Abstract WP163: Predictors Of All-Cause Readmissions After Spinal Cord Infarctions: A Nationwide Analysis

Introduction: Spinal cord infarction (SCI) is a rare but serious neurological condition that accounts for 1-2% of all ischemic strokes and can lead to significant disability. Despite its clinical importance, existing studies are limited by small sample sizes that lack national generalizability, and little is known about post-discharge events such as hospital readmissions. We aimed to evaluate factors associated with all-cause hospital readmissions among hospitalizations with primary diagnosis of SCI, utilizing the Nationwide Readmissions Database (NRD). Methods: NRD data were abstracted from 2016 to 2022 including all hospitalizations of adult patients (≥18 years) with a primary diagnosis of SCI. Multiple-predictor logistic regression models were estimated to assess the association of demographic and clinical variables with all-cause 90-day readmissions. We included specific comorbidities often used in comorbidity scoring systems and added conditions or complications that are known to be associated with SCI. Statistically significant covariates from the unadjusted model were retained in the multiple-predictor model. Odds ratios (OR) with associated 95% Confidence intervals (CI) and p-values were reported. Results: From 2016 to 2022, there were an estimated 3321 hospitalizations with a primary diagnosis of SCI, out of which 27% (95% CI: 24-30%) were readmitted within 90 days. Demographic factors that were associated with lower odds of readmission included private insurance (vs. Medicare) and high-income quartile (Fig. 1). [AH1] [AAs2] Clinical factors that were associated with increased odds of readmission included autonomic dysfunction (OR = 5.44, 95% CI: 1.86 to 15.94), renal disease (OR = 3.57, 95% CI: 1.83 to 6.96), liver disease (OR = 2.40, 95% CI: 1.34 to 4.32), full clinical associations shown in Figure 2. Conclusion: We identified several clinical and demographic factors that were significantly associated with increased all-cause readmissions after SCI hospitalizations. Autonomic dysfunction and renal disease were among the top predictors of readmissions. We uncovered socioeconomic disparities in readmissions rates following SCI. This data may assist vascular neurologists, physicians and healthcare administrators in reducing readmissions after SCI and addressing these disparities. Additionally, our results can guide future studies focusing on the management of this rare entity.

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Journal
Stroke
Published
2026-01-29
DOI
https://doi.org/10.1161/str.57.suppl_1.wp163
Primary Topic
Spinal Cord Injury Research
Type
article
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0.00
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article

Abstract WP163: Predictors Of All-Cause Readmissions After Spinal Cord Infarctions: A Nationwide Analysis

Rohan Sharma, Ali Al-Salahat, Saif Bawaneh, Alexander Hall et al.
Stroke
Spinal Cord Injury Research
article

Abstract WP163: Predictors Of All-Cause Readmissions After Spinal Cord Infarctions: A Nationwide Analysis

Rohan Sharma, Ali Al-Salahat, Saif Bawaneh, Alexander Hall, Muhammad Roshan Asghar
article en

Abstract

Introduction: Spinal cord infarction (SCI) is a rare but serious neurological condition that accounts for 1-2% of all ischemic strokes and can lead to significant disability. Despite its clinical importance, existing studies are limited by small sample sizes that lack national generalizability, and little is known about post-discharge events such as hospital readmissions. We aimed to evaluate factors associated with all-cause hospital readmissions among hospitalizations with primary diagnosis of SCI, utilizing the Nationwide Readmissions Database (NRD). Methods: NRD data were abstracted from 2016 to 2022 including all hospitalizations of adult patients (≥18 years) with a primary diagnosis of SCI. Multiple-predictor logistic regression models were estimated to assess the association of demographic and clinical variables with all-cause 90-day readmissions. We included specific comorbidities often used in comorbidity scoring systems and added conditions or complications that are known to be associated with SCI. Statistically significant covariates from the unadjusted model were retained in the multiple-predictor model. Odds ratios (OR) with associated 95% Confidence intervals (CI) and p-values were reported. Results: From 2016 to 2022, there were an estimated 3321 hospitalizations with a primary diagnosis of SCI, out of which 27% (95% CI: 24-30%) were readmitted within 90 days. Demographic factors that were associated with lower odds of readmission included private insurance (vs. Medicare) and high-income quartile (Fig. 1). [AH1] [AAs2] Clinical factors that were associated with increased odds of readmission included autonomic dysfunction (OR = 5.44, 95% CI: 1.86 to 15.94), renal disease (OR = 3.57, 95% CI: 1.83 to 6.96), liver disease (OR = 2.40, 95% CI: 1.34 to 4.32), full clinical associations shown in Figure 2. Conclusion: We identified several clinical and demographic factors that were significantly associated with increased all-cause readmissions after SCI hospitalizations. Autonomic dysfunction and renal disease were among the top predictors of readmissions. We uncovered socioeconomic disparities in readmissions rates following SCI. This data may assist vascular neurologists, physicians and healthcare administrators in reducing readmissions after SCI and addressing these disparities. Additionally, our results can guide future studies focusing on the management of this rare entity.

StrokeVol. 57(Suppl_1)
Creighton University (US), Jacksonville College (US), WinnMed (US), Mayo Clinic in Florida (US)
No poverty
Openalex Percentile: Top 75%
Spinal Cord Injury Research
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