Mapping the Delirium Literature Through Probabilistic Topic Modeling and Network Analysis: A Computational Scoping Review

The occurrence of delirium diagnosis among youth hospitalizations in the United States: A Kids' Inpatient Database analysis

OBJECTIVES

Delirium is an acute neuropsychiatric condition associated with increased morbidity and mortality. There is increasing recognition of delirium as a substantial health burden in younger patients, although few studies have characterized its occurrence. This study analyzes the occurrence of delirium diagnosis, its comorbidities, and cost among youth hospitalized in the United States.

METHODS

The Kids' Inpatient Database, a national all-payers sample of pediatric hospitalizations in general hospitals, was examined for the year 2019. Hospitalizations with a discharge diagnosis of delirium among patients aged 1-20 years were included in the analysis.

RESULTS

Delirium was diagnosed in 43,138 hospitalizations (95% CI: 41,170-45,106), or 2.3% of studied hospitalizations. Delirium was diagnosed in a broad range of illnesses, with suicide and self-inflicted injury as the most common primary discharge diagnosis among patients with delirium. In-hospital mortality was seven times greater in hospitalizations caring a delirium diagnosis. The diagnosis of delirium was associated with an adjusted increased hospital cost of $8648 per hospitalization, or $373 million in aggregate cost.

CONCLUSIONS

Based on a large national claims database, delirium was diagnosed in youth at a lower rate than expected based on prospective studies. The relative absence of delirium diagnosis in claims data may reflect underdiagnosis, a failure to code, and/or a lower rate of delirium in general hospitals compared with other settings. Further research is needed to better characterize the incidence and prevalence of delirium in young people in the hospital setting.

Analysis of citation trends to identify articles on delirium worth reading using DDPP model with temporal heatmaps (THM): A bibliometric analysis

BACKGROUND

Delirium is one of the most common geriatric syndromes in older patients, accounting for 25% of hospitalized older patients, 31 to 35% of patients in the intensive care unit, and 8% to 17% of older patients in the emergency department (ED). A number of articles have been published in the literature regarding delirium. However, it is unclear about article citations evolving in the field. This study proposed a temporal heatmap (THM) that can be applied to all bibliographical studies for a better understanding of cited articles worth reading.

METHODS

As of November 25, 2022, 11,668 abstracts published on delirium since 2013 were retrieved from the Web of Science core collection. Research achievements were measured using the CJAL score. Social network analysis was applied to examine clusters of keywords associated with core concepts of research. A THM was proposed to detect articles worth reading based on recent citations that are increasing. The 100 top-cited articles related to delirium were displayed on an impact beam plot (IBP).

RESULTS

The results indicate that the US (12474), Vanderbilt University (US) (634), Anesthesiology (2168), and Alessandro Morandi (Italy) (116) had the highest CJAL scores in countries, institutes, departments, and authors, respectively. Articles worthy of reading were highlighted on a THM and an IBP when an increasing trend of citations over the last 4 years was observed.

CONCLUSION

The THM and IBP were proposed to highlight articles worth reading, and we recommend that more future bibliographical studies utilize the 2 visualizations and not restrict them solely to delirium-related articles in the future.

The State of the Catatonia Literature: Employing Bibliometric Analysis of Articles From 1965-2020 to Identify Current Research Gaps

BACKGROUND

Since Kahlbaum's classic 19th-century description of catatonia, our conceptualization of this syndrome, as well treatment options for it, has advanced considerably. However, little is known about the current state of the catatonia literature since a comprehensive bibliometric analysis of it has not yet been undertaken.

OBJECTIVE

The purpose of this study was to conduct a bibliometric analysis, along with a content analysis of articles reporting new findings, to better understand the catatonia literature and how catatonia research is changing.

METHODS

Using the search term "Title(catatoni∗)" in Web of Science Core Collection for all available years (1965-2020), all available publications (articles, proceeding papers, reviews) pertaining directly to catatonia were identified, and metadata extracted. Semantic and coauthorship network analyses were conducted. A content analysis was also conducted on all available case reports, case series, and research articles written in English.

