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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Filename: controllers/Detail.php
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Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
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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 stratify survivors based on late-effect risk, analyze follow-up care patterns, and determine factors associated with suboptimal follow-up care.
Procedure: The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD-O-3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits.
Results: Five hundred and ninety pediatric hematology-oncology (PHO) and 275 pediatric neuro-oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight-two survivors (32.6%) were not seen in any oncology-related subspecialty clinic at Duke 5-7 years after initial diagnosis. Factors associated with follow-up included age (p = .008), diagnosis (p < .001), race/ethnicity (p = .010), late-effect risk strata (p = .001), distance to treatment center (p < .0001), and area deprivation index (ADI) (p = .011). Multivariable logistic modeling attenuated the association for high-risk (OR 1.72; 95% CI 0.805, 3.66) and intermediate-risk (OR 1.23, 95% CI 0.644, 2.36) survivors compared to survivors at low risk of late effects among the PHO cohort. PNO survivors at high risk for late effects were more likely to follow up (adjusted OR 3.66; 95% CI 1.76, 7.61).
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.
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Source |
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http://dx.doi.org/10.1002/pbc.29014 | DOI Listing |
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