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验证英国常规电子医疗记录中特发性肺纤维化的记录。

Validation of the recording of idiopathic pulmonary fibrosis in routinely collected electronic healthcare records in England.

机构信息

School of Public Health, Imperial College London, Level 9, Sir Michael Uren Hub, White City Campus, 86 Wood Lane, W12 0BZ, London, UK.

National Heart and Lung Institute, Imperial College London, Level 9, Sir Michael Uren Hub, White City Campus, 86 Wood Lane, W12 0BZ, London, UK.

出版信息

BMC Pulm Med. 2023 Jul 11;23(1):256. doi: 10.1186/s12890-023-02550-0.

Abstract

BACKGROUND

Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.

METHOD

Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.

RESULT

A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.

CONCLUSION

High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation.

摘要

背景

常规收集的医疗保健数据为流行病学研究提供了有价值的资源。验证研究表明,对于大多数疾病,简单的临床代码列表可以可靠地用于初级保健中的病例发现,但是,对于特发性肺纤维化 (IPF) 等主要在二级保健中管理的疾病,缺乏对这种方法的稳健性的研究。

方法

我们使用英国的临床实践研究数据链接 (CPRD) Aurum 数据集,该数据集由与国家住院和死因数据相关联的患者级初级保健记录组成,比较了八种诊断算法的阳性预测值 (PPV)。算法是根据文献和 IPF 诊断指南使用初级和二级保健中的临床代码 (SNOMED-CT 或 ICD-10) 组合以及是否存在其他信息来开发的。使用死亡记录作为金标准,估算了每种算法的阳性预测值 (PPV)。观察了研究期间审查代码的使用情况,以评估随着时间的推移编码实践是否发生变化。

结果

在 2008 年至 2018 年期间,在我们的三个链接数据集之一或多个数据集中,共有 17559 人至少有一次记录表明患有 IPF。仅基于临床代码的病例发现算法的阳性预测值范围为 64.4%(95%CI:63.3-65.3),用于“广泛”代码集,74.9%(95%CI:72.8-76.9),用于包含高度特异性代码的“狭窄”代码集。添加确认证据,如 CT 扫描,将我们基于狭窄代码的算法的阳性预测值提高到 79.2%(95%CI:76.4-81.8%),但敏感性降低到 10%以下。将基于代码的孤立算法添加到住院证据中也提高了阳性预测值(PPV=78.4 比 64.4%;敏感性=53.5%比 38.1%)。IPF 编码实践随时间发生了变化,特定的 IPF 代码使用增加。

结论

通过使用一组受限的 IPF 代码集实现了高诊断准确性。虽然添加确认证据可以提高诊断准确性,但需要权衡这种方法的好处与不可避免的样本量和便利性损失。我们建议使用基于更广泛的 IPF 代码集的算法,并结合住院证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ecae/10337174/dbb9a77dd5be/12890_2023_2550_Fig1_HTML.jpg

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