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High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.

ABSTRACT: BACKGROUND:Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes. METHODS:We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes. Estimates of Research Domain Criteria loading were derived from a cohort of 3619 individuals with 4623 hospital admissions. We applied this tool to a large corpus of psychiatric inpatient admission and discharge notes (2010-2015), and using the same cohort we examined face validity, predictive validity, and convergent validity with gold standard annotations. RESULTS:In mixed-effect models adjusted for sociodemographic and clinical features, greater negative and positive symptom domains were associated with a shorter length of stay (? = -.88, p = .001 and ? = -1.22, p < .001, respectively), while greater social and arousal domain scores were associated with a longer length of stay (? = .93, p < .001 and ? = .81, p = .007, respectively). In fully adjusted Cox regression models, a greater positive domain score at discharge was also associated with a significant increase in readmission risk (hazard ratio = 1.22, p < .001). Positive and negative valence domains were correlated with expert annotation (by analysis of variance [df = 3], R2 = .13 and .19, respectively). Likewise, in a subset of patients, neurocognitive testing was correlated with cognitive performance scores (p < .008 for three of six measures). CONCLUSIONS:This shows that natural language processing can be used to efficiently and transparently score clinical notes in terms of cognitive and psychopathologic domains.

SUBMITTER: McCoy TH

PROVIDER: S-EPMC5972065 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.

McCoy Thomas H TH Yu Sheng S Hart Kamber L KL Castro Victor M VM Brown Hannah E HE Rosenquist James N JN Doyle Alysa E AE Vuijk Pieter J PJ Cai Tianxi T Perlis Roy H RH

Biological psychiatry 20180226 12

<h4>Background</h4>Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes.<h4>Methods</h4>We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative cli ...[more]

PMID: 29496195

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Project description:ObjectiveDeveloping algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.Materials and methodsPheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype's quantified concepts and uses them to calculate an individual's probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2.ResultsIn this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.ConclusionsPheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.

Project description:Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p?=?0.015), 0.09 (p?=?0.064), 0.13 (p?=?0.003), 0.00 (p?=?0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p?=?0.004). These h2g were lower or similar to the h2g observed by the ICCBD?+?PGCBD (0.23, p?=?3.17E-80, total N?=?33,181). However, the rg between ICCBD?+?PGCBD and the EHR-based cases were high for 95-NLP (0.66, p?=?3.69?×?10-5), coded-strict (1.00, p?=?2.40?×?10-4), and coded-broad (0.74, p?=?8.11?×?10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

Dataset Information

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.

Publications

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.

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