Impact of the COVID-19 pandemic on ischemic stroke hospitalizations and in-hospital outcomes in the United States: A survey-weighted cohort with predictive modeling.

INTRODUCTION

Ischemic stroke remains a leading cause of death in the United States, with the COVID-19 pandemic exacerbating disparities. Prior studies have been limited by small sample sizes and lack of generalizability. We used nationally representative data to assess the effects of COVID-19 on ischemic stroke care, outcomes, and predictive modeling.

METHODS

We performed a retrospective cohort study using the 2019-2021 HCUP National Inpatient Sample (NIS). Ischemic stroke hospitalizations and COVID-19 diagnosis were identified using ICD-10-CM diagnosis codes. Hospitalizations were categorized as pre-pandemic calendar year 2019 versus pandemic-era 2020-2021. Calendar-defined pandemic epochs were evaluated as temporal phases and were not interpreted as patient-level SARS-CoV-2 variant assignments. Survey-weighted analyses and machine-learning models were used to evaluate in-hospital mortality.

RESULTS

We identified 517,750 weighted ischemic stroke hospitalizations. Compared with the pre-pandemic year 2019, the pandemic era (2020-2021) had higher in-hospital mortality, slightly longer length of stay, higher total hospital charges, and shifted discharge disposition patterns. IV/IA thrombolysis-coded procedure rates were similar between eras, whereas mechanical endovascular reperfusion-coded procedures were more frequent during the pandemic. In the expanded survey-weighted mortality model, COVID-19 diagnosis was associated with higher in-hospital mortality (aOR 2.74, 95% CI 2.42-3.11). Most calendar-defined pandemic epochs were not independently associated with mortality, although the 2021Q4 Delta-to-Omicron transition epoch showed a modest association. In the NIHSS-coded subgroup, COVID-19 diagnosis remained associated with mortality after NIHSS adjustment. Admission-time machine-learning models showed modest discrimination, with AUROC values ranging from 0.62 to 0.64 and AUPRC values ranging from 0.12 to 0.14.

CONCLUSIONS

COVID-19 diagnosis was strongly associated with in-hospital mortality after ischemic stroke, whereas broad calendar-defined pandemic timing alone showed limited independent association. Calendar epochs should not be interpreted as variant-specific effects because NIS lacks patient-level SARS-CoV-2 sequencing, viral subtype, or variant data. Admission-time machine-learning models provided modest mortality prediction and require cautious interpretation.