Predicting intracranial pressure monitor placement in children with traumatic brain injury: a prospective cohort study to develop a clinical decision support tool.

Objective: Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available.

Materials And Methods: A prospective observational cohort study was conducted from September 2014 to January 2024.

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

Objective: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify children at low risk for BSI to improve blood culture diagnostic stewardship.

The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative.

Background: When coronavirus disease 2019 (COVID-19) mitigation efforts waned, viral respiratory infections (VRIs) surged, potentially increasing the risk of postviral invasive bacterial infections (IBIs). We sought to evaluate the change in epidemiology and relationships between specific VRIs and IBIs [complicated pneumonia, complicated sinusitis and invasive group A streptococcus (iGAS)] over time using the National COVID Cohort Collaborative (N3C) dataset.

Methods: We performed a secondary analysis of all prospectively collected pediatric (<19 years old) and adult encounters at 58 N3C institutions, stratified by era: pre-pandemic (January 1, 2018, to February 28, 2020) versus pandemic (March 1, 2020, to June 1, 2023).

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject.

Adiponectin and Glucocorticoids Modulate Risk for Preterm Birth: The Healthy Start Study.

Context: Adiponectin is a potent uterine tocolytic that decreases with gestational age, suggesting it could be a maternal metabolic quiescence factor. Maternal stress can influence preterm birth risk, and adiponectin levels may be stress responsive.

Objective: We characterized associations between adiponectin and glucocorticoids with preterm birth and modeled their predictive utility.

Open source and reproducible and inexpensive infrastructure for data challenges and education.

Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis.

Methods: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C).

Development and validation of a Bayesian network predicting neurosurgical intervention after injury in children and adolescents.

Background: Timely surgical decompression improves functional outcomes and survival among children with traumatic brain injury and increased intracranial pressure. Previous scoring systems for identifying the need for surgical decompression after traumatic brain injury in children and adults have had several barriers to use. These barriers include the inability to generate a score with missing data, a requirement for radiographic imaging that may not be immediately available, and limited accuracy.

A Nested Case-Control Study of Allopregnanolone and Preterm Birth in the Healthy Start Cohort.

Context: Chronic stress is a risk factor for preterm birth; however, objective measures of stress in pregnancy are limited. Maternal stress biomarkers may fill this gap. Steroid hormones and neurosteroids such as allopregnanolone (ALLO) play important roles in stress physiology and pregnancy maintenance and therefore may be promising for preterm birth prediction.

Radiographic assessment of acute vs chronic vertebral compression fractures.

Purpose: Distinguishing between acute and chronic vertebral compression fractures typically requires advanced imaging techniques such as magnetic resonance imaging (MRI). Recognizing specific radiographic findings associated with fracture acuity may improve the accuracy of radiographic assessment.

Methods: Patients with compression fractures that had both radiographic and MRI studies of the lumbar spine within a 30-day time frame were retrospectively reviewed.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program.

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID).

Objective: To identify risk factors associated with PASC/long-COVID.

Design: Retrospective case-control study.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission.

Background And Objectives: Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics.

Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative.

Importance: Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data.

Objective: To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C).

Design, Setting, And Participants: A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US.

Intracranial meningioma surveillance using volumetrics from T2-weighted MRI.

Background And Purpose: The gold standard for imaging of meningiomas is MRI with gadolinium-based contrast agent. Due to increased costs, time, and uncertain chronic effects of gadolinium exposure, use of noncontrast T2-weighted imaging (T2WI) in lieu of contrast-enhanced MRI has been an increasing focus of research across various diagnostic scenarios. The purpose of this study was to evaluate the diagnostic accuracy of T2WI in detecting changes in meningioma tumor volume.

Children with SARS-CoV-2 in the National COVID Cohort Collaborative (N3C).

Importance: SARS-CoV-2.

Objective: To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C).

Design: Prospective cohort study of patient encounters with end dates before May 27th, 2021.

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

Objective: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.

Materials And Methods: We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020.

Characteristics of epigenetic aging across gestational and perinatal tissues.

Background: Epigenetic clocks have been used to indicate differences in biological states between individuals of same chronological age. However, so far, only few studies have examined epigenetic aging in newborns-especially regarding different gestational or perinatal tissues. In this study, we investigated which birth- and pregnancy-related variables are most important in predicting gestational epigenetic age acceleration or deceleration (i.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

Background: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality.