Savard Noémie, Bédard Lucie, Allard Robert, Buckeridge David L
Department of Biostatistics, Epidemiology and Occupational Health, McGill University, Montréal, Québec, Canada
Direction de santé publique de l'Agence de la santé et des services sociaux de Montréal, Montréal, Québec, Canada Department of Social and Preventive Medicine, University of Montréal, Montréal, Québec, Canada.
J Am Med Inform Assoc. 2015 May;22(3):688-96. doi: 10.1093/jamia/ocu002. Epub 2015 Feb 26.
Markers of illness severity are increasingly captured in emergency department (ED) electronic systems, but their value for surveillance is not known. We assessed the value of age, triage score, and disposition data from ED electronic records for predicting influenza-related hospitalizations.
From June 2006 to January 2011, weekly counts of pneumonia and influenza (P&I) hospitalizations from five Montreal hospitals were modeled using negative binomial regression. Over lead times of 0-5 weeks, we assessed the predictive ability of weekly counts of 1) total ED visits, 2) ED visits with influenza-like illness (ILI), and 3) ED visits with ILI stratified by age, triage score, or disposition. Models were adjusted for secular trends, seasonality, and autocorrelation. Model fit was assessed using Akaike information criterion, and predictive accuracy using the mean absolute scaled error (MASE).
Predictive accuracy for P&I hospitalizations during non-pandemic years was improved when models included visits from patients ≥65 years old and visits resulting in admission/transfer/death (MASE of 0.64, 95% confidence interval (95% CI) 0.54-0.80) compared to overall ILI visits (0.89, 95% CI 0.69-1.10). During the H1N1 pandemic year, including visits from patients <18 years old, visits with high priority triage scores, or visits resulting in admission/transfer/death resulted in the best model fit.
Age and disposition data improved model fit and moderately reduced the prediction error for P&I hospitalizations; triage score improved model fit only during the pandemic year.
Incorporation of age and severity measures available in ED records can improve ILI surveillance algorithms.
疾病严重程度标志物越来越多地在急诊科（ED）电子系统中获取，但其用于监测的价值尚不清楚。我们评估了ED电子记录中的年龄、分诊分数和处置数据对预测流感相关住院的价值。
2006年6月至2011年1月，使用负二项回归对蒙特利尔五家医院每周的肺炎和流感（P&I）住院病例数进行建模。在0至5周的提前期内，我们评估了以下每周病例数的预测能力：1）急诊总就诊次数；2）流感样疾病（ILI）急诊就诊次数；3）按年龄、分诊分数或处置分层的ILI急诊就诊次数。模型针对长期趋势、季节性和自相关性进行了调整。使用赤池信息准则评估模型拟合度，使用平均绝对标度误差（MASE）评估预测准确性。
与总体ILI就诊次数（MASE为0.89，95%置信区间（95%CI）0.69 - 1.10）相比，当模型纳入65岁及以上患者的就诊次数以及导致入院/转院/死亡（MASE为0.64，95%CI 0.54 - 0.80）的就诊次数时，非大流行年份P&I住院病例数的预测准确性得到提高。在甲型H1N1流感大流行年份，纳入18岁以下患者的就诊次数、高优先级分诊分数的就诊次数或导致入院/转院/死亡的就诊次数可得到最佳模型拟合。
年龄和处置数据改善了模型拟合，并适度降低了P&I住院病例数的预测误差；分诊分数仅在大流行年份改善了模型拟合。
纳入ED记录中可用的年龄和严重程度测量指标可改善ILI监测算法。
