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

Establishment and validation of a 3-month prediction model for poor functional outcomes in patients with acute cardiogenic cerebral embolism related to non-valvular atrial fibrillation

Objectives

Cardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations.

Methods

We included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables.

Results

The 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model's Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854-0.902), 0.918 (95% CI, 0.857-0.979), and 0.839 (95% CI, 0.744-0.934), respectively.

Conclusion

The N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings.

Developing and externally validating a machine learning risk prediction model for 30-day mortality after stroke using national stroke registers in the UK and Sweden

OBJECTIVES

We aimed to develop and externally validate a generalisable risk prediction model for 30-day stroke mortality suitable for supporting quality improvement analytics in stroke care using large nationwide stroke registers in the UK and Sweden.

DESIGN

Registry-based cohort study.

SETTING

Stroke registries including the Sentinel Stroke National Audit Programme (SSNAP) in England, Wales and Northern Ireland (2013-2019) and the national Swedish stroke register (Riksstroke 2015-2020).

PARTICIPANTS AND METHODS

Data from SSNAP were used for developing and temporally validating the model, and data from Riksstroke were used for external validation. Models were developed with the variables available in both registries using logistic regression (LR), LR with elastic net and interaction terms and eXtreme Gradient Boosting (XGBoost). Performances were evaluated with discrimination, calibration and decision curves.

OUTCOME MEASURES

The primary outcome was all-cause 30-day in-hospital mortality after stroke.

RESULTS

In total, 488 497 patients who had a stroke with 12.4% 30-day in-hospital mortality were used for developing and temporally validating the model in the UK. A total of 128 360 patients who had a stroke with 10.8% 30-day in-hospital mortality and 13.1% all mortality were used for external validation in Sweden. In the SSNAP temporal validation set, the final XGBoost model achieved the highest area under the receiver operating characteristic curve (AUC) (0.852 (95% CI 0.848 to 0.855)) and was well calibrated. The performances on the external validation in Riksstroke were as good and achieved AUC at 0.861 (95% CI 0.858 to 0.865) for in-hospital mortality. For Riksstroke, the models slightly overestimated the risk for in-hospital mortality, while they were better calibrated at the risk for all mortality.

CONCLUSION

The risk prediction model was accurate and externally validated using high quality registry data. This is potentially suitable to be deployed as part of quality improvement analytics in stroke care to enable the fair comparison of stroke mortality outcomes across hospitals and health systems across countries.

Change in Hospital Risk-standardized Stroke Mortality Performance With and Without the Passive Surveillance Stroke Severity Score

BACKGROUND

Adjustment for baseline stroke severity is necessary for accurate assessment of hospital performance. We evaluated whether adjusting for the Passive Surveillance Stroke SeVerity (PaSSV) score, a measure of stroke severity derived using administrative data, changed hospital-specific estimated 30-day risk-standardized mortality rate (RSMR) after stroke.

METHODS

We used linked administrative data to identify adults who were hospitalized with ischemic stroke or intracerebral hemorrhage across 157 hospitals in Ontario, Canada between 2014 and 2019. We fitted a random effects logistic regression model using Markov Chain Monte Carlo methods to estimate hospital-specific 30-day RSMR and 95% credible intervals with adjustment for age, sex, Charlson comorbidity index, and stroke type. In a separate model, we additionally adjusted for stroke severity using PaSSV. Hospitals were defined as low-performing, average-performing, or high-performing depending on whether the RSMR and 95% credible interval were above, overlapping, or below the cohort's crude mortality rate.

RESULTS

We identified 65,082 patients [48.0% were female, the median age (25th,75th percentiles) was 76 years (65,84), and 86.4% had an ischemic stroke]. The crude 30-day all-cause mortality rate was 14.1%. The inclusion of PaSSV in the model reclassified 18.5% (n=29) of the hospitals. Of the 143 hospitals initially classified as average-performing, after adjustment for PaSSV, 20 were reclassified as high-performing and 8 were reclassified as low-performing. Of the 4 hospitals initially classified as low-performing, 1 was reclassified as high-performing. All 10 hospitals initially classified as high-performing remained unchanged.

CONCLUSION

PaSSV may be useful for risk-adjusting mortality when comparing hospital performance. External validation of our findings in other jurisdictions is needed.

Socioeconomic status and stroke severity: Understanding indirect effects via risk factors and stroke prevention using innovative statistical methods for mediation analysis

Background

Those with low socioeconomic status have an increased risk of stroke, more severe strokes, reduced access to treatment, and more adverse outcomes after stroke. The question is why these differences are present. In this study we investigate to which extent the association between low socioeconomic status and stroke severity can be explained by differences in risk factors and stroke prevention drugs.

