The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets.

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient's individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine's main objective-ensuring the optimum diagnosis, treatment and prognosis for each individual-investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data-and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization.

OBJECTIVE: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. METHODS: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. RESULTS: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. CONCLUSIONS: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

dbgap2x: an R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP).

SUMMARY: Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP. AVAILABILITY AND IMPLEMENTATION: dbgap2x is an R package with the code available at https://github.com/gversmee/dbgap2x. A containerized version including the package, a Jupyter server and with a Notebook example is available at https://hub.docker.com/r/gversmee/dbgap2x. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Population attitudes toward contraceptive methods over time on a social media platform.

BACKGROUND: Contraceptive method choice is often strongly influenced by the experiences and opinions of one's social network. Although social media, including Twitter, increasingly influences reproductive-age individuals, discussion of contraception in this setting has yet to be characterized. Natural language processing, a type of machine learning in which computers analyze natural language data, enables this analysis. OBJECTIVE: This study aimed to illuminate temporal trends in attitudes toward long- and short-acting reversible contraceptive methods in tweets between 2006 and 2019 and establish social media platforms as alternate data sources for large-scale sentiment analysis on contraception. STUDY DESIGN: We studied English-language tweets mentioning reversible prescription contraceptive methods between March 2006 (founding of Twitter) and December 2019. Tweets mentioning contraception were extracted using search terms, including generic or brand names, colloquial names, and abbreviations. We characterized and performed sentiment analysis on tweets. We used Mann-Kendall nonparametric tests to assess temporal trends in the overall number and the number of positive, negative, and neutral tweets referring to each method. The code to reproduce this analysis is available at https://github.com/hms-dbmi/contraceptionOnTwitter. RESULTS: We extracted 838,739 tweets mentioning at least 1 contraceptive method. The annual number of contraception-related tweets increased considerably over the study period. The intrauterine device was the most commonly referenced method (45.9%). Long-acting methods were mentioned more often than short-acting ones (58% vs 42%), and the annual proportion of long-acting reversible contraception-related tweets increased over time. In sentiment analysis of tweets mentioning a single contraceptive method (n=665,064), the greatest proportion of all tweets was negative (65,339 of 160,713 tweets with at least 95% confident sentiment, or 40.66%). Tweets mentioning long-acting methods were nearly twice as likely to be positive compared with tweets mentioning short-acting methods (19.65% vs 10.21%; P<.002). CONCLUSION: Recognizing the influence of social networks on contraceptive decision making, social media platforms may be useful in the collection and dissemination of information about contraception.

Treatment pathway analysis of newly diagnosed dementia patients in four electronic health record databases in Europe.

PURPOSE: Real-world studies to describe the use of first, second and third line therapies for the management and symptomatic treatment of dementia are lacking. This retrospective cohort study describes the first-, second- and third-line therapies used for the management and symptomatic treatment of dementia, and in particular Alzheimer's Disease. METHODS: Medical records of patients with newly diagnosed dementia between 1997 and 2017 were collected using four databases from the UK, Denmark, Italy and the Netherlands. RESULTS: We identified 191,933 newly diagnosed dementia patients in the four databases between 1997 and 2017 with 39,836 (IPCI (NL): 3281, HSD (IT): 1601, AUH (DK): 4474, THIN (UK): 30,480) fulfilling the inclusion criteria, and of these, 21,131 had received a specific diagnosis of Alzheimer's disease. The most common first line therapy initiated within a year (± 365 days) of diagnosis were Acetylcholinesterase inhibitors, namely rivastigmine in IPCI, donepezil in HSD and the THIN and the N-methyl-D-aspartate blocker memantine in AUH. CONCLUSION: We provide a real-world insight into the heterogeneous management and treatment pathways of newly diagnosed dementia patients and a subset of Alzheimer's Disease patients from across Europe.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Rcupcake: an R package for querying and analyzing biomedical data through the BD2K PIC-SURE RESTful API.

Motivation: In the era of big data and precision medicine, the number of databases containing clinical, environmental, self-reported and biochemical variables is increasing exponentially. Enabling the experts to focus on their research questions rather than on computational data management, access and analysis is one of the most significant challenges nowadays. Results: We present Rcupcake, an R package that contains a variety of functions for leveraging different databases through the BD2K PIC-SURE RESTful API and facilitating its query, analysis and interpretation. The package offers a variety of analysis and visualization tools, including the study of the phenotype co-occurrence and prevalence, according to multiple layers of data, such as phenome, exposome or genome. Availability and implementation: The package is implemented in R and is available under Mozilla v2 license from GitHub (https://github.com/hms-dbmi/Rcupcake). Two reproducible case studies are also available (https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/SSCcaseStudy_v01.ipynb, https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/NHANEScaseStudy_v01.ipynb). Contact: paul_avillach@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Associations of antepartum suicidal behaviour with adverse infant and obstetric outcomes.

