A Need for a Novel Survival Risk Scoring System for Intensive Care Admissions Due to Sepsis in Pediatric Hematology/Oncology Patients
Bibliographic record
Abstract
Background: Children with hemato-oncological diseases or following stem cell transplantation (SCT) are at high risk for life-threatening infections; sepsis in this population constitutes a substantial proportion of pediatric intensive care unit (PICU) admissions. The current pediatric prognostic scoring tools to evaluate illness severity and mortality risk are designed for the general pediatric population and may not be adequate for this vulnerable subpopulation. Methods: Retrospective analysis was performed on all PICU admissions for sepsis in children with hemato-oncological diseases or post-SCT, in a single tertiary pediatric hospital between 2008 and 2021 ( n = 233). We collected and analyzed demographic, clinical, and laboratory data and outcomes for all patients, and evaluated the accuracy of two major prognostic scoring tools, the Pediatric Logistic Organ Dysfunction-2 (PELOD-2) and the Pediatric Risk of Mortality III (PRISM III). Furthermore, we created a new risk-assessment model that contains additional parameters uniquely relevant to this population. Results: The survival rate for the cohort was 83%. The predictive accuracies of PELOD-2 and PRISM III, as determined by the area under the curve (AUC), were 83% and 78%, respectively. Nine new parameters were identified as clinically significant: age, SCT, viral infection, fungal infection, central venous line removal, vasoactive inotropic score, bilirubin level, C-reactive protein level, and prolonged neutropenia. Unique scoring systems were established by the integration of these new parameters into the algorithm; the new systems significantly improved their predictive accuracy to 91% ( p = 0.01) and 89% ( p < 0.001), respectively. Conclusions: The predictive accuracies (AUC) of the PELOD-2 and PRISM III scores are limited in children with hemato-oncological diseases admitted to PICU with sepsis. These results highlight the need to develop a risk-assessment tool adjusted to this special population. Such new scoring should represent their unique characteristics including their degree of immunosuppression and be validated in a large multi-center prospective study.
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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 | 0.000 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".