Prediction of Pediatric Critical Care Resource Utilization for Disaster Triage*
Bibliographic record
Abstract
OBJECTIVES: Pediatric protocols to guide allocation of limited resources during a disaster lack data to validate their use. The 2011 Pediatric Emergency Mass Critical Care Task Force recommended that expected duration of critical care be incorporated into resource allocation algorithms. We aimed to determine whether currently available pediatric illness severity scores can predict duration of critical care resource use. DESIGN: Retrospective cohort study. SETTING: Seattle Children's Hospital. PATIENTS: PICU patients admitted 2016-2018 for greater than or equal to 12 hours (n = 3,206). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed logistic and linear regression models in two-thirds of the cohort to predict need for and duration of PICU resources based on Pediatric Risk of Mortality-III, Pediatric Index of Mortality-3, and serial Pediatric Logistic Organ Dysfunction-2 scores. We tested the predictive accuracy of the models with the highest area under the receiver operating characteristic curve (need for each resource) and R (duration of use) in a validation cohort of the remaining one of three of the sample and among patients admitted during one-third of the sample and among patients admitted during surges of respiratory illness. Pediatric Logistic Organ Dysfunction score calculated 12 hours postadmission had higher predictive accuracy than either Pediatric Risk of Mortality or Pediatric Index of Mortality scores. Models incorporating 12-hour Pediatric Logistic Organ Dysfunction score, age, Pediatric Overall Performance Category, Pediatric Cerebral Performance Category, chronic mechanical ventilation, and postoperative status had an area under the receiver operating characteristic curve = 0.8831 for need for any PICU resource (positive predictive value 80.2%, negative predictive value 85.9%) and area under the receiver operating characteristic curve = 0.9157 for mechanical ventilation (positive predictive value 85.7%, negative predictive value 89.2%) within 7 days of admission. Models accurately predicted greater than or equal to 24 hours of any resource use for 78.9% of patients and greater than or equal to 24 hours of ventilation for 83.1%. Model fit and accuracy improved for prediction of resource use within 3 days of admission, and was lower for noninvasive positive pressure ventilation, vasoactive infusions, continuous renal replacement therapy, extracorporeal membrane oxygenation, and length of stay. CONCLUSIONS: A model incorporating 12-hour Pediatric Logistic Organ Dysfunction score performed well in estimating how long patients may require PICU resources, especially mechanical ventilation. A pediatric disaster triage algorithm that includes both likelihood for survival and for requiring critical care resources could minimize subjectivity in resource allocation decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".