Association Between the COI and Excess Health Care Utilization and Costs for ACSC.

BACKGROUND AND OBJECTIVES: The authors of previous work have associated the Childhood Opportunity Index (COI) with increased hospitalizations for ambulatory care sensitive conditions (ACSC). The burden of this inequity on the health care system is unknown. We sought to understand health care resource expenditure in terms of excess hospitalizations, hospital days, and cost. METHODS: We performed a retrospective cross-sectional study of the Pediatric Health Information Systems database, including inpatient hospitalizations between January 1, 2016 and December 31, 2022 for children <18 years of age. We compared ACSC hospitalizations, mortality, and cost across COI strata. RESULTS: We identified 2 870 121 hospitalizations among 1 969 934 children, of which 44.5% (1 277 568/2 870 121) were for ACSCs. A total of 49.1% (331 083/674 548) of hospitalizations in the very low stratum were potentially preventable, compared with 39.7% (222 037/559 003) in the very high stratum (P < .001). After adjustment, lower COI was associated with higher odds of potentially preventable hospitalization (odds ratio 1.18, 95% confidence interval [CI] 1.17-1.19). Compared with the very high COI stratum, there were a total of 137 550 (95% CI 134 582-140 517) excess hospitalizations across all other strata, resulting in an excess cost of $1.3 billion (95% CI $1.28-1.35 billion). Compared with the very high COI stratum, there were 813 (95% CI 758-871) excess deaths, with >95% from the very low and low COI strata. CONCLUSIONS: Children with lower neighborhood opportunity have increased risk of ACSC hospitalizations. The COI may identify communities in which targeted intervention could reduce health care utilization and costs.

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

OBJECTIVES: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. DESIGN: We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. SETTING: Twenty-five PICUs across the United States. PATIENTS: EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. CONCLUSIONS: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort.

Background: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions. Methods: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme. Findings: Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes. Interpretation: Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock.

Objective: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata. Design: We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata. Setting: 25 pediatric intensive care units (PICU) across the U.S. Patients: EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data. Interventions: None. Measurements and Main Results: The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS. Conclusions: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.

Evaluating Demographic Data to Improve Confidence in Equity Analytics in a Children's Hospital.

Healthcare institutions are placing greater emphasis on equitable care. To accurately track and validate equity metrics, Akron Children's Hospital evaluated how key fields are collected, analyzed, and visualized throughout the organization. Standardized recommendations in this area vary, and this investigation provided specific ways to advance analytics in this field. In addition, the technical infrastructure needed a comprehensive evaluation to increase confidence in using demographic data. Methods: First, we reviewed how staff are trained to collect data at registration. Next, the electronic health record team standardized race and ethnicity fields with federal definitions. We found that fields were not consistently accessible across reporting tools. However, when present, all fields are sourced from the same electronic health record field. Finally, 6 months of encounters were analyzed and validated, with limitations to a seldom-populated Race 2 field. Results: We compared data, including and excluding null values, to provide concise recommendations for standard visualizations. We uncovered many consistencies and a few inconsistencies that informed the next steps. Conclusions: The results informed 7 recommendations to align Akron Children's Hospital's advancement in analytics for health equity data: standardize race and ethnicity fields across all reporting tools, add Child Opportunity Index 2.0 to the enterprise data warehouse, utilize data at the time of the patient's encounter, include null fields (patient refused, unknown, and not specified) in analysis, increase reporting capabilities for social determinants of health (SDOH), standardize multiracial data visualizations, and optimize reliable upstream data collection to increase reliability for all health equity measures.

Benchmarking Pain Assessment Rate in Critical Care Transport.

The purpose of this study is to determine the rate of pain assessment in pediatric neonatal critical care transport (PNCCT). The GAMUT database was interrogated for an 18-month period and excluded programs with less than 10% pediatric or neonatal patient contacts and less than 3 months of any metric data reporting during the study period. We hypothesized pain assessment during PNCCT is superior to prehospital pain assessment rates, although inferior to in-hospital rates. Sixty-two programs representing 104,445 patient contacts were analyzed. A total of 21,693 (20.8%) patients were reported to have a documented pain assessment. Subanalysis identified 17 of the 62 programs consistently reporting pain assessments. This group accounted for 24,599 patients and included 7,273 (29.6%) neonatal, 12,655 (51.5%) pediatric, and 4,664 (19.0%) adult patients. Among these programs, the benchmark rate of pain assessment was 90.0%. Our analysis shows a rate below emergency medical services and consistent with published hospital rates of pain assessment. Poor rates of tracking of this metric among participating programs was noted, suggesting an opportunity to investigate the barriers to documentation and reporting of pain assessments in PNCCT and a potential quality improvement initiative.

Transport disposition using the Transport Risk Assessment in Pediatrics (TRAP) score.

BACKGROUND: Determining appropriate disposition for referred pediatric patients is difficult, since it relies primarily on a telephone description of the patient. In this study, we evaluate the Transport Risk Assessment in Pediatrics (TRAP) score's ability to assist in appropriate placement of these patients. This novel tool is derived from physiologic variables. OBJECTIVES: To determine the feasibility of calculating a TRAP score and whether a higher score correlates with pediatric intensive care unit (PICU) admission. METHODS: We performed an observational study of pediatric patients transported by a specialized team to a tertiary care center and the feasibility of implementing the TRAP tool. Patients were eligible if transported by the pediatric specialty transport team for direct admission to the children's hospital. The TRAP score was obtained either through chart review of the transport team's initial assessment or in real time by the transport team. RESULTS: A total of 269 patients were identified, with 238 patients included in the study. Using logistic regression, higher TRAP scores were associated with PICU admission (odds ratio [OR] 1.40, p < 0.001). Patients with a higher score were also less likely to leave the PICU within 24 hours (OR 0.79, p < 0.001). CONCLUSION: The TRAP score is a novel objective pediatric transport assessment tool where an elevated score is associated with PICU admission for more than 24 hours. This score may assist with the triage decisions for transported pediatric patients.