Characterizing suicidal ideation, suicidal behaviors, and service utilization among unhoused individuals using a health information exchange.

INTRODUCTION: Unhoused individuals have high rates of suicidal ideation (SI) and suicidal behaviors (SB), but few have studied the relative timing of homelessness and SI/SB. Our study examines the potential to use state-wide electronic health record data from Rhode Island's health information exchange (HIE) to identify temporal relationships, service utilization, and associations of SI/SB among unhoused individuals. METHODS: We use timestamped HIE data for 5368 unhoused patients to analyze service utilization and the relative timing of homelessness versus SI/SB onset. Multivariable models identified associations of SI/SB, hospitalization, and repeat acute care utilization within 30 days from clinical features representing 10,000+ diagnoses captured within the HIE. RESULTS: The onset of SI typically precedes homelessness onset, while the onset of SB typically follows. Weekly rates of suicide-related service utilization increased over 25 times the baseline rate during the week before and after homelessness onset. Over 50% of encounters involving SI/SB result in hospitalization. Of those engaging in acute care for suicide-related reasons, we found high rates of repeat acute care encounters. CONCLUSION: HIEs are a particularly valuable resource for understudied populations. Our study demonstrates how longitudinal, multi-institutional data from an HIE can be used to characterize temporal associations, service utilization, and clinical associations of SI and behaviors among a vulnerable population at scale. Increasing access to services that address co-occurring SI/SB, mental health, and substance use is needed.

The effect of levetiracetam treatment on survival in patients with glioblastoma: a systematic review and meta-analysis.

BACKGROUND: Levetiracetam (LEV) is an anti-epileptic drug (AED) that sensitizes glioblastoma (GBM) to temozolomide (TMZ) chemotherapy by inhibiting O6-methylguanine-DNA methyltransferase (MGMT) expression. Adding LEV to the standard of care (SOC) for GBM may improve TMZ efficacy. This study aimed to pool the existing evidence in the literature to quantify LEV's effect on GBM survival and characterize its safety profile to determine whether incorporating LEV into the SOC is warranted. METHOD: A search of CINAHL, Embase, PubMed, and Web of Science from inception to May 2021 was performed to identify relevant articles. Hazard ratios (HR), median overall survival, and adverse events were pooled using random-effect models. Meta-regression, funnel plots, and the Newcastle-Ottawa Scale were utilized to identify sources of heterogeneity, bias, and statistical influence. RESULTS: From 20 included studies, 5804 GBM patients underwent meta-analysis, of which 1923 (33%) were treated with LEV. Administration of LEV did not significantly improve survival in the entire patient population (HR 0.89, p = 0.094). Significant heterogeneity was observed during pooling of HRs (I2 = 75%, p < 0.01). Meta-regression determined that LEV treatment effect decreased with greater rates of MGMT methylation (RC = 0.03, p = 0.02) and increased with greater proportions of female patients (RC = - 0.05, p = 0.002). Concurrent LEV with the SOC for GBM did not increase odds of adverse events relative to other AEDs. CONCLUSIONS: Levetiracetam treatment may not be effective for all GBM patients. Instead, LEV may be better suited for treating specific molecular profiles of GBM. Further studies are necessary to identify optimal GBM candidates for LEV.

Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.

PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR. MATERIALS AND METHODS: Patient data from years 2012-2014 of the National Inpatient Sample were used to develop random forest machine learning models to predict iatrogenic pneumothorax after computed tomography-guided transthoracic biopsy (TTB), in-hospital mortality after transjugular intrahepatic portosystemic shunt (TIPS), and length of stay > 3 days after uterine artery embolization (UAE). Model performance was evaluated with area under the receiver operating characteristic curve (AUROC) and maximum F1 score. The threshold for AUROC significance was set at 0.75. RESULTS: AUROC was 0.913 for the TTB model, 0.788 for the TIPS model, and 0.879 for the UAE model. Maximum F1 score was 0.532 for the TTB model, 0.357 for the TIPS model, and 0.700 for the UAE model. The TTB model had the highest AUROC, while the UAE model had the highest F1 score. All models met the criteria for AUROC significance. CONCLUSIONS: This study demonstrates that machine learning models may suitably predict a variety of different clinically relevant outcomes, including procedure-specific complications, mortality, and length of stay. Performance of these models will improve as more high-quality IR data become available.

Molecular convergence of neurodevelopmental disorders.

