Association of multimorbidity with mortality after stroke stratified by age, severity, etiology, and prior disability

BACKGROUND

Multimorbidity is common in patients with stroke and is associated with increased medium- to long-term mortality, but its value for clinical decision-making and case-mix adjustment will depend on other factors, such as age, stroke severity, etiological subtype, prior disability, and vascular risk factors.

AIMS

In the absence of previous studies, we related multimorbidity to long-term post-stroke mortality with stratification by these factors.

METHODS

In patients ascertained in a population-based stroke incidence study (Oxford Vascular Study; 2002-2017), we related pre-stroke multimorbidity (weighted/unweighted Charlson comorbidity index (CCI)) to all-cause/vascular/non-vascular mortality (1/5/10 years) using regression models adjusted/stratified by age, sex, predicted early outcome (THRIVE score), stroke severity (NIH stroke scale (NIHSS)), etiology (Trial of Org 10172 in Acute Stroke Treatment (TOAST)), premorbid disability (modified Rankin Scale (mRS)), and non-CCI risk factors (hypertension, hyperlipidemia, atrial fibrillation, smoking, deprivation, anxiety/depression).

RESULTS

Among 2454 stroke patients (M/SD age: 74.1/13.9 years; 48.9% male; M/SD NIHSS: 5.7/7.0), 1375/56.0% had ⩾ 1 CCI comorbidity and 685/27.9% had ⩾ 2. After age/sex adjustment, multimorbidity (unweighted CCI ⩾ 2 vs 0) predicted (all ps < 0.001) mortality at 1 year (aHR = 1.57, 95% CI = 1.38-1.78), 5 years (aHR = 1.73, 95% CI = 1.53-1.96), and 10 years (aHR = 1.79, 95% CI = 1.58-2.03). Although multimorbidity was independently associated with premorbid disability (mRS > 2: aOR = 2.76, 2.13-3.60) and non-CCI risk factors (hypertension: 1.56, 1.25-1.95; hyperlipidemia: 2.58, 2.03-3.28; atrial fibrillation: 2.31; 1.78-2.98; smoking: 1.37, 1.01-1.86), it predicted death after adjustment for all measured confounders (10-year-aHR = 1.56, 1.37-1.78, p < 0.001), driven mainly by non-vascular death (aHR = 1.89, 1.55-2.29). Predictive value for 10-year all-cause death was greatest in patients with lower expected early mortality: lower THRIVE score (pint < 0.001), age < 75 years (aHR = 2.27, 1.71-3.00), NIHSS < 5 (1.84, 1.53-2.21), and lacunar stroke (3.56, 2.14-5.91). Results were similar using the weighted CCI.

CONCLUSION

Pre-stroke multimorbidity is highly prevalent and is an independent predictor of death after stroke, supporting its inclusion in case-mix adjustment models and in informing decision-making by patients, families, and carers. Prediction in younger patients and after minor stroke, particularly for non-vascular death, suggests potential clinical utility in targeting interventions that require survival for 5-10 years to achieve a favorable risk/benefit ratio.

DATA ACCESS STATEMENT

Data requests will be considered by the Oxford Vascular Study (OXVASC) Study Director (P.M.R.-peter.rothwell@ndcn.ox.ac.uk).

Explaining predictors of discharge destination assessed along the patients' acute stroke journey

INTRODUCTION

Accurate prediction of outcome destination at an early stage would help manage patients presenting with stroke. This study assessed the predictive ability of three machine learning (ML) algorithms to predict outcomes at four different stages as well as compared the predictive power of stroke scores.

METHODS

Patients presenting with acute stroke to the Canberra Hospital between 2015 and 2019 were selected retrospectively. 16 potential predictors and one target variable (discharge destination) were obtained from the notes. k-Nearest Neighbour (kNN) and two ensemble-based classification algorithms (Adaptive Boosting and Bootstrap Aggregation) were employed to predict outcomes. Predictive accuracy was assessed at each of the four stages using both overall and per-class accuracy. The contribution of each variable to the prediction outcome was evaluated by the ensemble-based algorithm and using the Relief feature selection algorithm. Various combinations of stroke scores were tested using the aforementioned models.

RESULTS

Of the three ML models, Adaptive Boosting demonstrated the highest accuracy (90%) at Stage 4 in predicting death while the highest overall accuracy (81.7%) was achieved by kNN (k=2/City-block distance). Feature importance analysis has shown that the most important features are the 24-hour Scandinavian Stroke Scale (SSS) and 24-hour National Institutes of Health Stroke Scale (NIHSS) scores, dyslipidaemia, hypertension and premorbid mRS score. For the initial and 24-hour scores, there was a higher correlation (0.93) between SSS scores than for NIHSS scores (0.81). Reducing the overall four scores to InitSSS/24hrNIHSS increased accuracy to 95% in predicting death (Adaptive Boosting) and overall accuracy to 85.4% (kNN). Accuracies at Stage 2 (pre-treatment, 11 predictors) were not far behind those at Stage 4.

