Children's Hospital of Orange County, Orange, California, USA.
Beckman Research Institute, City of Hope, Duarte, California, USA.
Cancer Rep (Hoboken). 2021 Jun;4(3):e1343. doi: 10.1002/cnr2.1343. Epub 2021 Feb 2.
Pediatric oncology patients have high rates of hospital readmission but there is a dearth of research into risk factors for unplanned 30-day readmissions among this high-risk population.
In this study, we built a statistical model to provide insight into risk factors of unplanned readmissions in this pediatric oncology.
We retrieved 32 667 encounters from 10 418 pediatric patients with a neoplastic condition from 16 hospitals in the Cerner Health Facts Database and built a mixed-effects model with patients nested within hospitals for inference on 75% of the data and reserved the remaining as an independent test dataset.
The mixed-effects model indicated that patients with acute lymphoid leukemia (in relapse), neuroblastoma, rhabdomyosarcoma, or bone/cartilage cancer have increased odds of readmission. The number of cancer medications taken by the patient and the administration of chemotherapy were associated with increased odds of readmission for all cancer types. Wilms Tumor had a significant interaction with administration of chemotherapy, indicating that the risk due to chemotherapy is exacerbated in patients with Wilms Tumor. A second two-way interaction between recent history of chemotherapy treatment and infections was associated with increased odds of readmission. The area under the receiver operator characteristic curve (and corresponding 95% confidence interval) of the mixed-effects model was 0.714 (0.702, 0.725) on the independent test dataset.
Readmission risk in oncology is modified by the specific type of cancer, current and past administration of chemotherapy, and increased health care utilization. Oncology-specific models can provide decision support where model built on other or mixed population has failed.
儿科肿瘤患者的住院再入院率较高，但针对该高风险人群中计划外 30 天再入院的危险因素研究甚少。
本研究旨在构建一个统计模型，深入了解儿科肿瘤患者计划外再入院的危险因素。
我们从 Cerner Health Facts 数据库的 16 家医院中检索了 10418 名患有肿瘤疾病的儿科患者的 32667 次就诊记录，并建立了一个混合效应模型，将患者嵌套在医院内进行推断，其中 75%的数据用于模型构建，其余 25%的数据作为独立测试数据集。
混合效应模型表明，患有急性淋巴细胞白血病（复发）、神经母细胞瘤、横纹肌肉瘤或骨/软骨癌的患者再入院的可能性增加。患者接受的癌症药物数量和化疗的应用与所有癌症类型的再入院风险增加相关。Wilms 瘤与化疗的应用存在显著的交互作用，表明化疗导致的风险在 Wilms 瘤患者中加剧。化疗治疗近期史与感染之间的第二次双向交互作用与再入院风险增加相关。混合效应模型在独立测试数据集上的接收者操作特征曲线下面积（及其相应的 95%置信区间）为 0.714（0.702，0.725）。
癌症患者的再入院风险受到癌症类型、当前和过去化疗的应用以及医疗保健利用增加的影响。针对特定肿瘤类型的模型可以提供决策支持，而基于其他或混合人群构建的模型可能会失败。
