nach oben

Erschienen in:

05.09.2023 | Original Communication

Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment

verfasst von: Stanislas Demuth, Joris Müller, Véronique Quenardelle, Valérie Lauer, Roxana Gheoca, Malwina Trzeciak, Irène Pierre-Paul, Jérôme De Sèze, Pierre-Antoine Gourraud, Valérie Wolff

Erschienen in: Journal of Neurology | Ausgabe 12/2023

Einloggen, um Zugang zu erhalten

Abstract

Background

Acute ischemic stroke (AIS) is an immediate emergency whose management is becoming more and more personalized while facing a limited number of neurologists with high expertise. Clinical decision support systems (CDSS) are digital tools leveraging information and artificial intelligence technologies. Here, we present the Strokecopilot project, a CDSS for the management of the acute phase of AIS. It has been designed to support the evidence-based medicine reasoning of neurologists regarding the indications of intravenous thrombolysis (IVT) and endovascular treatments (ET).

Methods

Reference populations were manually extracted from the field’s main guidelines and randomized clinical trials (RCT). Their characteristics were harmonized in a computerized reference database. We developed a web application whose algorithm identifies the reference populations matching the patient’s characteristics. It returns the latter’s outcomes in a graphical user interface (GUI), whose design has been driven by real-world practices.

Results

Strokecopilot has been released at www.digitalneurology.net. The reference database includes 25 reference populations from 2 guidelines and 15 RCTs. After a request, the reference populations matching the patient characteristics are displayed with a summary and a meta-analysis of their results. The status regarding IVT and ET indications are presented as “in guidelines”, “in literature”, or “outside literature references”. The GUI is updated to provide several levels of explanation. Strokecopilot may be updated as the literature evolves by loading a new version of the reference populations’ database.

Conclusion

Strokecopilot is a literature-based CDSS, developed to support neurologists in the management of the acute phase of AIS.

Nur mit Berechtigung zugänglich

GBD 2019 Stroke Collaborators (2021) Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 20:795–820. https://doi.org/10.1016/S1474-4422(21)00252-0CrossRef

Ma H, Campbell BCV, Parsons MW et al (2019) Thrombolysis guided by perfusion imaging up to 9 h after onset of stroke. N Engl J Med 380:1795–1803. https://doi.org/10.1056/NEJMoa1813046CrossRefPubMed

Thomalla G, Simonsen CZ, Boutitie F et al (2018) MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med 379:611–622. https://doi.org/10.1056/NEJMoa1804355CrossRefPubMed

Bivard A, Churilov L, Parsons M (2020) Artificial intelligence for decision support in acute stroke—current roles and potential. Nat Rev Neurol 16:575–585. https://doi.org/10.1038/s41582-020-0390-yCrossRefPubMed

Sutton RT, Pincock D, Baumgart DC et al (2020) An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 3:1–10. https://doi.org/10.1038/s41746-020-0221-yCrossRef

Kuczmarski RJ, Ogden CL, Guo SS et al (2002) 2000 CDC growth charts for the United States: methods and development. Vital Health Stat 11:1–190

Modasia KH, Kaliyadan F (2022) Digital tools for assessing disease severity in dermatology. Indian Dermatol Online J 13:190–198. https://doi.org/10.4103/idoj.idoj_636_21CrossRefPubMedPubMedCentral

Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) AI in health and medicine. Nat Med. https://doi.org/10.1038/s41591-021-01614-0CrossRefPubMed

Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J et al (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58:82–115. https://doi.org/10.1016/j.inffus.2019.12.012CrossRef

10.

Citerio G (2022) Big data and artificial intelligence for precision medicine in the neuro-ICU: Bla, Bla, Bla. Neurocrit Care 37:163–165. https://doi.org/10.1007/s12028-021-01427-6CrossRefPubMedPubMedCentral

11.

Ghassemi M, Oakden-Rayner L, Beam AL (2021) The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 3:e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9CrossRefPubMed

12.

Ed-Driouch C, Mars F, Gourraud P-A, Dumas C (2022) Addressing the challenges and barriers to the integration of machine learning into clinical practice: an innovative method to hybrid human-machine intelligence. Sensors 22:8313. https://doi.org/10.3390/s22218313CrossRefPubMedPubMedCentral

13.

