Ignite Creation Date:
2025-12-25 @ 1:28 AM
Ignite Modification Date:
2025-12-28 @ 1:36 AM
Study NCT ID:
NCT06245694
Status:
RECRUITING
Last Update Posted:
2024-11-22
First Post:
2024-01-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits
Sponsor:
Organization:
Raw JSON
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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': '26529161', 'type': 'BACKGROUND', '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.'}, {'pmid': '27002444', 'type': 'BACKGROUND', '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.'}, {'pmid': '29539284', 'type': 'BACKGROUND', '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.'}, {'pmid': '28982688', 'type': 'BACKGROUND', '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.'}, {'pmid': '29656897', 'type': 'BACKGROUND', '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."}, {'pmid': '25006139', 'type': 'BACKGROUND', '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.'}, {'pmid': '27694098', 'type': 'BACKGROUND', '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.'}, {'pmid': '26898852', 'type': 'BACKGROUND', '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.'}, {'pmid': '19359646', 'type': 'BACKGROUND', '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.'}, {'pmid': '29507784', 'type': 'BACKGROUND', '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.'}, {'pmid': '29286945', 'type': 'BACKGROUND', '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.'}, {'pmid': '28055930', 'type': 'BACKGROUND', '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.'}, {'pmid': '31038007', 'type': 'BACKGROUND', '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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nAim 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '120 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': "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.\n\nWe 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.\n\nFor the purpose of this study, only a fully pseudonomyzed dataset will be used.", 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Female and Male subjects\n* Age ≥ 18 years\n\nExclusion Criteria:\n\n\\- none'}, 'identificationModule': {'nctId': 'NCT06245694', 'acronym': 'PAN-EM-NEURO', 'briefTitle': 'Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits', 'organization': {'class': 'OTHER', 'fullName': 'Medical University of Vienna'}, 'officialTitle': 'Predictive and Advanced Analytics in Emergency Medicine - Neurological Deficits', 'orgStudyIdInfo': {'id': 'EK- Nr. 1738/2022'}}, 'contactsLocationsModule': {'locations': [{'zip': '1090', 'city': 'Vienna', 'status': 'RECRUITING', 'country': 'Austria', 'contacts': [{'name': 'Jan Niederdöckl, Dr.', 'role': 'CONTACT', 'email': 'jan.niederdoeckl@medunwien.ac.at'}, {'name': 'Alexander Simon, Dr.', 'role': 'CONTACT', 'email': 'alexander.simon@medunwien.ac.at'}], 'facility': 'Emergency Department, Medical University Vienna', 'geoPoint': {'lat': 48.20849, 'lon': 16.37208}}], 'centralContacts': [{'name': 'Jan Niederdöckl, MD', 'role': 'CONTACT', 'email': 'jan.niederdoeckl@meduniwien.ac.at', 'phone': '0042 40 400 19640'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Medical University of Vienna', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Priv. Doz. Dr. Jan Niederdöckl, PhD - senior researcher and head of the arrhythmias and cardiovascular biomarkers research group', 'investigatorFullName': 'Jan Niederdöckl', 'investigatorAffiliation': 'Medical University of Vienna'}}}}