ArticleDrugs & aging2024
Validation of MyFORTA: An Automated Tool to Improve Medications in Older People Based on the FORTA List.
Article in Drugs & aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Longitudinal Non-interventional Changes of the FORTA Score are Associated with Changes of Cognitive and Physical Function Tests in Community-Dwelling Older People.Drugs & aging · 2026Article
- Knowledge, attitude, and practice of dentists in India regarding geriatric oral healthcare and polypharmacy management: a cross-sectional study.BMC geriatrics · 2026Article
- Proton Pump Inhibitor Use in Older Adult Patients with Multiple Chronic Conditions: Clinical Risks and Best Practices.Journal of clinical medicine · 2025Review
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16 authors.
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Abstract
backgroundListing tools have been developed to improve medications in older patients, including the Fit fOR The Aged (FORTA) list, a clinically validated, positive-negative list of medication appropriateness. Here, we aim to validate MyFORTA, an automated tool for individualized application of the FORTA list.
methods331 participants of a multi-center cohort study (AgeCoDe) for whom the FORTA score (sum of overtreatment and undertreatment errors) had been determined manually (gold standard [GS]) were reassessed using the automated MyFORTA (MF) tool. This tool determines the score from ATC and ICD codes combined with clinical parameters.
resultsThe FORTA scores were 9.01 ± 2.91 (mean ± SD, MF) versus 6.02 ± 2.52 (GS) (p < 0.00001). Removing undertreatment errors for calcium/vitamin D (controversial guidelines) and influenza/pneumococcal vaccinations (no robust information in the database), the difference decreased: 7.5 ± 2.7 (MF) versus 5.98 ± 2.55 (GS) (p < 0.00001). The remaining difference was driven by, for example, missing nitro spray in coronary heart disease/acute coronary syndrome as the related information was rarely found in the database, but notoriously detected by MF. Three hundred and forty errors from those 100 patients with the largest score deviation accounted for 68% of excess errors by MF.
conclusionMF was more sensitive to detect medication errors than GS, all frequent errors only detected by MF were plausible, and almost no adaptations of the MF algorithm seem indicated. This automated tool to check medication appropriateness according to the FORTA list is now validated and represents the first clinically directed algorithm in this context. It should ease the application of FORTA and help to implement the proven beneficial effects of FORTA on clinical endpoints.
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