Sadiku MNO, Musa SM (2021) Augmented intelligence. In: Sadiku MNO, Musa SM (eds) A primer on multiple intelligences. Springer International Publishing, Cham, pp 191–199CrossRef

14.

Berge E, Whiteley W, Audebert H et al (2021) European Stroke Organisation (ESO) guidelines on intravenous thrombolysis for acute ischaemic stroke. Eur Stroke J 6:I–LXII. https://doi.org/10.1177/2396987321989865CrossRefPubMedPubMedCentral

15.

Turc G, Bhogal P, Fischer U et al (2019) European Stroke Organisation (ESO)—European Society for Minimally Invasive Neurological Therapy (ESMINT) guidelines on mechanical thrombectomy in acute ischaemic StrokeEndorsed by Stroke Alliance for Europe (SAFE). Eur Stroke J 4:6–12. https://doi.org/10.1177/2396987319832140CrossRefPubMedPubMedCentral

16.

Turc G, Tsivgoulis G, Audebert HJ et al (2022) European Stroke Organisation (ESO)-European Society for Minimally Invasive Neurological Therapy (ESMINT) expedited recommendation on indication for intravenous thrombolysis before mechanical thrombectomy in patients with acute ischemic stroke and anterior circulation large vessel occlusion. J Neurointerventional Surg 14:209. https://doi.org/10.1136/neurintsurg-2021-018589CrossRef

17.

Django. In: Django Proj. https://www.djangoproject.com/. Accessed 19 Dec 2022

18.

Wahlgren N, Ahmed N, Dávalos A et al (2007) Thrombolysis with alteplase for acute ischaemic stroke in the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST): an observational study. Lancet Lond Engl 369:275–282. https://doi.org/10.1016/S0140-6736(07)60149-4CrossRef

19.

Hongyong D PythonMeta: a Python module of meta-analysis, usually applied in systemtic reviews of evidence-based medicine. http://www.pymeta.com. Accessed 19 Dec 2022

20.

Masoumi S, Shahraz S (2022) Meta-analysis using Python: a hands-on tutorial. BMC Med Res Methodol 22:193. https://doi.org/10.1186/s12874-022-01673-yCrossRefPubMedPubMedCentral

21.

Demuth S, Wolff V (2021) Le projet Strokecopilot: une intelligence artificielle pour assister les alertes thrombolyses. Rev Neurol (Paris) 177:S82. https://doi.org/10.1016/j.neurol.2021.02.267CrossRef

22.

Nagenthiraja K, Walcott BP, Hansen MB et al (2013) Automated decision-support system for prediction of treatment responders in acute ischemic stroke. Front Neurol 4:140. https://doi.org/10.3389/fneur.2013.00140CrossRefPubMedPubMedCentral

23.

Alexander NVJ, Brunette CA, Guardino ET et al (2022) Performance of EHR classifiers for patient eligibility in a clinical trial of precision screening. Contemp Clin Trials 121:106926. https://doi.org/10.1016/j.cct.2022.106926CrossRefPubMedPubMedCentral

24.

Buchanan BG, Shortliffe EH (1984) Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading

25.

Barnett GO, Cimino JJ, Hupp JA, Hoffer EP (1987) DXplain: an evolving diagnostic decision-support system. JAMA 258:67–74. https://doi.org/10.1001/jama.1987.03400010071030CrossRefPubMed

26.

Brochado AP, Muras AC, Oyarzun-Irazu I et al (2022) Drip and ship and mothership models of mechanical thrombectomy result in similar outcomes in acute ischemic stroke of the anterior circulation. J Stroke Cerebrovasc Dis Off J Natl Stroke Assoc 31:106733. https://doi.org/10.1016/j.jstrokecerebrovasdis.2022.106733CrossRef

27.

Ed-Driouch C, Chéneau F, Simon F et al (2022) Multiple sclerosis clinical decision support system based on projection to reference datasets. Ann Clin Transl Neurol 9:1863–1873. https://doi.org/10.1002/acn3.51649CrossRefPubMedPubMedCentral

28.

Gourraud P-A, Henry RG, Cree BAC et al (2014) Precision medicine in chronic disease management: the multiple sclerosis BioScreen. Ann Neurol 76:633–642. https://doi.org/10.1002/ana.24282CrossRefPubMedPubMedCentral

29.

