Camden Cheek, Huiyong Zheng, Brian R Hallstrom, Richard E Hughes
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The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2018-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/ed/10.1177_1179597218756896.PMC5826097.pdf","citationCount":"0","resultStr":"{\"title\":\"Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data.\",\"authors\":\"Camden Cheek, Huiyong Zheng, Brian R Hallstrom, Richard E Hughes\",\"doi\":\"10.1177/1179597218756896\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of \\\"outlier\\\" devices that have an especially high risk of reoperation (\\\"revision\\\"). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outlier implants such as the ASR metal-on-metal hip resurfacing device that was recalled. Although identifying an implant as an outlier implies a causal relationship between the implant and revision risk, traditional signal detection methods use classical biostatistical methods. The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.</p>\",\"PeriodicalId\":42484,\"journal\":{\"name\":\"Biomedical Engineering and Computational Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2018-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/ed/10.1177_1179597218756896.PMC5826097.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering and Computational Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/1179597218756896\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1179597218756896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
要提高髋关节置换术(置换)患者的治疗质量,就必须对植入物的临床表现进行系统评估,并找出再次手术("翻修")风险特别高的 "异常 "植入物。对关节置换术植入物的市场后监测主要依靠对大型患者登记资料的分析,这种方法能有效识别出异常植入物,如被召回的 ASR 金属髋关节置换植入物。虽然将植入物识别为异常点意味着植入物与翻修风险之间存在因果关系,但传统的信号检测方法使用的是经典的生物统计方法。因果关系概率图形建模领域已开发出用于严格分析观察数据中因果关系的工具。本研究旨在评估一种因果关系发现算法(PC),以确定其是否适用于髋关节置换术植入信号检测。使用患者和植入物特征的分布生成模拟数据,并使用 TETRAD 软件包进行因果发现。模拟了两种规模的登记处:(1) 密歇根州的全州登记处;(2) 英国的全国登记处。结果显示,该算法在模拟大型全国性登记处时表现更好。结论是本研究中使用的因果发现算法可能是大型关节成形术登记处检测植入物信号的有用工具;地区登记处可能只能检测到表现特别差的植入物。
Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data.
Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of "outlier" devices that have an especially high risk of reoperation ("revision"). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outlier implants such as the ASR metal-on-metal hip resurfacing device that was recalled. Although identifying an implant as an outlier implies a causal relationship between the implant and revision risk, traditional signal detection methods use classical biostatistical methods. The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.