Multiple Chronic Conditions Explain Ethnic Differences in Functional Outcome Among Patients With Ischemic Stroke

Stroke ◽

10.1161/strokeaha.120.032595 ◽

2021 ◽

Author(s):

Xiaqing Jiang ◽

Lewis B. Morgenstern ◽

Christine T. Cigolle ◽

Lu Wang ◽

Edward S. Claflin ◽

...

Keyword(s):

Ischemic Stroke ◽

Functional Outcome ◽

Chronic Conditions ◽

Ethnic Difference ◽

Effect Modification ◽

Multiple Chronic Conditions ◽

White People ◽

Discharge Data ◽

Stroke Outcomes ◽

Hispanic White

Background and Purpose: Mexican Americans (MAs) have worse stroke outcomes and a different profile of multiple chronic conditions (MCC) compared with non-Hispanic White people. MCC has implications for stroke treatment, complications, and poststroke care, which impact poststroke functional outcome (FO). We sought to assess the contribution of MCC to the ethnic difference in FO at 90 days between MAs and non-Hispanic White people. Methods: In a prospective cohort of ischemic stroke patients (2008–2016) from Nueces County, Texas, data were collected from patient interviews, medical records, and hospital discharge data. MCC was assessed using a stroke-specific and function-relevant index (range, 0–35; higher scores greater MCC burden). Poststroke FO was measured by an average score of 22 activities of daily living (ADLs) and instrumental ADLs at 90 days (range, 1–4; higher scores worse FO). The contribution of MCC to the ethnic difference in FO was assessed using Tobit regression. Effect modification by ethnicity was examined. Results: Among the 896 patients, 70% were MA and 51% were women. Mean age was 68±12.2 years; 33% of patients were dependent in ADL/instrumental ADLs (FO score >3, representing a lot of difficulty with ADL/instrumental ADLs) at 90 days. MAs had significantly higher age-adjusted MCC burden compared with non-Hispanic White people. Patients with high MCC score (at the 75th percentile) on average scored 0.70 points higher in the FO score (indicating worse FO) compared with those with low MCC score (at the 25th percentile) after adjusting for age, initial National Institutes of Health Stroke Scale, and sociodemographic factors. MCC explained 19% of the ethnic difference in FO, while effect modification by ethnicity was not statistically significant. Conclusions: MAs had a higher age-adjusted MCC burden, which partially explained the ethnic difference in FO. The prevention and treatment of MCC could potentially mitigate poststroke functional impairment and lessen ethnic disparities in stroke outcomes.

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Abstract WP239: Ethnic Differences in Multiple Chronic Conditions Among Ischemic Stroke Patients From the Brain Attack Surveillance in Corpus Christi Project

Stroke ◽

10.1161/str.51.suppl_1.wp239 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Xiaqing Jiang ◽

Lu Wang ◽

Christine Cigolle ◽

Lynda Lisabeth

Keyword(s):

Ischemic Stroke ◽

Ethnic Differences ◽

Chronic Conditions ◽

Ethnic Difference ◽

Stroke Onset ◽

Multiple Chronic Conditions ◽

Stroke Patients ◽

Discharge Data ◽

Stroke Outcomes ◽

Younger Patients

Introduction: The risk of developing multiple chronic conditions (MCC) increases with age. MCC predicts stroke outcomes and impairs prestroke reserve that aids the neuropsychological process of stroke recovery. Mexican Americans (MAs) have increased stroke risk, worse stroke outcomes and a different profile of pre-stroke comorbidities compared to non-Hispanic whites (NHWs). We assessed ethnic differences in the overall burden of MCC among ischemic stroke patients from a bi-ethnic, population-based stroke study. Methods: We studied patients with ischemic stroke between November 2008 and March 2017. Twenty-two chronic conditions (prevalence 1%-83%) were identified from medical records and ICD-9 and 10 codes from hospital discharge data. MCC burden was measured by the total number of chronic conditions. Ethnic differences in terms of the odds of experiencing none (<2 conditions), low MCC (2-3 conditions), or high MCC (>4 conditions) were assessed using a proportional odds model adjusting for age (at stroke onset). Effect modification by age was also investigated. Results: Of 1,656 stroke patients, 68% were MA, 51% were female, mean age was 69 (SD=13), median number of MCC was 4 (IQR: 2-6). MAs were younger at stroke onset, but more likely to have higher age-adjusted MCC burden (OR 1.32, 95% CI: 1.07-1.62) compared to NHWs. The difference in MCC burden was modified by age (p= 0.02), with greater ethnic difference in MCC burden among younger patients (Figure). Conclusion: MAs have greater MCC burden at stroke onset compared to NHWs, especially in younger patients. The contribution of this ethnic difference in MCC burden to ethnic disparities in stroke outcome needs further investigation.

