Validation of knee osteoarthritis case identification algorithms in a large electronic health record database.
Validation of knee osteoarthritis case identification algorithms in a large electronic health record database.
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在大型电子健康记录数据库中验证膝骨关节炎病例识别算法。
DOI：
10.1016/j.ocarto.2021.100229
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Felson,DavidT
中科院分区：
文献类型：
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作者：
Yau,MichelleS;Dubreuil,Maureen;Li,Shanshan;Inamdar,Vibha;Peloquin,Christine;Felson,DavidT
PurposeTo促进膝关节骨关节炎（OA）在大型数据库中的研究，病例发现算法与高水平的诊断performance.MethodsFrom英国全科医生（GP）的做法衍生的数据库，我们选择了成年人年龄40-90岁的会议算法，包括各种组合的代码为膝关节OA或膝关节疼痛和成像。向每位患者的全科医生邮寄了一份调查问卷，以评估膝关节疼痛的原因，并提供膝关节X线和/或MRI结果。我们认为膝关节疼痛的X线和/或MRI结果与OA一致是金标准。我们计算阳性预测值（PPV）和敏感性的情况下identification algorithm.ResultsOf 100份问卷发送，93人返回，我们排除了8例有其他风湿性疾病或全膝关节置换术。在有一个OA代码的患者中，PPV为64%（95% CI = 49%-79%），当需要在6个月内有两个或更多OA代码时，PPV增加到92%（95% CI = 76%-100%）。PPV增加伴随着灵敏度从44%（95% CI = 31%-57%）降至19%（95% CI = 9%-30%）。使用一个疼痛代码产生了与使用一个OA代码类似的结果。在6个月内需要两个或更多个膝关节疼痛代码产生的PPV为68%（95%CI = 49%-88%）和灵敏度为26%（95%CI = 15%-38%）。DiscussionA的情况下，识别算法需要两个或更多个膝关节OA代码产生了最高的PPV的敏感性降低的成本。在选择病例识别算法时，需要与研究目标一起权衡PPV和灵敏度之间的权衡。
PurposeTo facilitate studies of knee osteoarthritis (OA) in large databases, case finding algorithms with high levels of diagnostic performance are needed.MethodsFrom a UK general practitioner (GP) practice derived database, we selected adults ages 40–90 years meeting algorithms that included various combinations of codes for knee OA or knee pain and imaging. The GP for each patient was mailed a questionnaire to assess the cause of knee pain and provide knee x-ray and/or MRI findings. We considered knee pain with x-ray and/or MRI findings consistent with OA the gold standard. We calculated positive predictive values (PPV) and sensitivity for case identification algorithms.ResultsOf 100 questionnaires sent, 93 were returned; we excluded 8 subjects who had other rheumatic disorders or total knee replacements. Among those with one code for OA, the PPV was 64% (95% CI = 49%–79%) and it increased to 92% (95% CI = 76%–100%) when two or more OA codes over six months were required. The increase in PPV was accompanied by a drop in sensitivity from 44% (95% CI = 31%–57%) to 19% (95% CI = 9%–30%). Use of one pain code yielded similar results to use of one OA code. Requiring two or more knee pain codes over six months yielded a PPV of 68% (95% CI = 49%–88%) and sensitivity of 26% (95% CI = 15%–38%).DiscussionA case identification algorithm requiring two or more knee OA codes yielded the highest PPV at the cost of reduced sensitivity. Tradeoffs between PPV and sensitivity will need to be weighed alongside study goals when selecting a case identification algorithm.