Improving Delirium Assessments in Vanderbilt Pediatric and Pediatric Cardiovascular Intensive Care Units.

Introduction: Delirium is a disturbance of attention and awareness that represents a change from baseline mental status. Accurate diagnosis of delirium is of paramount importance to improving the management of pediatric delirium in the intensive care unit. Despite ongoing education, inconsistencies in delirium assessments occur. Here, we aimed to determine the extent of the problem and increase compliance with delirium assessments. Methods: We collected preintervention data to assess baseline compliance of delirium assessments in the Pediatric Intensive Care Unit (PICU) and Pediatric Cardiac Intensive Care Unit (PCICU) at Monroe Carell Jr Children's Hospital at Vanderbilt in November 2020. We executed 2 Plan-Do-Study-Act cycles with different interventions and collected data after each and approximately 1 year after the interventions. The first intervention consisted of virtual lectures on delirium assessments for the nursing staff. The second intervention included an educational handout and a new electronic medical record documentation tool. Results: Five hundred five individual nurse-patient encounters were assessed and collected throughout the project. The mean compliance of delirium documentation before the interventions was 52.5%. Target compliance after interventions was 70%. Mean compliance was 70% after cycle 1, 78% after cycle 2, and 86% in March 2022. Conclusions: Using pre- and postintervention data from chart reviews and nurse interviews regarding delirium screenings, we found that interventions targeting nurse education and EMR flowsheet improved compliance with delirium assessment and documentation in the PICU and PCICU. Future work should focus on assessing the clinical implications of this project in diagnosing and treating delirium.

An updated, computable MEDication-Indication resource for biomedical research.

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.

ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.

OBJECTIVE: Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. METHODS: We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. RESULTS: We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). CONCLUSION: ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.

ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms.

Objective: Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods: Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms. Results: We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including "absent sense of smell" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "with cough fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion: ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.

Developing image sets for inducing obsessive-compulsive checking symptoms.

Visual stimuli are often used for obsessive-compulsive (OC) symptom provocation in research studies. We tested the induction of anxiety and OC checking symptoms across different types of checking provocation stimuli in three populations: individuals with obsessive compulsive disorder (OCD), individuals with checking symptoms but without a diagnosis of OCD, and control individuals with neither checking symptoms nor a clinical diagnosis. One set of provocative images depicted objects that are commonly associated with checking anxiety. Another set ('enhanced provocative images') depicted similar objects but also included contextual cues suggesting a specific harmful scenario that could occur. As expected, the enhanced provocative images were more effective at inducing anxiety and OC symptoms than the standard provocative images. Future studies requiring checking symptom provocation should therefore consider incorporating similarly suggestive images. Individuals with clinical OCD reported the greatest provocation in response to these images, followed by those with nonclinical checking, followed by control individuals. Thus, these stimuli are able to provoke OC checking symptoms and anxiety differentially across groups, with the intensity of provocation reflecting diagnostic status. All groups demonstrated a similar qualitative pattern of provocation across images. Finally, in all groups, reported anxiety closely tracked intrusive thoughts and checking urges.