CONCLUSION

Our findings suggest that even in the early stages of management, a clinically useful prediction regarding discharge destination can be made. Adaptive Boosting might be the best ML model, especially when it comes to predicting death. The predictors' importance analysis also showed that dyslipidemia and hypertension contributed to the discharge outcome even more than expected. Further, surprisingly using mixed score systems might also lead to higher prediction accuracies.

Large-scale implementation of stroke early supported discharge: the WISE realist mixed-methods study

Background

In England, the provision of early supported discharge is recommended as part of an evidence-based stroke care pathway.

Objectives

To investigate the effectiveness of early supported discharge services when implemented at scale in practice and to understand how the context within which these services operate influences their implementation and effectiveness.

Design

A mixed-methods study using a realist evaluation approach and two interlinking work packages was undertaken. Three programme theories were tested to investigate the adoption of evidence-based core components, differences in urban and rural settings, and communication processes.

Setting and interventions

Early supported discharge services across a large geographical area of England, covering the West and East Midlands, the East of England and the North of England.

Participants

Work package 1: historical prospective patient data from the Sentinel Stroke National Audit Programme collected by early supported discharge and hospital teams. Work package 2: NHS staff (n = 117) and patients (n = 30) from six purposely selected early supported discharge services.

Data and main outcome

Work package 1: a 17-item early supported discharge consensus score measured the adherence to evidence-based core components defined in an international consensus document. The effectiveness of early supported discharge was measured with process and patient outcomes and costs. Work package 2: semistructured interviews and focus groups with NHS staff and patients were undertaken to investigate the contextual determinants of early supported discharge effectiveness.

Results

A variety of early supported discharge service models had been adopted, as reflected by the variability in the early supported discharge consensus score. A one-unit increase in early supported discharge consensus score was significantly associated with a more responsive early supported discharge service and increased treatment intensity. There was no association with stroke survivor outcome. Patients who received early supported discharge in their stroke care pathway spent, on average, 1 day longer in hospital than those who did not receive early supported discharge. The most rural services had the highest service costs per patient. NHS staff identified core evidence-based components (e.g. eligibility criteria, co-ordinated multidisciplinary team and regular weekly multidisciplinary team meetings) as central to the effectiveness of early supported discharge. Mechanisms thought to streamline discharge and help teams to meet their responsiveness targets included having access to a social worker and the quality of communications and transitions across services. The role of rehabilitation assistants and an interdisciplinary approach were facilitators of delivering an intensive service. The rurality of early supported discharge services, especially when coupled with capacity issues and increased travel times to visit patients, could influence the intensity of rehabilitation provision and teams’ flexibility to adjust to patients’ needs. This required organising multidisciplinary teams and meetings around the local geography. Findings also highlighted the importance of good leadership and communication. Early supported discharge staff highlighted the need for collaborative and trusting relationships with patients and carers and stroke unit staff, as well as across the wider stroke care pathway.

Limitations

Work package 1: possible influence of unobserved variables and we were unable to determine the effect of early supported discharge on patient outcomes. Work package 2: the pragmatic approach led to ‘theoretical nuggets’ rather than an overarching higher-level theory.

Conclusions

The realist evaluation methodology allowed us to address the complexity of early supported discharge delivery in real-world settings. The findings highlighted the importance of context and contextual features and mechanisms that need to be either addressed or capitalised on to improve effectiveness.

Trial registration

Current Controlled Trials ISRCTN15568163.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 22. See the NIHR Journals Library website for further project information.

Effect of stroke early supported discharge on length of hospital stay: analysis from a national stroke registry

OBJECTIVE

The first observational study to investigate the impact of early supported discharge (ESD) on length of hospital stay in real-world conditions.

DESIGN

Using historical prospective Sentinel Stroke National Audit Programme (SSNAP) data (1 January 2013-31 December 2016) and multilevel modelling, cross-sectional (2015-2016; 30 791 patients nested within 55 hospitals) and repeated cross-sectional (2013-2014 vs 2015-2016; 49 266 patients nested within 41 hospitals) analyses were undertaken.

SETTING

Hospitals were sampled across a large geographical area of England covering the West and East Midlands, the East of England and the North of England.

PARTICIPANTS

Stroke patients whose data were entered into the SSNAP database by hospital teams.

INTERVENTIONS

Receiving ESD along the patient care pathway.

PRIMARY AND SECONDARY OUTCOME MEASURES

Length of hospital stay.

