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

Breaking the Cycle: Predicting Agitation Crises in Child and Adolescent Inpatient Psychiatry

This study examined biopsychosocial factors associated with the use of intramuscular (IM) agitation emergency medication in child and adolescent psychiatric inpatients. A retrospective review of 1,101 patients hospitalized between June 2018-November 2021 at an urban teaching hospital identified predictors of IM medication use through linear regression analysis. Among these patients, 196 received IM medication during their stay. Female sex was associated with a lower likelihood of receiving IM treatment, while factors such as prior involvement with child protective services, a history of violence, previous psychiatric hospitalizations, and use of multiple home psychiatric medications increased the likelihood. Agitation episodes pose risks to both patients and staff, underscoring the importance of early identification and intervention. Understanding these risk factors may guide proactive strategies to reduce the frequency and severity of agitation and limit reliance on emergency pharmacological interventions. Further research is needed to refine predictive models and explore non-pharmacological management approaches.

Identifying data-driven subtypes of major depressive disorder with electronic health records

BACKGROUND

Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration.

METHODS

We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups.

RESULTS

Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %).

LIMITATIONS

Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions.

CONCLUSION

These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes

BACKGROUND

Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.

METHODS

We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both.

RESULTS

We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35-2.51, p < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86-1.89, p = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10-2.25, p = 0.012 vs non-agitated phenotype; OR 4.61, 2.14-9.90, p < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07-23.79, p = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80-20.64, p = 0.004).

CONCLUSIONS

NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design.

NANDA nursing diagnoses associated with the occurrence of psychomotor agitation in patients with severe mental disorder: a cross-sectional study

BACKGROUND

Psychomotor agitation is increased psychomotor activity, restlessness and irritability. People with psychomotor agitation respond by overreacting to intrinsic and extrinsic stimuli, experiencing stress and/or cognitive impairment. the aim was to analyse the association of nursing diagnoses with the disinhibition dimension, the aggressiveness dimension and the lability dimension of the Corrigan Agitated Behaviour Scale.

METHODS

This study was conducted in Spain using a multicentre cross-sectional convenience sample of 140 patients who had been admitted to psychiatric hospital units and had presented an episode of psychomotor agitation between 2018 and 2021.

RESULTS

The Corrigan Agitated Behaviour Scale was used to assess psychomotor agitation. Associated nursing diagnoses, violence directed at professionals and the environment are shown to be predictive values for the severity of the agitation episode. Moderate-severe psychomotor agitation episodes are shown as predictors of violence directed mainly at professionals and the environment.

CONCLUSIONS

There is an urgent need for mental health nurses to have knowledge of the extended clinic in order to care for users and improve their health conditions in dealing with people, with their social, subjective and biological dimension.

Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology

Introduction

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records.

Methods

We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures.

Results

Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes.

Discussion

This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology

INTRODUCTION

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records.

METHODS

We adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimer's disease (AD) neuropathological measures.

RESULTS

Our results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß=0.38, p=0.0004), parietal (ß=0.35, p=0.0008), temporal (ß=0.37, p=0. 0004) and occipital (ß=0.37, p=0.0003) lobes.

DISCUSSION

This proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

Clinical and Sociodemographic Profile of Psychomotor Agitation in Mental Health Hospitalisation: A Multicentre Study

Psychomotor agitation is characterised by an increase in psychomotor activity, restlessness and irritability. People with psychomotor agitation respond by over-reacting to both intrinsic and extrinsic stimuli, experiencing stress and/or altered cognition. The objective of this study is to assess the clinical and sociodemographic profile of psychomotor agitation in patients with severe mental disorders. The study was carried out in Spain by means of multicentre cross-sectional convenience sampling involving 140 patients who had been admitted to psychiatric hospital units and had experienced an episode of psychomotor agitation between 2018 and 2021.Corrigan's Agitated Behaviour Scale was used to assess psychomotor agitation. The results show that the predominant characteristic in psychomotor agitation is aggressiveness, which is also the most reported factor in patients with severe mental disorder. Patients who also have anxiety develop psychomotor agitation symptoms of moderate/severe intensity. The clinical and sociodemographic profile found in our study is consistent with other studies on the prevalence of psychomotor agitation.

Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods

BACKGROUND

Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.

METHODS

Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.

RESULTS

A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.

CONCLUSIONS

We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.

Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks