AzTrials
AzTrials

Artificial IntelligencePAN-EM-NEURONCT06245694

Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits

An observational study of Artificial Intelligence and Resource Allocation, run by Medical University of Vienna at 1 site in Austria.

Recruiting

Looking for participants now. Contact the study team or a site below to ask about taking part.

Sites and contacts
Official record
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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. Read more about this study

Who is it for?
Ages 18 to 120 years
How many people
50,000 participants
Where
1 siteAustria
Study dates
1 Jan 2022to 1 Jan 2030
Phase
Observational
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 to 120 years
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Study population: Retrospective analysis of routinely collected data. We aim to find data patterns associated with in-hospital resource utilization of patients hospitalized by emergency medical services for suspected acute neurologic deficit. We will use only information from the routine electronic medical documentation system for this study. No linkage to other datasets will be performed. Data in the system includes patient demographics, initial symptoms, prehospital vital signs and scores, suspected diagnosis by emergency medical services, inhospital vital signs and scores, inhospital diagnostic (computed tomography CT, magnetic resonance imaging MRI) and therapeutic (catheter intervention) procedures and final diagnosis. See 'variables' for a full set of variables. For the purpose of this study, only a fully pseudonomyzed dataset will be used.

Who may be able to join

  • Female and Male subjects
  • Age ≥ 18 years

Who cannot join

  • none

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Jan Niederdöckl, MD contact

0042 40 400 19640

jan.niederdoeckl@meduniwien.ac.at

Austria1 site, 1 recruiting

  • Emergency Department, Medical University Vienna

    Vienna, 1090

    Jan Niederdöckl, Dr., jan.niederdoeckl@medunwien.ac.at

    Alexander Simon, Dr., alexander.simon@medunwien.ac.at

    Recruiting

Other trials in Austria

About this study

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.

Medical subject headings
Neurologic Manifestations

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Prediction model

    to be developed

    Measured: 1.1.2025

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted7 Feb 2024
  2. Study start1 Jan 2022
  3. Primary completion1 Jan 2025 (estimated)
  4. Study completion1 Jan 2030 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Retrospective
Enrolment
50,000 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06245694
Sponsor's protocol ID
EK- Nr. 1738/2022
Acronym
PAN-EM-NEURO
Status last verified
Nov 2024
First submitted
30 Jan 2024
Last updated
22 Nov 2024
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • 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.

    PubMed 26443830

  • 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.

    PubMed 26529161

  • 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.

    PubMed 27002444

  • 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.

    PubMed 29539284

  • 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.

    PubMed 28982688

  • 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.

    PubMed 29656897

  • 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.

    PubMed 25006139

  • 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.

    PubMed 27694098

  • 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.

    PubMed 26898852

  • 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.

    PubMed 19359646

  • 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.

    PubMed 29507784

  • 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.

    PubMed 29286945

  • 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.

    PubMed 28055930

  • 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.

    PubMed 31038007

Questions about this trial

What is this clinical trial studying?

An observational study of Artificial Intelligence and Resource Allocation, run by Medical University of Vienna at 1 site in Austria.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: looking for participants now, as of the sponsor's last update on 22 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Austria. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact Jan Niederdöckl, MD (0042 40 400 19640, jan.niederdoeckl@meduniwien.ac.at), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Prediction model (time frame: 1.1.2025). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06245694, which also lists 14 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Nov 2024.

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