Methods

The study included 86 316 patients registered with an ischemic stroke in the Swedish Stroke Register (Riksstroke) 2012–2016. Data on socioeconomic status was retrieved from the Longitudinal integrated database for health insurance and labour market studies (LISA) by individual linkage. We used education level as proxy for socioeconomic status, with primary school education classified as low education. Stroke severity was measured using the Reaction Level Scale, with values above 1 classified as severe strokes. To investigate the pathways via risk factors and stroke prevention drugs we performed a mediation analysis estimating indirect and direct effects.

Results

Low education was associated with an excess risk of a severe stroke compared to mid/high education (absolute risk difference 1.4%, 95% CI: 1.0%-1.8%), adjusting for confounders. Of this association 28.5% was an indirect effect via risk factors (absolute risk difference 0.4%, 95% CI: 0.3%-0.5%), while the indirect effect via stroke prevention drugs was negligible.

Conclusion

Almost one third of the association between low education and severe stroke was explained by risk factors, and clinical effort should be taken to reduce these risk factors to decrease stroke severity among those with low socioeconomic status.

Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study

Backgrounds

We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care.

Methods

Data from the UK Sentinel Stroke National Audit Program between 2013 to 2019 were used. Models were developed using XGBoost, Logistic Regression (LR), LR with elastic net with/without interaction terms using 80% randomly selected admissions from 2013 to 2018, validated on the 20% remaining admissions, and temporally validated on 2019 admissions. The models were developed with 30 variables. A reference model was developed using LR and 4 variables. Performances of all models was evaluated in terms of discrimination, calibration, reclassification, Brier scores and Decision-curves.

Results

In total, 488,497 stroke patients with a 12.3% 30-day mortality rate were included in the analysis. In 2019 temporal validation set, XGBoost model obtained the lowest Brier score (0.069 (95% CI: 0.068–0.071)) and the highest area under the ROC curve (AUC) (0.895 (95% CI: 0.891–0.900)) which outperformed LR reference model by 0.04 AUC (p < 0.001) and LR with elastic net and interaction term model by 0.003 AUC (p < 0.001). All models were perfectly calibrated for low (< 5%) and moderate risk groups (5–15%) and ≈1% underestimation for high-risk groups (> 15%). The XGBoost model reclassified 1648 (8.1%) low-risk cases by the LR reference model as being moderate or high-risk and gained the most net benefit in decision curve analysis.

Conclusions

All models with 30 variables are potentially useful as benchmarking models in stroke-care quality improvement with ML slightly outperforming others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02722-1.

A register-based study comparing planned rehabilitation following acute stroke in 2011 and 2017

This cross-sectional, register-based study aimed to explore patterns of planned rehabilitation at discharge from stroke units in Sweden in 2011 and 2017 and identify explanatory variables for planned rehabilitation. Multivariable binary logistic regression was used to identify variables that could explain planned rehabilitation. There were 19,158 patients in 2011 and 16,508 patients in 2017 with stroke, included in the study. In 2011, 57% of patients were planned for some form of rehabilitation at discharge from stroke unit, which increased to 72% in 2017 (p < 0.001). Patients with impaired consciousness at admission had increased odds for planned rehabilitation (hemorrhage 2011 OR 1.43, 95% CI 1.13–1.81, 2017 OR 1.66, 95% CI 1.20–2.32), (IS 2011 OR 1.21, 95% CI 1.08–1.34, 2017 OR 1.49, 95% CI 1.28–1.75). Admission to a community hospital (hemorrhage 2011 OR 0.56, 95% CI 0.43–0.74, 2017 OR 0.39, 95% CI 0.27–0.56) (IS 2011 OR 0.63, 95% CI 0.58–0.69, 2017 OR 0.54, 95% CI 0.49–0.61) or to a specialized non-university hospital (hemorrhage 2017 OR 0.66, 95% CI 0.46–0.94), (IS 2011 OR 0.90, 95% CI 0.82–0.98, 2017 OR 0.76, 95% CI 0.68–0.84) was associated with decreased odds of receiving planned rehabilitation compared to admission to a university hospital. As a conclusion severe stroke was associated with increased odds for planned rehabilitation and patients discharged from non-university hospitals had consistently decreased odds for planned rehabilitation.

Outliers from national audits: their analysis and use by the Care Quality Commission in quality assurance and regulation of healthcare services in England

The Care Quality Commission (CQC) is the independent regulator of health and adult social care in England. As part of the intelligence-driven approach to regulation, the CQC works closely with national clinical audit bodies to identify key metrics which reflect quality of care and track the performance of providers against these metrics. Where outliers on national audits are identified that may reflect risks to patients, the CQC encourages the hospital to identify any learning points and implement changes to improve patient care. In this article, we describe the role of national audit outcomes in the regulatory process and how providers can use national audits to inform both quality assurance and quality improvement processes, with two illustrative case studies. We discuss the ongoing challenges with using audit data in the regulatory process and how these could be addressed.

