Identification of cancer chemotherapy regimens and patient cohorts in administrative claims: challenges, opportunities, and a proposed algorithm

Real-world evidence is a valuable source of information in healthcare. This study describes the challenges and successes during algorithm development to identify cancer cohorts and multi-agent chemotherapy regimens from claims data to perform a comparative effectiveness analysis of granulocyte colony stimulating factor (G-CSF) use. Using the Biologics and Biosimilars Collective Intelligence Consortium’s Distributed Research Network, we iteratively developed and tested a de novo algorithm to accurately identify patients by cancer diagnosis, then extract chemotherapy and G-CSF administrations for a retrospective study of prophylactic G-CSF. After identifying patients with cancer and subsequent chemotherapy exposures, we observed only 12% of patients with cancer received chemotherapy, which is fewer than expected based on prior analyses. Therefore, we reversed the initial inclusion criteria to identify chemotherapy receipt, then prior cancer diagnosis, which increased the number of patients from 2,814 to 3,645, or 68% of patients receiving chemotherapy had diagnoses of interest. Additionally, we excluded patients with cancer diagnoses that differed from those of interest in the 183 days before the index date of G-CSF receipt, including early-stage cancers without G-CSF or chemotherapy exposure. By removing this criterion, we retained 77 patients who were previously excluded. Finally, we incorporated a 5-day window to identify all chemotherapy drugs administered (excluding oral prednisone and methotrexate, as these medications may be used for other non-malignant conditions) as patients may fill oral prescriptions days to weeks prior to infusion. This increased the number of patients with chemotherapy exposures of interest to 6,010. The final cohort of included patients, based on G-CSF exposure, increased from 420 from the initial algorithm to 886 using the final algorithm. Medications used for multiple indications, sensitivity and specificity of administrative codes, and relative timing of medication exposure must all be evaluated to identify patient cohorts receiving chemotherapy from claims data.

真实世界证据（Real-world evidence）是医疗保健领域极具价值的信息来源。本研究阐述了从理赔数据中识别癌症队列与多药联合化疗方案的算法开发过程中所面临的挑战与取得的成果，以此开展粒细胞集落刺激因子（granulocyte colony stimulating factor, G-CSF）使用的比较有效性分析。借助生物制品与生物类似药联合智能联盟（Biologics and Biosimilars Collective Intelligence Consortium）的分布式研究网络，我们迭代开发并测试了一款全新算法，以精准基于癌症诊断识别患者，随后提取化疗与G-CSF给药数据，用于预防性G-CSF使用的回顾性研究。在识别出癌症患者及其后续化疗暴露情况后，我们发现仅12%的癌症患者接受了化疗，这一比例低于此前分析的预期值。因此，我们调整了初始纳入标准，改为先识别接受化疗的患者，再追溯其既往癌症诊断史，这使得符合条件的患者数量从2814例增至3645例，其中68%的化疗患者具有目标癌症诊断。此外，我们排除了在G-CSF给药索引日期前183天内患有非目标癌症诊断的患者，包括未接受G-CSF或化疗的早期癌症患者。移除该排除标准后，我们保留了此前被排除的77例患者。最后，我们设置了5天的时间窗以识别所有给药的化疗药物（排除口服泼尼松与甲氨蝶呤，因为这两类药物可用于其他非恶性疾病的治疗），因为患者可能会在静脉输注前数日至数周开具口服处方。这使得具有目标化疗暴露的患者数量增至6010例。最终基于G-CSF暴露纳入的患者队列规模，从初始算法得到的420例增至最终算法下的886例。若要从理赔数据中识别接受化疗的患者队列，需对多适应证药物、行政编码的灵敏度与特异度，以及药物暴露的相对时间窗进行全面评估。