Implementing an EMA burst design in community-engaged research: participant and researcher perspectives and recommendations.

Background: Implementing ecological momentary assessment (EMA) methodology to evaluate the substance use disorder (SUD) treatment pipeline has clear advantages, including learning about participants' day-to-day experiences to aid in the improvement of services and accessibility for those seeking treatment. Given that the SUD treatment pipeline spans long periods of time, EMA burst designs (deployment of multiple short EMA periods spread over time) can be advantageous for evaluating the treatment pipeline over time while keeping participant burden low.Objectives: This feasibility study describes (1) the process and study design of implementing EMA burst methodology to evaluate the SUD treatment pipeline experience; (2) study implementation from the perspective of researchers, including discussion of collaboration with community partners; and (3) participant feedback on the experience of engaging with this type of research.Method: EMA metrics, feasibility ratings, and general experience ratings in the study are presented from 22 participants (64% women) who participated in a parent EMA study evaluating the SUD treatment pipeline and 8 who provided feedback in a follow-up survey.Results: Participants found the EMA burst design to be acceptable and not burdensome, although technology issues were present for some participants. Steps to partnering with community treatment programs and implementation of a burst design are outlined.Conclusions: Strategies and recommendations for implementation of an EMA burst study with community partners are provided, including aspects of study design, technology issues, retention, and funding.

Examining associations between social anhedonia and convergent thinking using the Remote Associates Test.

Introduction: Social anhedonia (SocAnh) predicts increased risk of schizophrenia-spectrum disorders, with evidence that these disorders are associated with increased creativity. However, it is still largely unknown whether SocAnh is associated with one central aspect of creative thinking, convergent thinking.Methods: In two studies, college students with either extreme levels of SocAnh (n = 44 and n = 70) or controls with an average level of SocAnh (n = 111 and n = 100) completed a convergent thinking task, the Remote Associates Test, and also completed measures of current affect. In the second study, participants also completed a divergent thinking task.Results: In both studies, the SocAnh group had better performance than controls on the convergent thinking task. Further, this group difference remained after removing shared variance with current affect. In Study 2, groups did not differ on divergent thinking.Conclusions: Overall, consistent with research linking schizophrenia-spectrum disorders and creativity, the current research suggests that SocAnh is associated with increases in some aspects of creativity.

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort.

BACKGROUND: Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied. METHODS: We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures. RESULTS: Among the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both. CONCLUSIONS: While passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.

Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method.

BACKGROUND: Agitation is a common feature of many neuropsychiatric disorders. OBJECTIVE: Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases. METHODS: We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined. RESULTS: Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype. CONCLUSION: Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Differences among Research Domain Criteria score trajectories by Diagnostic and Statistical Manual categorical diagnosis during inpatient hospitalization.

With brief psychiatric hospitalizations, the extent to which symptoms change is rarely characterized. We sought to understand symptomatic changes across Research Domain Criteria (RDoC) dimensions, and the extent to which such improvement might be associated with risk for readmission. We identified 3,634 individuals with 4,713 hospital admissions to the psychiatric inpatient unit of a large academic medical center between 2010 and 2015. We applied a natural language processing tool to extract estimates of the five RDoC domains to the admission note and discharge summary and calculated the change in each domain. We examined the extent to which symptom domains changed during admission, and their relationship to baseline clinical and sociodemographic features, using linear regression. Symptomatic worsening was rare in the negative valence (0.4%) and positive valence (5.1%) domains, but more common in cognition (25.8%). Most diagnoses exhibited improvement in negative valence, which was associated with significant reduction in readmission risk. Despite generally brief hospital stays, we detected reduction across multiple symptom domains, with greatest improvement in negative symptoms, and greatest probability of worsening in cognitive symptoms. This approach should facilitate investigations of other features or interventions which may influence pace of clinical improvement.

Integrating questionnaire measures for transdiagnostic psychiatric phenotyping using word2vec.

BACKGROUND: Recent initiatives in psychiatry emphasize the utility of characterizing psychiatric symptoms in a multidimensional manner. However, strategies for applying standard self-report scales for multiaxial assessment have not been well-studied, particularly where the aim is to support both categorical and dimensional phenotypes. METHODS: We propose a method for applying natural language processing to derive dimensional measures of psychiatric symptoms from questionnaire data. We utilized nine self-report symptom measures drawn from a large cellular biobanking study that enrolled individuals with mood and psychotic disorders, as well as healthy controls. To summarize questionnaire results we used word embeddings, a technique to represent words as numeric vectors preserving semantic and syntactic meaning. A low-dimensional approximation to the embedding space was used to derive the proposed succinct summary of symptom profiles. To validate our embedding-based disease profiles, these were compared to presence or absence of axis I diagnoses derived from structured clinical interview, and to objective neurocognitive testing. RESULTS: Unsupervised and supervised classification to distinguish presence/absence of axis I disorders using survey-level embeddings remained discriminative, with area under the receiver operating characteristic curve up to 0.85, 95% confidence interval (CI) (0.74,0.91) using Gaussian mixture modeling, and cross-validated area under the receiver operating characteristic curve 0.91, 95% CI (0.88,0.94) using logistic regression. Derived symptom measures and estimated Research Domain Criteria scores also associated significantly with performance on neurocognitive tests. CONCLUSIONS: Our results support the potential utility of deriving dimensional phenotypic measures in psychiatric illness through the use of word embeddings, while illustrating the challenges in identifying truly orthogonal dimensions.

Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records.

BACKGROUND: Investigation of personality traits and pathology in large, generalizable clinical cohorts has been hindered by inconsistent assessment and failure to consider a range of personality disorders (PDs) simultaneously. METHODS: We applied natural language processing (NLP) of electronic health record notes to characterize a psychiatric inpatient cohort. A set of terms reflecting personality trait domains were derived, expanded, and then refined based on expert consensus. Latent Dirichlet allocation was used to score notes to estimate the extent to which any given note reflected PD topics. Regression models were used to examine the relationship of these estimates with sociodemographic features and length of stay. RESULTS: Among 3623 patients with 4702 admissions, being male, non-white, having a low burden of medical comorbidity, being admitted through the emergency department, and having public insurance were independently associated with greater levels of disinhibition, detachment, and psychoticism. Being female, white, and having private insurance were independently associated with greater levels of negative affectivity. The presence of disinhibition, psychoticism, and negative affectivity were each significantly associated with a longer stay, while detachment was associated with a shorter stay. CONCLUSIONS: Personality features can be systematically and scalably measured using NLP in the inpatient setting, and some of these features associate with length of stay. Developing treatment strategies for patients scoring high in certain personality dimensions may facilitate more efficient, targeted interventions, and may help reduce the impact of personality features on mental health service utilization.

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

INTRODUCTION: Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high-risk individuals. METHODS: We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow-up. RESULTS: Among 267,855 hospitalized patients with 1,251,858 patient years of follow-up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54-1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients. DISCUSSION: A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.

Prevalence of Financial Considerations Documented in Primary Care Encounters as Identified by Natural Language Processing Methods.

