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.

Background: This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to risk stratify patients for serious adverse health outcomes, analyze follow-up care patterns, and determine factors associated with suboptimal follow-up care. Procedure: The survivorship cohort included patients ≤18 years of age with a diagnosis of a malignancy reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. ICD-O-3 coding and treatment exposures facilitated risk stratification of survivors. All follow-up visits were extracted from the EHR through linkage to the cancer registry based on medical record number (MRN). Results: Eight-hundred-and-sixty-five survivors were included in the final analytic cohort, of whom 191, 496, and 158 were considered low, intermediate, and high risk survivors, respectively. Two-hundred-and-eight-two survivors (32.6%) were not seen in any oncology-related subspecialty clinic at Duke five to seven years after initial diagnosis. Factors associated with a clinic visit included younger age (p=0.008), acute lymphoblastic leukemia (ALL) as the primary diagnosis (p<0.001), race/ethnicity (p=0.010), risk strata (p=0.001), distance to treatment center (p<0.0001), and lower ADI (p=0.011). Multivariable logistic modeling with adjustment for diagnosis of ALL, gender, age at diagnosis, and race/ethnicity attenuated the association between follow-up care and risk strata (p=0.17) Conclusions: Nearly a third of survivors received suboptimal follow-up care. This study provides a reproducible model to integrate cancer registry and EHR data to construct risk-stratified survivorship cohorts to assess follow-up care.