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如何从意大利行政数据库中区分非小细胞肺癌(NSCLC)病例?一项用于评估新型算法性能的回顾性、二次数据使用研究。

How to discriminate non-small cell lung cancer (NSCLC) cases from an Italian administrative database? A retrospective, secondary data use study for evaluating a novel algorithm performance.

机构信息

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy.

Outcome Research, Healthcare Administration, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) " Dino Amadori", Meldola, Emilia-Romagna, Italy

出版信息

BMJ Open. 2021 Sep 24;11(9):e048188. doi: 10.1136/bmjopen-2020-048188.

Abstract

OBJECTIVES

To evaluate an algorithm developed for identifying non-small cell lung cancer (NSCLC) candidates among patients with lung cancer with a diagnosis International Classification of Diseases: ninth revision (ICD-9) 162.x code in administrative databases. Algorithm could then be applied for identifying the NSCLC population in order to assess the appropriateness and quality of care of the NSCLC care pathway.

DESIGN

Algorithm discrimination capacity to select both NSCLC or non-NSCLC was carried out on a sample for which electronic health record (EHR) diagnosis was available. A bivariate frequency distribution and other measures were used to evaluate algorithm's performances. Associations between possible factors potentially affecting algorithm accuracy were investigated.

SETTING

Administrative databases used in a specific geographical area of Emilia-Romagna region, Italy.

PARTICIPANTS

Algorithm was carried out on patients aged >18 years, with a lung cancer diagnosis from January to December 2017 and resident in Emilia-Romagna region who have been hospitalised at IRST or in one of the hospitals placed in the Forlì-Cesena area and for which EHR diagnosis data were available.

OUTCOME MEASURES

Overall accuracy, positive (PPV) and negative (NPV) predictive values, sensitivity and specificity, positive and negative likelihood ratios and diagnostic OR were calculated.

RESULTS

A total of 430 patients were identified as lung cancer cases based on ICD-9 diagnosis. Focusing on the total incident cases (n=314), the algorithm had an overall accuracy of 82.8% with a sensitivity of 88.8%. The analysis confirmed a high level of PPV (90.2%), but lower specificity (53.7%) and NPV (50%). Higher length of stay seemed to be associated with a correct classification. Hospitalisation regimen and a supply of antiblastic therapy seemed to increase the level of PPV.

CONCLUSION

The algorithm demonstrated a strong validity for identifying NSCLC among patients with lung cancer in hospital administrative databases and can be used to investigate the quality of cancer care for this population.

TRIAL REGISTRATION NUMBER

NCT04676321.

摘要

目的

评估一种用于在行政数据库中通过国际疾病分类第 9 版(ICD-9)162.x 代码诊断为肺癌的患者中识别非小细胞肺癌(NSCLC)候选者的算法。然后可以应用该算法来识别 NSCLC 人群,以评估 NSCLC 护理途径的适当性和护理质量。

设计

对电子病历(EHR)诊断可用的样本进行算法区分能力,以选择 NSCLC 或非 NSCLC。使用双变量频率分布和其他措施来评估算法的性能。调查了可能影响算法准确性的潜在因素之间的关联。

设置

在意大利艾米利亚-罗马涅地区特定地理区域使用的行政数据库。

参与者

该算法针对年龄> 18 岁、2017 年 1 月至 12 月诊断为肺癌且居住在艾米利亚-罗马涅地区、曾在 IRST 或福尔利-切塞纳地区的一家医院住院且可获得 EHR 诊断数据的患者进行。

结果

根据 ICD-9 诊断,共有 430 名患者被确定为肺癌病例。在所有的新发病例(n=314)中,该算法的总体准确率为 82.8%,敏感度为 88.8%。分析证实了高阳性预测值(90.2%),但特异性(53.7%)和阴性预测值(50%)较低。较长的住院时间似乎与正确分类有关。住院方案和提供的抗肿瘤治疗似乎会提高阳性预测值。

结论

该算法在医院行政数据库中用于识别肺癌患者中的 NSCLC 具有较强的有效性,可用于调查该人群的癌症护理质量。

临床试验注册号

NCT04676321。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70cf/8475132/f2d0f1fafe25/bmjopen-2020-048188f01.jpg

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