Sex Differences in Stroke Care and Outcome 2005-2018: Observations From the Swedish Stroke Register

BACKGROUND AND PURPOSE

Previous studies of stroke management and outcome in Sweden have revealed differences between men and women. We aimed to analyze if differences in stroke incidence, care, and outcome have altered over time.

METHODS

All stroke events registered in the Swedish Stroke Register 2005 to 2018 were included. Background variables and treatment were collected during the acute hospital stay. Survival data were obtained from the national cause of death register by individual linkage. We used unadjusted proportions and estimated age-adjusted marginal means, using a generalized linear model, to present outcome.

RESULTS

We identified 335 183 stroke events and a decreasing incidence in men and women 2005 to 2018. Men were on average younger than women (73.3 versus 78.1 years) at stroke onset. The age-adjusted proportion of reperfusion therapy 2005 to 2018 increased more rapidly in women than in men (2.3%-15.1% in men versus 1.4%-16.9% in women), but in 2018, women still had a lower probability of receiving thrombolysis within 30 minutes. Among patients with atrial fibrillation, oral anticoagulants at discharge increased more rapidly in women (31.2%-78.6% in men versus 26.7%-81.9% in women). Statins remained higher in men (36.9%-83.7% in men versus 32.3%-81.2% in women). Men had better functional outcome and survival after stroke. After adjustment for women's higher age, more severe strokes, and background characteristics, the absolute difference in functional outcome was <1% and survival did not differ.

CONCLUSIONS

Stroke incidence, care, and outcome show continuous improvements in Sweden, and previously reported differences between men and women become less evident. More severe strokes and older age in women at stroke onset are explanations to persisting differences.

[Opportunities and limitations of risk adjustment of quality indicators based on inpatient administrative health data - a workshop report]

INTRODUCTION

The quality indicators of the Initiative Qualitätsmedizin e. V. (IQM) have been developed as triggers to examine treatment processes for opportunities for improvement. Published quality results have partly been used for external quality comparisons in the media. Therefore, member hospitals of IQM demanded to investigate if methods of risk adjustment should be applied in the calculation of the quality indicators. After a hearing of experts had been held, a task force was founded to conduct test calculations on risk adjustment methods.

METHODS

Specific risk adjustment models for mortality in myocardial infarction, heart failure, stroke, pneumonia, and colectomy in colorectal cancer were developed in the database of national German DRG data of the year 2016. These models were used to calculate standardized mortality ratios (SMR) per indicator in a sample of 172 member hospitals of IQM based on the data of the year 2018. Median SMR per indicator were compared to median SMR based on a standardization by age and gender, which is the standard procedure in IQM. Correlations between the different SMR were calculated. Quality of care was judged by two different approaches: a) a descriptive discrepancy of |0.1| from the SMR value of 1, and b) a significant discrepancy from 1 using the 95% confidence limits. The effect of using the specific risk adjustment in relation to the standard procedure was investigated for both approaches (a and b).

RESULTS

The specific risk adjustment methods showed an area under the curve between 0.72 and 0.84. The median differences between the SMR based on standardization by age and gender and the SMR based on specific risk adjustment were small (between 0 and 0.4); Spearman's correlations were between 0.90 and 0.99. Changes in the judgement of quality of care in comparison to the national average occurred in 3.9% (mortality from pneumonia) to 20.6% of the hospitals (mortality from heart failure) in descriptive comparisons. When the judgement was based on confidence limits changes were observed in 1.6% (mortality after colectomy) to 17.4% of the hospitals (mortality from heart failure).

DISCUSSION

Implementing specific risk adjustment models had only minor effects on the distribution of risk-adjusted mortality compared to the standard procedure, but the judgement of quality of care could change for a fifth of the hospitals in individual indicators. Concerning methodological and practical reasons, the task force recommends further development of risk adjustment methods for selected indicators. This should be accompanied by studies on the validity of inpatient administrative data for quality management as well as by efforts to improve the usefulness of these data for such purposes.

Health insurance utilisation after ischaemic stroke in Sweden: a retrospective cohort study in a system of universal healthcare and social insurance

BACKGROUND

Stroke is one of the largest single-condition sources of the global burden of non-communicable disease in terms of disability-adjusted life-years and monetary costs, directly as well as indirectly in terms of informal care and productivity loss. The objective was to assess the population afflicted with ischaemic stroke in working age in the context of universal healthcare and social insurance; to estimate the levels of absence from work, the indirect costs related to that and to assess the associated patient characteristics.

METHODS

This was a retrospective register-based study; all individuals registered with an ischaemic stroke during 2008-2011 in seven Swedish regions, covering the largest cities as well as more rural areas, were included. Individual-level data were used to compute net days of sick leave and disability pension, indirect costs due to productivity loss and to perform regression analysis on net absence from work to assess the associated factors. Costs related to productivity loss were estimated using the human capital approach.

