Phan Thanh G, Kooblal Talvika, Matley Chelsea, Singhal Shaloo, Clissold Benjamin, Ly John, Thrift Amanda G, Srikanth Velandai, Ma Henry
Stroke and Ageing Research (STARC), Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
Department of Neurology, Monash Health, Monash University, Melbourne, VIC, Australia.
Front Neurol. 2019 Jan 29;10:16. doi: 10.3389/fneur.2019.00016. eCollection 2019.
Post-stroke pneumonia is a feared complication of stroke as it is associated with greater mortality and disability than in those without pneumonia. Patients are often kept "Nil By Mouth" (NBM) after stroke until after receiving a screen for dysphagia and declared safe to resume oral intake. We aimed to assess the proportional contribution of stroke severity and dysphagia screen to pneumonia by borrowing idea from coalition game theory on fair distribution of marginal profit (Shapley value). Retrospective study of admissions to the stroke unit at Monash Medical Center in 2015. Seventy-five percent of data were partitioned into training set and the remainder (25%) into validation set. Variables associated with pneumonia ( < 0.1) were entered into Shapley value regression and conditional decision tree analysis. In 2015, there were 797 admissions and 617 patients with ischemic and hemorrhagic stroke (age 69.9 ± 16.2, male = 55.0%, National Institute of Health Stroke Scale/NIHSS 8.1 ± 7.9). The frequency of pneumonia was 6.6% (41/617). In univariable analyses NIHSS, time to dysphagia screen, Charlson comorbidity index (CCI), and age were significantly associated with pneumonia but not weekend admission. Shapley value regression showed that the largest contributor to the model was stroke severity (72.8%) followed by CCI (16.2%), dysphagia screen (3.8%), and age (7.2%). Decision tree analysis yielded an NIHSS threshold of 14 for classifying people with (27% of 75 patients) and without pneumonia (2.5% of 308 patients). The area under the ROC curve for training data was 0.83 (95% CI 0.75-0.91) with no detectable difference between the training and test data ( = 0.4). Results were similar when dysphagia was exchanged for the variable dysphagia screen. Stroke severity status, and not dysphagia or dysphagia screening contributed to the decision tree model of post stroke pneumonia. We cannot exclude the chance that using dysphagia screen in this cohort had minimized the impact of dysphagia on development of pneumonia.
卒中后肺炎是一种令人担忧的卒中并发症,因为与未患肺炎的患者相比,它与更高的死亡率和残疾率相关。卒中后患者通常在接受吞咽困难筛查并被宣布恢复经口进食安全之前一直保持“禁食”状态。我们旨在借鉴联盟博弈论中关于边际利润公平分配的思想(夏普值),评估卒中严重程度和吞咽困难筛查对肺炎的比例贡献。对2015年莫纳什医疗中心卒中单元收治患者进行回顾性研究。75%的数据被划分为训练集,其余25%为验证集。将与肺炎相关的变量(<0.1)纳入夏普值回归和条件决策树分析。2015年,共收治797例患者,其中617例为缺血性和出血性卒中患者(年龄69.9±16.2岁,男性占55.0%,美国国立卫生研究院卒中量表/NIHSS评分为8.1±7.9)。肺炎发生率为6.6%(41/617)。在单因素分析中,NIHSS、吞咽困难筛查时间、查尔森合并症指数(CCI)和年龄与肺炎显著相关,但周末入院与肺炎无关。夏普值回归显示,模型中最大的贡献因素是卒中严重程度(72.8%),其次是CCI(16.2%)、吞咽困难筛查(3.8%)和年龄(7.2%)。决策树分析得出,将有肺炎患者(75例中的27%)和无肺炎患者(308例中的2.5%)进行分类的NIHSS阈值为14。训练数据的ROC曲线下面积为0.83(95%CI 0.75 - 0.91),训练数据和测试数据之间无显著差异(=0.4)。当将吞咽困难替换为吞咽困难筛查变量时,结果相似。卒中严重程度状态而非吞咽困难或吞咽困难筛查对卒中后肺炎的决策树模型有影响。我们不能排除在该队列中使用吞咽困难筛查使吞咽困难对肺炎发生的影响最小化的可能性。