Bordeaux PharmacoEpi, INSERM CIC-P 1401, University of Bordeaux, Bordeaux, France.
Pharmacoepidemiol Drug Saf. 2024 Apr;33(4):e5781. doi: 10.1002/pds.5781.
This paper aims to introduce an algorithm designed to identify Venous Thromboembolism (VTE) in the French National Healthcare Database (SNDS) and to estimate its positive predictive value.
A case-identifying algorithm was designed using SNDS inpatient and outpatient encounters, including hospital stays with discharge diagnoses, imaging procedures and drugs dispensed, of French patients aged at least 18 years old to whom baricitinib or Tumor Necrosis Factor Inhibitors (TNFi) were dispensed between September 1, 2017, and December 31, 2018. An intra-database validation study was then conducted, drawing 150 cases identified as VTE by the algorithm and requesting four vascular specialists to assess them. Patient profiles used to conduct the case adjudication were reconstituted from de-identified pooled and formatted SNDS data (i.e., reconstituted electronic health records-rEHR) with a 6-month look-back period prior to the supposed VTE onset and a 12-month follow-up period after. The positive predictive value (PPV) with its 95% confidence interval (95% CI) was calculated as the number of expert-confirmed VTE divided by the number of algorithm-identified VTE. The PPV and its 95% CI were then recomputed among the same patient set initially drawn, once the VTE-identifying algorithm was updated based on expert recommendation.
For the 150 patients identified with the first VTE-identifying algorithm, the adjudication committee confirmed 92 cases, resulting in a PPV of 61% (95% CI = [54-69]). The final VTE-identifying algorithm including expert suggestions showed a PPV of 92% (95% CI = [86-98]) with a total of 87 algorithm-identified cases, including 80 retrieved from the 92 confirmed by experts.
The identification of VTE in the SNDS is possible with a good PPV.
本文旨在介绍一种旨在识别法国国家医疗保健数据库（SNDS）中静脉血栓栓塞症（VTE）的算法，并估计其阳性预测值。
使用 SNDS 的住院和门诊就诊记录设计了一种病例识别算法，包括有出院诊断的住院治疗、影像学检查和配药，患者年龄至少为 18 岁，于 2017 年 9 月 1 日至 2018 年 12 月 31 日期间接受了巴瑞替尼或肿瘤坏死因子抑制剂（TNFi）的治疗。然后进行了一项内部数据库验证研究，从算法识别的 150 例 VTE 中抽取病例，并要求四名血管专家对其进行评估。用于进行病例裁决的患者概况是使用经过去识别的汇集和格式化的 SNDS 数据（即，重新构成的电子健康记录-rEHR）构建的，该数据在假定的 VTE 发病前有 6 个月的回溯期，在 VTE 发病后有 12 个月的随访期。阳性预测值（PPV）及其 95%置信区间（95%CI）是通过将专家确认的 VTE 数量除以算法识别的 VTE 数量计算得出的。根据专家建议更新 VTE 识别算法后，在最初抽取的相同患者组中重新计算了 PPV 及其 95%CI。
对于使用第一个 VTE 识别算法识别的 150 例患者，裁决委员会确认了 92 例，PPV 为 61%（95%CI=[54-69]）。包含专家建议的最终 VTE 识别算法的 PPV 为 92%（95%CI=[86-98]），共识别了 87 例算法病例，其中包括 80 例从专家确认的 92 例中检索到的病例。
SNDS 中 VTE 的识别具有较高的阳性预测值。