Neurodevelopmental disorders (NDDs) are caused by mutations in diverse genes involved in different cellular functions, although there can be crosstalk, or convergence, between molecular pathways affected by different NDDs. To assess molecular convergence, we generated human neural progenitor cell models of 9q34 deletion syndrome, caused by haploinsufficiency of EHMT1, and 18q21 deletion syndrome, caused by haploinsufficiency of TCF4. Using next-generation RNA sequencing, methylation sequencing, chromatin immunoprecipitation sequencing, and whole-genome miRNA analysis, we identified several levels of convergence. We found mRNA and miRNA expression patterns that were more characteristic of differentiating cells than of proliferating cells, and we identified CpG clusters that had similar methylation states in both models of reduced gene dosage. There was significant overlap of gene targets of TCF4 and EHMT1, whereby 8.3% of TCF4 gene targets and 4.2% of EHMT1 gene targets were identical. These data suggest that 18q21 and 9q34 deletion syndromes show significant molecular convergence but distinct expression and methylation profiles. Common intersection points might highlight the most salient features of disease and provide avenues for similar treatments for NDDs caused by different genetic mutations.

Predictors of Cognitive and Functional Decline in Patients With Alzheimer Disease Dementia From Brazil.

Little is known on how risk factors for Alzheimer disease (AD) dementia affect disease progression, much less for populations with low mean schooling, whereas the transcription of APOE may be regulated by nongenetic factors. In this 44-month cohort study, 214 consecutive outpatients with late-onset AD were assessed for rates of cognitive and functional decline by way of Clinical Dementia Rating and Mini-Mental State Examination (MMSE) scores, keeping blinded assessment of APOE haplotypes. Subjects were evaluated for sex, schooling, age of dementia onset, and cerebrovascular risk factors (including Framingham risk scores). Of the 214 patients, there were 146 (68.2%) women and 113 (52.8%) APOE4+ carriers. The mean age of AD onset was 73.4±6.5 years-old, negatively correlated with time to Clinical Dementia Rating >1.0 (ß=-0.132; ρ<0.001), MMSE=20 (ß=-0.105; ρ<0.001), and MMSE=15 (ß=-0.124; ρ=0.003), more significantly for women and APOE4+ carriers. Mean schooling was 4.18±3.7 years, correlated with time to MMSE=20 and MMSE=15 for women and APOE4+ carriers. Body mass index was correlated with time to MMSE=20 only for men (ρ=0.006). The 10-year coronary heart disease risk was correlated with time to MMSE=20 only for APOE4+ carriers (ρ=0.015). These outcomes suggest interactions among genomic effects of cognitive reserve, cerebral perfusion, and hormonal changes over mechanisms of neurodegeneration.

DNA hypomethylation of Synapsin II CpG islands associates with increased gene expression in bipolar disorder and major depression.

BACKGROUND: The Synapsins (SYN1, SYN2, and SYN3) are important players in the adult brain, given their involvement in synaptic transmission and plasticity, as well as in the developing brain through roles in axon outgrowth and synaptogenesis. We and others previously reported gene expression dysregulation, both as increases and decreases, of Synapsins in mood disorders, but little is known about the regulatory mechanisms leading to these differences. Thus, we proposed to study DNA methylation at theses genes' promoter regions, under the assumption that altered epigenetic marks at key regulatory sites would be the cause of gene expression changes and thus part of the mood disorder etiology. METHODS: We performed CpG methylation mapping focusing on the three genes' predicted CpG islands using the Sequenom EpiTYPER platform. DNA extracted from post-mortem brain tissue (BA10) from individuals who had lived with bipolar disorder (BD), major depressive disorder (MDD), as well as psychiatrically healthy individuals was used. Differences in methylation across all CpGs within a CpG island and between the three diagnostic groups were assessed by 2-way mixed model analyses of variance. RESULTS: We found no significant results for SYN1 or SYN3, but there was a significant group difference in SYN2 methylation, as well as an overall pattern of hypomethylation across the CpG island. Furthermore, we found a significant inverse correlation of DNA methylation with SYN2a mRNA expression. CONCLUSIONS: These findings contribute to previous work showing dysregulation of Synapsins, particularly SYN2, in mood disorders and improve our understanding of the regulatory mechanisms that precipitate these changes likely leading to the BD or MDD phenotype.

Investigation of genes important in neurodevelopment disorders in adult human brain.