Importance: Quantifying patient-physician cost conversations is challenging but important as out-of-pocket spending by US patients increases and patients are increasingly interested in discussing costs with their physicians. Objective: To characterize the prevalence of financial considerations documented in narrative clinical records of primary care encounters and their association with patient-level features. Design, Setting, and Participants: This cohort study applied natural language processing to narrative clinical notes obtained from electronic health records for adult primary care visits. Participants included patients aged 18 years and older with at least 1 primary care visit for an annual preventive examination at outpatient clinics at a US academic health system between January 2, 2008, and July 30, 2013. Data were analyzed in March 2019. Main Outcomes and Measures: Presence of financial content documented in narrative clinical notes. Results: The data set included 222 457 primary care visits for 46 244 individuals aged 18 years and older; 30 556 patients (60.1%) were female, 27 869 patients (60.3%) were white, and the mean (SD) age was 51.3 (17.7) years. In total, 6058 patients (13.1%) had at least 1 narrative clinical note indicating a financial conversation with their physician. In fully adjusted regression models, the odds of having a financial note were greater among patients with Medicare (odds ratio [OR], 1.27; 95% CI, 1.15-1.41; P < .001) or Medicaid (OR, 1.43; 95% CI, 1.25-1.64; P < .001) insurance, those residing in zip codes with lower median income (OR, 0.97; 95% CI, 0.96-0.98; P < .001), black individuals (OR, 1.40; 95% CI, 1.28-1.53; P < .001), Hispanic individuals (OR, 1.10; 95% CI, 1.01-1.20; P = .03), and those who were unmarried (OR, 1.23; 95% CI, 1.15-1.33; P < .001). Conclusions and Relevance: Cost considerations were more likely to be noted in annual preventive examinations than previously observed in intensive care unit admissions, but still infrequently. Associations with particular patient subgroups may indicate differential financial burden or willingness to discuss financial concerns.

Association between child psychiatric emergency room outcomes and dimensions of psychopathology.

OBJECTIVE: To determine the degree to which dimensional psychopathology predicts length of stay in an emergency department (ED) and need for hospital admission among children with psychiatric complaints. METHOD: Electronic health records of children age 4-17 years who presented to the ED of a large academic medical center were analyzed using a natural language processing tool to estimate Research Domain Criteria (RDoC) symptom scores. These scores' association with length of stay and probability of admission versus discharge to home were evaluated. RESULTS: We identified 3061 children and adolescents who presented to the ED and were evaluated by the psychiatry service between November 2008 and March 2015. Median length of stay was 7.8 h (interquartile range 5.2-14.3 h) and 1696 (55.4%) were admitted to the hospital. Higher estimated RDoC arousal, cognitive, positive, and social domain scores were associated with increased length of stay in multiple regression models, adjusted for age, sex, race, private insurance, voluntary admission, and diagnostic categories. In similarly adjusted models, odds of hospital admission were increased by higher RDoC arousal and cognitive domain scores and decreased by higher negative domain scores. CONCLUSIONS: A natural language processing tool to characterize dimensional psychopathology identified features associated with differential outcomes in children in the psychiatric ED, most notably symptoms reflecting arousal and cognitive function. Methodologically, this in silico approach to risk stratification should facilitate precision psychiatry in children within the emergency setting.

Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death.

BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital discharge provides an opportunity to stratify risk using readily available clinical records and details. METHODS: We applied a validated natural language processing tool to generate estimated Research Domain Criteria (RDoC) scores for a cohort of 444,317 individuals drawn from 815,457 hospital discharges between 2005 and 2013. We used survival analysis to examine the association of this risk with suicide and accidental death, adjusted for sociodemographic features. RESULTS: In adjusted models, symptoms in each of the five domains contributed to incremental risk (log rank P < 0.001), with greatest increase observed with positive valence. The contribution of each domain to risk was time dependent. CONCLUSIONS: RDoC symptom scores parsed from clinical documentation are associated with suicide and illustrates that multiple domains contribute to risk in a time-varying fashion.

Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8.

While nearly all common genomic variants associated with schizophrenia have no known function, one corresponds to a missense variant associated with change in efficiency of a metal ion transporter, ZIP8, coded by SLC39A8. This variant has been linked to a range of phenotypes and is believed to be under recent selection pressure, but its impact on health is poorly understood. We sought to understand phenotypic implications of this variant in a large genomic biobank using an unbiased phenome-wide approach. Specifically, we generated 50 topics based on diagnostic codes using latent Dirichlet allocation, and examined them for association with the risk variant. Then, any significant topics were further characterized by examining association with individual diagnostic codes contributing to the topic. Among 50 topics, 1 was associated at an experiment-wide significance threshold (beta = 0.003, uncorrected p = 0.00049), comprising predominantly brain-related codes, including intracranial hemorrhage, cerebrovascular disease, and delirium/dementia. These results suggest that a functional variant previously associated with schizophrenia risk also increases liability to cerebrovascular disease. They further illustrate the utility of a topic-based approach to phenome-wide association.

