Predictors of early mortality in patients with ischemic stroke

Predictive Role of hsCRP in Recurrent Stroke Differed According to Severity of Cerebrovascular Disease: Analysis from a Prospective Cohort Study

Elevated levels of high-sensitivity C-reactive protein (hsCRP) were associated with an increased risk of recurrent stroke. However, it is still unknown whether the predictive value of hsCRP differed according to the severity of cerebrovascular disease. We used the cohort of the prospective multicenter cohort study of the Third China National Stroke Registry (CNSR-III), in which 10,765 consecutive patients with acute ischemic stroke or transient ischemic attack (TIA) had hsCRP levels measured. Patients were classified into minor stroke, or TIA, and non-minor stroke. The primary outcome was a new stroke within 1 year. Cox proportional hazards models were used to assess the association of hsCRP and its outcome. Elevated levels of hsCRP were associated with an increased risk of recurrent stroke in minor stroke or TIA patients, irrespective of using a National Institutes of Health Stroke Scale (NIHSS) score of ≤3 (the highest quartile vs. the lowest quartile: adjusted hazard ratio, 1.48; 95% CI, 1.12-1.97; p = 0.007) or ≤5 (the highest quartile vs. the lowest quartile: adjusted hazard ratio, 1.45; 95% CI, 1.15-1.84; p = 0.002) to define minor stroke. Such association was more apparent in the large-artery atherosclerosis subtype. However, for the patients with non-minor stroke, the association of hsCRP with recurrent stroke disappeared.

NIH stroke scale and unfavourable outcomes in acute ischaemic stroke: retrospective study

OBJECTIVES

To evaluate the determining factors of severe functional impairment (SFI) outcome at discharge and in-hospital mortality in patients who had an acute ischaemic stroke and thus favouring early implementation of primary palliative care (PC).

METHODS

A retrospective descriptive study by the analysis of 515 patients who had an acute ischaemic stroke admitted at stroke unit, aged≥18 years, from January 2017 to December 2018. Previous clinical and functional status data, National Institute of Health Stroke Scale (NIHSS) on admission, and data related to the evolution during hospitalisation were evaluated, relating them to the SFI outcome at discharge and death. The significance level was set at 5%.

RESULTS

Of 515 patients included, 15% (77) died, 23.3%(120) had an SFI outcome and 9.1% (47) were evaluated by the PC team. It was observed that NIHSS Score≥16 is responsible for a 15.5-fold increase in the occurrence of death outcome. The presence of atrial fibrillation was responsible for a 3.5-fold increase in the risk of this outcome.

CONCLUSION

NIHSS Score is an independent predictor of in-hospital death and SFI outcomes at discharge. Knowledge about the prognosis and risk of developing unfavourable outcomes is important for planning the care of patients affected by a potentially fatal and limiting acute vascular insult.

Validity of National Institutes of Health Stroke Scale for Severity of Stroke to Predict Mortality Among Patients Presenting With Symptoms of Stroke

Introduction

Cerebrovascular accident (CVA), also termed as stroke, is the third leading cause of mortality and the most common cause of disability globally. The National Institutes of Health Stroke Scale (NIHSS) is a valid assessment tool utilized to determine the severity of the stroke and can be used to prioritize patients to design treatment plans, rehabilitation, and better clinical outcomes. The primary objective of this study was to determine the validity of the NIHSS to predict mortality among patients presenting with symptoms of a stroke.

Material and methods

This was a descriptive case-series conducted over a period of six months between September 2019 and February 2020 at a tertiary care hospital in Nawabshah, Pakistan. The sample population included 141 patients admitted within 24 hours of the onset of symptoms of a stroke. A neurological examination of the patients was performed. On admission, stroke severity was evaluated with the NIHSS. After an initial clinical evaluation, patients underwent a non-enhanced computed tomography (CT) scan of the brain. The score of NIHSS and mortality at 72 hours were recorded on the pre-defined proforma by the investigators. All statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 23.0 (Armonk, NY: IBM Corp).

Results

The mean age of the participants was 52.37±8.61 years. 68.1% of patients were hypertensive, 29.1% were diabetic, and 36.9% of patients were found with hyperlipidemia. The mortality rate was 41.1%. The mean NIHSS score was 16.68±6.72 points. The findings of this study demonstrated that the score of 14.9% cases was good (0-6 points), the score of 29.1% cases was moderate (7-15 points), and the score of 56% cases was poor (≥16 points). There was a significant association of NIHSS score with mortality (p<0.001).

Conclusions

Baseline NIHSS score has a profound association with mortality after acute stroke. It can help clinicians decide whether to provide thrombolytic treatment, rehabilitation or a combination of both in these patients and decrease the mortality rate. However, more studies are needed to potentiate these conclusions.

Bispectral Index in predicting in-hospital mortality in patients with ischemic stroke: A methodological study

Background:

Ischemic stroke is a leading cause of death and functional disability worldwide. Several clinical scores or stroke scales, biological test or markers, clinical signs, and radiological imaging have been performed to predict both worse neurologic outcome and mortality for ischemic stroke.

Objectives:

The aim of our study was to investigate the association between early Bispectral Index scores and in-hospital mortality in patients with ischemic stroke.

Methods:

This is a comparative prospective methodological study, in which we evaluated the predictive accuracies of Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index for in-hospital mortality of patients with ischemic stroke. Receiver operating characteristic analysis was used for comparing the accuracy of the scoring systems, areas under receiver operating characteristic curves were calculated, and Youden J index was used for estimating associated cut-off values.