Several neurodevelopmental disorders (NDDs) are caused by mutations in genes expressed in fetal brain, but little is known about these same genes in adult human brain. Here, we test the hypothesis that genes associated with NDDs continue to have a role in adult human brain to explore the idea that NDD symptoms may be partially a result of their adult function rather than just their neurodevelopmental function. To demonstrate adult brain function, we performed expression analyses and ChIPseq in human neural stem cell(NSC) lines at different developmental stages and adult human brain, targeting two genes associated with NDDs, SATB2 and EHMT1, and the WNT signaling gene TCF7L2, which has not been associated with NDDs. Analysis of DNA interaction sites in neural stem cells reveals high (40-50 %) overlap between proliferating and differentiating cells for each gene in temporal space. Studies in adult brain demonstrate that consensus sites are similar to NSCs but occur at different genomic locations. We also performed expression analyses using BrainSpan data for NDD-associated genes SATB2, EHMT1, FMR1, MECP2, MBD5, CTNND2, RAI1, CHD8, GRIN2A, GRIN2B, TCF4, SCN2A, and DYRK1A and find high expression of these genes in adult brain, at least comparable to developing human brain, confirming that genes associated with NDDs likely have a role in adult tissue. Adult function of genes associated with NDDs might be important in clinical disease presentation and may be suitable targets for therapeutic intervention.

Disruption of a large intergenic noncoding RNA in subjects with neurodevelopmental disabilities.

Large intergenic noncoding (linc) RNAs represent a newly described class of ribonucleic acid whose importance in human disease remains undefined. We identified a severely developmentally delayed 16-year-old female with karyotype 46,XX,t(2;11)(p25.1;p15.1)dn in the absence of clinically significant copy number variants (CNVs). DNA capture followed by next-generation sequencing of the translocation breakpoints revealed disruption of a single noncoding gene on chromosome 2, LINC00299, whose RNA product is expressed in all tissues measured, but most abundantly in brain. Among a series of additional, unrelated subjects referred for clinical diagnostic testing who showed CNV affecting this locus, we identified four with exon-crossing deletions in association with neurodevelopmental abnormalities. No disruption of the LINC00299 coding sequence was seen in almost 14,000 control subjects. Together, these subjects with disruption of LINC00299 implicate this particular noncoding RNA in brain development and raise the possibility that, as a class, abnormalities of lincRNAs may play a significant role in human developmental disorders.

Identifying and Characterizing the Transgender and Nonbinary Population Presenting to Pediatric Psychiatry Emergency Services.

Transgender and nonbinary (TGNB) individuals have an increased risk of certain mental health outcomes, such as depression and suicide attempts. This population skews younger in the United States and prior studies have not included TGNB patients for the entire pediatric age range in an emergency department (ED) setting. The present study aimed to examine gender identity documentation in the electronic health record and then use that information to identify and further characterize the pediatric TGNB population presenting to a psychiatric emergency service. Preliminary findings include a greater percentage of TGNB patients compared to non-TGNB individuals who had repeat visits to the ED for high acuity psychiatric concerns. A larger portion of TGNB patients also had at least one evaluation that included suicidal ideation. These results call for increased attention on the quality of mental healthcare for TGNB youth both inside and outside of the ED.

Characterizing Autism Spectrum Disorder and Predicting Suicide Risk for Pediatric Psychiatric Emergency Services Encounters.

Individuals diagnosed with autism spectrum disorder (ASD) are at a higher risk for mental health concerns including suicidal thoughts and behaviors (STB). Limited studies have focused on suicidal risk factors that are more prevalent or unique to the population with ASD. This study sought to characterize and classify youth presenting to the psychiatric emergency department (ED) for a chief complaint of STB. The results of this study validated that a high number of patients with ASD present to the ED with STB. There were important differences in clinical characteristics to those with ASD versus those without. Clinical features that showed important impact in predicting high suicide risk in the ASD cases include elements of the mental status exam such as affect, trauma symptoms, abuse history, and auditory hallucinations. Focused attention is needed on these unique differences in ASD cases so that suicide risk level can be appropriately and promptly addressed.

An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities.

The COVID-19 pandemic continues to be widespread, and little is known about mental health impacts from dealing with the disease itself. This retrospective study used a deidentified health information exchange (HIE) dataset of electronic health record data from the state of Rhode Island and characterized different subgroups of the positive COVID-19 population. Three different clustering methods were explored to identify patterns of condition groupings in this population. Increased incidence of mental health conditions was seen post-COVID-19 diagnosis, and these individuals exhibited higher prevalence of comorbidities compared to the negative control group. A self-organizing map cluster analysis showed patterns of mental health conditions in half of the clusters. One mental health cluster revealed a higher comorbidity index and higher severity of COVID-19 disease. The clinical features identified in this study motivate the need for more in-depth analysis to predict and identify individuals at high risk for developing mental illness post-COVID-19 diagnosis.