RESULTS

Women had significantly fewer net days of sick leave and disability pension than men after multivariable adjustment, and high-income groups had higher levels of sick leave than low-income groups. There were no significant differences for participants regarding educational level, region of birth or civil status. Indirect monetary costs amounted to €17 400 per stroke case during the first year, totalling approximately €169 million in Sweden.

CONCLUSION

The individual's burden of stroke is heavy in terms of morbidity, and the related productivity loss for society is immense. Income-group differences point to a socioeconomic gradient in the utilisation of the Swedish social insurance.

A Dynamic Model for Predicting Survival up to 1 Year After Ischemic Stroke

BACKGROUND

This study developed and validated a dynamic prediction model for survival after ischaemic stroke up to 1 year.

METHODS

Patients with stroke (n = 425) who participated in a sub-study (2002-2004) from the South London Stroke Register (SLSR) were selected for model derivation. The model was developed using the extended Cox model with time-dependent covariates. The two temporal validation cohorts from SLSR included 1735 (1995-2002) and 2155 patients (2004-2016). The discrimination, calibration and clinical utility of the model were assessed.

RESULTS

Six strong predictors were used in the model, namely, age, sex, stroke subtype, stroke severity and pre-stroke and post-stroke disabilities. The c-statistics was 0.822 at 1 year in the derivation cohort. The model had a fair performance with prognostic accuracies of 77%-83% in the validation 1 cohort and 70%-75% in the validation 2 cohort. A good calibration was observed in the derivation cohort.

CONCLUSION

The proposed model can accurately predict survival up to 1 year after ischaemic stroke.

Differences in self-perceived general health, pain, and depression 1 to 5 years post-stroke related to work status at 1 year

Stroke is one of the most common diseases and has several potential consequences, such as psychological problems and pain. Return to work (RTW) after stroke in working-age individuals is incomplete. The present study aimed to investigate differences in self-perceived general health, pain, and depression between 1 and 5 years post-stroke related to RTW status. The study was nationwide, registry-based and the study population (n = 398) consisted of working-age people who had a stroke in 2011 and participated in 1-year and 5-year follow-up questionnaire surveys. Shift analyses with the Wilcoxon signed rank test and logistic regression were used. RTW within the first year post-stroke was associated with better self-perceived general health, less pain, and less depression both at 1 and 5 years post-stroke, compared with the no-RTW group. However, the RTW group had significant deterioration in general health and pain between 1 and 5 years, while the no-RTW group had no significant change. RTW was a significant predictor of lower odds of improvement in general health and pain between 1 and 5 years. This emphasizes the need for continued follow-up and support to ensure a balance between work and health for RTW individuals after stroke.

Socioeconomic status and survival after stroke - using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding

Background

Although it has been established that low socioeconomic status is linked to increased risk of death after stroke, the mechanisms behind this link are still unclear. In this study we aim to shed light on the relationship between income level and survival after stroke by investigating the extent to which differences in stroke severity account for differences in survival.

Methods

The study was based on patients registered in Riksstroke (the Swedish stroke register) with first time ischemic stroke (n = 51,159) or intracerebral hemorrhage (n = 6777) in 2009–2012. We used causal mediation analysis to decompose the effect of low income on 3-month case fatality into a direct effect and an indirect effect due to stroke severity. Since causal mediation analysis relies on strong assumptions regarding residual confounding of the relationships involved, recently developed methods for sensitivity analysis were used to assess the robustness of the results to unobserved confounding.

Results

After adjustment for observed confounders, patients in the lowest income tertile had a 3.2% (95% CI: 0.9–5.4%) increased absolute risk of 3-month case fatality after intracerebral hemorrhage compared to patients in the two highest tertiles. The corresponding increase for case fatality after ischemic stroke was 1% (0.4–1.5%). The indirect effect of low income, mediated by stroke severity, was 1.8% (0.7–2.9%) for intracerebral hemorrhage and 0.4% (0.2–0.6%) for ischemic stroke. Unobserved confounders affecting the risk of low income, more severe stroke and case fatality in the same directions could explain the indirect effect, but additional adjustment to observed confounders did not alter the conclusions.

Conclusions

This study provides evidence that as much as half of income-related inequalities in stroke case fatality is mediated through differences in stroke severity. Targeting stroke severity could therefore lead to a substantial reduction in inequalities and should be prioritized. Sensitivity analysis suggests that additional adjustment for a confounder of greater impact than age would be required to considerably alter our conclusions.

Association of prestroke medicine use and health outcomes after ischaemic stroke in Sweden: a registry-based cohort study

OBJECTIVE

The objective was to investigate if there is a relationship between preischaemic stroke medicine use and health outcomes after stroke.

SETTING

This registry-based study covered Swedish stroke care, both primary and secondary care, including approximately 60% of the Swedish stroke cases from seven Swedish regions.

