Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports. Issue 7 (1st September 2017)

• Record Type: Journal Article
• Authors: Kothari, Cartik; Wack, Maxime; Hassen‐Khodja, Claire; Finan, Sean; Savova, Guergana; O'Boyle, Megan; Bliss, Geraldine; Cornell, Andria; Horn, Elizabeth J.; Davis, Rebecca; Jacobs, Jacquelyn; Kohane, Isaac; Avillach, Paul
• Is Part Of: American journal of medical genetics. Volume 177:Issue 7(2018)
• Page Start: 613
• Page End: 624
• Publication Date: 2017-09-01
• Subjects: clinical notes -- knowledge extraction -- knowledge integration -- neuropsychiatric disorders -- patient reported outcomes -- rare; Neuropsychiatry -- Periodicals; Medical genetics -- Periodicals; 616.8904205
• DOI: 10.1002/ajmg.b.32579
• Languages: English
• ISSNs: 1552-4841

Abstract

Abstract : The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan‐McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan‐McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype–genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes—two heterogeneous, underutilized sources of knowledge about patient phenotypes—with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portalhttps://pmsdn.hms.harvard.edu . PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high‐quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients.