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Informatics Tools to Implement Late Cardiovascular Risk Prediction Modeling for Population Management of High-Risk Childhood Cancer Survivors
  • +7
  • David H. Noyd,
  • Sixia Chen,
  • Anna M. Bailey,
  • Amanda Janitz,
  • Ashley Baker,
  • William H. Beasley,
  • Nancy C. Etzold,
  • David C. Kendrick,
  • Warren A. Kibbe,
  • Kevin Oeffinger
David H. Noyd
The University of Oklahoma Department of Pediatrics

Corresponding Author:[email protected]

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Sixia Chen
The University of Oklahoma Hudson College of Public Health
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Anna M. Bailey
The University of Oklahoma Hudson College of Public Health
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Amanda Janitz
The University of Oklahoma Hudson College of Public Health
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Ashley Baker
The University of Oklahoma Department of Pediatrics
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William H. Beasley
The University of Oklahoma Department of Pediatrics
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Nancy C. Etzold
The University of Oklahoma Health Sciences Center
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David C. Kendrick
The University of Oklahoma Health Sciences Center
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Warren A. Kibbe
Duke University Department of Biostatistics and Bioinformatics
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Kevin Oeffinger
Duke University Department of Medicine
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Abstract

Background: Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators. Methods: The Oklahoma cohort (n=365) harnessed data elements from Passport for Care (PFC) and the Duke cohort (n=274) integrated cancer registry and electronic health record data, using standard query language, to automatically extract chemotherapy exposures for survivors <18 years old at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented and risk groups for heart failure were compared to the Children’s Oncology Group (COG) Long-Term Follow-up Guidelines. Results: The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure with weighted Kappa statistics of 0.70 and 0.75, respectively. Low-risk groups showed excellent concordance (Kappa >0.9). Moderate and high-risk groups showed moderate concordance (Kappa 0.44-0.60 across both cohorts). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent care for echocardiogram surveillance compared with survivors <13 years old at diagnosis (OR 0.22; 95% CI 0.10-0.49). Conclusions: Clinical informatics tools represent a feasible approach to leverage discrete data elements regarding key treatment exposures from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Real-world evidence on the concordance of CCSS, COG, and IGHG risk groups promises to refine current guidelines and identify inequities in guideline-adherent care.
10 Feb 2023Submission Checks Completed
10 Feb 2023Assigned to Editor
10 Feb 2023Submitted to Pediatric Blood & Cancer
10 Feb 2023Review(s) Completed, Editorial Evaluation Pending
10 Feb 2023Reviewer(s) Assigned
26 Feb 2023Editorial Decision: Revise Major
05 May 2023Submission Checks Completed
05 May 2023Assigned to Editor
05 May 20231st Revision Received
05 May 2023Review(s) Completed, Editorial Evaluation Pending
05 May 2023Reviewer(s) Assigned
17 May 2023Editorial Decision: Accept