Ingrasciotta Ylenia, Isgrò Valentina, Foti Saveria Serena, Ientile Valentina, Fontana Andrea, L'Abbate Luca, Benoni Roberto, Fiore Elena Sofia, Tari Michele, Alibrandi Angela, Trifirò Gianluca
Department of Diagnostics and Public Health, University of Verona, Verona, Italy.
Academic Spin-off "INSPIRE - Innovative Solutions for Medical Prediction and Big Data Integration in Real World Setting" - Azienda Ospedaliera Universitaria "G. Martino", Messina, Italy.
Clin Epidemiol. 2023 Mar 11;15:309-321. doi: 10.2147/CLEP.S383738. eCollection 2023.
Inflammatory bowel diseases (IBDs), Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that have been increasingly treated with biological drugs in recent years. Newly developed coding algorithms for IBD identification using claims databases are needed to improve post-marketing surveillance of biological drugs.
To test algorithms to identify CD and UC, as indication for use of biological drugs approved for IBD treatment, using a claims database.
Data were extracted from the Caserta Local Health Unit database between 2015 and 2018. CD/UC diagnoses reported by specialists in electronic therapeutic plans (ETPs) were considered as gold standard. Five algorithms were developed based on ICD-9-CM codes as primary cause of hospital admissions, exemption from healthcare service co-payment codes and drugs dispensing with only indication for CD/UC. The accuracy was assessed by sensitivity (Se), specificity (Sp), positive (PPV) and negative predicted values (NPV) along with computation of the Youden Index and F-score.
In the study period, 1205 subjects received at least one biological drug dispensing approved for IBD and 134 (11.1%) received ≥1 ETP with IBD as use indication. Patients with CD and CU were 83 (61.9%) and 51 (38.1%), respectively. Sensitivity of the different algorithms ranged from 71.1% (95% CI: 60.1-80.5) to 98.8 (95% CI: 93.5-100.0) for CD and from 64.7% (95% CI: 50.1-77.6) to 94.1 (95% CI: 83.8-98.8) for UC, while specificity was always higher than 91%. The best CD algorithm was "Algorithm 3", based on hospital CD diagnosis code OR CD exemption code OR [IBD exemption code AND dispensing of non-biological drugs with only CD indication] (Se: 98.8%; Sp: 97.2%; PPV: 84.5%, NPV: 99.8%), achieving the highest diagnostic accuracy (Youden Index=0.960). The best UC algorithm was "Algorithm 3", based on specific hospital UC diagnosis code OR UC exemption code OR [IBD exemption code AND golimumab dispensing] OR dispensing of non-biological drugs with only UC indication (Se: 94.1%; Sp: 91.6%; PPV: 50.0%; NPV: 99.4%), and achieving the highest diagnostic accuracy (Youden Index=0.857).
In a population-based claims database, newly coding algorithms including diagnostic and exemption codes plus specific drug dispensing yielded highly accurate identification of CD and UC as distinct indication for biological drug use.
炎症性肠病(IBD),包括克罗恩病(CD)和溃疡性结肠炎(UC),是近年来越来越多地使用生物药物治疗的慢性疾病。需要新开发的使用理赔数据库识别IBD的编码算法,以改善生物药物的上市后监测。
使用理赔数据库测试识别CD和UC的算法,作为批准用于IBD治疗的生物药物的使用指征。
从卡塞塔地方卫生单位数据库中提取2015年至2018年的数据。电子治疗计划(ETP)中专科医生报告的CD/UC诊断被视为金标准。基于国际疾病分类第九版临床修正版(ICD-9-CM)编码开发了五种算法,作为住院的主要原因、医疗服务共付豁免编码以及仅用于CD/UC指征的药物配药。通过灵敏度(Se)、特异度(Sp)、阳性预测值(PPV)和阴性预测值(NPV)评估准确性,并计算尤登指数和F分数。
在研究期间,1205名受试者接受了至少一种批准用于IBD的生物药物配药,134名(11.1%)接受了≥1次以IBD为使用指征的ETP。患有CD和UC的患者分别为83名(61.9%)和51名(38.1%)。不同算法对CD的灵敏度范围为71.1%(95%置信区间:60.1-80.5)至98.8%(95%置信区间:93.5-100.0),对UC的灵敏度范围为64.7%(95%置信区间:50.1-77.6)至94.1%(95%置信区间:83.8-98.8),而特异度始终高于91%。最佳的CD算法是“算法3”,基于医院CD诊断编码或CD豁免编码或[IBD豁免编码以及仅用于CD指征的非生物药物配药](Se:98.8%;Sp:97.2%;PPV:84.5%,NPV:99.8%),实现了最高的诊断准确性(尤登指数=0.960)。最佳的UC算法是“算法3”,基于特定的医院UC诊断编码或UC豁免编码或[IBD豁免编码以及戈利木单抗配药]或仅用于UC指征的非生物药物配药(Se:94.1%;Sp:91.6%;PPV:50.0%;NPV:99.4%),并实现了最高的诊断准确性(尤登指数=0.857)。
在基于人群的理赔数据库中,新的编码算法,包括诊断和豁免编码以及特定的药物配药,能够高度准确地识别CD和UC,作为生物药物使用的不同指征。