Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.
Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data.
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使用临床和可穿戴数据预测脓毒症患者的再入院率。
DOI：
10.1101/2023.04.10.23288368
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Nemati,Shamim
中科院分区：
文献类型：
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作者：
Amrollahi,Fatemeh;Shashikumar,SupreethPrajwal;Yhdego,Haben;Nayebnazar,Arshia;Yung,Nathan;Wardi,Gabriel;Nemati,Shamim
脓毒症是一种危及生命的疾病，由于宿主对感染的反应失调而发生。最近的数据表明，脓毒症患者的再入院风险明显高于其他常见疾病，如心力衰竭、肺炎和心肌梗死以及相关的经济负担。先前的研究表明，患者的身体活动水平与再入院风险之间存在关联。在本研究中，我们发现脓毒症患者出院前和出院后的活动水平分布可预测90天内非计划再住院（p值<1e-3）。我们的初步结果表明，将Fitbit数据与临床测量相结合可以提高模型预测90天再入院的性能。脓毒症，活动水平，再入院，可穿戴数据
Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient’s physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.Clinical relevance Sepsis, Activity level, Hospital readmission, Wearable data