RESULTS

When adjusted for important case-mix variables, patients who received ESD on their stroke care pathway spent longer in hospital, compared with those who did not receive ESD. The percentage increase was 15.8% (95% CI 12.3% to 19.4%) for the 2015-2016 cross-sectional analysis and 18.8% (95% CI 13.9% to 24.0%) for the 2013-2014 versus 2015-2016 repeated cross-sectional analysis. On average, the increased length of hospital stay was approximately 1 day.

CONCLUSIONS

This study has shown that by comparing ESD and non-ESD patient groups matched for important patient characteristics, receiving ESD resulted in a 1-day increase in length of hospital stay. The large reduction in length of hospital stay overall, since original trials were conducted, may explain why a reduction was not observed. The longer term benefits of accessing ESD need to be investigated further.

TRIAL REGISTRATION NUMBER

http://www.isrctn.com/ISRCTN15568163.

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

Background

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

Aim

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

Methods

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

Results

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

Conclusions

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

Effectiveness of Stroke Early Supported Discharge: Analysis From a National Stroke Registry

Supplemental Digital Content is available in the text.

Background:

Implementation of stroke early supported discharge (ESD) services has been recommended in many countries’ clinical guidelines, based on clinical trial evidence. This is the first observational study to investigate the effectiveness of ESD service models operating in real-world conditions, at scale.

Methods and Results:

Using historical prospective data from the United Kingdom Sentinel Stroke National Audit Programme (January 1, 2016–December 31, 2016), measures of ESD effectiveness were “days to ESD” (number of days from hospital discharge to first ESD contact; n=6222), “rehabilitation intensity” (total number of treatment days/total days with ESD; n=5891), and stroke survivor outcome (modified Rankin scale at ESD discharge; n=6222). ESD service models (derived from Sentinel Stroke National Audit Programme postacute organizational audit data) were categorized with a 17-item score, reflecting adoption of ESD consensus core components (evidence-based criteria). Multilevel modeling analysis was undertaken as patients were clustered within ESD teams across the Midlands, East, and North of England (n=31). A variety of ESD service models had been adopted, as reflected by variability in the ESD consensus score. Controlling for patient characteristics and Sentinel Stroke National Audit Programme hospital score, a 1-unit increase in ESD consensus score was significantly associated with a more responsive ESD service (reduced odds of patient being seen after ≥1 day of 29% [95% CI, 1%–49%] and increased treatment intensity by 2% [95% CI, 0.3%–4%]). There was no association with stroke survivor outcome measured by the modified Rankin Scale.

Conclusions:

This study has shown that adopting defined core components of ESD is associated with providing a more responsive and intensive ESD service. This shows that adherence to evidence-based criteria is likely to result in a more effective ESD service as defined by process measures.

Registration:

URL: http://www.isrctn.com/; Unique identifier: ISRCTN15568163.

Gap Analysis Regarding Prognostication in Neurocritical Care: A Joint Statement from the German Neurocritical Care Society and the Neurocritical Care Society

Background/Objective

Prognostication is a routine part of the delivery of neurocritical care for most patients with acute neurocritical illnesses. Numerous prognostic models exist for many different conditions. However, there are concerns about significant gaps in knowledge regarding optimal methods of prognostication.

Methods

As part of the Arbeitstagung NeuroIntensivMedizin meeting in February 2018 in Würzburg, Germany, a joint session on prognostication was held between the German NeuroIntensive Care Society and the Neurocritical Care Society. The purpose of this session was to provide presentations and open discussion regarding existing prognostic models for eight common neurocritical care conditions (aneurysmal subarachnoid hemorrhage, intracerebral hemorrhage, acute ischemic stroke, traumatic brain injury, traumatic spinal cord injury, status epilepticus, Guillain–Barré Syndrome, and global cerebral ischemia from cardiac arrest). The goal was to develop a qualitative gap analysis regarding prognostication that could help inform a future framework for clinical studies and guidelines.

Results

Prognostic models exist for all of the conditions presented. However, there are significant gaps in prognostication in each condition. Furthermore, several themes emerged that crossed across several or all diseases presented. Specifically, the self-fulfilling prophecy, lack of accounting for medical comorbidities, and absence of integration of in-hospital care parameters were identified as major gaps in most prognostic models.

Conclusions

Prognostication in neurocritical care is important, and current prognostic models are limited. This gap analysis provides a summary assessment of issues that could be addressed in future studies and evidence-based guidelines in order to improve the process of prognostication.

Value-based healthcare in ischemic stroke care: case-mix adjustment models for clinical and patient-reported outcomes

Background

Patient-Reported Outcome Measures (PROMs) have been proposed for benchmarking health care quality across hospitals, which requires extensive case-mix adjustment. The current study’s aim was to develop and compare case-mix models for mortality, a functional outcome, and a patient-reported outcome measure (PROM) in ischemic stroke care.

