NCT06245694

Brief 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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
50,000

participants targeted

Target at P75+ for all trials

Timeline
43mo left

Started Jan 2022

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress56%
Jan 2022Jan 2030

Study Start

First participant enrolled

January 1, 2022

Completed
2.1 years until next milestone

First Submitted

Initial submission to the registry

January 30, 2024

Completed
8 days until next milestone

First Posted

Study publicly available on registry

February 7, 2024

Completed
11 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2025

Completed
5 years until next milestone

Study Completion

Last participant's last visit for all outcomes

January 1, 2030

Expected
Last Updated

November 22, 2024

Status Verified

November 1, 2024

Enrollment Period

3 years

First QC Date

January 30, 2024

Last Update Submit

November 19, 2024

Conditions

Artificial IntelligenceResource AllocationNeurologic Manifestations

Outcome Measures

Primary Outcomes (1)

  • Prediction model

    to be developed

    1.1.2025

Eligibility Criteria

Age18 Years - 120 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
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.

You may qualify if:

  • Female and Male subjects
  • Age ≥ 18 years

You may not qualify if:

  • \- none

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Emergency Department, Medical University Vienna

Vienna, 1090, Austria

RECRUITING

Related 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.

    PMID: 26443830BACKGROUND

  • 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.

    PMID: 26529161BACKGROUND

  • 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.

    PMID: 27002444BACKGROUND

  • 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.

    PMID: 29539284BACKGROUND

  • 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.

    PMID: 28982688BACKGROUND

  • 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.

    PMID: 29656897BACKGROUND

  • 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.

    PMID: 25006139BACKGROUND

  • 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.

    PMID: 27694098BACKGROUND

  • 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.

    PMID: 26898852BACKGROUND

  • 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.

    PMID: 19359646BACKGROUND

  • 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.

    PMID: 29507784BACKGROUND

  • 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.

    PMID: 29286945BACKGROUND

  • 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.

    PMID: 28055930BACKGROUND

  • 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.

    PMID: 31038007BACKGROUND

MeSH Terms

Conditions

Neurologic Manifestations

Condition Hierarchy (Ancestors)

Nervous System DiseasesSigns and SymptomsPathological Conditions, Signs and Symptoms

Central Study Contacts

Jan Niederdöckl, MD

CONTACT

0042 40 400 19640jan.niederdoeckl@meduniwien.ac.at

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Priv. Doz. Dr. Jan Niederdöckl, PhD - senior researcher and head of the arrhythmias and cardiovascular biomarkers research group

Study Record Dates

First Submitted

January 30, 2024

First Posted

February 7, 2024

Study Start

January 1, 2022

Primary Completion

January 1, 2025

Study Completion (Estimated)

January 1, 2030

Last Updated

November 22, 2024

Record last verified: 2024-11

Data Sharing

IPD Sharing
Will not share

Locations

AU(1)