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Prediction of blood pressure variability during thrombectomy using supervised machine learning and outcomes of patients with ischemic stroke from large vessel occlusion

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Abstract

Mechanical thrombectomy (MT) is the standard of care for patients with acute ischemic stroke from large vessel occlusion (AIS-LVO). The association of blood pressure variability (BPV) during MT and outcomes are unknown. We leveraged a supervised machine learning algorithm to predict patient characteristics that are associated with BPV indices. We performed a retrospective review of our comprehensive stroke center’s registry of all adult patients undergoing MT between 01/01/2016 and 12/31/2019. The primary outcome was poor functional independence, defined as 90-day modified Rankin Scale (mRS) ≥ 3. We used probit analysis and multivariate logistic regressions to evaluate the association of patients’ clinical factors and outcomes. We applied a machine learning algorithm (random forest, RF) to determine predictive factors for the different BPV indices during MT. Evaluation was performed with root-mean-square error (RMSE) and normalized-RMSE (nRMSE) metrics. We analyzed 375 patients with mean age (± standard deviation [SD]) of 65 (15) years. There were 234 (62%) patients with mRS ≥ 3. Univariate probit analysis demonstrated that BPV during MT was associated with poor functional independence. Multivariable logistic regression showed that age, admission National Institutes of Health Stroke Scale (NIHSS), mechanical ventilation, and thrombolysis in cerebral infarction (TICI) score (OR 0.42, 95% CI 0.17–0.98, P = 0.044) were significantly associated with outcome. RF analysis identified that the interval from last-known-well time-to-groin puncture, age, and mechanical ventilation were among important factors significantly associated with BPV. BPV during MT was associated with functional outcome in univariate probit analysis but not in multivariable regression analysis, however, NIHSS and TICI score were. RF algorithm identified risk factors influencing patients’ BPV during MT. While awaiting further studies’ results, clinicians should still monitor and avoid high BPV during thrombectomy while triaging AIS-LVO candidates quickly to MT.

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Acknowledgements

The authors would like to thank Professor Ruoqing Zhu, Ph.D. from the University of Illinois Urbana-Champaign for his assistance with R.

Funding

The authors received no funding for data collection or preparation of the manuscript.

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Author notes

  1. Daniel Najafali and Thomas Johnstone have contributed equally, co-primary authors.

Authors and Affiliations

  1. Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Daniel Najafali

  2. Stanford University School of Medicine, Stanford, CA, USA

    Thomas Johnstone

  3. Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA

    Melissa Pergakis, Karen L. Yarbrough, Michael S. Phipps & Gaurav **dal

  4. Department of Emergency Medicine, Mercer University at Coliseum Medical Center, Macon, GA, USA

    Adelina Buganu

  5. The Research Associate Program in Emergency Medicine and Critical Care, Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Muhammad Ullah, Kim Vuong & Bhakti Panchal

  6. The Critical Care Resuscitation Unit, University of Maryland Medical Center, Baltimore, MD, USA

    Quincy K. Tran

  7. Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Mark Sutherland & Quincy K. Tran

  8. Department of Neuroradiology, University of Maryland School of Medicine, Baltimore, MD, USA

    Gaurav **dal

  9. The R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, 22 South Greene Street, Suite T3N45, Baltimore, MD, USA

    Quincy K. Tran

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Contributions

DN: Conceptualization, Data Analysis, Data Curation, Writing—Original Draft Preparation, Writing—Reviewing and Editing. TJ: Conceptualization, Data Analysis, Writing—Original Draft Preparation, Writing—Reviewing and Editing. MP: Conceptualization, Data Curation, Writing—Reviewing and Editing. AB: Conceptualization, Data Curation, Writing—Reviewing and Editing. MU: Data Curation, Writing—Reviewing and Editing. KV: Data Curation, Writing—Reviewing and Editing. BP: Data Curation, Writing—Reviewing and Editing. MS: Writing—Reviewing and Editing. KLY: Conceptualization, Data Curation, Writing—Reviewing and Editing. MSP: Conceptualization, Data Curation, Writing—Reviewing and Editing. GJ: Conceptualization, Data Curation, Writing—Reviewing and Editing. QKT: Conceptualization, Supervision, Data Curation, Data Curation, Writing—Original Draft Preparation, Writing—Reviewing and Editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Quincy K. Tran.

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Ethical approval

This study was approved by the University of Maryland Baltimore’s IRB (IRB number: HP-00084554).

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Data from this article were presented virtually in part at the 2021 Annual Congress of the Society of Critical Care Medicine.

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Najafali, D., Johnstone, T., Pergakis, M. et al. Prediction of blood pressure variability during thrombectomy using supervised machine learning and outcomes of patients with ischemic stroke from large vessel occlusion. J Thromb Thrombolysis 56, 12–26 (2023). https://doi.org/10.1007/s11239-023-02796-9

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