2012/2013至2017/2018年新冠疫情之前，西班牙巴伦西亚地区成年住院患者中ICD流感出院诊断编码的准确性
Accuracy of ICD Influenza Discharge Diagnosis Codes in Hospitalized Adults From the Valencia Region, Spain, in the Pre-COVID-19 Period 2012/2013 to 2017/2018.
作者信息
Mira-Iglesias Ainara, López-Lacort Mónica, Bricout Hélène, Loiacono Matthew, Carballido-Fernández Mario, Mollar-Maseres Joan, Tortajada-Girbés Miguel, Schwarz-Chávarri Germán, López-Labrador F Xavier, Puig-Barberà Joan, Díez-Domingo Javier, Orrico-Sánchez Alejandro
机构信息
Área de Investigación en Vacunas, Fundación Para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO-Public Health), Valencia, Spain.
CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain.
出版信息
Influenza Other Respir Viruses. 2025 Feb;19(2):e70069. doi: 10.1111/irv.70069.
BACKGROUND
International Classification of Diseases (ICD) codes obtained from real-world data can be used to identify influenza cases for epidemiological research but, without validation, may introduce biases. The objective of this study was to validate ICD influenza discharge diagnoses using real-time reverse transcription-polymerase chain reaction (RT-PCR) laboratory-confirmed influenza (LCI) results.
METHODS
The study was conducted during six influenza seasons (2012/2013-2017/2018) in the Valencia Hospital Surveillance Network for the Study of Influenza (VAHNSI). Patients aged 18+ years were identified via active-surveillance and had to meet an influenza-like illness (ILI) case definition to be included. All patients were tested for influenza by real-time RT-PCR. Main and secondary influenza discharge diagnosis codes were extracted from hospital discharge letters. Positive predictive values (PPVs) and the complementary of the sensitivities (1-Sensitivity) of ICD codes with corresponding 95% credible intervals (CrIs) were estimated via binomial Bayesian regression models.
RESULTS
A total of 13,545 patients were included, with 2257 (17%) positive for influenza. Of 2257 LCI cases, 1385 (61%) were not ICD-coded as influenza. Overall, 74.73% (95% CrI: 63.24-84.44) of LCI were not-ICD coded as influenza (1-Sensitivity) after adjustment. Sensitivity improved across seasons and with increasing age. Average PPV was 74.02% (95% CrI: 68.58-79.17), ranging from 43.71% to 81.57% between seasons.
CONCLUSION
Using only main and secondary discharge diagnosis codes for influenza detection markedly underestimates the full burden of influenza in hospitalized patients. Future studies, including post-COVID context, using prospective surveillance for ILI are required to assess the validity of hospital discharge data as a tool for determining influenza-related burden of disease.
背景
从真实世界数据中获取的国际疾病分类（ICD）编码可用于识别流感病例以进行流行病学研究，但未经验证可能会引入偏差。本研究的目的是使用实时逆转录聚合酶链反应（RT-PCR）实验室确诊的流感（LCI）结果来验证ICD流感出院诊断。
方法
该研究在巴伦西亚医院流感研究监测网络（VAHNSI）的六个流感季节（2012/2013 - 2017/2018）期间进行。通过主动监测识别18岁及以上的患者，这些患者必须符合流感样疾病（ILI）病例定义才能被纳入。所有患者均通过实时RT-PCR检测流感。从医院出院信件中提取主要和次要流感出院诊断编码。通过二项式贝叶斯回归模型估计ICD编码的阳性预测值（PPV）和敏感性的互补值（1 - 敏感性）及其相应的95%可信区间（CrI）。
结果
共纳入13545例患者，其中2257例（占17%）流感检测呈阳性。在2257例LCI病例中，1385例（占61%）未被ICD编码为流感。总体而言，调整后74.73%（95% CrI：63.24 - 84.44）的LCI未被ICD编码为流感（1 - 敏感性）。敏感性在不同季节以及随着年龄增长而提高。平均PPV为74.02%（95% CrI：68.58 - 79.17），各季节之间范围为43.71%至81.57%。
结论
仅使用主要和次要出院诊断编码来检测流感会显著低估住院患者流感的全部负担。未来的研究，包括在新冠疫情后的背景下，需要采用对ILI的前瞻性监测来评估医院出院数据作为确定流感相关疾病负担工具的有效性。
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