de Berker Henry, de Berker Archy, Aung Htin, Duarte Pedro, Mohammed Salman, Shetty Hamsaraj, Hughes Tom
Royal Manchester Children's Hospital, Manchester, UK
joint first authors.
Clin Med (Lond). 2021 Mar;21(2):e186-e191. doi: 10.7861/clinmed.2020-0834.
Reliable prediction of discharge destination in acute stroke informs discharge planning and can determine the expectations of patients and carers. There is no existing model that does this using routinely collected indices of pre-morbid disability and stroke severity.
Age, gender, pre-morbid modified Rankin Scale (mRS) and National Institutes of Health Stroke Scale (NIHSS) were gathered prospectively on an acute stroke unit from 1,142 consecutive patients. A multiclass random forest classifier was used to train and validate a model to predict discharge destination.
Used alone, the mRS is the strongest predictor of discharge destination. The NIHSS is only predictive when combined with our other variables. The accuracy of the final model was 70.4% overall with a positive predictive value (PPV) and sensitivity of 0.88 and 0.78 for home as the destination, 0.68 and 0.88 for continued inpatient care, 0.7 and 0.53 for community hospital, and 0.5 and 0.18 for death, respectively.
Pre-stroke disability rather than stroke severity is the strongest predictor of discharge destination, but in combination with other routinely collected data, both can be used as an adjunct by the multidisciplinary team to predict discharge destination in patients with acute stroke.
对急性卒中患者出院去向进行可靠预测，有助于出院计划的制定，并能明确患者及其照料者的期望。目前尚无利用常规收集的病前残疾指标和卒中严重程度指标来进行此类预测的模型。
前瞻性收集了急性卒中单元1142例连续患者的年龄、性别、病前改良Rankin量表（mRS）评分及美国国立卫生研究院卒中量表（NIHSS）评分。使用多类随机森林分类器训练并验证一个预测出院去向的模型。
单独使用时，mRS是出院去向最强的预测指标。NIHSS仅在与其他变量结合时具有预测性。最终模型的总体准确率为70.4%，以回家为出院去向时的阳性预测值（PPV）和敏感度分别为0.88和0.78，继续住院治疗时为0.68和0.88，去社区医院时为0.7和0.53，死亡时为0.5和0.18。
卒中前残疾而非卒中严重程度是出院去向最强的预测指标，但结合其他常规收集的数据，两者均可被多学科团队用作预测急性卒中患者出院去向的辅助手段。
