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- Title
Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs.
- Authors
Cascella, Marco; Racca, Emanuela; Nappi, Anna; Coluccia, Sergio; Maione, Sabatino; Luongo, Livio; Guida, Francesca; Avallone, Antonio; Cuomo, Arturo
- Abstract
Background: Unplanned hospital readmissions (HRAs) are very common in cancer patients. These events can potentially impair the patients' health-related quality of life and increase cancer care costs. In this study, data-driven prediction models were developed for identifying patients at a higher risk for HRA. Methods: A large dataset on cancer pain and additional data from clinical registries were used for conducting a Bayesian network analysis. A cohort of gastrointestinal cancer patients was selected. Logical and clinical relationships were a priori established to define and associate the considered variables including cancer type, body mass index (BMI), bone metastasis, serum albumin, nutritional support, breakthrough cancer pain (BTcP), and radiotherapy. Results: The best model (Bayesian Information Criterion) demonstrated that, in the investigated setting, unplanned HRAs are directly related to nutritional support (p = 0.05) and radiotherapy. On the contrary, BTcP did not significantly affect HRAs. Nevertheless, the correlation between variables showed that when BMI ≥ 25 kg/m2, the spontaneous BTcP is more predictive for HRAs. Conclusions: Whilst not without limitations, a Bayesian model, combined with a careful selection of clinical variables, can represent a valid strategy for predicting unexpected HRA events in cancer patients. These findings could be useful for calibrating care interventions and implementing processes of resource allocation.
- Subjects
CANCER patient psychology; CANCER pain; PATIENT readmissions; HOSPITAL care; QUALITY of life; RESEARCH funding; DESCRIPTIVE statistics; PREDICTION models; BODY mass index; DATA analysis software; MEDICAL needs assessment; BREAKTHROUGH pain; LONGITUDINAL method
- Publication
Healthcare (2227-9032), 2022, Vol 10, Issue 10, pN.PAG
- ISSN
2227-9032
- Publication type
Article
- DOI
10.3390/healthcare10101853