Results:

Among the 80 ischemic stroke patients, in-hospital mortality rate was 38.8% (n = 31). The areas under receiver operating characteristic curves were 0.984, 0.960, and 0.863 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. The difference between areas under receiver operating characteristic curves for Bispectral Index and Glasgow Coma Scale was statistically similar. Besides, the difference between areas under receiver operating characteristic curves for Bispectral Index and Charlson Comorbidity Index, and the difference between areas under receiver operating characteristic curves for Glasgow Coma Scale and Charlson Comorbidity Index were statistically significant. The associated cut-off values were ⩽74, ⩽12, and >4 for Bispectral Index, Glasgow Coma Scale, and Charlson Comorbidity Index, respectively. For these cut-off points, sensitivity and specificity of Bispectral Index were 93.6% and 95.9%, sensitivity and specificity of Glasgow Coma Scale were 100.0% and 83.7%, and sensitivity and specificity of Charlson Comorbidity Index were 83.9% and 69.4%, respectively. However, accuracy of Bispectral Index was 95.0%, accuracy of Glasgow Coma Scale was 90.0%, and accuracy of Charlson Comorbidity Index was 75.0.

Conclusion:

Knowledge of the risk factors for mortality in patients with ischemic stroke can help to identify which patients have a higher risk of fatal outcome. The Bispectral Index score improved discrimination and classified patients with higher mortality better than both Glasgow Coma Scale and Charlson Comorbidity Index.

Population-based Stroke Atlas for outcome prediction: method and preliminary results for ischemic stroke from CT

BACKGROUND AND PURPOSE

Knowledge of outcome prediction is important in stroke management. We propose a lesion size and location-driven method for stroke outcome prediction using a Population-based Stroke Atlas (PSA) linking neurological parameters with neuroimaging in population. The PSA aggregates data from previously treated patients and applies them to currently treated patients. The PSA parameter distribution in the infarct region of a treated patient enables prediction. We introduce a method for PSA calculation, quantify its performance, and use it to illustrate ischemic stroke outcome prediction of modified Rankin Scale (mRS) and Barthel Index (BI).

METHODS

The preliminary PSA was constructed from 128 ischemic stroke cases calculated for 8 variants (various data aggregation schemes) and 3 case selection variables (infarct volume, NIHSS at admission, and NIHSS at day 7), each in 4 ranges. Outcome prediction for 9 parameters (mRS at 7th, and mRS and BI at 30th, 90th, 180th, 360th day) was studied using a leave-one-out approach, requiring 589,824 PSA maps to be analyzed.

RESULTS

Outcomes predicted for different PSA variants are statistically equivalent, so the simplest and most efficient variant aiming at parameter averaging is employed. This variant allows the PSA to be pre-calculated before prediction. The PSA constrained by infarct volume and NIHSS reduces the average prediction error (absolute difference between the predicted and actual values) by a fraction of 0.796; the use of 3 patient-specific variables further lowers it by 0.538. The PSA-based prediction error for mild and severe outcomes (mRS  =  [2]-[5]) is (0.5-0.7). Prediction takes about 8 seconds.

CONCLUSIONS

PSA-based prediction of individual and group mRS and BI scores over time is feasible, fast and simple, but its clinical usefulness requires further studies. The case selection operation improves PSA predictability. A multiplicity of PSAs can be computed independently for different datasets at various centers and easily merged, which enables building powerful PSAs over the community.

Emergency department triaging of admitted stroke patients--a Bayesian Network analysis

This study uses hospital administrative data to ascertain the differences in the patient characteristics, process and outcomes of care between the Emergency Department (ED) triage categories of patients admitted from an ED presentation into a large metropolitan teaching hospital with a Stroke Care Unit. Bayesian Networks (BNs) derived from the administrative data were used to provide the descriptive models. Nearly half the patients in each stroke subtype were triaged as 'Urgent' (to be seen within 30 minutes). With a decrease in the urgency of triage categories, the proportion admitted within 8 hours decreased dramatically and the proportion of formal discharge increased. Notably, 45% of transient ischaemic attacks (TIAs) were categorized as 'Semi-urgent' (to be attended within 60 minutes), indicating an opportunity to improve emergency assessment of TIAs. The results illustrate the utility of hospital administrative data and the applicability of BNs for review of the current triage practices and subsequent impact.

Formal-Transfer In and Out of Stroke Care Units

The positive impact of stroke care units (SCUs) on patient outcome has been previously reported. In this study, long-term stroke patients that are formally admitted to teaching-hospitals are compared with and without SCUs. The authors focus on the patients’ experience with ongoing care or formal transfers following current care as this cohort is often high users of the system with associated high costs. Bayesian Networks were employed to analyze routinely collected public-hospital administrative data. The results illustrate that the teaching-hospitals with SCUs, while achieving shorter length of stay, in fact deal with younger patients with lower overall patient complexity than non-SCU teaching-hospitals. Other differences include SCUs predominantly treating subarachnoid hemorrhages whereas the non-SCUs treat more cerebral infarctions. This study illustrates the power of Bayesian Networks to expose the nature of caseload and outcomes recorded in hospital-administrative data as a means to gain insight on current practice and create opportunities for benchmarking and improving care.

Recorded Categories of Non-Principal Diagnoses in Victorian Public Hospital Transient Ischaemic Attack and Stroke Admissions

Information about the number and types of non-principal diagnoses (NPDs) would make an important contribution to prediction of outcome and hence patient management. The study reported here is based on analysis of three fiscal years of the Victorian public hospital transient ischaemic attack (TIA) and stroke admissions. The incidence of NPDs and co-occurrence of NPD-associated prefix categories (that identify the onset or relevance of each condition to the episode) are described in each of the broad stroke subtypes. The distributions of length of stay and in-hospital deaths in the cohorts without and with NPDs and in the various prefix categories are determined. The study also compares the age and gender distribution in the various subpopulations of interest. The importance of collecting complete and accurate data on nature of NPDs and its potential in describing the complexity of presentation are discussed.