RESULTS

A total of 1015 articles were identified representing 2861 authors, 346 journals, and 15,639 references. The average number of publications per year over the last 20 years (31.3) more than doubled in comparison to that in the 20 years prior (12.8). The top 3 most common journals were Psychosomatics/Journal of the Academy of Consultation-Liaison Psychiatry, Journal of ECT, and Schizophrenia Research, which represented 12.6% of all publications. Content analysis revealed that catatonia articles are increasingly published in nonpsychiatric journals. There was a notable paucity of clinical trials throughout the study period. Since 2003, articles on catatonia secondary to a general medical condition, as well as articles including child/adolescent patients and patients with autism spectrum disorder or intellectual disability, have made up increasing shares of the literature, with a smaller proportion of articles reporting periodic or recurrent catatonia. We noted a decrease in the proportion of articles detailing animal/in vitro studies, genetic/heredity studies, and clinical trials, along with stagnation in the proportion of neuroimaging studies.

CONCLUSIONS

The catatonia literature is growing through contributions from authors and institutions across multiple countries. However, recent growth has largely been driven by increased case reports, with significant downturns observed in both clinical and basic science research articles. A dearth of clinical trials evaluating potential treatments remain a critical gap in the catatonia literature.

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients

OBJECTIVE

To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

METHOD

Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

RESULTS

Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave.

CONCLUSION

This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Bibliometric Analysis of 100 Most-Cited Articles in Delirium

Delirium is a cognitive disorder with complex etiology, which brings a great burden to social health care. Articles with high citation frequency can provide important information about the current research situation in a certain field. Web of Science was used to search the 100 most-cited articles and we extracted key information, such as the authors, countries/regions, institutions, journals, and study types of these articles. CiteSpace was used to visually analyze the keywords. Our bibliometric analysis shows that the attention in this field continues to rise. Authors from the United States published the most articles and Inouye SK is the most influential author in the field. The journals that published these articles have high impact factors. Cohort studies are the main cited articles in this field, and there are a large number of systematic reviews or meta-analyses of cohort studies. Risk factors for delirium, psychometric evaluation, hospital care, and various clinical study design are still the focus of research. In short, we summarized the 100 most-cited articles in the field of delirium to identify the current status and global trends. These results enable researchers to understand the quality and trend of research in the field of delirium and make better use of the classical literature.

A scientometric study of publications on delirium from 2001 to 2020

AIM

This study aims to evaluate the publications on delirium by using bibliometric analysis.

METHODOLOGY

The Scopus database was evaluated for publications on delirium, during the period of 2001-20. The search results were analyzed for the origin of country, origin of institution, authorship, collaborations, type of article, source of funding, and number of citations.

RESULTS

The searches of Scopus database yielded 22,941 publications, originating from 139 countries. Compared to the decade of 2001-2010, the number of publications on delirium doubled in the decade of 2011-2020. The majority of the papers were research articles (58.26%), and the papers were cited for mean number of 20.53 times. Only a small proportion of the papers were based on funding (13.14%). Maximum number of papers emerged from United States of America. In terms of institutional affiliations, among the authors from top 20 institutes, 15 were from United States, 2 from Netherlands and 1 each from Canada, Germany and United Kingdom. In terms of authors, the research productivity of the top 20 most productive authors varied from 172 to 612 publications with 12 authors belonging to United States, 2 from Italy and 1 each from Canada, Greece, India, Ireland, Netherland and the United Kingdom. The maximum number of papers were published in Journal of the American Geriatric Society and based on the number of citations the New England Journal of Medicine was the most impactful journal.

CONCLUSION

Over the years number of publications on delirium have increased, majority of the publications have emerged from United States.

Involving stakeholders in research priority setting: a scoping review

BACKGROUND

This scoping review provides a thorough analysis of how stakeholders have so far been involved in research priority setting. The review describes, synthesizes, and evaluates research priority setting projects not only for the field of health-as previous reviews have done-but does so on a much broader scale for any research area.