Methods

Data from ischemic stroke patients, admitted to four stroke centers in the Netherlands between 2014 and 2016 with available outcome information (N = 1022), was analyzed. Case-mix adjustment models were developed for mortality, modified Rankin Scale (mRS) scores and EQ-5D index scores with respectively binary logistic, proportional odds and linear regression models with stepwise backward selection. Predictive ability of these models was determined with R-squared (R²) and area-under-the-receiver-operating-characteristic-curve (AUC) statistics.

Results

Age, NIHSS score on admission, and heart failure were the only common predictors across all three case-mix adjustment models. Specific predictors for the EQ-5D index score were sex (β = 0.041), socio-economic status (β = − 0.019) and nationality (β = − 0.074). R²-values for the regression models for mortality (5 predictors), mRS score (9 predictors) and EQ-5D utility score (12 predictors), were respectively R² = 0.44, R² = 0.42 and R² = 0.37.

Conclusions

The set of case-mix adjustment variables for the EQ-5D at three months differed considerably from the set for clinical outcomes in stroke care. The case-mix adjustment variables that were specific to this PROM were sex, socio-economic status and nationality. These variables should be considered in future attempts to risk-adjust for PROMs during benchmarking of hospitals.

Impact of Missing Stroke Severity Data on the Accuracy of Hospital Ischemic Stroke Mortality Profiling

BACKGROUND

The Centers for Medicare and Medicaid Services have proposed 30-day ischemic stroke risk-standardized mortality rates that include adjustment for stroke severity using the National Institute of Health Stroke Scale (NIHSS), which is often undocumented. We used simulations to quantify the effect of missing NIHSS data on the accuracy of hospital-level ischemic stroke risk-standardized mortality rate profiling for 100 hypothetical hospitals with different case volumes.

METHODS AND RESULTS

We generated simulated data sets of patients with NIHSS scores and other predictors of 30-day mortality based on empirical analysis of data from 7654 patients with ischemic stroke in the Michigan Stroke Registry. We assigned and rank-ordered a true (known) 30-day mortality rate to each hospital in the simulated data sets of N=100 hospitals with either low (100 patients with stroke), medium (300), or high (500) case volumes. We then estimated and rank-ordered 30-day risk-standardized mortality rates for the N=100 hospitals in each simulated data set using hierarchical logistic regression models. In each data set, we systematically varied the rate of missing NIHSS data and whether missing NIHSS data was independent (missing completely at random) or dependent (missing not at random) on the NIHSS score. With no missing NIHSS data, the Spearman correlation between the true hospital performance rank order assigned during data set generation and the estimated 30-day risk-standardized mortality rate rank order was 0.72, 0.88, and 0.91 in low, medium, and high volume hospitals, respectively. However, this fell to as low as 0.50, 0.71, and 0.79 as missing NIHSS data reached 70%.

CONCLUSIONS

Missing NIHSS data had substantial detrimental effects on the accuracy of profiling of ischemic stroke mortality, especially in lower volume hospitals. Even with complete NIHSS documentation, significant limitations in ischemic stroke mortality profiling remain.

What is the impact of large-scale implementation of stroke Early Supported Discharge? A mixed methods realist evaluation study protocol

BACKGROUND

Stroke Early Supported Discharge (ESD) is a service innovation that facilitates discharge from hospital and delivery of specialist rehabilitation in patients' homes. There is currently widespread implementation of ESD services in many countries, driven by robust clinical trial evidence. In England, the type of ESD service patients receive on the ground is variable, and in some regions, ESD is still not offered at all. This protocol presents a study designed to investigate the mechanisms and outcomes of implementing ESD at scale in real-world conditions. This will help to establish which models of ESD are most effective and in what context.

METHODS

A realist evaluation approach composed of two interlinking work packages will be adopted to investigate how and why ESD works, for whom and in what circumstances. Work package 1 (WP1) will begin with a rapid evidence synthesis to formulate preliminary realist hypotheses. Quantitative analyses of historical prospective Sentinel Stroke National Audit Programme (SSNAP) data will be performed to evaluate service outcomes based on the degree to which evidence-based ESD has been implemented. Work package 2 (WP2) will involve the qualitative investigation of purposively selected case study sites featuring in WP1 and covering different regions in England. The perspectives of clinicians, managers, commissioners, and service users will be explored qualitatively. Cost implications of ESD models will be examined using a cost-consequence analysis. Cross-case comparisons and triangulation of the data sources from both work packages will be performed to test, revise, and refine initial programme theories and address research aims.

DISCUSSION

This study will investigate whether and how current large-scale implementation of ESD is achieving the outcomes suggested by the evidence base. The theory-driven evaluation approach will highlight key mechanisms and contextual conditions necessary to optimise outcomes and allow us to draw transferable lessons to inform the effective implementation and sustainability of ESD in clinical practice. In addition, the methodological framework will progress the theoretical understanding of implementation and evaluation of complex rehabilitation interventions in stroke care.

