电子算法优于医院出院诊断代码，可用于诊断历史队列中的妊娠高血压疾病。
Electronic Algorithm Is Superior to Hospital Discharge Codes for Diagnoses of Hypertensive Disorders of Pregnancy in Historical Cohorts.
机构信息
Division of Nephrology and Hypertension, University of Belgrade, Belgrade, Serbia; Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia.
Department of Obstetrics and Gynecology, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN.
出版信息
Mayo Clin Proc. 2018 Dec;93(12):1707-1719. doi: 10.1016/j.mayocp.2018.08.031.
OBJECTIVES
To develop and validate criteria for the retrospective diagnoses of hypertensive disorders of pregnancy that would be amenable to the development of an electronic algorithm, and to compare the accuracy of diagnoses based on both the algorithm and diagnostic codes with the gold standard, of physician-made diagnoses based on a detailed review of medical records using accepted clinical criteria.
PATIENTS AND METHODS
An algorithm for hypertensive disorders of pregnancy was developed by first defining a set of criteria for retrospective diagnoses, which included relevant clinical variables and diagnosis of hypertension that required blood pressure elevations in greater than 50% of readings ("the 50% rule"). The algorithm was validated using the Rochester Epidemiology Project (Rochester, Minnesota). A stratified random sample of pregnancies and deliveries between January 1, 1976, and December 31, 1982, with the algorithm-based diagnoses was generated for review and physician-made diagnoses (normotensive, gestational hypertension, and preeclampsia), which served as the gold standard; the targeted cohort size for analysis was 25 per diagnosis category according to the gold standard. Agreements between (1) algorithm-based diagnoses and (2) diagnostic codes and the gold standard were analyzed.
RESULTS
Sensitivities of the algorithm for 25 normotensive pregnancies, 25 with gestational hypertension, and 25 with preeclampsia were 100%, 88%, and 100%, respectively, and specificities were 94%, 100%, and 100%, respectively. Diagnostic code sensitivities were 96% for normotensive pregnancies, 32% for gestational hypertension, and 96% for preeclampsia, and specificities were 78%, 96%, and 88%, respectively.
CONCLUSION
The electronic diagnostic algorithm was highly sensitive and specific in identifying and classifying hypertensive disorders of pregnancy and was superior to diagnostic codes.
目的
制定并验证适用于回顾性诊断妊娠高血压疾病的标准，以便开发电子算法，并将基于该算法和诊断代码的诊断准确性与基于详细病历回顾的医生诊断的金标准进行比较，这些病历采用了公认的临床标准。
方法
通过首先定义一组用于回顾性诊断的标准来开发妊娠高血压疾病的算法，这些标准包括相关的临床变量和高血压诊断，需要血压升高超过 50%的读数（“50%规则”）。该算法使用罗切斯特流行病学项目（明尼苏达州罗切斯特）进行验证。根据算法生成了妊娠和分娩的分层随机样本（1976 年 1 月 1 日至 1982 年 12 月 31 日），并进行了回顾和医生诊断（正常血压、妊娠期高血压和子痫前期），作为金标准；根据金标准，分析的目标队列大小为每个诊断类别的 25 个。分析了（1）基于算法的诊断与（2）诊断代码与金标准之间的一致性。
结果
该算法对 25 例正常血压妊娠、25 例妊娠期高血压和 25 例子痫前期的敏感性分别为 100%、88%和 100%，特异性分别为 94%、100%和 100%。诊断代码的敏感性分别为正常血压妊娠 96%、妊娠期高血压 32%和子痫前期 96%，特异性分别为 78%、96%和 88%。
结论
电子诊断算法在识别和分类妊娠高血压疾病方面具有高度的敏感性和特异性，优于诊断代码。
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