METHODS

A comprehensive electronic literature search was conducted in the databases PubMed, Scopus, and Web of Science. Reflecting the importance of grey literature, Google Scholar and relevant websites were also screened for eligible publications. A computational approach was then used for the study selection. The final screening for inclusion was done manually.

RESULTS

The scoping review encompasses 731 research priority setting projects published until the end of 2020. Overall, the projects were conducted within the realm of 50 subject areas ranging from agriculture and environment over health to social work and technology. Key learnings include that nearly all priority setting projects aimed to identify research priorities for the field of health (93%), particularly for nursing and care, cancer, pediatrics, and mental, behavioral and neurodevelopmental disorders. Only 6% of the projects were not health-related and 1% identified research priorities at the interface between health and a non-health area. Over time, 30 different stakeholder groups took part in research priority setting. The stakeholders most frequently asked to identify research priorities were doctors, patients, academics/researchers, nurses, allied healthcare professionals, family members, friends, and carers. Nearly two thirds of all projects have been conducted in Europe and North America. Overall, only 9% of the projects emphasized the importance of stakeholders in their goals and rationales and actively involved them. In around a quarter of the projects, stakeholders deliberated on their research priorities throughout the entire process.

CONCLUSION

By mapping out the complex landscape of stakeholder involvement in research priority setting, this review guides future efforts to involve stakeholders effectively, inclusively, and transparently, which in turn may increase the overall value of research for society. As a practical addition to this review, the first worldwide research priority setting database was created: https://ois.lbg.ac.at/en/project-database . The database contains all the projects analyzed for this review and is constantly updated with the latest published research priority setting projects.

Identifying patients with delirium based on unstructured clinical notes. (Preprint)

Background

Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate.

Objective

We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes.

Methods

We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators.

Results

The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs –0.028).

Conclusions

Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019

Background

The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives

To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods

We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results

Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion

Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Stratified delirium risk using prescription medication data in a state-wide cohort

OBJECTIVE

Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list.

METHOD

Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort.

RESULTS

The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender.

CONCLUSION

Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.

Diagnostic Performance of Delirium Assessment Tools in Critically Ill Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Critical care nurses are in the best position to detect and monitor delirium in critically ill patients. Therefore, an optimum delirium assessment tool with strong evidence should be identified with critical care nurses to perform in the daily assessment.

AIM

To evaluate and compare the diagnostic performance of delirium assessment tools in diagnosing delirium in critically ill patients.

METHODS

We searched five electronic databases including the Cochrane Library, PubMed, Embase, CINAHL, and a Chinese database for eligible diagnostic studies published in English or Mandarin up to December 2018. This diagnostic test accuracy meta-analysis was limited to studies in intensive care unit (ICU) settings, using the Diagnostic and Statistical Manual of Mental Disorders (DSM) as a standard reference to test the accuracy of delirium assessment tools. Eligible studies were critically appraised by two investigators independently. The summary of evidence was conducted for pooling and comparing diagnostic accuracy by a bivariate random effects meta-analysis model. The pooled sensitivities and specificities, summary receiver operating characteristic curve (sROC), the area under the curve (AUC), and diagnostic odds ratio (DOR) were calculated and plotted. The possibility of publication bias was assessed by Deeks' funnel plot.

DATA SYNTHESIS

We identified and evaluated 23 and 8 articles focused on CAM-ICU and ICDSC, respectively. The summary sensitivities of 0.85 and 0.87, and summary specificities of 0.95 and 0.91 were found for CAM-ICU and ICDSC, respectively. The AUC of the CAM-ICU was 0.96 (95% CI, 0.94-0.98), with DOR at 99 (95% CI, 55-177). The AUC of the ICDSC was 0.95 (95% CI, 0.92-0.96), and the DOR was 65 (95% CI, 27-153).

LINKING EVIDENCE TO ACTION

CAM-ICU demonstrated higher diagnostic test accuracy and is recommended as the optimal delirium assessment tool. However, the results should be interpreted with caution due to the between-study heterogeneity of this diagnostic test accuracy meta-analysis.