Although there are several reports of previous studies on the exploration of ADEs using SRS, reports that consider factors other than the drug–AE pairs, such as the primary disease affecting the patient, are limited. The reason for this is that in disproportionality methods, considering additional factors requires an enormous number of combinations to be taken into account when signal detection is performed and the respective risk indicators are calculated. Therefore, considering additional factors seems to be impractical as an exploration method.
A potential solution to this issue would be to utilize AR. However, although AR is often used to efficiently analyze large data, there are only a few examples of it being used in the medical field, especially in SRS analysis [14,15,16,17]. Signal detection using AR has already been validated as an effective method for the initial identification of “multi-item ADEs” in a study by Harpaz et al. [14]. Furthermore, although information on primary diseases was not included similar to this study, the use of AR in drug–AE pairs was compared with the conventional signal detection method by Wang et al. [17]. Currently, signal detection using AR is not used in pharmacovigilance at regulatory authorities, but is considered very useful for performing complicated analysis considering the patient’s background as primary disease.
Therefore, in this study, kidney injury or liver injury as primary disease was considered in addition to the drug–AE pairs, and signal detection was performed using AR method. The conventional PRR method was also used, and the signal detection powers of each method were compared.
In this study, the detection criteria for AR were nAB 1 ≥ 3, lift > 1, and conviction > 1. For kidney or liver injury, in the AR method, both sensitivities were greater than 99%, and both specificities were greater than 94%. The same detection results were also obtained using the PRR method, indicating that the detection powers of the AR and PRR methods are similar. In addition, the AUC was 0.974 for kidney injury and 0.940 for liver injury. These high values suggest that the AR method is also highly accurate.
However, the NPV was greater than 99.9% for both kidney and liver injury, but the PPV was only 68.08% for kidney injury and 67.88% for liver injury.
These results suggest that the AR method may have detected signals that could not be detected by the conventional PRR method. However, unfortunately, we cannot prove our hypothesis, because we did not have “the true risk”. The true risk dataset containing “unknown AEs” does not exist.
Because SRS is the result of voluntary reporting and is influenced by reporting bias including underreporting, and the value of the signal easily changes depending on the timing of the analysis, signal detection is not necessarily the true risk, but is limited to the hypothesis of risk. In other words, PRR signals and AR signals are limited to the hypothesis of risk, but they are not the true risk.
The detected signals are hypotheses to be clinically noticed until pharmacologic verification is completed. For the AR method to be an alternative to the PRR method, a correlation with the magnitude of the signal value is desirable.
The magnitude of the lift values as AR signals and the PRR signals intensity are positively correlated. Thus, the correlation of the signal values of each method also makes the AR method easy to use for pharmacovigilance.
The conventional PRR method involves extracting data for each of the primary diseases, as shown in Figs. 1, 2 and 4, and constructing a 2 × 2 drug–AE k × m table. If a similar calculation method as shown in Figs. 2 and 3 that simply creates combinations from the database was used for AR method, the number of the combinations considered would be enormous and it would be difficult to calculate within a realistic time, even if AR method is used.
However, in the AR method, the “apriori algorithm” can be used to reduce the number of calculations. The apriori algorithm is based on the principle that “support of a certain item set is always less than or equal to support of its partial item set” [12]. Therefore, it is unnecessary to calculate the risk index for all combinations, which is required in the conventional method.
In this study, the AR method proposed also requires verification for primary diseases other than kidney injury and liver injury. However, it was suggested that computation using the “apriori algorithm” of AR method might be simple with the detection power equivalent to that of the conventional PRR method.