Pediatric Critical Care Illness Severity Toolkit: Stata Commands for Calculation of Pediatric Index of Mortality and Pediatric Logistic Organ Dysfunction Scores.

Introduction: Illness severity scoring tools, such as PRISM III/IV, PIM-3, and PELOD-2, are widely used in pediatric critical care research. However, their application is hindered by complex calculation processes, privacy concerns with third-party online calculators, and challenges in accurate implementation within statistical packages.

Methods: We have developed a comprehensive, open-source toolkit for implementing the PIM-3, Simplified PIM-3, and PELOD-2 scores.

Application of Participatory Ergonomics to the Dissemination of a Quality Improvement Program for Optimizing Blood Culture Use.

Background: Blood cultures are overused in pediatric ICUs (PICUs), which may lead to unnecessary antibiotic use and antibiotic resistance. Using a participatory ergonomics (PE) approach, the authors disseminated a quality improvement (QI) program for optimizing blood culture use in PICUs to a national 14-hospital collaborative. The objective of this study was to evaluate the dissemination process and its impact on blood culture reduction.

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

Background: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method.

Aims: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day.

Predicting respiratory decompensation in mechanically ventilated adult ICU patients.

Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model.

A pilot study to predict cardiac arrest in the pediatric intensive care unit.

Background: Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance.

Methods: Our data consists of 240 Hz ECG waveform data, 0.

Examining Diurnal Differences in Multidisciplinary Care Teams at a Pediatric Trauma Center Using Electronic Health Record Data: Social Network Analysis.

Background: The care of pediatric trauma patients is delivered by multidisciplinary care teams with high fluidity that may vary in composition and organization depending on the time of day.

Objective: This study aims to identify and describe diurnal variations in multidisciplinary care teams taking care of pediatric trauma patients using social network analysis on electronic health record (EHR) data.

Methods: Metadata of clinical activities were extracted from the EHR and processed into an event log, which was divided into 6 different event logs based on shift (day or night) and location (emergency department, pediatric intensive care unit, and floor).

Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.

High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV).

A Research Agenda for Diagnostic Excellence in Critical Care Medicine.

Diagnosing critically ill patients in the intensive care unit is difficult. As a result, diagnostic errors in the intensive care unit are common and have been shown to cause harm. Research to improve diagnosis in critical care medicine has accelerated in past years.

Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit.

The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients. The design of the study is a retrospective observational cohort study. The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD.

Culture Ordering for Patients with New-onset Fever: A Survey of Pediatric Intensive Care Unit Clinician Practices.

Introduction: Accurate assessment of infection in critically ill patients is vital to their care. Both indiscretion and under-utilization of diagnostic microbiology testing can contribute to inappropriate antibiotic administration or delays in diagnosis. However, indiscretion in diagnostic microbiology cultures may also lead to unnecessary tests that, if false-positive, would incur additional costs and unhelpful evaluations.

Prediction of Impending Septic Shock in Children With Sepsis.

Objectives: Sepsis and septic shock are leading causes of in-hospital mortality. Timely treatment is crucial in improving patient outcome, yet treatment delays remain common. Early prediction of those patients with sepsis who will progress to its most severe form, septic shock, can increase the actionable window for interventions.

Consensus Recommendations for Blood Culture Use in Critically Ill Children Using a Modified Delphi Approach.

Objectives: Blood cultures are fundamental in evaluating for sepsis, but excessive cultures can lead to false-positive results and unnecessary antibiotics. Our objective was to create consensus recommendations focusing on when to safely avoid blood cultures in PICU patients.

Design: A panel of 29 multidisciplinary experts engaged in a two-part modified Delphi process.

Diagnostic Errors in Pediatric Critical Care: A Systematic Review.

Objectives: To summarize the literature on prevalence, impact, and contributing factors related to diagnostic error in the PICU.

Data Sources: Search of PubMed, EMBASE, and the Cochrane Library up to December 2019.

Study Selection: Studies on diagnostic error and the diagnostic process in pediatric critical care were included.

Diagnostic Stewardship of Endotracheal Aspirate Cultures in a PICU.

Background: Clinicians commonly obtain endotracheal aspirate cultures (EACs) in the evaluation of suspected ventilator-associated infections. However, bacterial growth in EACs does not distinguish bacterial colonization from infection and may lead to overtreatment with antibiotics. We describe the development and impact of a clinical decision support algorithm to standardize the use of EACs from ventilated PICU patients.

Spectral clustering of risk score trajectories stratifies sepsis patients by clinical outcome and interventions received.

Sepsis is not a monolithic disease, but a loose collection of symptoms with diverse outcomes. Thus, stratification and subtyping of sepsis patients is of great importance. We examine the temporal evolution of patient state using our previously-published method for computing risk of transition from sepsis into septic shock.