TRIAL REGISTRATION

ISRCTN: 15568163, registration date: 26 October 2018.

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.

Stroke Administrative Severity Index: using administrative data for 30-day poststroke outcomes prediction

Aim:

Current stroke severity scales cannot be used for archival data. We develop and validate a measure of stroke severity at hospital discharge (Stroke Administrative Severity Index [SASI]) for use in billing data.

Methods:

We used the NIH Stroke Scale (NIHSS) as the theoretical framework and identified 285 relevant International Classification of Diseases, 9th Revision diagnosis and procedure codes, grouping them into 23 indicator variables using cluster analysis. A 60% sample of stroke patients in Medicare data were used for modeling risk of 30-day postdischarge mortality or discharge to hospice, with validation performed on the remaining 40% and on data with NIHSS scores.

Results:

Model fit was good (p > 0.05) and concordance was strong (C-statistic = 0.76–0.83). The SASI predicted NIHSS at discharge (C = 0.83).

Conclusion:

The SASI model and score provide important tools to control for stroke severity at time of hospital discharge. It can be used as a risk-adjustment variable in administrative data analyses to measure postdischarge outcomes.

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.

External Validation of a Case-Mix Adjustment Model for the Standardized Reporting of 30-Day Stroke Mortality Rates in China

BACKGROUND AND PURPOSE

A case-mix adjustment model has been developed and externally validated, demonstrating promise. However, the model has not been thoroughly tested among populations in China. In our study, we evaluated the performance of the model in Chinese patients with acute stroke.

METHODS

The case-mix adjustment model A includes items on age, presence of atrial fibrillation on admission, National Institutes of Health Stroke Severity Scale (NIHSS) score on admission, and stroke type. Model B is similar to Model A but includes only the consciousness component of the NIHSS score. Both model A and B were evaluated to predict 30-day mortality rates in 13,948 patients with acute stroke from the China National Stroke Registry. The discrimination of the models was quantified by c-statistic. Calibration was assessed using Pearson's correlation coefficient.

RESULTS

The c-statistic of model A in our external validation cohort was 0.80 (95% confidence interval, 0.79-0.82), and the c-statistic of model B was 0.82 (95% confidence interval, 0.81-0.84). Excellent calibration was reported in the two models with Pearson's correlation coefficient (0.892 for model A, p<0.001; 0.927 for model B, p = 0.008).

CONCLUSIONS

The case-mix adjustment model could be used to effectively predict 30-day mortality rates in Chinese patients with acute stroke.

A comparison of stroke rehabilitation; data from two national cohorts

OBJECTIVES

Inpatient rehabilitation is a commonly used complex intervention to improve a person's independence after stroke. Evaluation and comparison of the effects of routine clinical practice could provide a contribution towards optimization of stroke care. The aim of this study is to describe results of inpatient rehabilitation as a complex intervention for persons after stroke and explore possible differences between two countries.

METHODS

Data from 1055 Latvian and 1748 Swedish adult patients after stroke receiving inpatient rehabilitation, during 2011-2013, were used for this retrospective cohort study. Qualitative description of systems, as well as information on basic medical and sociodemographic information, and organizational aspects were reported. Change in the Functional Independence Measure during rehabilitation was investigated. In six domains of the instrument, the shifts for three levels of dependence were analysed using ordinal regression analysis.

RESULTS

The components of stroke care seem to be similar in Latvia and Sweden. However, the median time since stroke onset until the start of rehabilitation was 13 weeks in Latvia and 2 weeks in Sweden. The median length of rehabilitation was 12 and 49 days, respectively. The level of dependency at start, time since stroke onset and length of the period had an impact on the results of the rehabilitation.

CONCLUSIONS

Although components of the rehabilitation are reported as being the same, characteristics and the outcome of the inpatient rehabilitation are different. Therefore, comparison of stroke rehabilitation between countries requires caution.

Validation and Recalibration of Two Multivariable Prognostic Models for Survival and Independence in Acute Stroke

INTRODUCTION

Various prognostic models have been developed for acute stroke, including one based on age and five binary variables ('six simple variables' model; SSVMod) and one based on age plus scores on the National Institutes of Health Stroke Scale (NIHSSMod). The aims of this study were to externally validate and recalibrate these models, and to compare their predictive ability in relation to both survival and independence.

METHODS

Data from a large clinical trial of oxygen therapy (n = 8003) were used to determine the discrimination and calibration of the models, using C-statistics, calibration plots, and Hosmer-Lemeshow statistics. Methods of recalibration in the large and logistic recalibration were used to update the models.

RESULTS

For discrimination, both models functioned better for survival (C-statistics between .802 and .837) than for independence (C-statistics between .725 and .735). Both models showed slight shortcomings with regard to calibration, over-predicting survival and under-predicting independence; the NIHSSMod performed slightly better than the SSVMod. For the most part, there were only minor differences between ischaemic and haemorrhagic strokes. Logistic recalibration successfully updated the models for a clinical trial population.

CONCLUSIONS

Both prognostic models performed well overall in a clinical trial population. The choice between them is probably better based on clinical and practical considerations than on statistical considerations.

A comparison of stroke severity proxy measures for claims data research: a population-based cohort study

PURPOSE

Confounding by disease severity has been viewed as an intractable problem in claims-based studies. A novel 7-variable stroke severity index (SSI) was designed for estimating stroke severity by using claims data. This study compared the performance of mortality models with various proxy measures of stroke severity, including the SSI, in patients hospitalized for acute ischemic stroke (AIS).

METHODS

Data from the Taiwan National Health Insurance Research Database (NHIRD) were analyzed. Three proxy measures of stroke severity were evaluated: Measure 1, the SSI; Measure 2, intensive care unit admission and length of stay; and Measure 3, surgical operation, mechanical ventilation, hemiplegia or hemiparesis, and residual neurological deficits. We performed logistic regression by including age, sex, vascular risk factors, Charlson comorbidity index, and one of the proxy measures as covariates to predict 30-day and 1-year mortality after AIS. Model discrimination was evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS

We identified 7551 adult patients with AIS. Models using the SSI (Measure 1) outperformed models using the other proxy measures in predicting 30-day mortality (AUC 0.892 vs 0.851, p < 0.001 for Measure 2; 0.892 vs 0.853, p < 0.001 for Measure 3) and 1-year mortality (AUC 0.816 vs 0.784, p < 0.001 for Measure 2; 0.816 vs 0.782, p < 0.001 for Measure 3).

CONCLUSIONS

Using the SSI facilitated risk adjustment for stroke severity in mortality models for patients with AIS. The SSI is a viable methodological tool for stroke outcome studies using the NHIRD.

Which stroke patients gain most from intermittent pneumatic compression: further analyses of the CLOTS 3 trial

BACKGROUND

The CLOTS 3 trial showed that intermittent pneumatic compression (IPC) reduced the risk of DVT and improved survival after stroke.

AIMS

To provide additional information which may help clinicians target IPC on the most appropriate patients by exploring the variation in its effects on subgroups defined by predicted prognosis.

METHODS

A multicentre, parallel group, randomized trial enrolled immobile acute stroke patients and allocated them to IPC or no IPC. The primary outcome was proximal DVT at 30 days. Secondary outcomes at six-months included survival, disability, quality of life, and hospital costs. We stratified patients into quintiles according to their predicted prognosis at randomization, based on the Six Simple Variable model.

RESULTS

Between December 2008 and September 2012, we enrolled 2876 patients in 94 UK hospitals. Patients with the best predicted outcome had the lowest absolute risk of proximal DVT (6·7%) and death by six-months (9·3%). Allocation to IPC had little effect on DVT, survival, disability, quality of life, hospital length of stay, or costs. In patients with the worst predicted outcomes, the overall risk of DVT and death was 16·0% and 51·3%, respectively. IPC reduced DVT (odds reduction 34%) and improved survival 17% and significantly increased length of stay and hospital costs. In the three intermediate quintiles, IPC reduced the odds of DVT (35-43%) and improved survival (11-13%). Disability and quality of life at six-months depended on baseline severity but was not influenced significantly by IPC.

CONCLUSIONS

IPC appears to reduce the risk of DVT and probably improves survival in all immobile stroke patients, other than the fifth with the best prognosis. It therefore seems reasonable to recommend that IPC should be considered in all immobile stroke patients, but that the final decision should be based on a judgment about the individual's prognosis. In some, their prognosis for survival with an acceptable quality of life will be so poor that use of IPC might be considered futile, while at the other end of the spectrum, patients' risk of DVT, and of dying from VTE, may not be high enough to justify the modest cost and inconvenience of IPC use.

Life-and-death decision-making in the acute phase after a severe stroke: Interviews with relatives

BACKGROUND

Decision-making in the acute phase after a severe stroke is complex and may involve life-and-death decisions. Apart from the medical condition and prognosis, quality of life and the deliberation of palliative care should be part of the decision-making process. Relatives play an important role by informing physicians about the patient's values and preferences. However, little is known about how the patients' relatives experience the decision-making process.

AIM

To elicit the perspective of relatives of severe stroke patients with regard to the decision-making process in the acute phase in order to understand how they participate in treatment decisions.

DESIGN

An exploratory qualitative interview approach guided by the principles of grounded theory.

