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Key summary points Aim We aim to provide the most accurate data about frailty detection tools used by healthcare professionals. Findings The number of criteria used by tools to detect frailty is broad and distributed in a total of nine different domains. There is a lack of homogeneity in the evaluation of the tools. Message The great heterogeneity in the criteria and domains used to detect frailty and in the evaluation of the tools highlights a lack of strong recommendations for the creation, evaluation, and validation of frailty detection tools. Background There is a wide variety of frailty detection tools, but no gold standard. Choosing the most appropriate tool can therefore be complicated. Our systematic review seeks to provide useful data on the frailty detection tools available to help healthcare professionals in choosing a tool. Method We systematically searched for articles published between January 2001 and December 2022 in three electronic databases. Articles were to be written in English or French and were to discuss a frailty detection tool used by healthcare professionals in a population without specific health conditions. Any self-testing, physical testing or biomarkers were excluded. Systematic reviews and meta-analyses were also excluded. Data were extracted from two coding grids; one for the criteria used by the tools to detect frailty and the other for the evaluation of clinimetric parameters. The quality of the articles was assessed using QUADAS-2. Results A total of 52 articles, covering 36 frailty detection tools, were included and analysed in the systematic review. Forty-nine different criteria were identified, with a median of 9 (IQR 6–15) criteria per tool. Regarding the evaluation of tool performances, 13 different clinimetric properties were identified, with a mean of 3.6 (± 2.2) properties assessed per tool. Conclusion There is considerable heterogeneity in the criteria used to detect frailty, as well as in the way tools are evaluated.

Background Potentially inappropriate prescribing (PIP) is usually associated with a higher risk of adverse health outcomes. It is therefore important to identify PIP in older adults. However, there are no clear prioritisation strategies to select patients requiring prescription reviews. Aim The aim of this study was to assess the association between the identification of seniors at risk (ISAR) score and the number of PIPs. Method A 12-month retrospective hospital-based study was conducted. PIPs, including potentially inappropriate medications (PIMs) and potential prescribing omissions (PPOs), were detected using the STOPP/START tool. Multivariate linear regressions were conducted to identify factors associated with the number of PIPs. Sensitivity, specificity, Youden index, and ROC curve were calculated to determine the predictive power of ISAR score. Results This study included 266 records. The analysis led to the detection of 420 PIMs and 210 PPOs, with a prevalence of 80.1% and 54.9%, respectively. Multivariate linear regression revealed that the ISAR score ( p  = 0.041), and the number of medications ( p  < 0.001) were determinants of PIP. The number of medications remained the sole determinant of the number of PIMs ( p  < 0.001), while living in a nursing home was the only determinant of the number of PPOs ( p  = 0.036). Conclusion The study showed that the ISAR score and the number of medications were independently associated with the number of PIPs. Considering the use of the ISAR score and the number of medications may be useful strategies to prioritise patients for whom prescribing appropriateness should be assessed using explicit criteria.