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Record W4385605972 · doi:10.1101/2023.07.31.23293444

Critical Illness Risk and Long-Term Outcomes Following Intensive Care in Pediatric Hematopoietic Cell Transplant Recipients

2023· preprint· en· W4385605972 on OpenAlexaffabout
Matt S. Zinter, Ruta Brazauskas, Joelle Strom, Stella Chen, Stephanie Bo‐Subait, Akshay Sharma, Amer Beitinjaneh, Dimana Dimitrova, Greg Guilcher, Jaime M. Preussler, Kasiani C. Myers, Neel S. Bhatt, Olle Ringdén, Peiman Hematti, Robert J. Hayashi, Sagar S. Patel, Satiro De Oliveira, Seth J. Rotz, Sherif M. Badawy, Taiga Nishihori, David Buchbinder, Betty K. Hamilton, Bipin N. Savani, Hélène Schoemans, Mohamed L. Sorror, Lena E. Winestone, Christine Duncan, Rachel Phelan, Christopher C. Dvorak

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsAlberta Children's Hospital
FundersCancer MoonshotMoonshot Research and Development ProgramNational Institute of Allergy and Infectious DiseasesBe The Match FoundationOffice of Naval ResearchHealth Resources and Services AdministrationAstellas PharmaActinium PharmaceuticalsNational Heart, Lung, and Blood InstituteAdaptive BiotechnologiesNational Cancer InstituteNational Institutes of HealthMedical College of WisconsinAmgen
KeywordsMedicineIntensive careUmbilical cordHematopoietic cellHematopoietic stem cell transplantationPediatricsTransplantationCumulative incidenceIncidence (geometry)Intensive care medicineInternal medicineHaematopoiesisStem cellImmunology

Abstract

fetched live from OpenAlex

ABSTRACT Background Allogeneic hematopoietic cell transplantation (HCT) can be complicated by the development of organ toxicity and infection necessitating intensive care. Risk factors for intensive care admission are unclear due to heterogeneity across centers, and long-term outcome data after intensive care are sparse due to a historical paucity of survivors. Methods The Center for International Blood and Marrow Transplant Research (CIBMTR) was queried to identify patients age ≤21 years who underwent a 1 st allogeneic HCT between 2008-2014 in the United States or Canada. Records were cross-referenced with the Virtual Pediatric Systems pediatric ICU database to identify intensive care admissions. CIBMTR follow-up data were collected through the year 2020. Results We identified 6,995 pediatric HCT patients from 69 HCT centers, of whom 1,067 required post-HCT intensive care. The cumulative incidence of PICU admission was 8.3% at day +100, 12.8% at 1 year, and 15.3% at 5 years post HCT. PICU admission was linked to younger age, lower median zip code income, Black or multiracial background, pre-transplant organ toxicity, pre-transplant CMV seropositivity, use of umbilical cord blood and/or HLA-mismatched allografts, and the development of post-HCT graft-versus-host disease or malignancy relapse. Among PICU patients, survival to ICU discharge was 85.7% but more than half of ICU survivors were readmitted to a PICU during the study interval. Overall survival from the time of 1 st PICU admission was 52.5% at 1 year and 42.6% at 5 years. Long-term post-ICU survival was worse among patients with malignant disease (particularly if relapsed), as well as those with poor pre-transplant organ function and alloreactivity risk-factors. In a landmark analysis of all 1-year HCT survivors, those who required intensive care in the first year had 10% lower survival at 5 years (77.1% vs. 87.0%, p<0.001) and developed new dialysis-dependent renal failure at a greater rate (p<0.001). Conclusions Intensive care management is common in pediatric HCT patients. Survival to ICU discharge is high, but ongoing complications necessitate recurrent ICU admission and lead to a poor 1-year outcome in many patients. Together, these data suggest an ongoing burden of toxicity in pediatric HCT patients that continues to limit long-term survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.313
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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