Zhou Huaqiong, Della Phillip R, Roberts Pamela, Goh Louise, Dhaliwal Satvinder S
Clinical Nurse, General Surgical Ward, Princess Margaret Hospital for Children, Perth, Western Australia, Australia School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
BMJ Open. 2016 Jun 27;6(6):e011060. doi: 10.1136/bmjopen-2016-011060.
To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.
Systematic review.
SETTING/DATA SOURCE: CINAHL, Embase, MEDLINE from 2011 to 2015.
All studies of 28-day and 30-day readmission predictive model.
Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.
Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21-0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables 'comorbidities', 'length of stay' and 'previous admissions' were frequently cited across 73 models. The variables 'laboratory tests' and 'medication' had more weight in the models for cardiovascular disease and medical condition-related readmissions.
The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.
更新之前关于28天或30天非计划住院再入院预测模型的系统评价。
系统评价。
设置/数据来源：2011年至2015年的CINAHL、Embase、MEDLINE。
所有关于28天和30天再入院预测模型的研究。
纳入研究的特征、所识别预测模型的性能以及模型中包含的关键预测变量。
在7310条记录中，共有60项研究及73个独特的预测模型符合纳入标准。模型的应用结局包括全因再入院、包括肺炎在内的心血管疾病、内科疾病、外科疾病以及与心理健康状况相关的再入院。总体而言，56/60项研究报告了广泛的C统计量（0.21 - 0.88）。在13个与内科疾病相关再入院的预测模型中，有11个被发现具有一致的中度区分能力（C统计量≥0.7）。仅有两个模型是针对潜在可预防/可避免的再入院设计的，且C统计量>0.8。“合并症”“住院时长”和“既往住院史”这几个变量在73个模型中被频繁提及。“实验室检查”和“用药”变量在心血管疾病及内科疾病相关再入院的模型中权重更大。
关注与一般内科疾病相关的非计划住院再入院的预测模型显示出中度区分能力。两个针对潜在可预防/可避免再入院的模型显示出高区分能力。然而，本次更新的系统评价发现，纳入的73个独特风险预测模型的性能不一致。在选择预测模型之前，明确应用结局和可获取数据源的类型至关重要。对具有中度至高区分能力的预测模型进行严格验证至关重要，尤其是针对两个潜在可预防/可避免再入院的模型。鉴于现有证据有限，开发专门针对儿科28天全因非计划住院再入院的预测模型是当务之急。