SETTINGS/PARTICIPANTS

Relatives of severe stroke patients (n = 15) were interviewed about their experiences in the decision-making process in the acute phase.

RESULTS

Four categories reflecting relatives' experiences were identified: (1) making decisions under time pressure, (2) the feeling of 'who am I' to decide, (3) reluctance in saying 'let her die' and (4) coping with unexpected changes. Following the treatment proposal of the physician was found to be the prevailing tendency of relatives in the decision-making process.

CONCLUSION

A better understanding of the latent world of experiences of relatives that influence the decision-making process may help physicians and other health-care providers to better involve relatives in decision-making and enhance the care, including palliative care, for patients with severe stroke in line with their values and preferences. Communication between physician and relatives seems vital in this process.

The impact of stroke unit care on outcome in a Scottish stroke population, taking into account case mix and selection bias

BACKGROUND AND AIM

Randomised trials indicate that stroke unit care reduces morbidity and mortality after stroke. Similar results have been seen in observational studies but many have not corrected for selection bias or independent predictors of outcome. We evaluated the effect of stroke unit compared with general ward care on outcomes after stroke in Scotland, adjusting for case mix by incorporating the six simple variables (SSV) model, also taking into account selection bias and stroke subtype.

METHODS

We used routine data from National Scottish datasets for acute stroke patients admitted between 2005 and 2011. Patients who died within 3 days of admission were excluded from analysis. The main outcome measures were survival and discharge home. Multivariable logistic regression was used to estimate the OR for survival, and adjustment was made for the effect of the SSV model and for early mortality. Cox proportional hazards model was used to estimate the hazard of death within 365 days.

RESULTS

There were 41 692 index stroke events; 79% were admitted to a stroke unit at some point during their hospital stay and 21% were cared for in a general ward. Using the SSV model, we obtained a receiver operated curve of 0.82 (SE 0.002) for mortality at 6 months. The adjusted OR for survival at 7 days was 3.11 (95% CI 2.71 to 3.56) and at 1 year 1.43 (95% CI 1.34 to 1.54) while the adjusted OR for being discharged home was 1.19 (95% CI 1.11 to 1.28) for stroke unit care.

CONCLUSIONS

In routine practice, stroke unit admission is associated with a greater likelihood of discharge home and with lower mortality up to 1 year, after correcting for known independent predictors of outcome, and excluding early non-modifiable mortality.

Developing a stroke severity index based on administrative data was feasible using data mining techniques

OBJECTIVES

Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data.

STUDY DESIGN AND SETTING

We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database.

RESULTS

We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models.

CONCLUSION

The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data.

Neuroendocrine hormones as prognostic biomarkers in the setting of acute stroke: overcoming the major hurdles

Stroke represents one of the major causes of disability and mortality worldwide and prediction of outcome represents a challenge for both clinicians and researchers. In the past years, many blood markers have been associated with stroke outcome but despite this evidence, no biomarker is routinely used in stroke management. In this review, we focus on markers of the neuroendocrine system, which represent potential candidates to be implemented in clinical practice. Moreover, we present a systematic review and literature-based meta-analysis for copeptin, a new biomarker of the hypothalamo-pituitary-adrenal axis that has shown additional predictive value over clinical information in a large prospective study. The meta-analysis of the included 7 studies, with more than 2000 patients, reinforced its association with poor outcome (pooled odds ratio: 2.474 [1.678-3.268]) and mortality (pooled OR: 2.569 [1.642-3.495]). We further review the current situation of the topic and next steps to implement these tools by clinicians.

Derivation and External Validation of a Case Mix Model for the Standardized Reporting of 30-Day Stroke Mortality Rates

Background and Purpose—

Case mix adjustment is required to allow valid comparison of outcomes across care providers. However, there is a lack of externally validated models suitable for use in unselected stroke admissions. We therefore aimed to develop and externally validate prediction models to enable comparison of 30-day post-stroke mortality outcomes using routine clinical data.

Methods—

Models were derived (n=9000 patients) and internally validated (n=18 169 patients) using data from the Sentinel Stroke National Audit Program, the national register of acute stroke in England and Wales. External validation (n=1470 patients) was performed in the South London Stroke Register, a population-based longitudinal study. Models were fitted using general estimating equations. Discrimination and calibration were assessed using receiver operating characteristic curve analysis and correlation plots.

Results—

Two final models were derived. Model A included age (<60, 60–69, 70–79, 80–89, and ≥90 years), National Institutes of Health Stroke Severity Score (NIHSS) on admission, presence of atrial fibrillation on admission, and stroke type (ischemic versus primary intracerebral hemorrhage). Model B was similar but included only the consciousness component of the NIHSS in place of the full NIHSS. Both models showed excellent discrimination and calibration in internal and external validation. The c-statistics in external validation were 0.87 (95% confidence interval, 0.84–0.89) and 0.86 (95% confidence interval, 0.83–0.89) for models A and B, respectively.

