Silveira Fernanda P, Saul Melissa, Nowalk Mary Patricia, Saul Sean, Sax Theresa M, Eng Heather, Zimmerman Richard K, Balasubramani Goundappa K
Department of Medicine, School of Medicine, University of Pittsburgh, Pennsylvania.
Department of Family Medicine, School of Medicine, University of Pittsburgh, Pennsylvania.
Open Forum Infect Dis. 2019 Jun 10;6(6):ofz231. doi: 10.1093/ofid/ofz231. eCollection 2019 Jun.
A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for a test-negative, case-control study to determine influenza vaccine effectiveness, to improve enrollment over manual records review. Further testing may enhance the CIA for increased efficiency.
The CIA generated a daily screening list by querying all medical record databases for patients admitted in the last 3 days, using specified terms and diagnosis codes located in admission notes, emergency department notes, chief complaint upon registration, or presence of a respiratory viral panel charge or laboratory result (RVP). Classification and regression tree analysis (CART) and multivariable logistic regression were used to refine the algorithm.
Using manual records review, 204 patients (<4/day) were approached and 144 were eligible in the 2014-2015 season compared with 3531 (12/day) patients who were approached and 1136 who were eligible in the 2016-2017 season using a CIA. CART analysis identified RVP as the most important indicator from the CIA list for determining eligibility, identifying 65%-69% of the samples and predicting 1587 eligible patients. RVP was confirmed as the most significant predictor in regression analysis, with an odds ratio (OR) of 4.9 (95% confidence interval [CI], 4.0-6.0). Other significant factors were indicators in admission notes (OR, 2.3 [95% CI, 1.9-2.8]) and emergency department notes (OR, 1.8 [95% CI, 1.4-2.3]).
This study supports the benefits of a CIA to facilitate recruitment of eligible participants in clinical research over manual records review. Logistic regression and CART identified potential eligibility screening criteria reductions to improve the CIA's efficiency.
开发了一种临床信息学算法（CIA），用于系统地识别检测呈阴性的病例对照研究的潜在参与者，以确定流感疫苗的有效性，从而提高与人工病历审查相比的招募效率。进一步测试可能会增强CIA以提高效率。
CIA通过查询过去3天内入院患者的所有病历数据库，使用入院记录、急诊科记录、登记时的主要诉求中指定的术语和诊断代码，或存在呼吸道病毒检测费用或实验室结果（RVP）来生成每日筛查清单。使用分类与回归树分析（CART）和多变量逻辑回归来优化该算法。
在2014 - 2015季节，通过人工病历审查，接触了204名患者（每天<4名），其中144名符合条件；而在2016 - 2017季节，使用CIA接触了3531名患者（每天12名），其中1136名符合条件。CART分析确定RVP是CIA清单中用于确定资格的最重要指标，识别出65% - 69%的样本并预测出1587名符合条件的患者。在回归分析中，RVP被确认为最显著的预测因子，优势比（OR）为4.9（95%置信区间[CI]，4.0 - 6.0）。其他重要因素是入院记录中的指标（OR，2.3[95%CI，1.9 - 2.8]）和急诊科记录中的指标（OR，1.8[95%CI，1.4 - 2.3]）。
本研究支持CIA在促进临床研究中招募符合条件的参与者方面优于人工病历审查。逻辑回归和CART确定了潜在的资格筛选标准减少方案，以提高CIA的效率。
