Abstract TP306: Characteristics and Outcomes Among Patients Transferred to Regional Stroke Centers Across the United States for Specialized Stroke Care

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Abstract

Intro: Many patients are transferred to stroke centers for advanced stroke care, especially after IV tPA. We sought to determine differences in the baseline characteristics and outcomes between AIS cases presenting directly to stroke centers’ front doors vs. transfers-in from another regional acute care hospital.

Methods: Using data from the national GWTG-Stroke registry, we analyzed 970,390 AIS cases (01/2010 - 03/14). Patients at hospitals with high transfer-in rates (>15%) were selected (284 hospitals, 303,739 patients). Due to large sample size, instead of p-values, standardized differences were reported. Multivariable model (MV) examined the association of transfer-in vs. front door with the primary and secondary outcomes, adjusting for patient and hospital characteristics including NIHSS.

Results: High volume transfer-in hospitals admitted 31% of their patients via transfer. Transfer-in patients were younger, more often white and non-Hispanic. They had similar stroke risk factors except for hypertension and previous stroke/TIA which were less common. Transfer-in had worse initial NIHSS, more often had altered consciousness and language disturbance. Transfer-in patients had longer length of hospital stay, higher mRS at discharge, and were less often discharged home. In-hospital mortality was ∼ 3% higher in transfer-in as compared with front-door. Among tPA treated patients, sICH < 36hr was more common in transfer-in patients. On MV, transfer-in patients had overall worse outcomes as shown by the higher odds of in-hospital mortality, longer length of stay, and not able to ambulate independently at discharge (Table).

Conclusion: Many hospitals receive high volumes of stroke patients via transfer. Because transfer-in patients have worse outcomes, these patients have the potential to negatively influence institutional outcomes rates. Transfer-in patients should be carefully accounted for in risk adjusted models of hospital outcomes.

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