Conclusions—

We have derived and externally validated 2 models to predict mortality in unselected patients with acute stroke using commonly collected clinical variables. In settings where the ability to record the full NIHSS on admission is limited, the level of consciousness component of the NIHSS provides a good approximation of the full NIHSS for mortality prediction.

Ethical considerations in stroke patients

PURPOSE OF REVIEW

Medical decision-making in stroke patients can be complex and often involves ethical challenges, from the perspective of healthcare providers as well as patients and their families. Awareness of these challenges and knowledge of current ethical topics in stroke may improve the quality of care provided to stroke patients.

RECENT FINDINGS

Predictive scores are increasingly available to estimate prognosis following stroke, though their usefulness in decision-making for individual patients remains unclear. Medical decisions requiring a surrogate decision-maker can be challenging; surrogates may also be susceptible to systematic biases in their decision-making. Variations in care are common and possibly related to under-utilization or over-utilization of resources. However, patient preferences may explain some of the variability as well. Early mortality may be related to patient and family preferences regarding life-sustaining measures rather than the provision of care that is not well tolerated or evidence-based.

SUMMARY

Ethical challenges are common in the care of stroke patients. An effective understanding of these topics is essential for clinicians to deliver patient-centered, preference-sensitive care.

Predicting patient-reported stroke outcomes: a validation of the six simple variable prognostic model

Background

Case-mix represents the range of disease severity and baseline characteristics that may be the cause of variation in outcomes between individuals and populations. Adjustment for case-mix is therefore important to allow meaningful comparison of healthcare outcomes. The best available case-mix adjustment model for stroke (the Six Simple Variable [SSV] model) was developed to adjust the hard endpoints of independent survival, survival and alive and living at home. There is increasing interest in the measurement of patient-reported outcomes through self-completed questionnaires, though there are currently no robust adjustment models for any such outcome. We aimed to determine whether the SSV prognostic model derived to predict 6-month post-stroke independent survival has wider utility in case-mix adjustment of a patient-reported functional outcome measure, the Subjective Index of Physical and Social Outcome (SIPSO), collected by post 6 months after stroke onset.

Methods

We examined data from 176 patients admitted following an acute stroke and recruited into a prospective cohort study in three participating acute hospitals in Yorkshire, UK. Patients in receipt of palliative care or with transient ischaemic attack were excluded. Using the beta coefficients from the published SSV model to predict independent survival, individual probabilities of ‘good’ outcome as measured with the dichotomised SIPSO collected by post 6 months after stroke onset were calculated. The ability of the SSV case-mix adjustment model to discriminate patients with ‘good’ over ‘poor’ outcome was assessed through calculation of C statistics. Correct predictions were visualised with calibration plots.

Results

The C statistics for the SSV model to predict the physical and social subscales of the SIPSO outcome measure were 0.73 (95% CI 0.65-0.79) and 0.66 (0.58-0.82), respectively. Inclusion of patients who died prior to follow-up and ascribing them a score of 0 improved the discrimination (0.76 [0.70-0.82] and 0.70 [0.64-0.76], respectively). Calibration plots demonstrated a tendency to over-optimistic predictions, although confidence limits were wide.

Conclusions

The SSV model predicts adequately the physical component of the SIPSO patient-reported outcome measure and may be useful to adjust this outcome for case-mix following stroke in survivors to follow-up. This could be of benefit in observational studies, stratified randomisation for trials, and in comparison of between-institution clinical trials. Further exploration of the generalizability of the model to adjust other patient-reported stroke outcomes may be warranted.

Estimating and communicating prognosis in advanced neurologic disease

Prognosis can no longer be relegated behind diagnosis and therapy in high-quality neurologic care. High-stakes decisions that patients (or their surrogates) make often rest upon perceptions and beliefs about prognosis, many of which are poorly informed. The new science of prognostication--the estimating and communication "what to expect"--is in its infancy and the evidence base to support "best practices" is lacking. We propose a framework for formulating a prediction and communicating "what to expect" with patients, families, and surrogates in the context of common neurologic illnesses. Because neurologic disease affects function as much as survival, we specifically address 2 important prognostic questions: "How long?" and "How well?" We provide a summary of prognostic information and highlight key points when tailoring a prognosis for common neurologic diseases. We discuss the challenges of managing prognostic uncertainty, balancing hope and realism, and ways to effectively engage surrogate decision-makers. We also describe what is known about the nocebo effects and the self-fulfilling prophecy when communicating prognoses. There is an urgent need to establish research and educational priorities to build a credible evidence base to support best practices, improve communication skills, and optimize decision-making. Confronting the challenges of prognosis is necessary to fulfill the promise of delivering high-quality, patient-centered care.