Sex disparity in long-term stroke recurrence and mortality in a rural population in the United States

Lambert, Clare; Chaudhary, Durgesh; Olulana, Oluwaseyi; Shahjouei, Shima; Avula, Venkatesh; Li, Jiang; Abedi, Vida; Zand, Ramin

doi:10.1177/1756286420971895

Cited by 16 publications

(17 citation statements)

References 38 publications

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“…An alternate explanation is that the mean age in POINT was relatively young, with a relatively low prevalence of high-risk conditions such as atrial fibrillation. [19][20][21] Additional prior studies on the association between sex and stroke recurrence include analyses of administrative claims data from a rural region of the United States 12 and for diabetic patients in Ontario, Canada. 13 Though these data were from narrowly defined populations and lacked granular clinical data, their findings were similar to ours.…”

Section: Discussionmentioning

confidence: 99%

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Sex differences in the risk of recurrent ischemic stroke after ischemic stroke and transient ischemic attack

Basu

Omran²,

Kamel

et al. 2021

European Stroke Journal

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Background Sex differences in stroke outcomes have been noted, but whether this extends to stroke recurrence is unclear. We examined sex differences in recurrent stroke using data from the Platelet-Oriented Inhibition in New TIA and Minor Ischemic Stroke (POINT) trial. Patients and methods We assessed the risk of recurrent stroke in women compared to men using data from the POINT trial. Adults >18 years old were randomized within 12 hours of onset of minor ischemic stroke or transient ischemic attack (TIA), and followed for up to 90 days for ischemic stroke, our primary outcome. We used Cox proportional hazards model adjusted for demographics and stroke risk factors to evaluate the association between sex and stroke recurrence. We used interaction term testing and prespecified subgroup analyses to determine if the association between sex and recurrent stroke differed by age (<60 versus >60 years old), locale (US versus non-US), and index event type (stroke versus TIA). Last, we evaluated whether sex modified the effect of common stroke risk factors on stroke recurrence. Results Of 4,881 POINT trial participants with minor stroke or high-risk TIA, 2,195 (45%) were women. During the 90-day follow-up period, 267 ischemic strokes occurred; 121 were in women and 146 in men. The cumulative risk of recurrent ischemic stroke was not significantly different among women (5.76%; 95% CI, 4.84%–6.85%) compared to men (5.67%; 95% CI, 4.83%–6.63%). Women were not at a different risk of recurrent ischemic stroke compared to men (hazard ratio [HR], 1.02; 95% CI, 0.80–1.30) in unadjusted models or after adjusting for covariates. However, there was a significant interaction of age with sex (P=0.04). Among patients <60 years old, there was a non-significantly lower risk of recurrent stroke in women compared to men (HR 0.66; 95% CI 0.42–1.05). Last, sex did not modify the association between common stroke risk factors and recurrent stroke risk. Discussion and Conclusion Among patients with minor stroke or TIA, the risk of recurrent ischemic stroke and the impact of common stroke risk factors did not differ between men and women.

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Section: Discussionmentioning

confidence: 99%

“…6,9,10 Whether these differences impact stroke recurrence risk has not been definitively assessed. Prior studies, which included a large proportion of stroke mimics 11 or used non-adjudicated administrative claims data, 12,13 did not find clear evidence of a sex difference in stroke recurrence.…”

Section: Introductionmentioning

confidence: 96%

Sex differences in the risk of recurrent ischemic stroke after ischemic stroke and transient ischemic attack

Basu

Omran²,

Kamel

et al. 2021

European Stroke Journal

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“…The results of the ROSEsampled XGBoost model indicated that age, hypertension, and gender-in this specific cohort-were not significantly associated with 30-day readmission after ischemic stroke. We have also performed a detailed analysis of our Geisinger cohort and identified that sex was not an independent risk factor for allcause mortality and ischemic stroke recurrence (33). Finally, the identification of malnutrition provides potential new venues to improve secondary prevention and outcome (34).…”

Section: Clinical Features Highly Associated With 30-day Readmissionmentioning

confidence: 95%

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

et al. 2021

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Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

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“…GNSIS contains longitudinal clinical data for ischemic stroke patients compiled from various sources, including electronic health records data (EHR), social security death data, and quality data. The GNSIS database has been described in detail in previous studies [ 13 , 14 , 15 ]. The study was reviewed and approved by the Geisinger Institutional Review Board.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Long-Term Outcomes and Associated Factors between Younger and Older Rural Ischemic Stroke Patients

Chaudhary

Anyaehie

Demiraj

et al. 2022

JCM

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Introduction: The rise of ischemic stroke among young adults has stressed the need to understand their risk profiles and outcomes better. This study aimed to examine the five-year ischemic stroke recurrence and survival probability among young patients in rural Pennsylvania. Methods: This retrospective cohort study included first-time ischemic stroke patients from the Geisinger Health System between September 2003 and May 2014. The outcomes included all-cause mortality and ischemic stroke recurrence at five years. Kaplan-Meier estimator, cumulative incidence function, Cox proportional hazards model, and Cause-specific hazard model were used to examine the association of independent variables with the outcomes. Results: A total of 4459 first-time ischemic stroke patients were included in the study, with 664 (14.9%) patients in the 18–55 age group and 3795 (85.1%) patients in the >55 age group. In the 18–55 age group, the five-year survival probability was 87.2%, and the cumulative incidence of recurrence was 8%. Patients in the 18–55 age group had significantly lower hazard for all-cause mortality (HR = 0.37, 95% CI 0.29–0.46, p < 0.001), and non-significant hazard for five-year recurrence (HR = 0.81, 95% CI 0.58–1.12, p = 0.193) compared to the >55 age group. Chronic kidney disease was found to be associated with increased mortality in the 18–55 age group. Conclusion: In our rural population, younger ischemic stroke patients were at the same risk of long-term ischemic stroke recurrence as the older ischemic stroke patients. Identifying the factors and optimizing adequate long-term secondary prevention may reduce the risk of poor outcomes among younger ischemic stroke patients.

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Sex disparity in long-term stroke recurrence and mortality in a rural population in the United States

Cited by 16 publications

References 38 publications

Sex differences in the risk of recurrent ischemic stroke after ischemic stroke and transient ischemic attack

Sex differences in the risk of recurrent ischemic stroke after ischemic stroke and transient ischemic attack

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

Comparison of Long-Term Outcomes and Associated Factors between Younger and Older Rural Ischemic Stroke Patients

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