PARTICIPANTS

The Sveus research database was used, including 35 913 patients (33 943 with full information on confounding factors) with an ischaemic stroke (International Classification of Diseases, 10th Revision (ICD-10) I63*) between 2009 and 2011 registered both in the regions' patient administrative systems and in the Swedish Stroke Register. Patients with haemorrhagic stroke (ICD-10 I61*) were excluded.

PRIMARY OUTCOME

The primary outcome was the association, expressed in ORs, of prestroke medicine use (oral anticoagulants, statins, antihypertensives, antidepressants, non-steroidal anti-inflammatory drugs (NSAIDs) and antidiabetic drugs) and health outcomes 1 and 2 years poststroke (survival, activities of daily living dependency and modified Rankin Scale (mRS) 0-2), adjusted for patient characteristics and stroke severity at stroke onset.

RESULTS

The multivariate analysis indicated that patients on drugs for hypertension, diabetes, oral anticoagulants and antidepressants prestroke had worse odds for health outcomes in both survival (OR 0.65, 95% CI 0.60 to 0.69; OR 0.77, 95% CI 0.71 to 0.83; OR 0.72, 95% CI 0.66 to 0.80; OR 0.91, 95% CI 0.84 to 0.98, respectively, for survival at 2 years) and functional outcome (OR 0.82, 95% CI 0.75 to 0.89; OR 0.61, 95% CI 0.55 to 0.68; OR 0.83, 95% CI 0.72 to 0.95; OR 0.58, 95% CI 0.52 to 0.65, respectively, for mRS 0-2 at 1 year), whereas patients on statins and NSAIDS had significantly better odds for survival (OR 1.16, 95% CI 1.08 to 1.25 and OR 1.12, 95% CI 1.00 to 1.25 for 1-year survival, respectively), compared with patients without these treatments prior to stroke.

CONCLUSIONS

The results indicated that there are differences in health outcomes between patients who had different common prestroke treatments, patients on drugs for hypertension, diabetes, oral anticoagulants and antidepressants had worse health outcomes, whereas patients on statins and NSAIDS had significantly better survival, compared with patients without these treatments prior to stroke.

Return to work after stroke: A Swedish nationwide registry-based study

OBJECTIVES

A substantial proportion of individuals with stroke are of working age. After stroke, it is important to return to work (RTW), both for the individual's satisfaction with life and economically for society. The current comprehensive, long-term study aimed at investigating in what time period the RTW continues after stroke and what factors could predict RTW.

MATERIALS AND METHODS

All individuals registered in the registry Riksstroke with stroke in Sweden at ages 18-58 years during 2011 were eligible for participation. RTW was based on sickness absence data from the Social Insurance Agency covering 1 year prestroke to 5 years post-stroke. Time to RTW was analyzed with Kaplan-Meier curves. Potential predictors of RTW were analyzed with Cox regression and logistic regression.

RESULTS

For RTW analyses, 1695 participants were included. Almost 50% RTW within 3 months, 70% within 1 year, and 80% within 2 years post-stroke. However, the RTW continued for several years, with a total of 85% RTW. Predictors of favorable time to RTW were male sex, ischemic stroke, and long university education compared with primary school education. Predictors of unfavorable times to RTW were higher stroke severity, defined by the level of consciousness, and older ages. Participants with self-expectations of RTW 1 year post-stroke had higher odds of RTW within 5 years.

CONCLUSIONS

The RTW continues for a longer time after stroke than previously known. Both self-expectations and demographical, socioeconomic, stroke-related factors were important predictors of RTW. This knowledge could assist healthcare professionals to individualize the rehabilitation post-stroke.

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.

Time Trends and Monthly Variation in Swedish Acute Stroke Care

Background and Purpose: Studies of monthly variation in acute stroke care have led to conflicting results. Our objective was to study monthly variation and longitudinal trends in quality of care and patient survival following acute stroke.

Methods: Our nationwide study included all adult patients (≥18 years) with acute stroke (ischemic or hemorrhagic), admitted to Swedish hospitals from 2011 to 2016, and that were registered in The Swedish Stroke Register (Riksstroke). We studied how month of admission and longitudinal trends affected acute stroke care and survival. We also studied resilience to this variation among hospitals with different levels of specialization.

Results: We included 132,744 stroke admissions. The 90-day survival was highest in May and lowest in January (84.1 vs. 81.5%). Thrombolysis rates and door-to-needle time within 30 min increased from 2011 to 2016 (respectively, 7.3 vs. 12.8% and 7.7 vs. 28.7%). Admission to a stroke unit as first destination of hospital care was lowest in January and highest in June (78.3 vs. 80.5%). Stroke unit admission rates decreased in university hospitals from 2011 to 2016 (83.4 vs. 73.9%), while no such trend were observed in less specialized hospitals. All the differences above remained significant (p < 0.05) after adjustment for possible confounding factors.

