Validation of MyFORTA: An Automated Tool to Improve Medications in Older People Based on the FORTA List
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Validation of MyFORTA: An Automated Tool to Improve Medications in Older People Based on the FORTA List. / Wehling, Martin; Weindrich, Johannes; Weiss, Christel; Heser, Kathrin; Pabst, Alexander; Luppa, Melanie; Bickel, Horst; Weyerer, Siegfried; Pentzek, Michael; König, Hans-Helmut; Lühmann, Dagmar; van der Leeden, Carolin; Scherer, Martin; Riedel-Heller, Steffi G; Wagner, Michael; Pazan, Farhad.
in: DRUG AGING, Jahrgang 41, Nr. 6, 07.06.2024, S. 555-564.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Validation of MyFORTA: An Automated Tool to Improve Medications in Older People Based on the FORTA List
AU - Wehling, Martin
AU - Weindrich, Johannes
AU - Weiss, Christel
AU - Heser, Kathrin
AU - Pabst, Alexander
AU - Luppa, Melanie
AU - Bickel, Horst
AU - Weyerer, Siegfried
AU - Pentzek, Michael
AU - König, Hans-Helmut
AU - Lühmann, Dagmar
AU - van der Leeden, Carolin
AU - Scherer, Martin
AU - Riedel-Heller, Steffi G
AU - Wagner, Michael
AU - Pazan, Farhad
N1 - © 2024. The Author(s).
PY - 2024/6/7
Y1 - 2024/6/7
N2 - BACKGROUND: Listing 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.METHODS: 331 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.RESULTS: The 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.CONCLUSION: MF 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.
AB - BACKGROUND: Listing 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.METHODS: 331 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.RESULTS: The 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.CONCLUSION: MF 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.
U2 - 10.1007/s40266-024-01120-1
DO - 10.1007/s40266-024-01120-1
M3 - SCORING: Journal article
C2 - 38848020
VL - 41
SP - 555
EP - 564
JO - DRUG AGING
JF - DRUG AGING
SN - 1170-229X
IS - 6
ER -
