The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Causes and Predictors of 30-Day Readmission in Patients With Stroke Undergoing Mechanical Thrombectomy: A Large Single-Center Experience

BACKGROUND AND OBJECTIVES

The 30-day readmission rate has emerged as a metric of quality care and is associated with increased health care expenditure. We aim to identify the rate and causes of 30-day readmission after mechanical thrombectomy and provide the risk factors of readmission to highlight high-risk patients who may require closer care.

METHODS

This is a retrospective study from a prospectively maintained database of 703 patients presenting for mechanical thrombectomy between 2017 and 2023. All patients who presented with a stroke and underwent a mechanical thrombectomy were included in this study. Patients who were deceased on discharge were excluded from this study.

RESULTS

Our study comprised 703 patients, mostly female (n = 402, 57.2%) with a mean age of 70.2 years ±15.4. The most common causes of readmission were cerebrovascular events (stroke [n = 21, 36.2%], intracranial hemorrhage [n = 9, 15.5%], and transient ischemic attack [n = 1, 1.7%]).Other causes of readmission included cardiovascular events (cardiac arrest [n = 4, 6.9%] and bradycardia [n = 1, 1.7%]), infection (wound infection postcraniectomy [n = 3, 5.2%], and pneumonia [n = 1, 1.7%]). On multivariate analysis, independent predictors of 30-day readmission were history of smoking (odds ratio [OR]: 2.2, 95% CI: 1.1-4.2) P = .01), distal embolization (OR: 3.2, 95% CI: 1.1-8.7, P = .03), decompressive hemicraniectomy (OR: 9.3, 95% CI: 3.2-27.6, P < .01), and intracranial stent placement (OR: 4.6, 95% CI: 2.4-8.7) P < .01).

CONCLUSION

In our study, the rate of 30-day readmission was 8.3%, and the most common cause of readmission was recurrent strokes. We identified a history of smoking, distal embolization, decompressive hemicraniectomy, and intracranial stenting as independent predictors of 30-day readmission in patients with stroke undergoing mechanical thrombectomy.

The Rate and Predictors of 30-Day Readmission in Patients Treated for Unruptured Cerebral Aneurysms: A Large Single-Center Study

BACKGROUND AND OBJECTIVES

Numerous studies of various populations and diseases have shown that unplanned 30-day readmission rates are positively correlated with increased morbidity and all-cause mortality. In this study, we aim to provide the rate and predictors of 30-day readmission in patients undergoing treatment for unruptured intracranial aneurysms.

METHODS

This is a retrospective study of 525 patients presenting for aneurysm treatment between 2017 and 2022. All patients who were admitted and underwent a successful treatment of their unruptured intracerebral aneurysms were included in the study. The primary outcome was the rate and predictors of 30-day readmission.

RESULTS

The rate of 30-day readmission was 6.3%, and the mean duration to readmission was 7.8 days ± 6.9. On univariate analysis, factors associated with 30-day readmission were antiplatelet use on admission (odds ratio [OR]: 0.4, P = .009), peri-procedural rupture (OR: 15.8, P = .007), surgical treatment of aneurysms (OR: 2.2, P = .035), disposition to rehabilitation (OR: 9.5, P < .001), and increasing length of stay (OR: 1.1, P = .0008). On multivariate analysis, antiplatelet use on admission was inversely correlated with readmission (OR: 0.4, P = .045), whereas peri-procedural rupture (OR: 9.5, P = .04) and discharge to rehabilitation (OR: 4.5, P = .029) were independent predictors of 30-day readmission.

CONCLUSION

In our study, risk factors for 30-day readmission were aneurysm rupture during the hospital stay and disposition to rehabilitation, whereas the use of antiplatelet on admission was inversely correlated with 30-day readmission. Although aneurysm rupture is a nonmodifiable risk factor, more studies are encouraged to focus on the correlation of antiplatelet use and rehabilitation disposition with 30-day readmission rates.