PDF(Pubmed)
Abstract：
Reactive syphilis serologies are investigated by health departments to determine if they represent new infection, reinfection, or treatment failure. Serologies prioritized for investigation based on nontreponemal test titer and age (using a \"reactor grid\") undergo manual record search and review. We developed a computerized algorithm that automates the record search and review.
We developed and tested the algorithm using a Florida Department of Health data set containing serologies reported January 2016 to December 2018 and previous records linked to each individual. The algorithm was based on the syphilis case definition, which requires (except primary cases with signs and symptoms) (1) a positive treponemal test result and a newly positive nontreponemal test result or (2) a 4-fold increase in nontreponemal test titer. Two additional steps were added to avoid missing cases. New York City Department of Health and Mental Hygiene validated this algorithm.
The algorithm closed more investigations (49.9%) than the reactor grid (27.0%). The algorithm opened 99.4% of the individuals investigated and labeled as cases by the health department; it missed 75 cases. Many investigations opened by the algorithm were closed by the reactor grid; we could not assess how many would have been cases. In New York City, the algorithm closed 70.9% of investigations, likely because more individuals had previous test in the database (88.2%) compared with Florida (56.5%).
The automated algorithm successfully searched and reviewed records to help identify cases of syphilis. We estimate the algorithm would have saved Florida 590 workdays for 3 years.
We developed and tested the algorithm using a Florida Department of Health data set containing serologies reported January 2016 to December 2018 and previous records linked to each individual. The algorithm was based on the syphilis case definition, which requires (except primary cases with signs and symptoms) (1) a positive treponemal test result and a newly positive nontreponemal test result or (2) a 4-fold increase in nontreponemal test titer. Two additional steps were added to avoid missing cases. New York City Department of Health and Mental Hygiene validated this algorithm.
The algorithm closed more investigations (49.9%) than the reactor grid (27.0%). The algorithm opened 99.4% of the individuals investigated and labeled as cases by the health department; it missed 75 cases. Many investigations opened by the algorithm were closed by the reactor grid; we could not assess how many would have been cases. In New York City, the algorithm closed 70.9% of investigations, likely because more individuals had previous test in the database (88.2%) compared with Florida (56.5%).
The automated algorithm successfully searched and reviewed records to help identify cases of syphilis. We estimate the algorithm would have saved Florida 590 workdays for 3 years.
摘要：
卫生部门对反应性梅毒血清学进行调查，以确定它们是否代表新的感染，再感染,或治疗失败。根据非密螺旋体测试滴度和年龄（使用“反应器网格”）优先进行调查的血清学经过手动记录搜索和审查。我们开发了一种计算机化算法，可以自动进行记录搜索和审核。
我们使用佛罗里达州卫生部数据集开发并测试了该算法，该数据集包含2016年1月至2018年12月报告的血清学以及与每个个体相关的先前记录。该算法基于梅毒病例定义，这需要（除了有体征和症状的原发性病例）（1）螺旋体试验结果阳性和非螺旋体试验结果新阳性或（2）非螺旋体试验滴度增加4倍。增加了两个额外的步骤以避免遗漏病例。纽约市卫生与心理卫生部对该算法进行了验证。
该算法比反应堆网格（27.0％）关闭了更多的研究（49.9％）。该算法打开了99.4％的被卫生部门调查并标记为病例的个体；它错过了75例。该算法打开的许多调查都被反应堆网格关闭；我们无法评估有多少案例。在纽约市,该算法关闭了70.9%的调查，可能是因为与佛罗里达州（56.5％）相比，更多的人以前在数据库中进行过测试（88.2％）。
自动算法成功地搜索和审查了记录，以帮助识别梅毒病例。我们估计该算法将在3年内为佛罗里达州节省590个工作日。
我们使用佛罗里达州卫生部数据集开发并测试了该算法，该数据集包含2016年1月至2018年12月报告的血清学以及与每个个体相关的先前记录。该算法基于梅毒病例定义，这需要（除了有体征和症状的原发性病例）（1）螺旋体试验结果阳性和非螺旋体试验结果新阳性或（2）非螺旋体试验滴度增加4倍。增加了两个额外的步骤以避免遗漏病例。纽约市卫生与心理卫生部对该算法进行了验证。
该算法比反应堆网格（27.0％）关闭了更多的研究（49.9％）。该算法打开了99.4％的被卫生部门调查并标记为病例的个体；它错过了75例。该算法打开的许多调查都被反应堆网格关闭；我们无法评估有多少案例。在纽约市,该算法关闭了70.9%的调查，可能是因为与佛罗里达州（56.5％）相比，更多的人以前在数据库中进行过测试（88.2％）。
自动算法成功地搜索和审查了记录，以帮助识别梅毒病例。我们估计该算法将在3年内为佛罗里达州节省590个工作日。
