Multicenter study of risk factors of unplanned 30-day readmissions in pediatric oncology

Kamila Hoenk1,2, Lilibeth Torno1, William Feaster1

  • 1Children's Hospital of Orange County, Orange, California, USA.

Insights

Unplanned 30-day readmissions in pediatric oncology are influenced by specific cancer types, chemotherapy, and infections. Oncology-specific models can improve risk prediction for these high-risk patients.

Area of Science:

  • Oncology
  • Health Services Research
  • Biostatistics

Background:

  • Pediatric oncology patients experience high hospital readmission rates.
  • Research into risk factors for unplanned 30-day readmissions in this population is limited.

Purpose of the Study:

  • To develop a statistical model identifying risk factors for unplanned 30-day readmissions in pediatric oncology patients.
  • To provide insights into factors influencing readmission risk within this specific patient group.

Main Methods:

  • A mixed-effects statistical model was developed using data from 32,667 encounters of 10,418 pediatric patients with neoplastic conditions.
  • Data were sourced from the Cerner Health Facts Database across 16 hospitals.
  • The model was trained on 75% of the data and validated on an independent test dataset.

Main Results:

  • Specific cancers (acute lymphoid leukemia in relapse, neuroblastoma, rhabdomyosarcoma, bone/cartilage cancer) increased readmission odds.
  • Number of cancer medications and chemotherapy administration were associated with higher readmission odds for all cancer types.
  • Interactions between Wilms Tumor and chemotherapy, and between recent chemotherapy and infections, significantly increased readmission risk. The model achieved an AUC of 0.714 on the test dataset.

Conclusions:

  • Readmission risk in pediatric oncology is significantly influenced by cancer type, chemotherapy exposure, and healthcare utilization.
  • Oncology-specific predictive models are crucial for effective decision support, outperforming models developed for mixed populations.
Abstract

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