Person:

Holm, Ingrid

Purpose Much information on parental perspectives on the return of individual research results (IRR) in pediatric genomic research is based on hypothetical rather than actual IRR. Our aim was to understand how the expected utility to parents who received IRR on their child from a genetic research study compared to the actual utility of the IRR received. Methods: We conducted individual telephone interviews with parents who received IRR on their child through participation in the Manton Center for Orphan Disease Research Gene Discovery Core (GDC) at Boston Children’s Hospital (BCH). Results: Five themes emerged around the utility that parents expected and actually received from IRR: predictability, management, family planning, finding answers, and helping science and/or families. Parents expressing negative or mixed emotions after IRR return were those who did not receive the utility they expected from the IRR. Conversely, parents who expressed positive emotions were those who received as much or greater utility than expected. Conclusions: Discrepancies between expected and actual utility of IRR affect the experiences of parents and families enrolled in genetic research studies. An informed consent process that fosters realistic expectations between researchers and participants may help to minimize any negative impact on parents and families.

Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. Methods: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children’s Hospital (BCH) (N = 150) and Cincinnati Children’s Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. Results: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children’s Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. Conclusions: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.