Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

Abstract Submission

Attendees interested in presenting a poster should complete the form below to submit their work. A confirmation email will be sent upon successful submission.

Best Abstract Award

Abstracts will be evaluated based on the following criteria:

Significance and Impact:
- Relevance of the topic and its potential contribution
- Evidence of innovative data use and/or potential to increase relevant data for sharing in the CCDI Ecosystem
Scientific Rigor:
- Clearly stated research question
- Well-suited methodologies to address the problem
- Detailed experimental methods
Clarity: Content is well-organized, easy to follow, and effectively communicated

The deadline for abstracts for poster presentations closes on August 21, 2026. If you have questions about submitting abstracts for poster presentations, contact NCIChildhoodCancerDataInitiative@mail.nih.gov.

Submission information

Submission Number: 48

Submission ID: 150078

Submission UUID: 458369c0-9547-47ef-aec4-6bc56dbec5e2

Submission URI: /nci/ccdisymposium/abstract?cid=eb_govdel_ccdi_events

Submission Update: /nci/ccdisymposium/abstract?cid=eb_govdel_ccdi_events&token=d80kU0p6IObVw_7bw-zPf8Kd5fwovrK6xb-TLXL2DEM

Created: Thu, 08/28/2025 - 08:32

Completed: Thu, 08/28/2025 - 08:35

Changed: Thu, 08/28/2025 - 08:35

Remote IP address: 10.208.28.90

Submitted by: Anonymous

Language: English

Is draft: No

Webform: Childhood Cancer Data Initiative Annual Symposium (Abstracts Submission)

Submitted to: Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

Abstract Submission for Poster Presentation

Poster size should not exceed 4 ft. (height) by 6 ft. (width)

Abstract Title:

Abstract:

Background:
The National Cancer Institute (NCI) Childhood Cancer Data Initiative (CCDI) has enabled the distribution of previously unshared data. A critical component to maximize the utility of these data is to have longitudinal clinical data, such as treatment data. Manual abstraction has typically been used to capture treatment data, but this process is time consuming and subject to human error. This study aimed to use automated extraction and processing of electronic health record (EHR) data using the ExtractEHR+ Toolkit to describe chemotherapy exposures for children enrolled in the Children’s Brain Tumor Network (CBTN) who have contributed data to the CCDI.

Methods:
Patients enrolled in CBTN at two hospitals were included. ExtractEHR extracted all medication orders and administrations, including outpatient prescriptions, heights, and weights. Medication order and administration data were merged and MedCleanEHR centrally cleaned these data to identify unique chemotherapy exposures and dose amounts. Height and weight data were used to calculate body surface area and merged with medication data. For prescriptions where a discrete dose field was not included, regular expressions were used to identify doses in free text fields. MedCleanEHR calculated cumulative chemotherapy doses each patient received.

Results:
The cohort included 1628 patients. ExtractEHR successfully pulled 3,849,099 medication administrations and 839,736 medication orders. Once cleaned, there were 98,877 unique chemotherapy administrations and 8,781 chemotherapy prescriptions.

Conclusions:
The ExtractEHR+ Toolkit can efficiently ascertains chemotherapy exposures and dosing for patients in the CBTN cohort. These data will be transferred to the CCDI to enhance clinical data in the CCDI ecosystem.

Authors:

Please use the following formatting for these degrees: M.D. Ph.D. MPH

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