Algorithm to derive progression-based lines of therapy from a real-world non-small cell lung cancer (NSCLC) dataset.

Abstract

e21172 Background: Analysis of Real World Data (RWD) from Electronic Health Records (EHR) for applications such as Health Economics and Outcomes Research (HEOR) or regulatory submissions requires identification of the lines of therapy (LoT) patients have received. LoTs are typically not captured in EHR and must be manually abstracted. As the use of RWD increases, there is a growing need to create algorithms that can work on RWD to extract LoT information in an automated manner with high accuracy. We present here the results of such an algorithm created on NSCLC RWD. Methods: 10950 advanced NSCLC patients from the ConcertAI Oncology RWD database who had received anti-neoplastic treatment after advanced diagnosis were used to build and validate this algorithm. These data were further enriched by expert nurse curators to fill in missing oral drug information and identify progression events. We developed a progression-based LoT (pLoT) model that identified LoT changes in sync with tumor progressions. If patients received multiple regimens before progression they were captured as nested regimens within the LoT. The algorithm uses complex rules to define combination of drugs as regimens (combination rule), identify resumption of regimens (gap rule) or dropping of drugs from regimens as new lines and to handle noisiness in RWD etc. Results: The LoT model accurately captures line changes triggered by progression events as well as any nested regimen changes due to adverse events etc. Patient level validation of LoT was carried out by clinical experts using an in-house tool and found to be consistent with literature & individual drug data. Cohort level analysis of top 3 combinations of therapies used in 1st & 2nd line treatment between 2015-2020 (8200 patients) are shown in Table. Sensitivity analysis on the combination rule showed that this parameter can be changed between 28-33 days without significantly impacting the LoT output (<1% impact). We use a 30 day combination rule as the default. Similarly, the gap rule parameter is quite robust and does not show significant variation between 45 – 90 days (<2% impact). We use 63 days. Conclusions: We have developed a robust algorithm to derive pLoT on RWD at scale assuming availability of curated progression data which can be used to support use cases such as HEOR, clinical development and regulatory submissions. pLoT is better suited for outcomes analysis compared to regimen based LoT since it distinguishes changes in treatment due to progression events from changes due to toxicity, drug availability, etc., and allows analysis on a more homogeneous patient population relating to their past clinical experience. [Table: see text]