The role of H3K9 acetylation and gene expression in different brain regions of Alzheimer's disease patients.

Aims: To evaluate H3K9 acetylation and gene expression profiles in three brain regions of Alzheimer's disease (AD) patients and elderly controls, and to identify AD region-specific abnormalities. Methods: Brain samples of auditory cortex, hippocampus and cerebellum from AD patients and controls underwent chromatin immunoprecipitation sequencing, RNA sequencing and network analyses. Results: We found a hyperacetylation of AD cerebellum and a slight hypoacetylation of AD hippocampus. The transcriptome revealed differentially expressed genes in the hippocampus and auditory cortex. Network analysis revealed Rho GTPase-mediated mechanisms. Conclusions: These findings suggest that some crucial mechanisms, such as Rho GTPase activity and cytoskeletal organization, are differentially dysregulated in brain regions of AD patients at the epigenetic and transcriptomic levels, and might contribute toward future research on AD pathogenesis.

Alzheimer's disease (AD) is the most common form of dementia affecting the elderly population. The onset and progression of AD are influenced by environmental factors, which are able to promote epigenetic changes on the DNA and/or the DNA-associated proteins called histones. We investigated a specific epigenetic modification of histones (H3K9 acetylation) in three brain regions of AD patients and compared them with elderly controls. We found increased levels of H3K9 acetylation in the cerebellum of AD patients, as well as a slight decrease of this modification in the hippocampus of the same patients. These brain tissues from AD patients showed abnormal gene expression patterns when compared with elderly controls. These findings contribute to understanding the molecular changes that occur in AD, and provide a basis for future research or drug development for AD treatment.

Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.

Clinical notes are a rich source of biomedical data for natural language processing (NLP). The identification of note sections represents a first step in creating portable NLP tools. Here, a system that used a heterogeneous hidden Markov model (HMM) was designed to identify seven note sections: (1) Medical History, (2) Medications, (3) Family and Social History, (4) Physical Exam, (5) Labs and Imaging, (6) Assessment and Plan, and (7) Review of Systems. Unified Medical Language System (UMLS) concepts were identified using MetaMap, and UMLS semantic type distributions for each section type were empirically determined. The UMLS semantic type distributions were used to train the HMM for identifying clinical note sections. The system was evaluated relative to a template boundary model using manually annotated notes from the Medical Information Mart for Intensive Care III. The results show promise for an approach to segment clinical notes into sections for subsequent NLP tasks.

Predicting open wound mortality in the ICU using machine learning.

BACKGROUND: Open wounds have a significant impact on the health of patients causing pain, loss of function, and death. Labeled as a comorbid condition, open wounds represent a "silent epidemic" that affect a large portion of the US population. Due to their burden of care, open wound patients face an increased risk of ICU stay and mortality. There is a dearth of studies that investigate mortality among wound patients in the ICU. We sought to develop a model that predicts the risk of mortality among wound patients in the ICU. METHODS: Random forest and binomial logistic regression models were developed to predict the risk of mortality among open wound patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. MIMIC-III includes de-identified data for patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Six variables were used to develop the model (wound location, gender, age, admission type, minimum platelet count and hyperphosphatemia). The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index were used to assess model strength. RESULTS: A total of 3,937 patients were included with a mean age of 76.57. Of those, 3,372 (85%) survived and 565 (15%) died during their ICU stay. The random forest model achieved an area under the curve (AUC) of 0.924. The CCI and Elixhauser models resulted in AUC of 0.528 and 0.565, respectively. CONCLUSIONS: Machine learning models may allow clinicians to provide better care and management to open wound patients in the ICU.

An automated technique for identifying associations between medications, laboratory results and problems.

BACKGROUND: The patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete. OBJECTIVE: To determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list. DESIGN: Association rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women's Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results. MEASUREMENTS: Candidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby's Diagnostic and Laboratory Test Reference for laboratory results. RESULTS: We were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest). CONCLUSION: Association rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches.

MeSHing molecular sequences and clinical trials: a feasibility study.

