Turcotte Luke A, Daniel Imtiaz, Hirdes John P
School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada.
Institute of Health Policy Management and Evaluation, University of Toronto, Ontario Hospital Association, Toronto, Ontario, Canada.
J Am Med Dir Assoc. 2020 Apr;21(4):538-544.e1. doi: 10.1016/j.jamda.2019.12.022. Epub 2020 Feb 20.
To describe and validate the Post-acute Delayed Discharge Risk Scale (PADDRS), which classifies patients by risk of delayed discharge on admission to post-acute care settings using information collected with the interRAI Minimum Data Set (MDS) 2.0 assessment.
Retrospective cohort study of individuals admitted to Ontario Complex Continuing Care (CCC) hospitals. Person-level linkage between interRAI MDS 2.0 assessments and Cancer Care Ontario Wait Time Information System records was performed.
Sample of 30,657 patients who received care in an Ontario CCC hospital and were assessed with the interRAI MDS 2.0 assessment between January 1, 2010, and March 31, 2013.
Alternate Level of Care (ALC) designation of 30 or more days was used as the marker of delayed discharge. Scale validation was performed through computation of class-level effect sizes and receiver operating characteristic curves for each of Ontario's geographic health regions. Additionally, Clinical Assessment Protocol (CAP) decision-support tool trigger rates by PADDRS risk level were computed for problem areas that are clinically relevant with the delayed discharge outcome.
Overall, 9.4% of the sample experienced the delayed discharge outcome. The PADDRS algorithm achieved an overall area under the curve (AUC) statistic of 0.74, which indicates good discriminatory ability for predicting delayed discharge. PADDRS is generalizable across geographic regions, with AUC statistics ranging between 0.61 and 0.81 across each of Ontario's 14 Local Health Integration Networks. PADDRS demonstrated strong concurrent validity, as the percentage of patients triggering CAPs increased with the risk of delayed discharge.
PADDRS combines numerous important clinical factors associated with delayed discharge from a post-acute hospital into a cohesive decision-support tool for use by discharge planners. In addition to early identification of patients who are most likely to experience delayed discharge, PADDRS has applications in risk-adjusted quality measurement of discharge planning efficiency.
描述并验证急性后期延迟出院风险量表（PADDRS），该量表利用通过interRAI最低数据集（MDS）2.0评估收集的信息，在患者入住急性后期护理机构时，根据延迟出院风险对患者进行分类。
对安大略省综合持续护理（CCC）医院收治的个体进行回顾性队列研究。对interRAI MDS 2.0评估与安大略省癌症护理等待时间信息系统记录进行个人层面的关联。
2010年1月1日至2013年3月31日期间在安大略省CCC医院接受护理并接受interRAI MDS 2.0评估的30657例患者样本。
30天或更长时间的替代护理级别（ALC）指定被用作延迟出院的标志。通过计算安大略省每个地理健康区域的类别水平效应量和受试者工作特征曲线来进行量表验证。此外，针对与延迟出院结果临床相关的问题领域，计算了按PADDRS风险水平划分的临床评估协议（CAP）决策支持工具触发率。
总体而言，9.4%的样本出现了延迟出院结果。PADDRS算法的曲线下面积（AUC）统计量总体为0.74，这表明其对预测延迟出院具有良好的辨别能力。PADDRS在各地理区域具有普遍性，在安大略省14个地方卫生整合网络中的每个网络中，AUC统计量在0.61至0.81之间。PADDRS显示出很强的同时效度，因为触发CAP的患者百分比随着延迟出院风险的增加而增加。
PADDRS将与急性后期医院延迟出院相关的众多重要临床因素整合到一个连贯的决策支持工具中，供出院计划者使用。除了早期识别最有可能经历延迟出院的患者外，PADDRS还可应用于出院计划效率的风险调整质量测量。
