Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits

Artificial Intelligence Clinical Trial

— PAN-EM-NEURO  

Official title:

Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06245694
• Other study ID #: EK- Nr. 1738/2022
• Status: Recruiting
• Start date: January 1, 2022
• Est. completion date: January 1, 2030

Study information

• Verified date: February 2024
• Source: Medical University of Vienna
• Contact: Jan Niederdöckl, MD
• Phone: 0042 40 400 19640
• Email: jan.niederdoeckl[@]meduniwien.ac.at
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Future predictive modeling in emergency medicine will likely combine the use of a wide range of data points such as continuous documentation, monitoring using wearables, imaging, biomarkers, and real-time administrative data from all health care providers involved. Subsequent extensive data sets could feed advanced deep learning and neural network algorithms to accurately predict the risk of specific health conditions. Moreover, predictive analytics steers towards the development of clinical pathways that are adaptive and continuously updated, and in which healthcare decision-making is supported by sophisticated algorithms to provide the best course of action effectively and safely. The potential for predictive analytics to revolutionize many aspects of healthcare seems clear in the horizon. Information on the use in emergency medicine is scarce.

Aim of the study is to evaluate the performance of using routine-data to predict resource usage in emergency medicine using the commonly encountered symptom of acute neurologic deficit. As an outlook, this might serve as a prototype for other, similar projects using routine medical data for predictive analytics in emergency medicine.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50000
• Est. completion date: January 1, 2030
• Est. primary completion date: January 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 120 Years

Eligibility

Inclusion Criteria:

• Female and Male subjects

• Age = 18 years

Exclusion Criteria:

• none

Related Conditions & MeSH terms

• Artificial Intelligence
• Emergencies
• Neurologic Manifestations

Locations

Country	Name	City	State
Austria	Emergency Department, Medical University Vienna	Vienna

Sponsors (1)

Lead Sponsor	Collaborator
Medical University of Vienna

Country where clinical trial is conducted

Austria, 

References & Publications (14)

Avasarala J. Letter by Avasarala Regarding Article, "2015 AHA/ASA Focused Update of the 2013 Guidelines for the Early Management of Patients With Acute Ischemic Stroke Regarding Endovascular Treatment: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association". Stroke. 2015 Nov;46(11):e234. doi: 10.1161/STROKEAHA.115.010716. Epub 2015 Oct 6. No abstract available. — View Citation

Badhiwala JH, Nassiri F, Alhazzani W, Selim MH, Farrokhyar F, Spears J, Kulkarni AV, Singh S, Alqahtani A, Rochwerg B, Alshahrani M, Murty NK, Alhazzani A, Yarascavitch B, Reddy K, Zaidat OO, Almenawer SA. Endovascular Thrombectomy for Acute Ischemic Stroke: A Meta-analysis. JAMA. 2015 Nov 3;314(17):1832-43. doi: 10.1001/jama.2015.13767. — View Citation

Bauchner H, Golub RM, Fontanarosa PB. Data Sharing: An Ethical and Scientific Imperative. JAMA. 2016 Mar 22-29;315(12):1237-9. doi: 10.1001/jama.2016.2420. No abstract available. — View Citation

Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018 Mar 15;378(11):981-983. doi: 10.1056/NEJMp1714229. No abstract available. — View Citation

Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2018 Mar 15;24(6):1248-1259. doi: 10.1158/1078-0432.CCR-17-0853. Epub 2017 Oct 5. — View Citation

Christiansen EM, Yang SJ, Ando DM, Javaherian A, Skibinski G, Lipnick S, Mount E, O'Neil A, Shah K, Lee AK, Goyal P, Fedus W, Poplin R, Esteva A, Berndl M, Rubin LL, Nelson P, Finkbeiner S. In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell. 2018 Apr 19;173(3):792-803.e19. doi: 10.1016/j.cell.2018.03.040. Epub 2018 Apr 12. — View Citation

Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff (Millwood). 2014 Jul;33(7):1139-47. doi: 10.1377/hlthaff.2014.0048. — View Citation

Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909. — View Citation

Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, Davalos A, Majoie CB, van der Lugt A, de Miquel MA, Donnan GA, Roos YB, Bonafe A, Jahan R, Diener HC, van den Berg LA, Levy EI, Berkhemer OA, Pereira VM, Rempel J, Millan M, Davis SM, Roy D, Thornton J, Roman LS, Ribo M, Beumer D, Stouch B, Brown S, Campbell BC, van Oostenbrugge RJ, Saver JL, Hill MD, Jovin TG; HERMES collaborators. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet. 2016 Apr 23;387(10029):1723-31. doi: 10.1016/S0140-6736(16)00163-X. Epub 2016 Feb 18. — View Citation

Heeley E, Anderson CS, Huang Y, Jan S, Li Y, Liu M, Sun J, Xu E, Wu Y, Yang Q, Zhang J, Zhang S, Wang J; ChinaQUEST Investigators. Role of health insurance in averting economic hardship in families after acute stroke in China. Stroke. 2009 Jun;40(6):2149-56. doi: 10.1161/STROKEAHA.108.540054. Epub 2009 Apr 9. — View Citation

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. eCollection 2017 Dec. — View Citation

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Crit Care Med. 2018 Apr;46(4):547-553. doi: 10.1097/CCM.0000000000002936. — View Citation

Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep Learning for Health Informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. doi: 10.1109/JBHI.2016.2636665. Epub 2016 Dec 29. — View Citation

Saber H, Somai M, Rajah GB, Scalzo F, Liebeskind DS. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurol Res. 2019 Aug;41(8):681-690. doi: 10.1080/01616412.2019.1609159. Epub 2019 Apr 30. — View Citation

* Note: There are 14 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Prediction model	to be developed	1.1.2025