroke from the Virtual International Stroke Trials Archive-Acute Collaboration

M. Kaste on työryhmän VISTA-Acute Collaboration jäsen.Background: There is increasing interest in the use of administrative data (incorporating comorbidity index) and stroke severity score to predict ischemic stroke mortality. The aim of this study was to determine the optimal timing for the collection of stroke severity data and the minimum clinical dataset to be included in models of stroke mortality. To address these issues, we chose the Virtual International Stroke Trials Archive (VISTA), which contains National Institutes of Health Stroke Scale (NIHSS) on admission and at 24 hours, as well as outcome at 90 days. Methods: VISTA was searched for patients who had baseline and 24-hour NIHSS. Improvement in regression models was performed by the net reclassification improvement (NRI) method. Results: The clinical data among 5206 patients were mean age, 69 +/- 13; comorbidity index, 3.3 +/- .9; median NIHSS at baseline, 12 (interquartile range [IQR] 8-17); NIHSS at 24 hours, 9 (IQR 8-15); and death at 90 days in 15%. The baseline model consists of age, gender, and comorbidity index. Adding the baseline NIHSS to model 1 improved the NRI by 0.671 (95% confidence interval [CI] 0.595-0.747) [or 67.1% correct reclassification between model 1 and model 2]. Adding the 24 hour NIHSS term to model 1 (model 3) improved the NRI by 0.929 (95% CI 0.857-1.000) for model 3 versus model 1. Adding the variable thrombolysis to model 3 (model 4) improve NRI by 0.1 (95% CI 0.023-0.178) [model 4 versus model 3]. Conclusion: The optimal model for the prediction of mortality was achieved by adding the 24-hour NIHSS and thrombolysis to the baseline model.Peer reviewe

Stroke Severity Versus Dysphagia Screen as Driver for Post-stroke Pneumonia

Background and Purpose: Post-stroke pneumonia is a feared complication of stroke as it is associated with greater mortality and disability than in those without pneumonia. Patients are often kept “Nil By Mouth” (NBM) after stroke until after receiving a screen for dysphagia and declared safe to resume oral intake. We aimed to assess the proportional contribution of stroke severity and dysphagia screen to pneumonia by borrowing idea from coalition game theory on fair distribution of marginal profit (Shapley value).Method: Retrospective study of admissions to the stroke unit at Monash Medical Center in 2015. Seventy-five percent of data were partitioned into training set and the remainder (25%) into validation set. Variables associated with pneumonia (p < 0.1) were entered into Shapley value regression and conditional decision tree analysis.Results: In 2015, there were 797 admissions and 617 patients with ischemic and hemorrhagic stroke (age 69.9 ± 16.2, male = 55.0%, National Institute of Health Stroke Scale/NIHSS 8.1 ± 7.9). The frequency of pneumonia was 6.6% (41/617). In univariable analyses NIHSS, time to dysphagia screen, Charlson comorbidity index (CCI), and age were significantly associated with pneumonia but not weekend admission. Shapley value regression showed that the largest contributor to the model was stroke severity (72.8%) followed by CCI (16.2%), dysphagia screen (3.8%), and age (7.2%). Decision tree analysis yielded an NIHSS threshold of 14 for classifying people with (27% of 75 patients) and without pneumonia (2.5% of 308 patients). The area under the ROC curve for training data was 0.83 (95% CI 0.75–0.91) with no detectable difference between the training and test data (p = 0.4). Results were similar when dysphagia was exchanged for the variable dysphagia screen.Conclusion: Stroke severity status, and not dysphagia or dysphagia screening contributed to the decision tree model of post stroke pneumonia. We cannot exclude the chance that using dysphagia screen in this cohort had minimized the impact of dysphagia on development of pneumonia

Prevalence of primary aldosteronism in acute stroke or transient ischemic attack: a systematic review and meta-analysis

Background and purposePrimary aldosteronism (PA) is the most common endocrine cause of secondary hypertension with a prevalence of 14% in patients with newly diagnosed hypertension. Patients with PA experience a higher rate of cardiovascular events including stroke when compared to those with blood pressure matched essential hypertension. This systematic review and meta-analysis summarize current evidence on the prevalence of PA in patients with acute stroke or transient ischemic attack (TIA).MethodsTwo reviewers independently reviewed the literature for observational studies on the prevalence of PA in patients with acute stroke or TIA. MEDLINE and Embase were searched for studies up to December 13, 2023.ResultsThree single center studies conducted in Japan, Singapore and China were found to meet the inclusion criteria. The reported prevalence of PA in two cohort studies of adults with stroke or TIA were 3.1% and 4.0% and a third cross-sectional study in adults under 45 years old revealed a prevalence rate of 12.9%. Following a meta-analysis, the pooled prevalence of PA in adults with stroke or TIA is 5.8% [95% CI 1.6%-12.3%].ConclusionsA considerable proportion of patients with stroke or TIA may have PA as the underlying cause of their hypertension. Given the increased risk of stroke associated with PA, clinicians should consider screening for PA in hypertensive patients with stroke or TIA. Further research is needed to evaluate the effect of timing and interfering medications on test results, which will inform an evidence-based approach to testing for PA following TIA or stroke.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022328644

