Pediatric intensive care unit admissions in children with respiratory technology dependence
Bibliographic record
Abstract
Children with medical complexity (CMC), defined as those with complex chronic conditions, functional limitations, technology dependence, and high healthcare utilization,1 account for a disproportionate amount of hospital inpatient and pediatric intensive care unit (PICU) admissions and have longer hospital and PICU lengths of stay.2-4 Many CMC utilize respiratory technology in outpatient settings.4, 5 Up to 20% of CMC use outpatient noninvasive positive pressure ventilation (NIPPV); an additional 9% have a tracheostomy with or without ventilator dependence.4, 5 Children with respiratory technology dependence (RTD) frequently require hospitalization and PICU admission.6, 7 However, few studies have focused specifically on PICU admissions in children with RTD,5, 7 limiting our understanding of their care needs. This study was approved by IWK Health (Halifax, Canada) Research Ethics Board (REB #1028128). Using the PICU administrative database, we generated a list of children (<18 years) admitted between June 2016 and May 2022. To identify children meeting inclusion criteria (i.e., RTD, defined as one of outpatient need for supplemental oxygen, NIPPV, tracheostomy with or without ventilation), we screened medical records of children with PICU database documentation of an underlying diagnosis (e.g., cerebral palsy, genetic/metabolic/syndromic condition, neuromuscular disease, chronic respiratory disease) or admission diagnosis (e.g., aspiration pneumonia, scoliosis repair, seizures/status epilepticus) suggestive of medical complexity with severe neurologic impairment. We also screened medical records of children identified by cross-referencing the list of PICU admissions with otolaryngology service lists of tracheostomy patients and respirology service lists of complex respiratory patients followed during the study period. Children who were admitted to PICU for a sleep study were excluded (n = 9). We compared Pediatric Risk of Mortality (PRISM) III,8 Therapeutic Intervention Scoring System for critically ill children (TISS-C)9 on PICU admission Day 1, and PICU length of stay between children with and without RTD using two-tailed independent samples t tests with unequal variances (Microsoft Excel, Version 16.58 for Mac, Microsoft Corporation, 2022). During the 6-year study period, we identified 50 children with RTD accounting for 119 (5.3%) of 2245 PICU admissions and 557 (5.6%) of 9895 PICU bed days. Patient characteristics are presented in Table 1. Compared to other PICU admissions, children with RTD had lower PRISM III scores (14 vs. 18, p < 0.001) and Day 1 TISS-C scores (22 vs. 25, p < 0.001). PICU length of stay was similar between children with RTD (median 3 days; interquartile range: 2–5 days) and without RTD (median 2 days; interquartile range: 2–4 days), p = 0.6. Although CMC account for a disproportionate amount of PICU resource utilization, with longer lengths of stay, higher severity of illness, and greater number of critical care interventions than other PICU admissions,2-4, 7 children with RTD admitted to our PICU accounted for 5% of admissions and bed days and, compared to other PICU admissions, had similar lengths of stay, lower severity of illness scores, and lower Day 1 estimates of nursing workload. These findings may be related to our organizational structure and institutional practices. We do not have an intermediate care unit (IMCU) and our PICU has a low threshold to admit children who require close observation and frequent noncritical care interventions (e.g., suctioning, cough assist, chest physiotherapy). This includes unplanned admissions of children with RTD whose respiratory support needs have increased from baseline during acute illness (53% of our cohort) and planned admissions for postoperative care (42% of our cohort). Our findings may not be generalizable to other centers, particularly those with IMCUs or well-resourced inpatient units where children are not often admitted to PICU unless they require invasive therapies. Our study was limited to a retrospective analysis of administrative data at a single center. However, our findings suggest that closer examination of rationale for admission and interventions required by children with RTD may identify patients who could be cared for outside the PICU if appropriate resources are reallocated to nonintensive care areas.10 Establishing high-dependency units with higher nurse-to-patient ratios than general wards, tracheostomy, and ventilator-trained nurses, and an increased number of interprofessional clinicians (i.e., respiratory therapists, physiotherapists) may allow for safe care of medically fragile children while conserving critical care resources. Marc Brousseau: Conceptualization; writing—original draft; methodology; writing—review and editing; formal analysis; investigation. Jennifer Foster: Conceptualization; methodology; writing—review and editing. Danielle Adam: Conceptualization; writing—review and editing; resources. Samantha Boggs: Conceptualization; writing—review and editing; methodology. Stacy Burgess: Conceptualization; writing—review and editing. Liane Johnson: Conceptualization; writing—review and editing; Resources. Sarah McMullen: Conceptualization; writing—review and editing. Kristina Krmpotic: Conceptualization; Investigation; writing—original draft; methodology; writing—review and editing; formal analysis; supervision; data curation; resources. Marc Brousseau received the Laila B Chase Studentship as part of Dalhousie Medicine's RIM Program. The authors have no funding to report. The authors declare no conflict of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".