Shilo S, Rossman H, Segal E (2020) Axes of a revolution: challenges and promises of big data in healthcare. Nat Med 26:29–38. https://doi.org/10.1038/s41591-019-0727-5CrossRefPubMed

30.

Lange S, Sauerland S, Lauterberg J, Windeler J (2017) The range and scientific value of randomized trials. Dtsch Arzteblatt Int 114:635–640. https://doi.org/10.3238/arztebl.2017.0635CrossRef

31.

Evidence-Based Medicine Working Group (1992) Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA 268:2420–2425. https://doi.org/10.1001/jama.1992.03490170092032CrossRef

32.

Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372:793–795. https://doi.org/10.1056/NEJMp1500523CrossRefPubMedPubMedCentral

33.

Merelli I, Pérez-Sánchez H, Gesing S, D’Agostino D (2014) Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives. BioMed Res Int 2014:134023. https://doi.org/10.1155/2014/134023CrossRefPubMedPubMedCentral

34.

Bates DW (2023) How to regulate evolving AI health algorithms. Nat Med 29:26–26. https://doi.org/10.1038/s41591-022-02165-8CrossRefPubMed

35.

Deans KJ, Sabihi S, Forrest CB (2018) Learning health systems. Semin Pediatr Surg 27:375–378. https://doi.org/10.1053/j.sempedsurg.2018.10.005CrossRefPubMed

Titel: Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment
verfasst von: Stanislas Demuth
Joris Müller
Véronique Quenardelle
Valérie Lauer
Roxana Gheoca
Malwina Trzeciak
Irène Pierre-Paul
Jérôme De Sèze
Pierre-Antoine Gourraud
Valérie Wolff
Publikationsdatum: 05.09.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Neurology / Ausgabe 12/2023
Print ISSN: 0340-5354
Elektronische ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-023-11979-6

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Schützt Olivenöl vor dem Tod durch Demenz?

10.05.2024 Morbus Alzheimer Nachrichten

Konsumieren Menschen täglich 7 Gramm Olivenöl, ist ihr Risiko, an einer Demenz zu sterben, um mehr als ein Vierten reduziert – und dies weitgehend unabhängig von ihrer sonstigen Ernährung. Dafür sprechen Auswertungen zweier großer US-Studien.

Bluttest erkennt Parkinson schon zehn Jahre vor der Diagnose

10.05.2024 Parkinson-Krankheit Nachrichten

Ein Bluttest kann abnorm aggregiertes Alpha-Synuclein bei einigen Menschen schon zehn Jahre vor Beginn der motorischen Parkinsonsymptome nachweisen. Mit einem solchen Test lassen sich möglicherweise Prodromalstadien erfassen und die Betroffenen früher behandeln.

Darf man die Behandlung eines Neonazis ablehnen?

08.05.2024 Gesellschaft Nachrichten

In einer Leseranfrage in der Zeitschrift Journal of the American Academy of Dermatology möchte ein anonymer Dermatologe bzw. eine anonyme Dermatologin wissen, ob er oder sie einen Patienten behandeln muss, der eine rassistische Tätowierung trägt.

Wartezeit nicht kürzer, aber Arbeit flexibler

Psychotherapie Medizin aktuell

Fünf Jahren nach der Neugestaltung der Psychotherapie-Richtlinie wurden jetzt die Effekte der vorgenommenen Änderungen ausgewertet. Das Hauptziel der Novellierung war eine kürzere Wartezeit auf Therapieplätze. Dieses Ziel wurde nicht erreicht, es gab jedoch positive Auswirkungen auf andere Bereiche.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Klimawandel und Gesundheit: Was Sie in der Hausarztpraxis wissen müssen und tun können

Springer Medizin

Abstract

Background

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 12/2023

Evaluating the prediction performance of objective physical activity measures for incident Parkinson’s disease in the UK Biobank

Increased brain age and relationships with blood-based biomarkers following concussion in younger populations

Cognitive impairment and dementia in young onset Parkinson’s disease

Rapid switching from levetiracetam to brivaracetam in pharmaco-resistant epilepsy in people with and without intellectual disabilities: a naturalistic case control study

“Perceived Intention”—Motor intention perceived as movement despite paralysis and retained insight