癌症患者非计划性再入院风险预测模型的范围综述

doi:10.3761/j.issn.0254-1769.2022.09.008

摘要/Abstract

摘要：

目的对癌症患者非计划性再入院的风险预测模型进行范围综述,为临床护理工作及未来研究提供借鉴。方法聚焦癌症患者非计划性再入院风险预测模型,系统检索中英文数据库,提取适用人群、非计划性再入院发生率、模型构建的方法学、预测因子及性能等信息。结果共纳入18项研究,涉及23个模型,研究人群集中于结直肠癌术后患者。癌症患者30 d内非计划性再入院的发生率为8.2%~19.0%。模型构建的方法多样,但预测性能总体表现欠佳。合并症、肿瘤分期、住院时长、年龄和术后并发症是预测癌症患者非计划性再入院的重要因子。结论临床护理人员应关注非计划性再入院的高危因素,选择性能优良的工具指导临床实践。未来可借助人工智能技术,构建预测性能佳、可操作性强的模型,并进行广泛的外部验证。

关键词: 癌症, 非计划性再入院, 预测模型, 风险评估, 范围综述, 护理

Abstract:

Objective A scoping review of unplanned readmission（UR) risk prediction models for cancer patients was conducted to provide a basis for clinical practice and research. Methods The UR risk prediction model of cancer patients was focused,and the Chinese and English databases were searched systematically. The extracted information of the model included applicable population,the incidence of UR,modeling methodology,predictors of the model and their performance. Results 18 studies involving 23 prediction models were included and the population focused on postoperative colorectal cancer patients. The incidence of 30 days UR in cancer patients ranged from 8.2% to 19.0%. The model development methods were various,but the overall prediction performance was poor. Comorbidities,TNM,length of stay,age and postoperative complications were important predictors of UR in cancer patients. Conclusion Clinical staff should pay attention to UR risk factors and choose excellent tools to guide clinical practice. Prediction models with high predictive performance and operability can be developed with artificial intelligence and verified extensively and externally.

Key words: Neoplasms, Unplanned Readmission, Prediction Model, Risk Assessment, Scoping Review, Nursing Care

李静, 侯云霞, 强万敏. 癌症患者非计划性再入院风险预测模型的范围综述[J]. 中华护理杂志, 2022, 57(9): 1079-1087.

LI Jing, HOU Yunxia, QIANG Wanmin. A scoping review of models for predicting the risk of unplanned readmission in cancer patients[J]. Chinese Journal of Nursing, 2022, 57(9): 1079-1087.

图/表 6

图1 文献筛选流程

Figure 1 Literature screening process

表1 纳入研究的偏倚风险评估（n=18)

Table 1 Risk assessment of bias for included studies（n=18)

表2 模型构建情况（n=14)

Table 2 Establishment of models（n=14)

表3 模型预测因子及性能（n=14)

Table 3 Performance and predictors of models（n=14)

表4 模型的风险预测因子分类

Table 4 Classification of risk predictors of the model

表5 其他模型在癌症患者非计划再入院风险预测效果的验证（n=5)

Table 5 Validation of other models in predicting risk of unplanned readmission for cancer patients（n=5)

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