Bastiaan Sallevelt

47 Performance of a trigger tool for detecting adverse drug reactions ADR causality method used; the relationship between the suspected drug and the identified ADRs in this French study was uncertain in over 80%. Removing the triggers for falls and delirium from the ADR trigger tool will increase the overall PPV of the ADR trigger tool from 41.8% to 62.2% (n = 255/410, Table 2). Nevertheless, we would not recommend excluding falls and delirium as triggers because these clinical events are often associated with drug-related admissions in older patients with polypharmacy [24]. In addition, a large proportion of ADRs would be excluded (35%, n = 138/393), and recognition by usual care for these triggers was lowest for ADRs of at least possible causality (Table 3). To increase the PPV, we would rather suggest to explore strategies for excluding drugs with a relatively low risk on the clinical event. A recent observational study compared the association of potentially inappropriate medication on inpatient falls listed in the explicit screening tools STOPP v2, STOPP v2 section K, and STOPPFall [26–28] Although all screening tools were independently associated with falls, the strongest effect was identified for STOPP section K [28]. This is plausible because STOPP section K is the most restrictive tool, including only four drug classes with highest risk of falls (i.e. benzodiazepines, hyponotic z-drugs, vasodilator drugs, and neuroleptic drugs). For delirium, selecting drugs with the highest anticholinergic burden will likely increase the PPV. However, a disadvantage of excluding drugs from the ADR trigger tool is that less ADRs may be detected. The difficulties in achieving a high PPV in ADR detection was illustrated in a systematic review on methods to detect drug-related problems. This systematic review identified 28 studies, three of which used a trigger tool to detect ADRs [29]. The PPVs of these ADR trigger tools ranged from 1.8%–32% [30–32]. The study with the lowest PPV (1.8%) was the only one performed in a geriatric population (rehabilitation ward) using a commercially available database grounded on potential ADRs extracted from a drug’s product information [30]. The highest PPV was reported in patients (age 16–90 years) admitted to a gastroenterology department using a trigger tool solely based on laboratory signals [32]. The use of trigger tools appeared to be the most labour-efficient method; however, incident report review generally showed a higher specificity compared to other methods. More recently, Zerah et al. evaluated the PPV of a trigger tool to detect adverse drug events (ADEs) and drug-related admissions (DRAs) in older people based on chart review [24]. The DRA trigger tool comprised 26 triggers and associated drugs frequently involved in ADEs. The DRA trigger tool was more comprehensive than the ADR trigger tool used in our study and included triggers to detect ADEs, including both ADRs and medication errors (i.e. underuse, overuse and misuse of drugs). The overall PPV for the detection of ADEs of the DRA trigger tool was 87% [24]. The better performance of the DRA trigger tool compared with the ADR trigger tool may be explained by the inclusion of medication errors, which had a large impact on the 2