Vigilance & Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.
Department of Laboratory Medicine, National University Hospital, Singapore.
Pharmacoepidemiol Drug Saf. 2024 Aug;33(8):e5875. doi: 10.1002/pds.5875.
Bleeding is an important health outcome of interest in epidemiological studies. We aimed to develop and validate rule-based algorithms to identify (1) major bleeding and (2) all clinically relevant bleeding (CRB) (composite of major and all clinically relevant nonmajor bleeding) within real-world electronic healthcare data.
We took a random sample (n = 1630) of inpatient admissions to Singapore public healthcare institutions in 2019 and 2020, stratifying by hospital and year. We included patients of all age groups, sex, and ethnicities. Presence of major bleeding and CRB were ascertained by two annotators through chart review. A total of 630 and 1000 records were used for algorithm development and validation, respectively. We formulated two algorithms: sensitivity- and positive predictive value (PPV)-optimized algorithms. A combination of hemoglobin test patterns and diagnosis codes were used in the final algorithms.
During validation, diagnosis codes alone yielded low sensitivities for major bleeding (0.16) and CRB (0.24), although specificities and PPV were high (>0.97). For major bleeding, the sensitivity-optimized algorithm had much higher sensitivity and negative predictive values (NPVs) (sensitivity = 0.94, NPV = 1.00), however false positive rates were also relatively high (specificity = 0.90, PPV = 0.34). PPV-optimized algorithm had improved specificity and PPV (specificity = 0.96, PPV = 0.52), with little reduction in sensitivity and NPV (sensitivity = 0.88, NPV = 0.99). For CRB events, our algorithms had lower sensitivities (0.50-0.56).
The use of diagnosis codes alone misses many genuine major bleeding events. We have developed major bleeding algorithms with high sensitivities, which can ascertain events within populations of interest.
出血是流行病学研究中一个重要的关注健康结果。我们旨在开发和验证基于规则的算法，以识别（1）主要出血和（2）所有临床相关出血（CRB）（主要出血和所有临床相关非主要出血的组合）在真实世界的电子医疗保健数据中。
我们从 2019 年和 2020 年新加坡公立医疗机构的住院患者中抽取了一个随机样本（n=1630），按医院和年份分层。我们纳入了所有年龄段、性别和种族的患者。两名注释员通过病历回顾确定主要出血和 CRB 的存在。总共使用了 630 个和 1000 个记录来开发和验证算法。我们制定了两种算法：敏感性和阳性预测值（PPV）优化算法。最终算法中使用了血红蛋白测试模式和诊断代码的组合。
在验证过程中，仅使用诊断代码对主要出血（敏感性为 0.16）和 CRB（敏感性为 0.24）的敏感性较低，尽管特异性和 PPV 较高（>0.97）。对于主要出血，敏感性优化算法的敏感性和阴性预测值（NPV）更高（敏感性=0.94，NPV=1.00），但假阳性率也相对较高（特异性=0.90，PPV=0.34）。PPV 优化算法的特异性和 PPV有所提高（特异性=0.96，PPV=0.52），敏感性和 NPV 略有降低（敏感性=0.88，NPV=0.99）。对于 CRB 事件，我们的算法的敏感性较低（0.50-0.56）。
单独使用诊断代码会错过许多真正的主要出血事件。我们已经开发了具有高敏感性的主要出血算法，可以确定感兴趣人群中的事件。