Stroke Incidence in Victoria, Australia—Emerging Improvements

BackgroundEvidence of a decline in the incidence of stroke has emerged from population-based studies. These have included retrospective and prospective cohorts. However, in Australia and other countries, government bodies and stroke foundations predict a rise in the prevalence of stroke that is anticipated to increase the burden of stroke across the entire domain of care. This increase in prevalence must be viewed as different from the decline in incidence being observed, a measure of new stroke cases. In Victoria, all public emergency department visits and public and private hospital admissions are reported to the Department of Health and Human Services and include demographic, diagnostic, and procedural/treatment information.MethodsWe obtained data from financial years 1997/1998 to 2007/2008 inclusive, for all cases with a primary stroke diagnosis (ICD-10-AM categories) with associated data fields. Incident cases were established by using a 5-year clearance period.ResultsFrom 2003/2004 to 2007/2008 inclusive, there were 53,425 patients with a primary stroke or TIA diagnosis. The crude incident stroke rate for first ever stroke was 211 per 100,000 per year (95% CI 205–217) [females—205 per 100,000 per year (95% CI 196–214) and males—217 per 100,000 per year (95% CI 210–224)]. The overall stroke rates were seen to significantly decline over the period [males (per 100,000 per year) 227 in 2003/2004 to 202 in 2007/2008 (p = 0.0157) and females (per 100,000 per year) 214 in 2003/2004 to 188 in 2007/2008 (p = 0.0482)]. Ischemic stroke rates also appeared to decline; however, this change was not significant.ConclusionThese results demonstrate a significant decline in stroke incidence during the study period and may suggest evidence for effectiveness of primary and secondary prevention strategies in cerebrovascular risk factor management

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records

Aim: To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques. Method: The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009–2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of “baseline” clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified. Results: The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input. Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes

Stroke incidence in Victoria, Australia-Emerging improvements

BACKGROUND: Evidence of a decline in the incidence of stroke has emerged from population-based studies. These have included retrospective and prospective cohorts. However, in Australia and other countries, government bodies and stroke foundations predict a rise in the prevalence of stroke that is anticipated to increase the burden of stroke across the entire domain of care. This increase in prevalence must be viewed as different from the decline in incidence being observed, a measure of new stroke cases. In Victoria, all public emergency department visits and public and private hospital admissions are reported to the Department of Health and Human Services and include demographic, diagnostic, and procedural/treatment information. METHODS: We obtained data from financial years 1997/1998 to 2007/2008 inclusive, for all cases with a primary stroke diagnosis (ICD-10-AM categories) with associated data fields. Incident cases were established by using a 5-year clearance period. RESULTS: From 2003/2004 to 2007/2008 inclusive, there were 53,425 patients with a primary stroke or TIA diagnosis. The crude incident stroke rate for first ever stroke was 211 per 100,000 per year (95% CI 205-217) [females-205 per 100,000 per year (95% CI 196-214) and males-217 per 100,000 per year (95% CI 210-224)]. The overall stroke rates were seen to significantly decline over the period [males (per 100,000 per year) 227 in 2003/2004 to 202 in 2007/2008 (p = 0.0157) and females (per 100,000 per year) 214 in 2003/2004 to 188 in 2007/2008 (p = 0.0482)]. Ischemic stroke rates also appeared to decline; however, this change was not significant. CONCLUSION: These results demonstrate a significant decline in stroke incidence during the study period and may suggest evidence for effectiveness of primary and secondary prevention strategies in cerebrovascular risk factor management

Predicting Disability after Ischemic Stroke Based on Comorbidity Index and Stroke Severity—From the Virtual International Stroke Trials Archive-Acute Collaboration

Background and aimThe availability and access of hospital administrative data [coding for Charlson comorbidity index (CCI)] in large data form has resulted in a surge of interest in using this information to predict mortality from stroke. The aims of this study were to determine the minimum clinical data set to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of outcome.MethodWe leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive. This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data, and outcome disability score such as 90-day Rankin Scale. We calculate CCI based on comorbidity data in this data set. For logistic regression, we used these calibration statistics: Nagelkerke generalised R2 and Brier score; and for discrimination we used: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex, and CCI.ResultsThe clinical data among 5,206 patients (55% males) were as follows: mean age 69 ± 13 years, CCI 4.2 ± 0.8, and median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h. In Model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65–0.68) to 0.79 (95% CI 0.78–0.81). In Model 3, adding 24-h NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89–0.91) and increased IDI by 0.23 (95% CI 0.22–0.24). Adding the variable recombinant tissue plasminogen activator did not result in a further change in AUC or IDI to this regression model. In Model 3, the variable NIHSS at 24 h explains 87.3% of the variance of Model 3, follow by age (8.5%), comorbidity (3.7%), and male sex (0.5%).ConclusionOur results suggest that prediction of disability after ischemic stroke should at least include 24-h NIHSS and age. The variable CCI is less important for prediction of disability in this data set