Multi-Modal Prediction of Extracorporeal Support—A Resource Intensive Therapy, Utilizing A Large National Database
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Moderator
Presenter
Statement of Purpose
ECMO is a scarce, resource-intensive therapy, and the COVID-19 pandemic exposed substantial gaps in timely identification of candidates and equitable allocation. Prior work has largely relied on static risk scores or conventional machine-learning models built on snapshot data, offering limited ability to capture evolving physiology or to support continuous, anticipatory triage decisions. These approaches often underutilize multimodal EHR data and lack mechanisms to translate temporal dynamics into actionable early warnings.
Our work advances the field by introducing PreEMPT-ECMO, a hierarchical deep learning framework that fuses static features with multi-granularity time series to produce continuous predictions of ECMO utilization up to 96 hours in advance. Trained on a large, multicenter N3C cohort, the model outperforms established methods across time horizons and provides trajectory-aware interpretability to contextualize changing risk drivers. This capability aligns with real-world decision-making, enabling earlier triage and more judicious resource allocation; it also establishes a platform for prospective validation and extension beyond COVID-19 to broader refractory respiratory failure, including pediatric applications.
Learning Objectives
- Explain the limitations of static risk scores and how continuous, time-aware modeling can enhance early ECMO risk assessment and clinical decision-making.
Additional Information
The target audience for this activity includes physicians, nurses, other healthcare providers, and medical informaticians.
No commercial support was received for this activity.
Completion of this “Other Activity (Regularly Scheduled Series – RSS)” is demonstrated by participating in the live webinar or viewing the on-demand recording, engaging with presenters during the live session by submitting questions, and completing the evaluation survey at the conclusion of the course.
Learners may claim credit and download a certificate upon submission of the evaluation. Participation in additional resources and the course forum is encouraged but optional.
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this Other activity (Regularly Scheduled Series (RSS)) for a maximum of 12 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
- Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
- Approved Contact Hours: 12 CME/CNE
It is the policy of the American Medical Informatics Association (AMIA) to ensure that Continuing Medical Education (CME) activities are independent and free of commercial bias. To ensure educational content is objective, balanced, and guarantee content presented is in the best interest of its learners and the public, the AMIA requires that everyone in a position to control educational content disclose all financial relationships with ineligible companies within the prior 24 months. An ineligible company is one whose primary business is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients. Examples can be found at accme.org.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to the planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
Faculty and planners who refuse to disclose any financial relationships with ineligible companies will be disqualified from participating in the educational activity.
For an individual with no relevant financial relationship(s), course participants must be informed that no conflicts of interest or financial relationship(s) exist.
Disclosures
Planning Committee
The planning committee and reviewers reported that they have no relevant financial relationship(s) with ineligible companies to disclose.
- Joanna Abraham, PhD, FACMI, FAMIA
- Jifan Gao, MS
- Frances Hsu, BS, MS
- Sonish Sivarajkumar
- Song Wang, MS
- Faisal Yaseen
Presenter
The following presenters have no relevant financial relationship(s) with ineligible companies to disclose.
AMIA Staff
The AMIA staff have no relevant financial relationship(s) with ineligible companies to disclose.
*All of the relevant financial relationships listed for these individuals have been mitigated.
