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基于电子健康记录的编码和叙述数据的膝关节骨关节炎发病率和患病率:一项基于人群的研究。

Incidence and Prevalence of Knee Osteoarthritis Using Codified and Narrative Data From Electronic Health Records: A Population-Based Study.

机构信息

Erasmus MC University Medical Center, Rotterdam, The Netherlands.

出版信息

Arthritis Care Res (Hoboken). 2022 Jun;74(6):937-944. doi: 10.1002/acr.24861. Epub 2022 Apr 9.

DOI:10.1002/acr.24861
PMID:35040591
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9325508/
Abstract

OBJECTIVE

To determine the incidence and prevalence of knee osteoarthritis (OA) using codified and narrative data from general practices throughout The Netherlands.

METHODS

This retrospective cohort study was conducted using the Integrated Primary Care Information database. Patients with codified knee OA were selected, and an algorithm was developed to identify patients with narratively diagnosed knee OA only. Point prevalence proportions and incidence rates among people age ≥30 years were assessed from 2008 to 2019. The association of comorbidities with codified knee OA was analyzed using multivariable logistic regression.

RESULTS

The positive predicted value of narratively diagnosed knee OA only was 94.0% (95% confidence interval [95% CI] 87.4-100%) and for codified knee OA 96.0% (95% CI 90.6-100%). Including narrative data in addition to codified data resulted in a prevalence 1.83-2.01 times higher (over the study years); prevalence increased from 5.8% to 11.8% between 2008 and 2019. The incidence rate was 1.93-2.28 times higher and increased from 9.98 per 1,000 person-years to 13.8 per 1,000 person-years between 2008 and 2019. Among patients with codified knee OA, 39.4% were previously diagnosed narratively with knee OA, on average ~3 years earlier. Comorbidities influenced the likelihood of being recorded with codified knee OA.

CONCLUSION

Our study of a Dutch primary care database showed that current incidence and prevalence estimates based on codified data alone from electronic health records are underestimated. Narrative data can be incorporated in addition to codified data to identify knee OA patients more accurately.

摘要

目的

利用荷兰各地全科医生的编码和描述性数据,确定膝关节骨关节炎(OA)的发病率和患病率。

方法

本回顾性队列研究使用综合初级保健信息数据库进行。选择有编码膝关节 OA 的患者,并开发了一个算法来仅识别有描述性诊断膝关节 OA 的患者。从 2008 年到 2019 年,评估了年龄≥30 岁人群的时点患病率和发病率。使用多变量逻辑回归分析了合并症与编码膝关节 OA 的关系。

结果

仅描述性诊断膝关节 OA 的阳性预测值为 94.0%(95%置信区间[95%CI]87.4-100%),而编码膝关节 OA 的阳性预测值为 96.0%(95%CI 90.6-100%)。在编码数据之外加入描述性数据,患病率会增加 1.83-2.01 倍(在研究期间);2008 年至 2019 年,患病率从 5.8%增加到 11.8%。发病率增加了 1.93-2.28 倍,从 2008 年的 9.98/1000 人年增加到 2019 年的 13.8/1000 人年。在有编码膝关节 OA 的患者中,39.4%的患者之前曾被描述性诊断为膝关节 OA,平均提前约 3 年。合并症影响了记录编码膝关节 OA 的可能性。

结论

我们对荷兰初级保健数据库的研究表明,仅基于电子健康记录中的编码数据估算的当前发病率和患病率被低估了。可以在编码数据之外加入描述性数据,以更准确地识别膝关节 OA 患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/0215817a5bed/ACR-74-937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/6a962cada5a9/ACR-74-937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/4e3dfdd879f1/ACR-74-937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/b826e1279b05/ACR-74-937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/0215817a5bed/ACR-74-937-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/6a962cada5a9/ACR-74-937-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/4e3dfdd879f1/ACR-74-937-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/b826e1279b05/ACR-74-937-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/873f/9325508/0215817a5bed/ACR-74-937-g004.jpg

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