Genomewide Association Study of Fracture Nonunion Using Electronic Health Records.

Nonunion is a clinically significant complication of fracture associated with worse outcomes, including increased pain, disability, and higher healthcare costs. The risk for nonunion is likely to be complex and multifactorial, and as such, the biology underlying such risk remains poorly understood. Genetic studies represent one approach to identify implicated biology for further investigation, but to date the lack of large cohorts for study has limited such efforts. We utilized the electronic health records of two large academic medical centers in Boston to identify individuals with fracture nonunion and control individuals with fracture but no evidence of nonunion. We conducted a genomewide association study among 1760 individuals of Northern European ancestry with upper or lower extremity fracture, including 131 with nonunion, to examine whether common variants were associated with nonunion in this cohort. In all, one locus in the Calcyon (CALY) gene exceeded a genomewide threshold for statistical significance (p = 1.95e-8), with eight additional loci associated with p < 5e-7. Previously reported candidate genes were not supported by this analysis. Electronic health records should facilitate identification of common genetic variations associated with adverse orthopedic outcomes. The loci we identified in this small cohort require replication and further study to characterize mechanism of action, but represent a starting point for the investigation of genetic liability for this costly outcome.

PET neuroimaging reveals histone deacetylase dysregulation in schizophrenia.

BACKGROUND: Patients with schizophrenia (SCZ) experience chronic cognitive deficits. Histone deacetylases (HDACs) are enzymes that regulate cognitive circuitry; however, the role of HDACs in cognitive disorders, including SCZ, remains unknown in humans. We previously determined that HDAC2 mRNA levels were lower in dorsolateral prefrontal cortex (DLPFC) tissue from donors with SCZ compared with controls. Here we investigated the relationship between in vivo HDAC expression and cognitive impairment in patients with SCZ and matched healthy controls using [11C]Martinostat positron emission tomography (PET). METHODS: In a case-control study, relative [11C]Martinostat uptake was compared between 14 patients with SCZ or schizoaffective disorder (SCZ/SAD) and 17 controls using hypothesis-driven region-of-interest analysis and unbiased whole brain voxel-wise approaches. Clinical measures, including the MATRICS consensus cognitive battery, were administered. RESULTS: Relative HDAC expression was lower in the DLPFC of patients with SCZ/SAD compared with controls, and HDAC expression positively correlated with cognitive performance scores across groups. Patients with SCZ/SAD also showed lower relative HDAC expression in the dorsomedial prefrontal cortex and orbitofrontal gyrus, and higher relative HDAC expression in the cerebral white matter, pons, and cerebellum compared with controls. CONCLUSIONS: These findings provide in vivo evidence of HDAC dysregulation in patients with SCZ and suggest that altered HDAC expression may impact cognitive function in humans. FUNDING: National Institute of Mental Health (NIMH), Brain and Behavior Foundation, Massachusetts General Hospital (MGH), Athinoula A. Martinos Center for Biomedical Imaging, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH Shared Instrumentation Grant Program.

Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records.

BACKGROUND: Genetic studies of neuropsychiatric disease strongly suggest an overlap in liability. There are growing efforts to characterize these diseases dimensionally rather than categorically, but the extent to which such dimensional models correspond to biology is unknown. METHODS: We applied a newly developed natural language processing method to extract five symptom dimensions based on the National Institute of Mental Health Research Domain Criteria definitions from narrative hospital discharge notes in a large biobank. We conducted a genome-wide association study to examine whether common variants were associated with each of these dimensions as quantitative traits. RESULTS: Among 4687 individuals, loci in three of five domains exceeded a genome-wide threshold for statistical significance. These included a locus spanning the neocortical development genes RFPL3 and RFPL3S for arousal (p = 2.29 × 10-8) and one spanning the FPR3 gene for cognition (p = 3.22 × 10-8). CONCLUSIONS: Natural language processing identifies dimensional phenotypes that may facilitate the discovery of common genetic variation that is relevant to psychopathology.

Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

Importance: Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve patient safety at periods of elevated risk. Objective: To determine the performance of a new time-series machine learning method for forecasting hospital discharge volume compared with simpler methods. Design: A retrospective cohort study of daily hospital discharge volumes at 2 large, New England academic medical centers between January 1, 2005, and December 31, 2014 (hospital 1), or January 1, 2005, and December 31, 2010 (hospital 2), comparing time-series forecasting methods for prediction was performed. Data analysis was conducted from February 28, 2017, to August 30, 2018. Group-level data for all discharges from inpatient units were included. In addition to conventional methods, a technique originally developed for allocating data center resources, and comparison strategies for incorporating prior data and frequency of model updates, was conducted to identify the model application that optimized forecast accuracy. Main Outcomes and Measures: Model calibration as measured by R2 and, secondarily, number of days with errors greater than 1 SD of daily volume. Results: During the forecasted year, hospital 1 had 54 411 discharges (daily mean, 149) and hospital 2 had 47 456 discharges (daily mean, 130). The machine learning method was well calibrated at both sites (R2, 0.843 and 0.726, respectively) and made errors greater than 1 SD of daily volume on only 13 and 22 days, respectively, of the forecast year at the 2 sites. Last-value-carried-forward models performed somewhat less well (calibration R2, 0.781 and 0.596, respectively) with 13 and 46 errors of 1 SD or greater, respectively. More frequent retraining and training sets of longer than 1 year had minimal effects on the machine learning method's performance. Conclusions and Relevance: Volume of hospital discharges can perhaps be reliably forecasted using simple carry-forward models as well as methods drawn from machine learning. The benefit of the latter does not appear to be dependent on extensive training data and may enable forecasts up to 1 year in advance with superior absolute accuracy to carry-forward models.

Prevalence and Nature of Financial Considerations Documented in Narrative Clinical Records in Intensive Care Units.

Importance: The extent to which financial considerations alter intensive care unit (ICU) decision making is poorly understood. Objectives: To characterize the prevalence and nature of financial considerations documented in narrative clinical records and their association with patient-level demographic and clinical features. Design, Setting, and Participants: In silico cohort study applying natural language processing to narrative notes from the Medical Information Mart for Intensive Care (MIMIC-III) study. Data from all individuals hospitalized between June 1, 2001, and October 31, 2012, in the ICU of Beth Israel Deaconess Medical Center were analyzed from April 1 to April 30, 2018. Main Outcomes and Measure: Presence of financial considerations in narrative clinical notes. Results: Among 46 146 index ICU admissions, 1936 patients (4.2%) were identified with at least 1 note reflecting financial considerations during the ICU stay. Of these 1936 patients, 1135 (58.6%) were male, with a mean (SD) age of 38.8 (28.4) years and mean (SD) length of stay of 21.7 (27.1) days. Among the remaining 44 210 admissions in the cohort, 24 780 (56.1%) were male, with a mean (SD) age of 48.6 (32.1) years and mean (SD) length of stay of 9.2 (11.4) days. Among the 46 146 admissions, 142 (0.3%) included notes describing a change in the discharge plan, 142 (0.3%) describing a change in the treatment plan, and 303 (0.7%) describing a change in medication or previous nonadherence to medication associated with financial considerations. In logistic regression models adjusted for age, sex, marital status, and insurance type, longer hospital stays were significantly associated with the presence of financial notes (odds ratio, 1.01; 95% CI, 1.01-1.01). Conclusions and Relevance: In this study, among patients in the ICU, clinical notes document the association of financial considerations with care decisions. Although such notes likely underestimate the frequency of such considerations, they highlight the need to develop better systematic approaches to understanding how financial constraints may alter care decisions in US health systems.