Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.
Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.
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DOI：
10.1371/journal.pone.0244746
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Kwong JC
中科院分区：
文献类型：
--
作者：
Hamilton MA;Calzavara A;Emerson SD;Djebli M;Sundaram ME;Chan AK;Kustra R;Baral SD;Mishra S;Kwong JC
常规收集的卫生管理数据可用于有效地评估大量人口的疾病负担，但重要的是评估这些数据的有效性。这项研究的目的是开发和验证国际疾病分类第10版(ICD-10)算法，该算法使用加拿大安大略省基于人口的卫生管理数据来识别实验室确认的流感或实验室确认的呼吸道合胞病毒(RSV)住院。2014-2015年、2015-16年、2016-17年和2017-18年呼吸道病毒季节的流感和RSV实验室数据从安大略省实验室信息系统(OLIS)获得，并与医院出院摘要数据联系起来，以生成流感和RSV参考队列。这些参考队列被用来评估ICD-10算法的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)。为了在未来的研究中最大限度地减少误分类，我们在选择性能最好的算法时优先考虑了特异性和PPV。83638名住院患者和61117名住院患者分别被纳入流感和RSV参考队列。最优流感算法的敏感性为73%(95%CI为72%~74%)，特异性为99%(95%CI为99%~99%)，PPV为94%(95%CI为94%~95%)，NPV为94%(95%CI为94%~95%)。最优RSV算法的敏感性为69%(95%CI为68%~70%)，特异性为99%(95%CI为99%~99%)，PPV为91%(95%CI为90%~91%)，NPV为97%(95%CI为97%~97%)。我们确定了两种高度特定的算法，以最好地确定感染流感或呼吸道合胞病毒住院的患者。如果没有实验室检测的数据，这些算法可以应用于住院患者，从而提高未来对流感、RSV和潜在的其他严重急性呼吸道感染的流行病学研究的能力。
Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. Influenza and RSV laboratory data from the 2014–15, 2015–16, 2016–17 and 2017–18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.
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