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Ischemic stroke
Original research
Exploring the use of ChatGPT in predicting anterior circulation stroke functional outcomes after mechanical thrombectomy: a pilot study
  1. Tiago Pedro1,
  2. José Maria Sousa1,
  3. Luísa Fonseca2,3,
  4. Manuel G. Gama2,3,
  5. Goreti Moreira2,3,
  6. Mariana Pintalhão2,3,
  7. Paulo C. Chaves2,3,
  8. Ana Aires3,4,
  9. Gonçalo Alves1,5,
  10. Luís Augusto1,5,
  11. Luís Pinheiro Albuquerque1,5,
  12. Pedro Castro4,6,
  13. Maria Luís Silva1,5
  1. 1Department of Neuroradiology, Centro Hospitalar Universitário de São João, Porto, Portugal
  2. 2Department of Medicine, University of Porto, Porto, Portugal
  3. 3Department of Internal Medicine, Centro Hospitalar Universitário de São João, Porto, Portugal
  4. 4Department of Neurology, Centro Hospitalar Universitário de São João, Porto, Portugal
  5. 5Centro de Referência de Neurorradiologia de Intervenção na Doença Cerebrovascular, Centro Hospitalar Universitário de São João, Porto, Portugal
  6. 6Department of Clinical Neurosciences and Mental Health, University of Porto, Porto, Portugal
  1. Correspondence to Dr Tiago Pedro, Department of Neuroradiology, Centro Hospitalar Universitário de São João, Porto, 4200-319, Portugal; tiagoliveirapedro{at}hotmail.com

Abstract

Background Accurate prediction of functional outcomes is crucial in stroke management, but this remains challenging.

Objective To evaluate the performance of the generative language model ChatGPT in predicting the functional outcome of patients with acute ischemic stroke (AIS) 3 months after mechanical thrombectomy (MT) in order to assess whether ChatGPT can used to be accurately predict the modified Rankin Scale (mRS) score at 3 months post-thrombectomy.

Methods We conducted a retrospective analysis of clinical, neuroimaging, and procedure-related data from 163 patients with AIS undergoing MT. The agreement between ChatGPT’s exact and dichotomized predictions and actual mRS scores was assessed using Cohen’s κ. The added value of ChatGPT was measured by evaluating the agreement of predicted dichotomized outcomes using an existing validated score, the MT-DRAGON.

Results ChatGPT demonstrated fair (κ=0.354, 95% CI 0.260 to 0.448) and good (κ=0.727, 95% CI 0.620 to 0.833) agreement with the true exact and dichotomized mRS scores at 3 months, respectively, outperforming MT-DRAGON in overall and subgroup predictions. ChatGPT agreement was higher for patients with shorter last-time-seen-well-to-door delay, distal occlusions, and better modified Thrombolysis in Cerebral Infarction scores.

Conclusions ChatGPT adequately predicted short-term functional outcomes in post-thrombectomy patients with AIS and was better than the existing risk score. Integrating AI models into clinical practice holds promise for patient care, yet refining these models is crucial for enhanced accuracy in stroke management.

  • Stroke

Data availability statement

Data are available upon reasonable request.

https://doi.org/10.1136/jnis-2024-021556

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    Data availability statement

    Data are available upon reasonable request.

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    Footnotes

    • Contributors TP conceived the study design; TP, LF, MGG, GM, MP, AA, PCC, PC, GA, LA, LPA, and MLS collected the data; TP analyzed the data; TP, LPA, ML, and PC interpreted the results; TP prepared the figures; TP and JMS drafted the manuscript; TP, JMS, LPA, PC, and MLS edited and revised the manuscript; All authors approved the final version of the manuscript. TP was guarantor for the overall content.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.