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New Index for Multiple Chronic Conditions Predicts Functional Outcome in Ischemic Stroke

Neurology ◽

10.1212/wnl.0000000000010992 ◽

2020 ◽

Vol 96 (1) ◽

pp. e42-e53

Author(s):

Xiaqing Jiang ◽

Lu Wang ◽

Lewis B. Morgenstern ◽

Christine T. Cigolle ◽

Edward S. Claflin ◽

...

Keyword(s):

Ischemic Stroke ◽

Functional Outcome ◽

Chronic Conditions ◽

Synergistic Effects ◽

Characteristic Curve ◽

Machine Learning Techniques ◽

Validation Dataset ◽

Multiple Chronic Conditions ◽

Corpus Christi ◽

Neurologic Disorders

ObjectiveTo determine whether a new index for multiple chronic conditions (MCCs) predicts poststroke functional outcome (FO), we developed and internally validated the new MCC index in patients with ischemic stroke.MethodsA prospective cohort of patients with ischemic stroke (2008–2017) was interviewed at baseline and 90 days in the Brain Attack Surveillance in Corpus Christi Project. An average of 22 activities of daily living (ADL)/instrumental ADL (IADL) items measured the FO score (range 1–4) at 90 days. A FO score >3 (representing a lot of difficulty with ADL/IADLs) was considered unfavorable FO. A new index was developed using machine learning techniques to select and weight conditions and prestroke impairments.ResultsPrestroke modified Rankin Scale (mRS) score, age, congestive heart failure (CHF), weight loss, diabetes, other neurologic disorders, and synergistic effects (dementia × age, CHF × renal failure, and prestroke mRS × prior stroke/TIA) were identified as important predictors in the MCC index. In the validation dataset, the index alone explained 31% of the variability in the FO score, was well-calibrated (p = 0.41), predicted unfavorable FO well (area under the receiver operating characteristic curve 0.81), and outperformed the modified Charlson Comorbidity Index in predicting the FO score and poststroke mRS.ConclusionsA new MCC index was developed and internally validated to improve the prediction of poststroke FO. Novel predictors and synergistic interactions were identified.Classification of EvidenceThis study provides Class II evidence that in patients with ischemic stroke, an index for MCC predicts FO at 90 days.

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Multiple Chronic Conditions and Functional Outcome after Ischemic Stroke: A Systematic Review and Meta-Analysis

Neuroepidemiology ◽

10.1159/000503900 ◽

2019 ◽

Vol 54 (3) ◽

pp. 205-213 ◽

Cited By ~ 2

Author(s):

Xiaqing Jiang ◽

Lewis B. Morgenstern ◽

Christine T. Cigolle ◽

Edward S. Claflin ◽

Lynda D. Lisabeth

Keyword(s):

Systematic Review ◽

Ischemic Stroke ◽

Functional Outcome ◽

Chronic Conditions ◽

Meta Analysis ◽

Multiple Chronic Conditions

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The Association between Baseline and 3-Month Albuminuria and 1-Year Prognosis of Ischemic Stroke

Cerebrovascular Diseases ◽

10.1159/000518180 ◽

2021 ◽

pp. 1-8

Author(s):

Dongxue Wang ◽

Yuesong Pan ◽

Hao Li ◽

Hongyi Yan ◽

Xia Meng ◽

...

Keyword(s):

Ischemic Stroke ◽

Functional Outcome ◽

Stroke Recurrence ◽

Stroke Patients ◽

Poor Functional Outcome ◽

Clinical Prognosis ◽

Stroke Outcomes ◽

Stroke Registry ◽

Confounding Variables ◽

Ischemic Attack

Introduction: The association between the changes in albuminuria levels and the clinical prognosis of stroke is unknown. The present study aimed to explore the relationships between changes in albuminuria and the risk of adverse stroke outcomes. Methods: The patients with ischemic stroke or transient ischemic attack from the Third China National Stroke Registry (CNSR-III) who had the urinary albumin-to-creatinine ratio (ACR) detected at baseline and 3-month were recruited. They were classified into 4 groups according to baseline and 3-month ACR and followed up for 1 year. Results: A total of 5,311 patients were finally included in the study. There were 3,738 (70.4%), 483 (9.1%), 451 (8.5%), and 639 (12.0%) patients with no albuminuria, baseline albuminuria, 3-month albuminuria, and persistent albuminuria, respectively. After adjustment for confounding variables, persistent albuminuria was independently associated with all-cause death (hazard ratio [HR], 2.23; 95% CI, 1.17–4.25; p = 0.02), stroke recurrence (HR, 1.55; 95% CI, 1.02–2.36; p = 0.04), and poor functional outcome (OR, 2.22; 95% CI, 1.66–2.96; p < 0.001). Baseline albuminuria was independently associated with poor functional outcome (OR, 1.65; 95% CI, 1.19–2.28; p = 0.003), while 3-month albuminuria was independently associated with stroke recurrence (HR, 1.68; 95% CI, 1.06–2.65; p = 0.03). Conclusions: Changes in albuminuria can predict adverse 1-year outcomes in Chinese ischemic stroke patients. In particular, persistent albuminuria was independently associated with 1-year all-cause death, stroke recurrence, and poor functional outcome.

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Association of white matter lesions and outcome after endovascular stroke treatment

Neurology ◽

10.1212/wnl.0000000000010994 ◽

2020 ◽

pp. 10.1212/WNL.0000000000010994

Author(s):

Sven P.R. Luijten ◽

Daniel Bos ◽

Kars C.J. Compagne ◽

Lennard Wolff ◽

Charles B.L.M. Majoie ◽

...

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Endovascular Treatment ◽

Acute Ischemic Stroke ◽

Functional Outcome ◽

Treatment Effect ◽

White Matter Lesions ◽

Effect Modification ◽

Stroke Treatment ◽

Logistic Regression Models

ObjectiveTo investigate the association between white matter lesions (WML) and functional outcome in patients with acute ischemic stroke (AIS) and the modification of the effect of endovascular treatment (EVT) by WML.MethodsWe used data from the Multicenter Randomized Clinical trial of Endovascular treatment for Acute ischemic stroke in the Netherlands (MR CLEAN) trial and assessed severity of WML on baseline non-contrast CT imaging (NCCT; n = 473) according to the Van Swieten Scale. Post-stroke functional outcome was assessed with the modified Rankin Scale (mRS). We investigated the association of WML with functional outcome using ordinal logistic regression models adjusted for age, sex, and other relevant cardiovascular and prognostic risk factors. In addition, an interaction term between treatment allocation and WML severity was used to assess treatment effect modification by WML.ResultsWe found an independent negative association between more severe WML and functional outcome (acOR 0.77 [95% CI 0.66–0.90]). Patients with absent to moderate WML had similar benefit of EVT on functional outcome (acOR 1.93 [95% CI 1.31–2.84]) as patients with severe WML (acOR 1.95 [95% CI 0.90–4.20]). No treatment effect modification of WML was found (p for interaction = 0.85).ConclusionsWe found that more severe WML predict poor functional outcome after acute ischemic stroke, but do not modify effect of EVT.Classification of evidencePrognostic accuracy. This study provides Class II evidence that for patients with AIS, the presence of WML on baseline NCCT is associated with worse functional outcomes.

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Abstract WP217: A New Index for Multiple Chronic Conditions Predicts Post-Stroke Functional Outcome: A Machine Learning Approach in the Brain Attack Surveillance in Corpus Christi Project

Stroke ◽

10.1161/str.51.suppl_1.wp217 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Xiaqing Jiang ◽

Lu Wang ◽

Lewis Morgenstern ◽

Christine Cigolle ◽

Lynda Lisabeth

Keyword(s):

Machine Learning ◽

Weight Loss ◽

Activities Of Daily Living ◽

Functional Outcome ◽

Neurological Disease ◽

Chronic Conditions ◽

Daily Living ◽

Multiple Chronic Conditions ◽

Post Stroke ◽

Other Neurological Disease

Introduction: Multiple chronic conditions (MCC) diminish the pre-stroke reserve that aids post-stroke adaptation and recovery. Through machine learning, we developed a MCC index that integrates pre-stroke comorbid conditions, functional and cognitive factors, as well as their interactions, to predict post-stroke functional outcome (FO) in a bi-ethnic, population-based cohort study. Methods: Ischemic stroke patients (2008-2017) were interviewed at baseline and 90 days. FO score (range 1-4, higher scores worse) at 90 days was measured by averaging 22 activities of daily living (ADL)/instrumental activities of daily living (IADL) and dichotomizing the score into favorable (1-3) and unfavorable FO (>3, a lot of difficulty with ADL/IADLs). Multiple linear regression was fit with a Lasso penalty to select predictors among 22 chronic conditions from ICD codes and medical records, pre-stroke function, cognitive impairment, social support, marital status, depression, age, initial stroke severity (NIHSS) and all pairwise interactions. We developed an MCC index by weighting selected predictors using β-coefficients. Adjusted R 2 , discrimination and calibration of the model were assessed. Results: Among 1,035 stroke survivors, 69% were Mexican American, 51% were female, mean age was 68 (SD=12), and median initial NIHSS was 4 (IQR:2-8). Median FO score was 2.36 (IQR:1.55-3.41); 32% had unfavorable FO. The final model contained the pre-stroke modified Rankin Score (mRS), initial NIHSS, age, congestive heart failure (CHF), weight loss, diabetes, other neurological disease, initial NIHSS х pre-stroke mRS, dementia х age, CHF х renal failure and pre-stroke mRS х history of stroke/TIA, which explained 44% of variability in FO score. The MCC index was well calibrated (p=0.28) and predicted unfavorable FO well (c-statistic, 0.84) in the internal validation dataset. Conclusion: A new MCC assessment tool was developed and validated to improve the prediction of post-stroke FO. Weight loss, other neurological disease and interactions between MCC were discovered as novel predictors. Efforts to improve stroke prognosis may benefit from a better understanding, prevention and management of MCC in population at high risk for stroke.

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