Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data

被引:1
|
作者
Cheek, Camden [1 ]
Zheng, Huiyong [2 ]
Hallstrom, Brian R. [2 ]
Hughes, Richard E. [1 ,2 ,3 ]
机构
[1] Univ Michigan, Dept Biomed Engn, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Dept Orthopaed Surg, 2003 BSRB,109 Zina Pitcher Pl, Ann Arbor, MI 48104 USA
[3] Univ Michigan, Dept Ind & Operat Engn, Ann Arbor, MI 48109 USA
来源
BIOMEDICAL ENGINEERING AND COMPUTATIONAL BIOLOGY | 2018年 / 9卷
关键词
Causal discovery; probabilistic graphical models; arthroplasty; hip;
D O I
10.1177/1179597218756896
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Early Benchmarking Total Hip Arthroplasty Implants Using Data from the Michigan Arthroplasty Registry Collaborative Quality Initiative (MARCQI)
    Chubb, Heather A.
    Cornish, Eric R.
    Hallstrom, Brian R.
    Hughes, Richard E.
    ORTHOPEDIC RESEARCH AND REVIEWS, 2021, 13 : 215 - 228
  • [32] Gene Pathways Discovery in Asbestos-Related Diseases using Local Causal Discovery Algorithm
    Yoo, Changwon
    Brilz, Erik M.
    Wilcox, Meredith
    Pershouse, Mark A.
    Putnam, Elizabeth A.
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2012, 41 (10) : 1840 - 1859
  • [33] Data quality audit of the arthroplasty clinical outcomes registry NSW
    Kurt G Seagrave
    Justine Naylor
    Elizabeth Armstrong
    Kwong-Ming Leong
    Joseph Descallar
    Ian A Harris
    BMC Health Services Research, 14
  • [34] Partial Information Decomposition for Causal Discovery With Application to Internet of Things
    Liang, Jessica E.
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (13): : 24289 - 24299
  • [35] On the Intersection Property of Conditional Independence and its Application to Causal Discovery
    Peters, Jonas
    JOURNAL OF CAUSAL INFERENCE, 2015, 3 (01) : 97 - 108
  • [36] Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
    Cinquini, Martina
    Giannotti, Fosca
    Guidotti, Riccardo
    2021 IEEE THIRD INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE (COGMI 2021), 2021, : 54 - 63
  • [37] Causal Discovery from Temporal Data: An Overview and New Perspectives
    Gong, Chang
    Zhang, Chuzhe
    Yao, Di
    Bi, Jingping
    Li, Wenbin
    Xu, Yongjun
    ACM COMPUTING SURVEYS, 2025, 57 (04)
  • [38] Discretization: Privacy-preserving data publishing for causal discovery
    Ahn, Youngmin
    Park, Woongjoon
    Park, Gunwoong
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2025, 209
  • [39] Causal discovery based on heterogeneous non-Euclidean data
    Wang X.
    Li S.
    Wang Y.
    Wan Y.
    Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2024, 44 (06): : 1987 - 2002
  • [40] Data-driven Causal Association Discovery in Manufacturing Industries
    Li, Yiming
    Xu, Jia
    Li, Li
    Iung, Benoit
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 5566 - 5571