Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow-up 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 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.

Development and validation of an electronic medical record (EMR)-based computed phenotype of HIV-1 infection.

Background: Electronic medical record (EMR) computed algorithms allow investigators to screen thousands of patient records to identify specific disease cases. No computed algorithms have been developed to detect all cases of human immunodeficiency virus (HIV) infection using administrative, laboratory, and clinical documentation data outside of the Veterans Health Administration. We developed novel EMR-based algorithms for HIV detection and validated them in a cohort of subjects in the Duke University Health System (DUHS). Methods: We created 2 novel algorithms to identify HIV-infected subjects. Algorithm 1 used laboratory studies and medications to identify HIV-infected subjects, whereas Algorithm 2 used International Classification of Diseases, Ninth Revision (ICD-9) codes, medications, and laboratory testing. We applied the algorithms to a well-characterized cohort of patients and validated both against the gold standard of physician chart review. We determined sensitivity, specificity, and prevalence of HIV between 2007 and 2011 in patients seen at DUHS. Results: A total of 172 271 patients were detected with complete data; 1063 patients met algorithm criteria for HIV infection. In all, 970 individuals were identified by both algorithms, 78 by Algorithm 1 alone, and 15 by Algorithm 2 alone. The sensitivity and specificity of each algorithm were 78% and 99%, respectively, for Algorithm 1 and 77% and 100% for Algorithm 2. The estimated prevalence of HIV infection at DUHS between 2007 and 2011 was 0.6%. Conclusions: EMR-based phenotypes of HIV infection are capable of detecting cases of HIV-infected adults with good sensitivity and specificity. These algorithms have the potential to be adapted to other EMR systems, allowing for the creation of cohorts of patients across EMR systems.