Pan Jie, Lee Seungwon, Cheligeer Cheligeer, Li Bing, Wu Guosong, Eastwood Catherine A, Xu Yuan, Quan Hude
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
Centre for Health Informatics, University of Calgary, Calgary, Alberta, Canada.
BMJ Health Care Inform. 2025 May 13;32(1):e101381. doi: 10.1136/bmjhci-2024-101381.
Administrative data are commonly used to inform chronic disease prevalence and support health informatic research. This study assessed the validity of coding comorbidities in the International Classification of Diseases, 10th Revision (ICD-10) administrative data.
We analysed three chart review cohorts (4008 patients in 2003, 3045 in 2015 and 9024 in 2022) in Alberta, Canada. Nurse reviewers assessed the presence of 17 clinical conditions using a consistent protocol. The reviews were linked with administrative data using unique patient identifiers. We compared the accuracy in coding comorbidity by ICD-10, using chart review data as the reference standard.
Our findings showed that the mean difference in prevalence between chart reviews and ICD-10 for these 17 conditions was 2.1% in 2003, 7.6% in 2015 and 6.3% in 2022. Some conditions were relatively stable, such as diabetes (1.9%, 2.1% and 1.1%) and metastatic cancer (0.3%, 1.1% and 0.4%). For these 17 conditions, the sensitivity ranged from 39.6-85.1% in 2003, 1.3%-85.2% in 2015 and 3.0-89.7% in 2022. The C-statistics for predicting in-hospital mortality using comorbidities by ICD-10 were 0.84 in 2003, 0.81 in 2015 and 0.78 in 2022.
The undercoding could be primarily due to the increase in hospital patient volumes and the limited time allocated to coding specialists. There is the potential to develop artificial intelligence methods based on electronic health records to support coding practices and improve data quality.
Comorbidities were increasingly undercoded over 20 years. The validity of ICD-10 decreased but remained relatively stable for certain conditions mandated for coding. The undercoding exerted minimal impact on in-hospital mortality prediction.
行政数据常用于了解慢性病患病率并支持健康信息学研究。本研究评估了国际疾病分类第十版（ICD - 10）行政数据中合并症编码的有效性。
我们分析了加拿大艾伯塔省的三个病历审查队列（2003年有4008名患者，2015年有3045名，2022年有9024名）。护士审查员使用一致的方案评估17种临床病症的存在情况。审查通过唯一的患者标识符与行政数据相关联。我们以病历审查数据作为参考标准，比较了ICD - 10对合并症编码的准确性。
我们的研究结果表明，2003年这17种病症在病历审查和ICD - 10之间的患病率平均差异为2.1%，2015年为7.6%，2022年为6.3%。一些病症相对稳定，如糖尿病（分别为1.9%、2.1%和1.1%）和转移性癌症（分别为0.3%、1.1%和0.4%）。对于这17种病症，2003年的敏感性范围为39.6% - 85.1%，2015年为1.3% - 85.2%，2022年为3.0% - 89.7%。2003年使用ICD - 10合并症预测住院死亡率的C统计量为0.84，2015年为0.81，2022年为0.78。
编码不足可能主要是由于医院患者数量增加以及分配给编码专家的时间有限。有可能基于电子健康记录开发人工智能方法来支持编码实践并提高数据质量。
20年来合并症编码不足的情况日益增加。ICD - 10的有效性有所下降，但对于某些规定编码的病症仍相对稳定。编码不足对住院死亡率预测的影响最小。
