Advances in Point-of-Care Ultrasound in Pediatric Acute Care Medicine.

Pediatric point-of-care ultrasonography (POCUS) has grown in utilization and is now an integral part of pediatric acute care. Applications within the pediatric critical care, neonatology and pediatric emergency were once limited to evaluation of undifferentiated shock states, abdominal free fluid assessments in trauma resuscitation and procedural guidance. The body of pediatric POCUS literature is ever expanding and recently published international consensus guidelines are available to guide implementation into clinical practice. The authors present a review of emerging applications and controversies within thoracic, hemodynamic, neurologic, and ocular POCUS in pediatric acute care medicine.

Diagnostic Identification of Acute Brain Dysfunction in Pediatric Sepsis and Septic Shock in the Electronic Health Record: A Comparison of Four Definitions in a Reference Dataset.

OBJECTIVES: Acute brain dysfunction (ABD) in pediatric sepsis has a prevalence of 20%, but can be difficult to identify. Our previously validated ABD computational phenotype (CP ABD ) used variables obtained from the electronic health record indicative of clinician concern for acute neurologic or behavioral change. We tested whether the CP ABD has better diagnostic performance to identify confirmed ABD than other definitions using the Glasgow Coma Scale or delirium scores. DESIGN: Diagnostic testing in a curated cohort of pediatric sepsis/septic shock patients. SETTING: Quaternary freestanding children's hospital. SUBJECTS: The test dataset comprised 527 children with sepsis/septic shock managed between 2011 and 2021 with a prevalence (pretest probability) of confirmed ABD of 30% (159/527). MEASUREMENTS AND MAIN RESULTS: CP ABD was based on use of neuroimaging, electroencephalogram, and/or administration of new antipsychotic medication. We compared the performance of the CP ABD with three GCS/delirium-based definitions of ABD-Proulx et al, International Pediatric Sepsis Consensus Conference, and Pediatric Organ Dysfunction Information Update Mandate. The posttest probability of identifying ABD was highest in CP ABD (0.84) compared with other definitions. CP ABD also had the highest sensitivity (83%; 95% CI, 76-89%) and specificity (93%; 95% CI, 90-96%). The false discovery rate was lowest in CP ABD (1-in-6) as was the false omission rate (1-in-14). Finally, the prevalence threshold for the definitions varied, with the CP ABD being the definition closest to 20%. CONCLUSIONS: In our curated dataset of pediatric sepsis/septic shock, CP ABD had favorable characteristics to identify confirmed ABD compared with GCS/delirium-based definitions. The CP ABD can be used to further study the impact of ABD in studies using large electronic health datasets.

State Department of Motor Vehicles Reporting Mandates of Dementia Diagnoses and Dementia Underdiagnosis.

Importance: With older drivers representing the fastest growing segment of the driver population and dementia prevalence increasing with age, policymakers face the challenge of balancing road safety and mobility of older adults. In states that require reporting a dementia diagnosis to the Department of Motor Vehicles (DMV), individuals with dementia may be reluctant to disclose symptoms of cognitive decline, and clinicians may be reluctant to probe for those symptoms, which may be associated with missed or delayed diagnoses. Objective: To assess whether DMV reporting policies for drivers with dementia are associated with primary care clinicians' underdiagnosing dementia. Design, Setting, and Participants: This cross-sectional study used data from the 100% Medicare fee-for-service program and the Medicare Advantage plans from 2017 to 2019 on 223 036 primary care clinicians with at least 25 Medicare patients. Statistical analysis was performed from July to October 2023. Exposures: State DMV reporting policies for drivers with dementia. Main Outcomes and Measures: The main outcome was a binary variable indicating whether the clinician underdiagnosed dementia or not. Each clinician's expected number of dementia cases was estimated using a predictive model based on patient characteristics. Comparing the estimation with observed dementia diagnoses identified clinicians who underdiagnosed dementia vs those who did not, after accounting for sampling errors. Results: Four states have clinician reporting mandates, 14 have mandates requiring drivers to self-report dementia diagnoses, and 32 states and the District of Columbia do not have explicit requirements. Among primary care clinicians in states with clinician reporting mandates (n = 35 620), 51.4% were female, 91.9% worked in a metropolitan area, and 19.9% of the patient panel were beneficiaries dually eligible for Medicare and Medicaid. Among primary care clinicians in states with patient self-reporting mandates (n = 57 548), 55.7% were female, 83.1% worked in a metropolitan area, and 15.4% of the patient panel were dually eligible for Medicare and Medicaid. Among clinicians in states without mandates, 55.7% were female, 83.0% worked in a metropolitan area, and 14.6% of the patient panel were dually eligible for Medicare and Medicaid. Clinicians in states with clinician reporting mandates had an adjusted 12.4% (95% CI, 10.5%-14.2%) probability of underdiagnosing dementia compared with 7.8% (95% CI, 6.9%-8.7%) in states with self-reporting and 7.7% (95% CI, 6.9%-8.4%) in states with no mandates, an approximately 4-percentage point difference (P < .001). Conclusions and Relevance: Results of this cross-sectional study of primary care clinicians suggest that mandatory DMV policies for clinicians to report patients with dementia may be associated with a higher risk of missed or delayed dementia diagnoses. Future research is needed to better understand the unintended consequences and the risk-benefit tradeoffs of these policies.

Evaluation of Paroxysmal Events in Critically Ill Patients: Relationship of Primary Diagnosis to Long-Term Electroencephalogram Yield.

Continuous EEG (cEEG) is indicated for the workup of paroxysmal events. We aimed to assess whether primary admission diagnoses predict the yield of cEEG when ordered for evaluating paroxysmal events. We identified patients in the ICU who underwent at least 6 hours of cEEG monitoring to evaluate paroxysmal events. Primary admission diagnoses were categorized into neurological or non-neurological conditions. cEEG results were dichotomized into presence or absence of epileptiform discharges. We identified 159 recordings that were obtained for the evaluation of paroxysmal events. Most patients (n = 100, 63%) were admitted with primary admission diagnoses of neurological disorders, such as ischemic stroke, or intracranial hemorrhage. We found that patients with primary neurological conditions were more likely to have brain surgeries, abnormal brain imaging, and focal neurological deficits on examination compared to those with primary non-neurological conditions. However, there was no significant difference in the prevalence of epileptiform discharges in cEEG among patients with primary diagnoses of neurological or non-neurological disorders. These results suggest that cEEG is often necessary to evaluate paroxysmal events, even among patients without primary neurological disorders.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

Importance: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-ß-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. Objective: To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. Design, Setting, and Participants: This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Exposures: Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Main Outcomes and Measures: Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Results: Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. Conclusions and Relevance: This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-ß-directed monoclonal antibody therapies.