Department of Hospital Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Oncol Pract. 2022 Jan;18(1):e117-e128. doi: 10.1200/OP.20.01077. Epub 2021 Aug 6.
Readmissions for the medical treatment of cancer have traditionally been excluded from readmission measures under the Hospital Readmissions Reduction Program. Patients with cancer often have higher readmission rates and may need heightened support to ensure effective care transitions after hospitalization. Estimating readmission risk before discharge may assist in discharge planning efforts and help promote care coordination at time of discharge.
We developed and validated a readmission risk scoring system among a cohort of adult cancer patients with solid tumor admitted at a comprehensive cancer center. Multivariate logistic regression analysis was used to develop the model. The model's discriminative capacity was evaluated through a receiver operating characteristic curve analysis. We further compared the performance of the developed score with existing risk scores for 30-day readmission.
The 30-day unplanned readmission rate in the total cohort was 16.0% (n = 1,078 of 6,720). After multivariate analysis, Cancer site, Recent emergency room visit within 30 days, non-English primary language, Anemia defined as hemoglobin < 10 g/dL, > 4 Days length of stay during the index admission, unmarried Marital status, Increased white blood cell count > 11 × 10/L, and distant Tumor spread were significantly associated with risk of unplanned 30-day readmission. The derived score, which we call the Cancer READMIT score, had modest discriminatory performance in predicting readmissions (area under the curve for the model receiver operating characteristic curve = 0.647).
The Cancer READMIT score was able to predict 30-day unplanned readmissions to our institution with fairly modest performance. External validation of our derived risk scoring system is recommended.
传统上，癌症的医疗再入院已被排除在医院再入院减少计划的再入院措施之外。癌症患者的再入院率往往较高，可能需要更多的支持，以确保住院后的有效护理过渡。在出院前估计再入院风险可能有助于出院计划，并有助于在出院时促进护理协调。
我们在一家综合癌症中心收治的成年实体瘤癌症患者队列中开发和验证了一种再入院风险评分系统。多变量逻辑回归分析用于建立模型。通过接收者操作特征曲线分析评估模型的区分能力。我们进一步比较了所开发评分与 30 天再入院的现有风险评分的性能。
总队列中 30 天非计划性再入院率为 16.0%（6720 例中 1078 例）。多变量分析后，癌症部位、30 天内最近急诊就诊、非英语母语、血红蛋白<10 g/dL 的贫血、指数住院期间>4 天的住院时间、未婚的婚姻状况、白细胞计数增加>11×10/L 和远处肿瘤扩散与 30 天非计划性再入院的风险显著相关。我们称之为癌症 READMIT 评分的评分系统在预测再入院方面具有中等的区分性能（模型接收者操作特征曲线的曲线下面积为 0.647）。
癌症 READMIT 评分能够预测我们机构的 30 天非计划性再入院，但其性能相当中等。建议对我们的风险评分系统进行外部验证。
