PDF(Pubmed)
Abstract：
We developed and validated the Influenza Severity Scale (ISS), a standardized risk assessment for influenza, to estimate and predict the probability of major clinical events in patients with laboratory-confirmed infection. Data from the Canadian Immunization Research Network\'s Serious Outcomes Surveillance Network (2011/2012-2018/2019 influenza seasons) enabled the selecting of all laboratory-confirmed influenza patients. A machine learning-based approach then identified variables, generated weighted scores, and evaluated model performance. This study included 12,954 patients with laboratory-confirmed influenza infections. The optimal scale encompassed ten variables: demographic (age and sex), health history (smoking status, chronic pulmonary disease, diabetes mellitus, and influenza vaccination status), clinical presentation (cough, sputum production, and shortness of breath), and function (need for regular support for activities of daily living). As a continuous variable, the scale had an AU-ROC of 0.73 (95% CI, 0.71-0.74). Aggregated scores classified participants into three risk categories: low (ISS < 30; 79.9% sensitivity, 51% specificity), moderate (ISS ≥ 30 but < 50; 54.5% sensitivity, 55.9% specificity), and high (ISS ≥ 50; 51.4% sensitivity, 80.5% specificity). ISS demonstrated a solid ability to identify patients with hospitalized laboratory-confirmed influenza at increased risk for Major Clinical Events, potentially impacting clinical practice and research.
摘要：
我们开发并验证了流感严重程度量表(ISS)，流感的标准化风险评估，评估和预测实验室确诊感染患者发生重大临床事件的概率。来自加拿大免疫研究网络的严重结果监测网络（2011/2012-2018/2019流感季节）的数据能够选择所有实验室确诊的流感患者。一种基于机器学习的方法，然后识别变量，生成的加权分数，并评估了模型性能。这项研究包括12,954例实验室确诊的流感感染患者。最佳量表包含十个变量：人口统计学(年龄和性别)，健康史(吸烟状况，慢性肺病，糖尿病,和流感疫苗接种状况)，临床表现(咳嗽，痰液生产，和呼吸急促)，和功能(需要定期支持日常生活活动)。作为连续变量，量表的AU-ROC为0.73(95%CI,0.71-0.74).综合得分将参与者分为三个风险类别：低(ISS<30；79.9%敏感度，51%特异性)，中等(ISS≥30但<50；54.5%灵敏度，55.9%的特异性)，和高(ISS≥50；51.4%灵敏度，80.5%特异性)。ISS表现出坚实的能力来识别住院实验室确诊的流感患者，其重大临床事件的风险增加。可能影响临床实践和研究。
