Objective
The CUSP was developed as an automated utility scoring system to support prioritisation of Individual Case Safety Reports (ICSRs) during clinical review. Its performance was determined based on a retrospective dataset. This study provides the first prospective examples following implementation.
Method
The CUSP methodology has been deployed as an assistive tool to support pharmacovigilance experts in reviewing large ICSR datasets to determine causality. In this study we assess the first two prospective uses to determine performance in a real-world setting.
Results
The examples where CUSP was used as an assistive tool were in the context of a safety assessment conducted at the request of a Health Authority, these assessments required review of 1139 and 632 ICSRs respectively.
In the first example, a head-to-head assessment was undertaken comparing CUSP to existing process of manual review. The total dataset consisted of 1139 cases where pharmacovigilance experts reviewed all cases and assessed 195 as key, 489 confounded and 455 poorly documented cases. The CUSP scores were retrieved for the same dataset, the average CUSP score was 15.4 (median: 16.0, range: 8.0-24.0). The same arbitrary cut off used in the analysis of retrospective data was applied (top 20% of all CUSP scores in the GSK Global Safety Database, for this drug-event pair this correlates to 65% of the total dataset). Utilising this arbitrary criterion resulted in the retrieval of 88% of key cases (172/195). The remainder of the key cases (n=23; 12%) where distributed above the 58th percentile comparative to all CUSP scores. The pharmacovigilance experts confirmed that the 23 cases not retrieved utilising the arbitrary cut off would not have changed their overall safety assessment outcome. Utilising CUSP in this example could have potentially led to a de-prioritisation of 399 cases (35%; 23 key, 127 confounded, 249 poor documented cases).
In the second example, CUSP was used on a total dataset of 632 cases (average: 17.7, median: 19.0, range: 5.0-27.0). Cases were reviewed sequentially by Pharmacovigilance experts, using a descending CUSP score, until they were confident that a sufficient number of key cases had been identified suggestive of a causal relationship. In this example 20 key cases were required to determine the safety assessment outcome, following review of 58 cases (CUSP ≥23), representing 9% of the total dataset (58/632).
Conclusion
This study provides a performance assessment of the first two prospective uses of CUSP in a real-world setting, based on the results of these the utilization of CUSP can offer significant time savings for some reviews affirming the results of the initial study.
CUSP remains a complementary tool available to pharmacovigilance experts to support prioritisation of ICSRs during signal assessments and other case series review activities when datasets are large adding an additional tool to the pharmacovigilance armamentarium.

**Objective**
The CUSP was developed as an automated utility scoring system to support prioritisation of Individual Case Safety Reports (ICSRs) during clinical review. Its performance was determined based on a retrospective dataset. This study provides the first prospective examples following implementation.
**Method**
The CUSP methodology has been deployed as an assistive tool to support pharmacovigilance experts in reviewing large ICSR datasets to determine causality. In this study we assess the first two prospective uses to determine performance in a real-world setting.
**Results**
The examples where CUSP was used as an assistive tool were in the context of a safety assessment conducted at the request of a Health Authority, these assessments required review of 1139 and 632 ICSRs respectively.
In the first example, a head-to-head assessment was undertaken comparing CUSP to existing process of manual review. The total dataset consisted of 1139 cases where pharmacovigilance experts reviewed all cases and assessed 195 as key, 489 confounded and 455 poorly documented cases. The CUSP scores were retrieved for the same dataset, the average CUSP score was 15.4 (median: 16.0, range: 8.0-24.0). The same arbitrary cut off used in the analysis of retrospective data was applied (top 20% of all CUSP scores in the GSK Global Safety Database, for this drug-event pair this correlates to 65% of the total dataset). Utilising this arbitrary criterion resulted in the retrieval of 88% of key cases (172/195). The remainder of the key cases (n=23; 12%) where distributed above the 58th percentile comparative to all CUSP scores. The pharmacovigilance experts confirmed that the 23 cases not retrieved utilising the arbitrary cut off would not have changed their overall safety assessment outcome. Utilising CUSP in this example could have potentially led to a de-prioritisation of 399 cases (35%; 23 key, 127 confounded, 249 poor documented cases).
In the second example, CUSP was used on a total dataset of 632 cases (average: 17.7, median: 19.0, range: 5.0-27.0). Cases were reviewed sequentially by Pharmacovigilance experts, using a descending CUSP score, until they were confident that a sufficient number of key cases had been identified suggestive of a causal relationship. In this example 20 key cases were required to determine the safety assessment outcome, following review of 58 cases (CUSP ≥23), representing 9% of the total dataset (58/632).
**Conclusion**
This study provides a performance assessment of the first two prospective uses of CUSP in a real-world setting, based on the results of these the utilization of CUSP can offer significant time savings for some reviews affirming the results of the initial study.
CUSP remains a complementary tool available to pharmacovigilance experts to support prioritisation of ICSRs during signal assessments and other case series review activities when datasets are large adding an additional tool to the pharmacovigilance armamentarium.

Clinical Utility Score for Prioritisation (CUSP), a Method to Identify High-Utility Safety Reports: A Prospective Assessment

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