Low-Resource Hospital Days for Children Following New Tracheostomy.

BACKGROUND AND OBJECTIVES: Children with new tracheostomy and invasive mechanical ventilation (IMV) require transitional care involving caregiver education and nursing support. To better understand hospital resource use during this transition, our study aimed to: (1) define and characterize low-resource days (LRDs) for this population and (2) identify factors associated with LRD occurrence. METHODS: This retrospective cohort analysis included children ≤21 years with new tracheostomy and IMV dependence admitted to an ICU from 2017 to 2022 using the Pediatric Health Information System database. A LRD was defined as a post tracheostomy day that accrued nonroom charges <10% of each patient's accrued nonroom charges on postoperative day 1. Factors associated with LRDs were analyzed using negative binomial regression. RESULTS: Among 4048 children, median post tracheostomy stay was 69 days (interquartile range 34-127.5). LRDs were common: 38.6% and 16.4% experienced ≥1 and ≥7 LRDs, respectively. Younger age at tracheostomy (0-7 days rate ratio [RR] 2.42 [1.67-3.51]; 8-28 days RR 1.8 (1.2-2.69) versus 29-365 days; Asian race (RR 1.5 [1.04-2.16]); early tracheostomy (0-7 days RR 1.56 [1.2-2.04]), and longer post tracheostomy hospitalizations (31-60 days RR 1.85 [1.44-2.36]; 61-90 days RR 2.14 [1.58-2.91]; >90 days RR 2.21 [1.71-2.86]) were associated with more LRDs. CONCLUSIONS: Approximately 1 in 6 children experienced ≥7 LRDs. Younger age, early tracheostomy, Asian race, and longer hospital stays were associated with increased risk of LRDs. Understanding the postacute phase, including bed utilization, serves as an archetype to explore care models for children with IMV dependence.

Machine learning models to predict and benchmark PICU length of stay with application to children with critical bronchiolitis.

OBJECTIVE: To create models for prediction and benchmarking of pediatric intensive care unit (PICU) length of stay (LOS) for patients with critical bronchiolitis. HYPOTHESIS: We hypothesize that machine learning models applied to an administrative database will be able to accurately predict and benchmark the PICU LOS for critical bronchiolitis. DESIGN: Retrospective cohort study. PATIENTS: All patients less than 24-month-old admitted to the PICU with a diagnosis of bronchiolitis in the Pediatric Health Information Systems (PHIS) Database from 2016 to 2019. METHODOLOGY: Two random forest models were developed to predict the PICU LOS. Model 1 was developed for benchmarking using all data available in the PHIS database for the hospitalization. Model 2 was developed for prediction using only data available on hospital admission. Models were evaluated using R2 values, mean standard error (MSE), and the observed to expected ratio (O/E), which is the total observed LOS divided by the total predicted LOS from the model. RESULTS: The models were trained on 13,838 patients admitted from 2016 to 2018 and validated on 5254 patients admitted in 2019. While Model 1 had superior R2 (0.51 vs. 0.10) and (MSE) (0.21 vs. 0.37) values compared to Model 2, the O/E ratios were similar (1.18 vs. 1.20). Institutional median O/E (LOS) ratio was 1.01 (IQR 0.90-1.09) with wide variability present between institutions. CONCLUSIONS: Machine learning models developed using an administrative database were able to predict and benchmark the length of PICU stay for patients with critical bronchiolitis.

Characteristics of Children Who Deteriorate After Transport and Associated Preadmission Factors.

OBJECTIVE: The incidence of deterioration and associated characteristics are largely unknown for children transported for admission from referring emergency departments (EDs) to general inpatient units. This study describes this population and identifies associated preadmission characteristics. METHODS: This single-center cohort study included children ≤ 18 years old transferred from an ED and directly admitted to general inpatient units from 2016 to 2019. Deterioration was defined as 1 or more of the following occurring within 24 hours of admission: rapid response team activation, transfer to the intensive care unit (ICU), or cardiac or respiratory arrest. ICU transfer was the secondary outcome. Logistic regression was performed. RESULTS: One thousand nine hundred eighty-eight patients were included; the median age was 4.2 years, 53.9% were male, and 44.1% had respiratory diagnoses. Deterioration occurred in 135 (6.8%) children overall and in 10.1% of children with respiratory complaints. Deterioration was associated with ≥ 2 complex chronic conditions (adjusted odds ratio [aOR] = 2.09; 95% confidence interval [CI], 1.04-4.19) and a longer stabilization time (per 10 minutes) (aOR = 1.17; 95% CI, 1.01-1.36). ICU transfer was associated with ≥ 2 complex chronic conditions (aOR = 2.33; 95% CI, 1.13-4.80), supplemental oxygen via nasal cannula (aOR = 2.13; 95% CI, 1.18-3.85), and nebulizer treatment (aOR = 2.77; 95% CI, 1.21-6.35). CONCLUSION: Deterioration was experienced by 7% of children admitted to a general unit, with the majority having respiratory complaints. Transport teams should consider the potential for increased risk of deterioration among children with respiratory disease, multiple complex chronic conditions, and a nasal cannula or nebulizer therapy. The clinical significance of marginally longer stabilization times is unclear and warrants further investigation.