开发和评估一种可计算的表型，以利用电子健康记录数据识别接受化疗治疗的小儿白血病和淋巴瘤患者。
Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data.
机构信息
Division of Oncology and Center for Childhood Cancer Research, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
出版信息
Pediatr Blood Cancer. 2019 Sep;66(9):e27876. doi: 10.1002/pbc.27876. Epub 2019 Jun 17.
BACKGROUND
Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies.
METHODS
This study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters.
RESULTS
The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99.
CONCLUSION
The EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.
背景
电子健康记录（EHR）的广泛应用为儿科肿瘤观察性研究创造了新的机会。但是，利用 EHR 数据来识别患有儿科血液恶性肿瘤的患者却很少受到关注。
方法
本研究使用儿科临床数据研究网络 PEDSnet 中的 EHR 衍生数据，开发并评估了一种可计算的表型算法，以识别接受化疗治疗的儿科白血病和淋巴瘤患者。为了指导早期开发，将多个可计算的表型定义队列与一个机构的肿瘤登记处进行了比较。选择最有前途的算法进行正式评估，该算法包括在 90 天内至少两次白血病/淋巴瘤诊断（医学系统命名法代码）、两次化疗暴露和三次血液肿瘤学提供者就诊。在评估过程中，针对来自三个大型机构的 2011 年至 2016 年的 EHR 数据执行了可计算的表型。通过将表型识别的患者与至少有三次血液肿瘤学就诊的对照组进行盲法病历审查，评估分类准确性。
结果
在开发机构中，该可计算的表型具有 100%（置信区间 [CI] 99%，100%）的敏感性、99%（CI 99%，100%）的特异性、100%的阳性预测值（PPV）和阴性预测值（NPV）以及 1 的 C 统计量。在两个测试机构中，该可计算的表型性能相似，敏感性为 100%（CI 99%，100%）、特异性为 99%（CI 99%，100%）、PPV 为 96%、NPV 为 100%和 C 统计量为 0.99。
结论
基于 EHR 的可计算表型是一种针对接受化疗治疗的儿科白血病和淋巴瘤患者的准确队列识别工具，可用于临床研究。
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