Neural networks to predict radiographic brain injury in pediatric patients treated with Extracorporeal Membrane Oxygenation

Brain injury is a significant source of morbidity and mortality for pediatric patients treated with Extracorporeal Membrane Oxygenation (ECMO). Our objective was to utilize neural networks to predict radiographic evidence of brain injury in pediatric ECMO-supported patients and identify specific variables that can be explored for future research. Data from 174 ECMO-supported patients were collected up to 24 h prior to, and for the duration of, the ECMO course. Thirty-five variables were collected, including physiological data, markers of end-organ perfusion, acid-base homeostasis, vasoactive infusions, markers of coagulation, and ECMO-machine factors. The primary outcome was the presence of radiologic evidence of moderate to severe brain injury as established by brain CT or MRI. This information was analyzed by a neural network, and results were compared to a logistic regression model as well as clinician judgement. The neural network model was able to predict brain injury with an Area Under the Curve (AUC) of 0.76, 73% sensitivity, and 80% specificity. Logistic regression had 62% sensitivity and 61% specificity. Clinician judgment had 39% sensitivity and 69% specificity. Sequential feature group masking demonstrated a relatively greater contribution of physiological data and minor contribution of coagulation factors to the model\u27s performance. These findings lay the foundation for further areas of research directions

Effectiveness of Personal Protective Equipment and Oseltamivir Prophylaxis during Avian Influenza A (H7N7) Epidemic, the Netherlands, 2003

TOC Summary: Only oseltamivir use significantly reduced the risk for human infection

State-level geographic variation in prompt access to care for children after motor vehicle crashes

Background: Motor vehicle crashes (MVCs) are a principal cause of death in children; fatal MVCs and pediatric trauma resources vary by state. We sought to examine state-level variability in and predictors of prompt access to care for children in MVCs. Materials and methods: Using the 2010-2014 Fatality Analysis Reporting System, we identified passengers aged (crashes on US public roads with ≥1 death, adult or pediatric, within 30 d). We included children requiring transport for medical care from the crash scene with documented time of hospital arrival. Our primary outcome was transport time to first hospital, defined as \u3e1 or ≤1 h. We used multivariable logistic regression to establish state-level variability in the percentage of children with transport time \u3e1 h, adjusting for injury severity (no injury, possible injury, suspected minor injury, suspected severe injury, fatal injury, and unknown severity), mode of transport (emergency medical services [EMS] air, EMS ground, and non-EMS), and rural roads. Results: We identified 18,116 children involved in fatal MVCs from 2010 to 2014; 10,407 (57%) required transport for medical care. Median transport time was 1 h (interquartile range: [1, 1]; range: [0, 23]). The percent of children with transport time \u3e1 h varied significantly by state, from 0% in several states to 69% in New Mexico. Children with no injuries identified at the scene and crashes on rural roads were more likely to have transport times \u3e1 h. Conclusions: Transport times for children after fatal MVCs varied substantially across states. These results may inform state-level pediatric trauma response planning

Factors associated with pediatric mortality from motor vehicle crashes in the United States: A state-based analysis

Objective: To examine geographic variation in motor vehicle crash (MVC)-related pediatric mortality and identify state-level predictors of mortality.Study design: Using the 2010-2014 Fatality Analysis Reporting System, we identified passengers \u3c15 years of age involved in fatal MVCs, defined as crashes on US public roads with ≥1 death (adult or pediatric) within 30 days. We assessed passenger, driver, vehicle, crash, and state policy characteristics as factors potentially associated with MVC-related pediatric mortality. Our outcomes were age-adjusted, MVC-related mortality rate per 100 000 children and percentage of children who died of those in fatal MVCs. Unit of analysis was US state. We used multivariable linear regression to define state characteristics associated with higher levels of each outcome.Results: Of 18 116 children in fatal MVCs, 15.9% died. The age-adjusted, MVC-related mortality rate per 100 000 children varied from 0.25 in Massachusetts to 3.23 in Mississippi (mean national rate of 0.94). Predictors of greater age-adjusted, MVC-related mortality rate per 100 000 children included greater percentage of children who were unrestrained or inappropriately restrained (P \u3c .001) and greater percentage of crashes on rural roads (P = .016). Additionally, greater percentages of children died in states without red light camera legislation (P \u3c .001). For 10% absolute improvement in appropriate child restraint use nationally, our risk-adjusted model predicted \u3e1100 pediatric deaths averted over 5 years.Conclusions: MVC-related pediatric mortality varied by state and was associated with restraint nonuse or misuse, rural roads, vehicle type, and red light camera policy. Revising state regulations and improving enforcement around these factors may prevent substantial pediatric mortality

Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree

BackgroundThe COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. ObjectiveWe sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. MethodsDue to the complex eligibility criteria for COVID-19 tests and the EHR implementation–related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. ResultsThe percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; P<.001). ConclusionsThe leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm