The implementation of artificial intelligence significantly reduces door-in-door-out times in a primary care center prior to transfer

Should they stay or should they go? Stroke transfers across a hospital network pre- and post-implementation of an automated image interpretation and communication platform

BACKGROUND

A key decision facing nonthrombectomy capable (spoke) hospitals is whether to transfer a suspected large vessel occlusion (LVO) patient to a comprehensive stroke center (CSC). In a retrospective cohort study, we investigated the rate of transfers resulting in endovascular thrombectomy (EVT) and associated costs before and after implementation of an artificial intelligence (AI)-based software.

METHODS

All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.

RESULTS

A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).

CONCLUSIONS

The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.

Impact and determinants of door in-door out time for stroke thrombectomy transfers in a large hub-and-spoke network

INTRODUCTION

The mantra "time is brain" cannot be overstated for patients suffering from acute ischemic stroke. This is especially true for those with large vessel occlusions (LVOs) requiring transfer to an endovascular thrombectomy (EVT) capable center. We sought to evaluate the spoke hospital door in-door out (DIDO) times for patients transferred to our hub center for EVT.

METHODS

Individuals who first presented with LVO to a spoke hospital and were then transferred to the hub for EVT were retrospectively identified from a prospectively maintained database from January 2019 to November 2022. DIDO was defined as the time between spoke hospital door in arrival and door out exit. Baseline characteristics, treatments, and outcomes were compared, dichotomizing DIDO at 90 minutes based in the American Heart Association goal for DIDO ≤90 minutes for 50% of transfers. Multivariable regression analyses were performed for determinants of the 90-day ordinal modified Rankin Scale (mRS) and DIDO.

RESULTS

We identified 194 patients transferred for EVT with available DIDO. The median age was 67 years (IQR 57-80), and 46% were female. The median National Institutes of Health Stroke Scale (NIHSS) was 16 (10-20), 50% were treated with intravenous thrombolysis at a spoke, and TICI 2B-3 reperfusion was achieved in 87% at the hub. The median DIDO was 120 minutes (97-149), with DIDO ≤90 minutes achieved in 18%. DIDO was a significant determinant of 90-day ordinal mRS (B = 0.007, 95% CI = 0.001-0.012, p = 0.013), even when accounting for the last known well-to-spoke door in, spoke door out-to-hub arrival, hub arrival-to-puncture, puncture-to-first pass, age, NIHSS, intravenous thrombolysis, TICI 2B-3, and symptomatic intracranial hemorrhage. Importantly, determinants of DIDO included Black race or Hispanic ethnicity (B = 0.918, 95% CI = 0.010-1.826, p = 0.048), atrial fibrillation or heart failure (B = 0.793, 95% CI = 0.257-1.329, p = 0.004), and basilar LVO location (B = 2.528, 95% CI = 1.154-3.901, p < 0.001).

CONCLUSION

Spoke DIDO was the most important period of time for long-term outcomes of LVO stroke patients treated with EVT. Targets were identified to reduce DIDO and improve patient outcomes.