BACKGROUND: Relatively little is known about antepartum suicidal behaviour and pregnancy outcomes. We examined associations of antepartum suicidal behaviour, alone and in combination with psychiatric disorders, with adverse infant and obstetric outcomes. METHODS: We included 188 925 singleton livebirths from a retrospective cohort (1996-2016). Suicidal behaviour, psychiatric disorders, and outcomes were derived from electronic medical records. We performed multivariable logistic regressions with generalised estimating equations to estimate adjusted odds ratios (aOR) with 95% confidence intervals (95%CI). RESULTS: The prevalence of antepartum suicidal behaviour was 152.44 per 100 000 singleton livebirths. Nearly two-thirds (64.24%) of women with suicidal behaviour also had psychiatric disorders. Compared to women without psychiatric disorders and suicidal behaviour, women with psychiatric disorders alone had 1.3-fold to 1.4-fold increased odds of delivering low birthweight or preterm infants and 1.2-fold increased odds of experiencing obstetric complications. Women with suicidal behaviour alone had increased odds of preterm labour (aOR 2.05, 95% CI 1.16, 3.62). Women with both suicidal behaviour and psychiatric disorders had > twofold increased odds of delivering low birthweight (aOR 2.52, 95% CI 1.40, 4.54), preterm birth (aOR 2.44, 95% CI 1.63, 3.66), and low birthweight/preterm birth (aOR 2.30, 95% CI 1.54, 3.44) infants; the odds of preterm labour (aOR 1.62, 95% CI 1.06, 2.47), placental abruption (aOR 2.33, 95% CI 1.20, 4.51), preterm rupture of membranes (aOR 1.63, 95% CI 1.08, 2.46), and postpartum haemorrhage (aOR 1.93, 95%CI 1.09, 3.40) were elevated. CONCLUSIONS: Antepartum suicidal behaviour, when co-occurring with psychiatric disorders, is associated with increased odds of adverse infant and obstetric outcomes. Future studies are warranted to understand the causal roles of suicidal behaviour and psychiatric disorders in pregnancy.

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.

We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured clinical notes, we first screened pregnant women in Partners HealthCare for suicidal behavior. Psychiatrists manually reviewed clinical charts to identify relevant features for suicidal behavior and to obtain gold-standard labels. Using the adaptive elastic net, we developed algorithms to classify suicidal behavior. We then validated algorithms in an independent validation dataset. From 275,843 women with codes related to pregnancy or delivery, 9331 women screened positive for suicidal behavior by either codified data (N = 196) or NLP (N = 9,145). Using expert-curated features, our algorithm achieved an area under the curve of 0.83. By setting a positive predictive value comparable to that of diagnostic codes related to suicidal behavior (0.71), we obtained a sensitivity of 0.34, specificity of 0.96, and negative predictive value of 0.83. The algorithm identified 1423 pregnant women with suicidal behavior among 9331 women screened positive. Mining unstructured clinical notes using NLP resulted in a 11-fold increase in the number of pregnant women identified with suicidal behavior, as compared to solely reliance on diagnostic codes.

Comparison of variation in frequency for SNPs associated with asthma or liver disease between Estonia, HapMap populations and the 1000 genome project populations.

Allele-specific analyses to understand frequency differences across populations, particularly populations not well studied, are important to help identify variants that may have a functional effect on disease mechanisms and phenotypic predisposition, facilitating new Genome-Wide Association Studies (GWAS). We aimed to compare the allele frequency of 11 asthma-associated and 16 liver disease-associated single nucleotide polymorphisms (SNPs) between the Estonian, HapMap and 1000 genome project populations. When comparing EGCUT with HapMap populations, the largest difference in allele frequencies was observed with the Maasai population in Kinyawa, Kenya, with 12 SNP variants reporting statistical significance. Similarly, when comparing EGCUT with 1000 genomes project populations, the largest difference in allele frequencies was observed with pooled African populations with 22 SNP variants reporting statistical significance. For 11 asthma-associated and 16 liver disease-associated SNPs, Estonians are genetically similar to other European populations but significantly different from African populations. Understanding differences in genetic architecture between ethnic populations is important to facilitate new GWAS targeted at underserved ethnic groups to enable novel genetic findings to aid the development of new therapies to reduce morbidity and mortality.

Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. It affects an estimated 20% of the general population, based on cohort studies of varying size and heterogeneous selection. However, the prevalence and incidence of recorded NAFLD diagnoses in unselected real-world health-care records is unknown. We harmonised health records from four major European territories and assessed age- and sex-specific point prevalence and incidence of NAFLD over the past decade. METHODS: Data were extracted from The Health Improvement Network (UK), Health Search Database (Italy), Information System for Research in Primary Care (Spain) and Integrated Primary Care Information (Netherlands). Each database uses a different coding system. Prevalence and incidence estimates were pooled across databases by random-effects meta-analysis after a log-transformation. RESULTS: Data were available for 17,669,973 adults, of which 176,114 had a recorded diagnosis of NAFLD. Pooled prevalence trebled from 0.60% in 2007 (95% confidence interval: 0.41-0.79) to 1.85% (0.91-2.79) in 2014. Incidence doubled from 1.32 (0.83-1.82) to 2.35 (1.29-3.40) per 1000 person-years. The FIB-4 non-invasive estimate of liver fibrosis could be calculated in 40.6% of patients, of whom 29.6-35.7% had indeterminate or high-risk scores. CONCLUSIONS: In the largest primary-care record study of its kind to date, rates of recorded NAFLD are much lower than expected suggesting under-diagnosis and under-recording. Despite this, we have identified rising incidence and prevalence of the diagnosis. Improved recognition of NAFLD may identify people who will benefit from risk factor modification or emerging therapies to prevent progression to cardiometabolic and hepatic complications.

Adverse obstetric and neonatal outcomes complicated by psychosis among pregnant women in the United States.

BACKGROUND: Adverse obstetric and neonatal outcomes among women with psychosis, particularly affective psychosis, has rarely been studied at the population level. We aimed to assess the risk of adverse obstetric and neonatal outcomes among women with psychosis (schizophrenia, affective psychosis, and other psychoses). METHODS: From the 2007 - 2012 National (Nationwide) Inpatient Sample, 23,507,597 delivery hospitalizations were identified. From the same hospitalization, International Classification of Diseases diagnosis codes were used to identify maternal psychosis and outcomes. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were obtained using logistic regression. RESULTS: The prevalence of psychosis at delivery was 698.76 per 100,000 hospitalizations. After adjusting for sociodemographic characteristics, smoking, alcohol/substance abuse, and pregnancy-related hypertension, women with psychosis were at a heightened risk for cesarean delivery (aOR = 1.26; 95% CI: 1.23 - 1.29), induced labor (aOR = 1.05; 95% CI: 1.02 - 1.09), antepartum hemorrhage (aOR = 1.22; 95% CI: 1.14 - 1.31), placental abruption (aOR = 1.22; 95% CI: 1.13 - 1.32), postpartum hemorrhage (aOR = 1.18; 95% CI: 1.10 - 1.27), premature delivery (aOR = 1.40; 95% CI: 1.36 - 1.46), stillbirth (aOR = 1.37; 95% CI: 1.23 - 1.53), premature rupture of membranes (aOR = 1.22; 95% CI: 1.15 - 1.29), fetal abnormalities (aOR = 1.49; 95% CI: 1.38 - 1.61), poor fetal growth (aOR = 1.26; 95% CI: 1.19 - 1.34), and fetal distress (aOR = 1.14; 95% CI: 1.10 - 1.18). Maternal death during hospitalizations (aOR = 1.00; 95% CI: 0.30 - 3.31) and excessive fetal growth (aOR = 1.06; 95% CI: 0.98 - 1.14) were not statistically significantly associated with psychosis. CONCLUSIONS: Pregnant women with psychosis have elevated risk of several adverse obstetric and neonatal outcomes. Efforts to identify and manage pregnancies complicated by psychosis may contribute to improved outcomes.

Screening pregnant women for suicidal behavior in electronic medical records: diagnostic codes vs. clinical notes processed by natural language processing.

BACKGROUND: We examined the comparative performance of structured, diagnostic codes vs. natural language processing (NLP) of unstructured text for screening suicidal behavior among pregnant women in electronic medical records (EMRs). METHODS: Women aged 10-64 years with at least one diagnostic code related to pregnancy or delivery (N = 275,843) from Partners HealthCare were included as our "datamart." Diagnostic codes related to suicidal behavior were applied to the datamart to screen women for suicidal behavior. Among women without any diagnostic codes related to suicidal behavior (n = 273,410), 5880 women were randomly sampled, of whom 1120 had at least one mention of terms related to suicidal behavior in clinical notes. NLP was then used to process clinical notes for the 1120 women. Chart reviews were performed for subsamples of women. RESULTS: Using diagnostic codes, 196 pregnant women were screened positive for suicidal behavior, among whom 149 (76%) had confirmed suicidal behavior by chart review. Using NLP among those without diagnostic codes, 486 pregnant women were screened positive for suicidal behavior, among whom 146 (30%) had confirmed suicidal behavior by chart review. CONCLUSIONS: The use of NLP substantially improves the sensitivity of screening suicidal behavior in EMRs. However, the prevalence of confirmed suicidal behavior was lower among women who did not have diagnostic codes for suicidal behavior but screened positive by NLP. NLP should be used together with diagnostic codes for future EMR-based phenotyping studies for suicidal behavior.