Conclusion: We found that month of admission and longitudinal trends both affect quality of care and survival of stroke patients in Sweden, and that the effects differ between hospital types. The observed variation suggests an opportunity to improve stroke care in Sweden. Future studies ought to focus on identifying the specific factors driving this variation, for subsequent targeting by quality improvement efforts.

Long-term outcome after ischemic stroke in relation to comorbidity - An observational study from the Swedish Stroke Register (Riksstroke)

Purpose

Comorbidity in stroke is common, but comprehensive reports are sparse. We describe prevalence of comorbidity and the prognostic impact on mortality and functional outcome in a large national ischemic stroke cohort.

Methods

We used outcome data from a long-term follow-up survey conducted in 2016 by the Swedish Stroke Register (Riksstroke). Those included in the study were 11 775 pre-stroke functionally independent patients with first-ever ischemic stroke followed up at three months and 12 months (all patients), and three years (2013 cohort) or five years (2011 cohort). Pre-stroke comorbidity data for 16 chronic conditions were obtained from the Swedish National Patient Register, the Swedish Prescribed Drugs Register and the Riksstroke register. Individuals were grouped according to number of conditions: none (0), low (1), moderate (2-3) or high (≥4). Co-occurrence was analysed using hierarchical clustering, and multivariable analyses were used to estimate the prognostic significance of individual conditions.

Results

The proportion of patients without comorbidity was 24.8%; 31.8% had low comorbidity; 33.5% had moderate comorbidity and 9.9% had high comorbidity. At 12 months, the proportion of poor outcome (dead or dependent: mRS ≥3) was 24.8% (no comorbidity), 34.7% (low), 45.2% (moderate) and 59.4% (high). At five years, these proportions were 37.7%, 50.3%, 64.3%, and 81.7%, respectively. There was clustering of cardiovascular conditions and substantial negative effects of dementia, kidney, and heart failure.

Conclusion

Comorbidity is common and has a strong impact on mortality and functional outcome. Our results highlight the need for health systems to shift focus to a comprehensive approach in stroke care that includes multimorbidity as a key component.

Diurnal variations in the quality of stroke care in Sweden

OBJECTIVES

A recent study of acute stroke patients in England and Wales revealed several patterns of temporal variation in quality of care. We hypothesized that similar patterns would be present in Sweden and aimed to describe these patterns. Additionally, we aimed to investigate whether hospital type conferred resilience against temporal variation.

MATERIALS AND METHODS

We conducted this nationwide registry-based study using data from the Swedish Stroke Register (Riksstroke) including all adult patients registered with acute stroke between 2011 and 2015. Outcomes included process measures and survival. We modeled time of presentation as on/off-hours, shifts, day of week, 4-hour, and 12-hour time blocks. We studied hospital resilience by comparing outcomes across hospital types.

RESULTS

A total of 113 862 stroke events in 72 hospitals were included. The process indicators and survival all showed significant temporal variation. Door-to-needle (DTN) time within 30 minutes was less likely during nighttime than daytime (OR 0.50; 95% CI 0.41-0.60). Patients admitted during off-hours had lower odds of direct stroke unit (SU) admission (OR 0.72; 95% CI 0.70-0.75). 30-day survival was lower in nighttime vs daytime presentations (OR 0.90, 95% CI 0.84-0.96). The effects of temporal variation differed significantly between hospital types for DTN time within 30 minutes and direct SU admission where university hospitals were more resilient than specialized non-university hospitals.

CONCLUSIONS

Our study shows that variation in quality of care and survival is present throughout the whole week. We also found that university hospitals were more resilient to temporal variation than specialized non-university hospitals.

The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data

Background

Administrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment.

Methods

We linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates.

Results

The performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7).

Conclusions

Rankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance.

Stroke mortality audit using the Structured Judgement Review method

Mortality data provided by the Sentinel Stroke National Audit Programme demonstrated the Royal Cornwall Hospitals Trust (RCHT) to have a higher than national average mortality ratio.1 In response to this, the RCHT stroke department undertook a mortality review of patients admitted with stroke making use of the Structured Judgement Review (SJR) process.2The review found all patients were deemed as receiving adequate, good or excellent care. There were no cases where death was deemed as definitely avoidable. The team found the SJR to be a useful, validated tool for mortality review though recognised specific limitations to its use and wider limitations within our review process. Focused areas for improvement derived from the review included improving compliance with local palliative care guides, improved documentation, links with primary care via Care Quality Commission atrial fibrillation group and consideration of improved scanning facilities. We also acknowledged wider unaccounted factors which may impact stroke mortality and thus influence perceived mortality ratios.

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

INTRODUCTION

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Hospital comparison of stroke care in Sweden: a register-based study

BACKGROUND AND PURPOSE

The objective of this study was to estimate the level of health outcomes and resource use at a hospital level during the first year after a stroke, and to identify any potential differences between hospitals after adjusting for patient characteristics (case mix).

METHOD

Data from several registries were linked on individual level: seven regional patient administrative systems, Swedish Stroke Register, Statistics Sweden, National Board of Health and Welfare and Swedish Social Insurance Agency. The study population consisted of 14 125 patients presenting with a stroke during 2010. Case-mix adjusted analysis of hospital differences was made on five aspects of health outcomes and resource use, 1 year post-stroke.

RESULTS

The results indicated that 26% of patients had died within a year of their stroke. Among those who survived, almost 5% had a recurrent stroke and 40% were left with a disability. On average, the patients had 22 inpatient days and 23 outpatient visits, and 13% had moved into special housing. There were significant variations between hospitals in levels of health outcomes achieved and resources used after adjusting for case mix.

CONCLUSION

Differences in health outcomes and resource use between hospitals were substantial and not entirely explained by differences in patient mix, indicating tendencies of unequal stroke care in Sweden. Healthcare organisation of regions and other structural features could potentially explain parts of the differences identified.

Evaluation of a single centre stroke service reconfiguration - the impact of transition from a combined (acute and rehabilitation) stroke unit to a hyperacute model of stroke care

We reorganised the combined (acute and rehab) stroke unit (SU) at Gloucestershire Royal Hospital into a hyperacute stroke unit (HASU) and a rehab SU where patients are moved after spending about 72 hours on HASU. Continuous monitoring of physiological variables was introduced and consultant job plans were reorganised to provide a HASU physician of the week model with enhanced 7-day senior presence along with redistribution of junior medical staff. Sentinel Stroke National Audit Programme (SSNAP) data for 14 months preceding the reorganisation (n=1,049) and 14 months after (n=974) were accessed for outcomes. More patients were admitted directly to the HASU with favourable reductions in time to computerised tomography scanning and stroke consultant assessment after the change. There were significant reductions in length of stay, pneumonia and urinary tract infections at 7 days and a favourable shift in modified Rankin scores (odds ratio 1.60, 95% confidence interval 1.36-1.89, p<0.001) on discharge from hospital.

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.

Doctor's follow-up after stroke in the south of Sweden: An observational study from the Swedish stroke register (Riksstroke)

Introduction

Information on follow-up practices after stroke in clinical routine are sparse. We studied the probability of doctor's follow-up within 90, 120, 180, and 365 days after hospital discharge, and how patient characteristics were associated with the probability of follow-up, in a large unselected stroke cohort.

Patients and methods

Data on patients living in southern Sweden, hospitalized with acute ischemic stroke or intracerebral hemorrhage 1 January 2008 to 31 December 2010, were obtained from the Swedish stroke register (Riksstroke) and merged with administrative data on doctor's visits during the year following stroke.

Results

Complete data were registered in 8164 patients. The cumulative probability of a doctor's follow-up was 76.3% within 90 days, 83.6% within 120 days, 88.7% within 180 days, and 93.1% within 365 days. Using Cox regression calculating hazard ratios (HR), factors associated with 90-day follow-up were: female sex HR = 1.066 (95%CI: 1.014-1.121), age: ages 65-74 HR = 0.928 (95%CI: 0.863-0.999), ages 75-84 HR = 0.943 (95%CI: 0.880-1.011), ages 85 + HR = 0.836 (95%CI: 0.774-0.904), pre-stroke dependency in activities of daily living (ADL): HR = 0.902 (95%CI = 0.819-0.994), prior stroke HR = 0.902 (95%CI: 0.764-0.872), and severe stroke HR = 0.506 (95%CI: 0.407-0.629). In patients discharged to assisted living, the following factors were associated with lower follow-up probability: living alone pre-stroke HR = 0.836 (95%CI: 0.736-0.949), and pre-stroke dependency HR = 0.887 (95%CI: 0.775-0.991).

Discussion

This study was based on hospital administrative data of post-stroke doctor's visits, but may be confounded by attendance for other conditions than stroke.

Conclusions

One in four stroke patients was not followed up within three months after hospital discharge. Vulnerable patients with high age, pre-stroke ADL dependency, and prior stroke were less likely to receive doctor's follow-up.

Ischemic stroke outcome: A review of the influence of post-stroke complications within the different scenarios of stroke care

Stroke remains one of the main causes of death and disability worldwide. The challenge of predicting stroke outcome has been traditionally assessed from a general point of view, where baseline non-modifiable factors such as age or stroke severity are considered the most relevant factors. However, after stroke occurrence, some specific complications such as hemorrhagic transformations or post stroke infections, which lead to a poor outcome, could be developed. An early prediction or identification of these circumstances, based on predictive models including clinical information, could be useful for physicians to individualize and improve stroke care. Furthermore, the addition of biological information such as blood biomarkers or genetic polymorphisms over these predictive models could improve their prognostic value. In this review, we focus on describing the different post-stroke complications that have an impact in short and long-term outcome across different time points in its natural history and on the clinical-biological information that might be useful in their prediction.

