目的:初次抗体缺乏(PAD)的诊断延迟与发病率增加有关,死亡率,和医疗费用。因此,以前开发了一种筛查算法,用于早期发现初级保健中存在PAD风险的患者.我们旨在通过将其应用于荷兰的初级保健数据库,对PAD筛查算法进行临床验证和优化。
方法:该算法应用于61,172个电子健康记录(EHR)的数据集。筛选了400个高分EHR的排除标准,其余患者被邀请进行血清免疫球蛋白分析,并在临床必要时转诊。
结果:在符合纳入条件的104名患者中,16人被他们的全科医生推荐为疑似PAD,其中10人被诊断为PAD。在通过筛选算法选择并纳入实验室分析的患者中,一般人群中PAD的患病率约为1:10,而1:1700-1:25,000。为了优化筛选过程的效率,我们用高风险患者的子集修改了算法,曲线下面积-接受者工作特征曲线值提高到0.80(95%置信区间0.63-0.97)。我们提出了一个两步筛选过程,首先应用原始算法区分高风险患者和低风险患者,然后应用优化算法选择高危患者进行血清免疫球蛋白分析。
结论:使用筛选算法,我们能够从初级保健人群中识别出10名新的PAD患者,从而减少诊断延迟。未来的研究应该解决其他人群的进一步验证和全面的成本效益分析。
背景:Clinicaltrials.gov记录编号NCT05310604,首次提交2022年3月25日。
The diagnostic delay of primary antibody deficiencies (PADs) is associated with increased morbidity, mortality, and healthcare costs. Therefore, a screening algorithm was previously developed for the early detection of patients at risk of PAD in primary care. We aimed to clinically validate and optimize the PAD screening algorithm by applying it to a primary care database in the Netherlands.
The algorithm was applied to a data set of 61,172 electronic health records (EHRs). Four hundred high-scoring EHRs were screened for exclusion criteria, and remaining patients were invited for serum immunoglobulin analysis and referred if clinically necessary.
Of the 104 patients eligible for inclusion, 16 were referred by their general practitioner for suspected PAD, of whom 10 had a PAD diagnosis. In patients selected by the screening algorithm and included for laboratory analysis, prevalence of PAD was ~ 1:10 versus 1:1700-1:25,000 in the general population. To optimize efficiency of the screening process, we refitted the algorithm with the subset of high-risk patients, which improved the area under the curve-receiver operating characteristics curve value to 0.80 (95% confidence interval 0.63-0.97). We propose a two-step screening process, first applying the original algorithm to distinguish high-risk from low-risk patients, then applying the optimized algorithm to select high-risk patients for serum immunoglobulin analysis.
Using the screening algorithm, we were able to identify 10 new PAD patients from a primary care population, thus reducing diagnostic delay. Future studies should address further validation in other populations and full cost-effectiveness analyses.
Clinicaltrials.gov record number NCT05310604, first submitted 25 March 2022.