Abstract 802P
Background
Post-marketing surveillance of antineoplastic agents is performed to evaluate the efficacy and safety in patients aiming at expanding drug indications and discovering potential adverse reactions. The real-world data is fraught with multiple missing values. Literature addressing different strategies for dealing with missing data in such a situation is scarce. Using machine learning algorithms for predicting therapeutic response to PD-1/PD-L1 Inhibitor has attracted attention in recent years. However, training a predictive model usually requires imaging or biomarker information, which is rarely available in the post-marketing surveillance data.
Methods
To address these challenges, we propose an ensemble learning-based framework that utilizes a multi-model fusion strategy at the stages of both data imputation and outcome prediction. The proposed method is evaluated on a gynecological cancer post-marketing surveillance dataset with 118 patient samples, collected from Sep 2019 to Feb 2021.
Results
Compared to several existing imputation algorithms, our method demonstrates a superior capability to obtain high-quality imputed values, effectively boosting the overall performance of outcome prediction. The study also compares three PD-1 inhibitors, including Camrelizumab (with 51 patient samples), Sintilimab (44 samples), and Toripalimab (23 samples). Statistical results show that Toripalimab presents a significant efficacy improvement in the RECIST metric, with 48% CR, 35% PR, 4% SD, and 13% PD, compared to Camrelizumab (29% CR, 22% PR, 14% SD, 24% PD) and Sintilimab (18% CR, 48% PR, 20% SD, 14% PD). The death rates for Camrelizumab, Sintilimab, and Toripalimab, are 17.6%, 13.6%, and 0%. With respect to adverse reactions (AE), the rates of organ dysfunction are 62.7%, 50.0%, and 43.5%, and the rates of general discomfort are 17.6%, 47.7%, and 17.4%, for Camrelizumab, Sintilimab, and Toripalimab, respectively.
Conclusions
Our method can be used as an effective analytical tool for efficacy and safety evaluation on an incomplete post-marketing surveillance dataset.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.