Stroke Severity Is a Crucial Predictor of Outcome: An International Prospective Validation Study

Background

Stroke is among the leading causes of morbidity and mortality worldwide. Without reliable prediction models and outcome measurements, comparison of care systems is impossible. We analyzed prospectively collected data from 4 countries to explore the importance of stroke severity in outcome prediction.

Methods and Results

For 2 months, all acute ischemic stroke patients from the hospitals participating in the Global Comparators Stroke GOAL (Global Outcomes Accelerated Learning) collaboration received a National Institutes of Health Stroke Scale (NIHSS) score on admission and a modified Rankin Scale score at 30 and 90 days. These data were added to the administrative data set, and risk prediction models including age, sex, comorbidity index, and NIHSS were derived for in‐hospital death within 7 days, all in‐hospital death, and death and good outcome at 30 and 90 days. The relative importance of each variable was assessed using the proportion of explained variation. Of 1034 admissions for acute ischemic stroke, 614 had a full set of NIHSS and both modified Rankin Scale values recorded; of these, 507 patients could be linked to administrative data. The marginal proportion of explained variation was 0.7% to 4.0% for comorbidity index, and 11.3 to 25.0 for NIHSS score. The percentage explained by the model varied by outcome (16.6–29.1%) and was highest for good outcome at 30 and 90 days. There was high agreement between 30‐ and 90‐day modified Rankin Scale scores (weighted κ=0.82).

Conclusions

In this prospective pilot study, the baseline NIHSS score was essential for prediction of acute ischemic stroke outcomes, followed by age; whereas traditional comorbidity index contributed little to the overall model. Future studies of stroke outcomes between different care systems will benefit from including a baseline NIHSS score.

Care-limiting decisions in acute stroke and association with survival: analyses of UK national quality register data

Background

Prognosis after intracerebral hemorrhage (ICH) is poor and care-limiting decisions may worsen outcomes.

Aims

To determine whether in current UK stroke practice, key acute care decisions are associated with stroke subtype (ICH/ischemic) and whether these decisions are independently associated with survival.

Methods

We extracted data describing all stroke patients included in a UK quality register between 1 April 2013 and 31 March 2014. Key care decisions in our analyses were transfer to higher level care on admission and palliation in the first 72 h. We used multivariable regression models to test for associations between stroke subtype (ICH/ischemic), key care decisions, and survival.

Results

A total of 65,818 patients were included in the final analysis. After ICH ( n = 7020/65,818, 10.7%), 10.5% were palliated on the day of admission and 19.3% by 72 h (vs. 0.7% and 3.3% for ischemic stroke). Although a greater proportion were admitted directly to higher level care after ICH (3.7% vs. 1.5% for ischemic stroke), ICH was not independently associated with the decision to admit to higher level care (adjusted odds ratio (OR): 1.12, 95% confidence interval (95%CI): 0.95–1.31, p = 0.183). However, ICH was strongly associated with the decision to commence palliative care on the day of admission (OR: 7.27, 95%CI: 6.31–8.37, p < 0.001). Palliative care was independently associated with risk of death by 30 days regardless of stroke subtype.

Conclusions

When compared to ischemic stroke, patients with ICH are much more likely to commence palliative care during the first 72 h of their care, independent of level of consciousness, age, and premorbid health.

Socioeconomic Inequalities in the Prescription of Oral Anticoagulants in Stroke Patients With Atrial Fibrillation

BACKGROUND AND PURPOSE

Oral anticoagulants (OACs) are effective against ischemic stroke in patients with atrial fibrillation. Our aim was to investigate differences in the prescribing of OACs after ischemic stroke in patients with atrial fibrillation based on age, sex, country of birth, and socioeconomic status.

METHODS

Patients with first-ever ischemic stroke and atrial fibrillation without OAC treatment were included from the Swedish stroke register from 2009 to 2012. The outcome was OAC prescribed at discharge. Income, education, country of birth, and risk factors were obtained from official registers. Risk factors and health status were controlled for in multivariable logistic regression.

RESULTS

Of 12 088 stroke patients, 36.3% were prescribed an OAC. Prescribing was less common with older age and, in patients born in other Nordic countries (odds ratio [OR], 0.82; 95% confidence interval [CI], 0.68-0.98) or countries outside of Europe (OR, 0.65; 95% CI, 0.42-0.99) compared with those born in Sweden. University education (OR, 1.20; 95% CI, 1.05-1.36) and highest income (OR, 1.19; 95% CI, 1.06-1.33) were associated with higher levels of OAC prescribing compared with those with primary school education or lowest income level.

CONCLUSION

Differences by age, income, education, and country of birth were found in the prescribing of OACs after stroke. Differences were not explained by common risk factors. This indicates socioeconomic inequalities in the prescribing of preventive treatment after stroke.