The centralized and public availability of molecular sequence and clinical trial data presents an opportunity to identify potentially valuable linkages across the bench-to-bedside "T1" translational barrier. In this study, we sought to leverage keyword metadata (Medical Subject Heading [MeSH] descriptors) to infer relationships between molecular sequences and clinical trials, as indexed by GenBank and ClinicalTrials.gov. The results of this feasibility study found that approximately 30% of sequences in GenBank could be linked to trials and over 90% of trials in ClinicalTrials.gov could be linked to sequences through MeSH descriptors. In a cursory evaluation, we were able to consistently identify meaningful linkages between molecular sequences and clinical trials. Based on our findings, there may be promise in subsequent studies aiming to identify linkages across the T1 translational barrier using existing large repositories.

Coding Free-Text Chief Complaints from a Health Information Exchange: A Preliminary Study.

Chief complaints are important textual data that can serve to enrich diagnosis and symptom data in electronic health record (EHR) systems. In this study, a method is presented to preprocess chief complaints and assign corresponding ICD-10-CM codes using the MetaMap natural language processing (NLP) system and Unified Medical Language System (UMLS) Metathesaurus. An exploratory analysis was conducted using a set of 7,942 unique chief complaints from the statewide health information exchange containing EHR data from hospitals across Rhode Island. An evaluation of the proposed method was then performed using a set of 123,086 chief complaints with corresponding ICD-10-CM encounter diagnoses. With 87.82% of MetaMap-extracted concepts correctly assigned, the preliminary findings support the potential use of the method explored in this study for improving upon existing NLP techniques for enabling use of data captured within chief complaints to support clinical care, research, and public health surveillance.

Mental Health Comorbidity Analysis in Pediatric Patients with Autism Spectrum Disorder Using Rhode Island Medical Claims Data.

Identification of comorbidity subgroups linked with Autism Spectrum Disorder (ASD) could provide promising insight into learning more about this disorder. This study sought to use the Rhode Island All-Payer Claims Database to examine mental health conditions linked to ASD. Medical claims data for ASD patients and one or more mental health conditions were analyzed using descriptive statistics, association rule mining (ARM), and sequential pattern mining (SPM). The results indicated that patients with ASD have a higher proportion of mental health diagnoses than the general pediatric population. ARM and SPM methods identified patterns of comorbidities commonly seen among ASD patients. Based on the observed patterns and temporal sequences, suicidal ideation, mood disorders, anxiety, and conduct disorders may need focused attention prospectively. Understanding more about groupings of ASD patients and their comorbidity burden can help bridge gaps in knowledge and make strides toward improved outcomes for patients with ASD.

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR phenotyping. Most STB phenotyping studies have used structured EHR data, but some are beginning to incorporate unstructured clinical text. In this study, we used a publicly-accessible natural language processing (NLP) program for biomedical text (MetaMap) and iterative elastic net regression to extract and select predictive text features from the discharge summaries of 810 inpatient admissions of interest. Initial sets of 5,866 and 2,709 text features were reduced to 18 and 11, respectively. The two models fit with these features obtained an area under the receiver operating characteristic curve of 0.866-0.895 and an area under the precision-recall curve of 0.800-0.838, demonstrating the approach's potential to identify textual features to incorporate in phenotyping models.

Applying FAIR Principles to Improve Data Searchability of Emergency Department Datasets: A Case Study for HCUP-SEDD.

BACKGROUND: There is a recognized need to improve how scholarly data are managed and accessed. The scientific community has proposed the findable, accessible, interoperable, and reusable (FAIR) data principles to address this issue. OBJECTIVE: The objective of this case study was to develop a system for improving the FAIRness of Healthcare Cost and Utilization Project's State Emergency Department Databases (HCUP's SEDD) within the context of data catalog availability. METHODS: A search tool, EDCat (Emergency Department Catalog), was designed to improve the "FAIRness" of electronic health databases and tested on datasets from HCUP-SEDD. ElasticSearch was used as a database for EDCat's search engine. Datasets were curated and defined. Searchable data dictionary-related elements and unified medical language system (UMLS) concepts were included in the curated metadata. Functionality to standardize search terms using UMLS concepts was added to the user interface. RESULTS: The EDCat system improved the overall FAIRness of HCUP-SEDD by improving the findability of individual datasets and increasing the efficacy of searches for specific data elements and data types. DISCUSSION: The databases considered for this case study were limited in number as few data distributors make the data dictionaries of datasets available. The publication of data dictionaries should be encouraged through the FAIR principles, and further efforts should be made to improve the specificity and measurability of the FAIR principles. CONCLUSION: In this case study, the distribution of datasets from HCUP-SEDD was made more FAIR through the development of a search tool, EDCat. EDCat will be evaluated and developed further to include datasets from other sources.