Validation of a novel causality assessment scale for adverse events in non-small cell lung carcinoma patients treated with platinum and pemetrexed doublet chemotherapy

Abstract

Aim: Accurate causality assessment (CA) of adverse events (AEs) is important in clinical research and routine clinical practice. The Naranjo scale (NS) used for CA lacks specificity, leading to a high rate of false positive causal associations. NS is a simple scale for CA; however, its limitations have reduced its popularity in favour of other scales. We therefore attempted to improvise the algorithm by addressing specific lacunae in NS. Methods: We attempted to modify the existing NS by (a) changing the weightage given to certain responses, (b) achieving higher resolution to certain responses for delineating drug related and unrelated AEs and (c) modifying the slabs for classification of association as ‘likely’ and ‘unlikely’. The new scale, named as the Sharma-Nookala-Gota (SNG) algorithm, was evaluated in a training set of 19 AEs in a tertiary care cancer hospital in western India, and further validated in a set of 104 AEs. Consensus of four physician opinion was taken as gold standard for comparison. Results: Of the 19 AEs in the training set, 6 were described by the treating physician as ‘not related’ and 13 as related to the drug. The SNG algorithm had 100% concordance with physician opinion, whereas the NS had only 73.7% concordance. NS showed a tendency to misclassify AEs as ‘related’ when they were indeed ‘not related’. In the validation set of 104 AEs, NS and SNG algorithms misclassified 30 and 2 AEs, respectively, leading to a concordance of 70.2% and 98.1%, respectively, with physician opinion. Conclusion: Decisive modifications of the NS resulted in the SNG scale, with superior specificity while retaining sensitivity against the gold standard. Plain Language Summary SNG algorithm – A novel tool for causality assessment of adverse drug reactions Adverse events (AEs) can cause increased morbidity, hospitalisation, and even death. Hence it is essential to recognise AEs and to establish their correct causal relationship to a drug. Many causality assessment methods, scales and algorithms are available to assess the relationship between an AE and a drug. The Naranjo algorithm is most commonly employed in spite of its many drawbacks as it is simple to use. Concerns have been raised regarding the performance of the scale, and researchers have tried to answer them, but none of them could address all issues satisfactorily. We too experienced many problems while using it in our routine clinical practice and in clinical trials. For instance, the Naranjo scale is non-specific and shows a bias toward implicating the drug as the causal factor for AEs. This improper assessment has often led to drug discontinuation, thereby compromising the efficacy of treatment. Hence, we modified the existing Naranjo scale to a new one (the Sharma-Nookala-Gota – SNG algorithm) to address these shortcomings. We piloted the SNG causality assessment algorithm in patients suffering from AEs due to various drugs. The SNG algorithm was found to have good concordance with the physicians’ assessment of causality. As a next step, we validated the SNG algorithm in patients receiving a standard drug combination of pemetrexed and carboplatin for lung cancer combination. Out of the 104 AEs observed in 65 patients, the SNG causality assessment algorithm showed good concordance (except in two cases) with the physicians’ decision of causality assessment, while the Naranjo algorithm was not so successful. Hence, the SNG algorithm can be a better guide for causality assessment of AEs.