Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.

Objectives: We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.

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.

Association of Dynamic Arterial Elastance With Fluid Responsiveness in Critically Ill Children.

Objectives: Cardiac mechanical efficiency has been shown to be a predictor of fluid responsiveness (FR) in adults. Our goal was to assess the association between mechanical efficiency as measured by dynamic arterial elastance (Eadyn) and mean arterial pressure (MAP) after fluid bolus in children with MAP less than or equal to 50th percentile for age.

Design: This was a retrospective, observational cohort study.

Derivation and Validation of Pediatric Sepsis-Associated Acute Kidney Injury Subphenotypes With Prognostic Relevance.

Objectives: Sepsis-associated acute kidney injury (SAKI) is a heterogeneous syndrome associated with poor outcomes. Subphenotypes of SAKI with prognostic and therapeutic relevance have been identified in adults, but not in children. We sought to identify reproducible and clinically relevant pediatric SAKI (pSAKI) subphenotypes using readily available clinical and laboratory data.

Experiences of recently diagnosed urban COVID-19 outpatients: A survey on patient worries, provider-patient interactions, and neutralizing monoclonal antibody treatment.

Background: COVID-19 patients have experienced worry, altered provider-patient interactions, and options to use novel treatments, initially with neutralizing monoclonal antibodies (mAbs). Limited research has been performed on these aspects of the COVID-19 outpatient experience.

Objective: This study aimed to evaluate the experiences of outpatients recently diagnosed with COVID-19, who were eligible for use of mAbs, during the diagnosis and treatment process based on sociodemographic and clinical factors.

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.

Personalizing the Pressure Reactivity Index for Quantifying Cerebral Autoregulation in Neurocritical Care.

Objective: The Pressure Reactivity Index (PRx) is a common metric for assessing cerebral autoregulation in neurocritical care. This study aimed to enhance the clinical utility of PRx by developing a personalized PRx algorithm (pPRx) and identifying ideal hyperparameters.

Methods: Algorithmic errors were quantified using simulated data and multimodal monitoring data from traumatic brain injury patients from the Track-TBI dataset.

Early Blood Biomarkers and MRI Injury After Cardiac Arrest: Secondary Analysis of the 2017-2020 Personalized Outcomes After Child Cardiac Arrest Study.

Objectives: Brain MRI is used to inform prognosis of pediatric cardiac arrest (CA). We analyzed the association between early levels of four brain injury biomarkers and pattern of brain injury on MRI.

Design, Setting, And Patients: This secondary analysis of a multicenter prospective cohort study in 14 U.

Children Requiring 3 or More Days of Invasive Ventilation: Secondary Analysis of Post-Discharge Change in Caregiver Employment.

Objectives: To describe frequency of, and risk factors, for change in caregiver employment among critically ill children with acute respiratory failure.

Design: Preplanned secondary analysis of prospective cohort dataset, 2018-2021.

Setting: Quaternary Children's Hospital PICU.

Physical Activity Monitoring in Children in the 1-Year After 3 or More Days of Invasive Ventilation: Feasibility of Using Accelerometers.

Objectives: To measure physical activity in a cohort of children who survived greater than or equal to 3 days of invasive ventilation.

Design: Prospective cohort study (2018-2021).

Setting: Quaternary children's hospital PICU.

Phoenix Sepsis Criteria in Critically Ill Children: Retrospective Validation Using a United States Nine-Center Dataset, 2012-2018.

Objectives: To perform: 1) external validation of the Phoenix Sepsis Score and Phoenix sepsis criteria in a multicenter cohort of critically ill children with infection and a comparison with the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria; 2) a study of Phoenix sepsis criteria performance in patient subgroups based on age and comorbidities; 3) an assessment of microbiological profile of children with Phoenix sepsis; and 4) a study of the performance of the Phoenix-8 score.

Design: Secondary, retrospective analysis of a multicenter cohort study from 2012 to 2018.

Setting: Nine PICUs in the United States.

External Validation, Re-Calibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children using Multi-Center Data.

Background: Acute kidney injury (AKI) is common among children with critical illness and is associated with high morbidity and mortality. Risk prediction models designed for clinical decision support implementation offer an opportunity to identify and proactively mitigate AKI risks. Existing models have been primarily validated on single-center data, owing partly to the lack of appropriately detailed multicenter datasets.

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).

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

Importance: During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics.

Objective: To evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.

Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world.

The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and-with the right planning-have the potential to reduce global health disparities.

Real-world evaluation of early remdesivir in high-risk COVID-19 outpatients during Omicron including BQ.1/BQ.1.1/XBB.1.5.

Background: A trial performed among unvaccinated, high-risk outpatients with COVID-19 during the delta period showed remdesivir reduced hospitalization. We used our real-world data platform to determine the effectiveness of remdesivir on reducing 28-day hospitalization among outpatients with mild-moderate COVID-19 during an Omicron period including BQ.1/BQ.