Beatrijs Mertens1,2, Julie Hias3,4, Laura Hellemans3,4, Karolien Walgraeve4, Isabel Spriet3,4, Jos Tournoy5,6, Lorenz Roger Van der Linden3,4. 1. Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium. beatrijs.1.mertens@uzleuven.be. 2. Pharmacy Department, University Hospitals Leuven, Leuven, Belgium. beatrijs.1.mertens@uzleuven.be. 3. Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium. 4. Pharmacy Department, University Hospitals Leuven, Leuven, Belgium. 5. Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium. 6. Department of Geriatric Medicine, University Hospitals Leuven, Leuven, Belgium.
Abstract
PURPOSE: Drug-related admissions (DRAs) are an important cause of preventable harm in older adults. Multiple algorithms exist to assess causality of adverse drug reactions, including the Naranjo algorithm and an adjusted version of the Kramer algorithm. The performance of these tools in assessing DRA causality has not been robustly shown. This study aimed to evaluate the ability of the adjusted Kramer algorithm to adjudicate DRA causality in geriatric inpatients. METHODS: DRAs were assessed in a convenience sample of patients admitted to the acute geriatric wards of an academic hospital. DRAs were identified by expert consensus and causality was evaluated using the Naranjo and the adjusted Kramer algorithms. Positive agreement with expert consensus was calculated for both algorithms. A multivariable logistic regression analysis was performed to explore determinants for a DRA. RESULTS: A total of 218 geriatric inpatients was included of whom 65 (29.8%) experienced a DRA. Positive agreement was 72.3% (95% confidence interval (CI), 59.6-82.3%) and 100% (95% CI, 93.0-100%) for the Naranjo and the adjusted Kramer algorithm, respectively. Diuretics were the main culprits and most DRAs were attributed to a fall (n = 18; 27.7%). A fall-related principal diagnosis was independently associated with a DRA (odds ratio 20.11; 95% CI, 5.60-72.24). CONCLUSION: The adjusted Kramer algorithm demonstrated a higher positive agreement with expert consensus in assessing DRA causality in geriatric inpatients compared to the Naranjo algorithm. Our results further support implementation of the adjusted Kramer algorithm as part of a standardized DRA assessment in older adults.
PURPOSE: Drug-related admissions (DRAs) are an important cause of preventable harm in older adults. Multiple algorithms exist to assess causality of adverse drug reactions, including the Naranjo algorithm and an adjusted version of the Kramer algorithm. The performance of these tools in assessing DRA causality has not been robustly shown. This study aimed to evaluate the ability of the adjusted Kramer algorithm to adjudicate DRA causality in geriatric inpatients. METHODS: DRAs were assessed in a convenience sample of patients admitted to the acute geriatric wards of an academic hospital. DRAs were identified by expert consensus and causality was evaluated using the Naranjo and the adjusted Kramer algorithms. Positive agreement with expert consensus was calculated for both algorithms. A multivariable logistic regression analysis was performed to explore determinants for a DRA. RESULTS: A total of 218 geriatric inpatients was included of whom 65 (29.8%) experienced a DRA. Positive agreement was 72.3% (95% confidence interval (CI), 59.6-82.3%) and 100% (95% CI, 93.0-100%) for the Naranjo and the adjusted Kramer algorithm, respectively. Diuretics were the main culprits and most DRAs were attributed to a fall (n = 18; 27.7%). A fall-related principal diagnosis was independently associated with a DRA (odds ratio 20.11; 95% CI, 5.60-72.24). CONCLUSION: The adjusted Kramer algorithm demonstrated a higher positive agreement with expert consensus in assessing DRA causality in geriatric inpatients compared to the Naranjo algorithm. Our results further support implementation of the adjusted Kramer algorithm as part of a standardized DRA assessment in older adults.
Authors: Chuenjid Kongkaew; Mark Hann; Jaydeep Mandal; Steven D Williams; David Metcalfe; Peter R Noyce; Darren M Ashcroft Journal: Pharmacotherapy Date: 2013-05-17 Impact factor: 4.705
Authors: Nibu Parameswaran Nair; Leanne Chalmers; Michael Connolly; Bonnie J Bereznicki; Gregory M Peterson; Colin Curtain; Ronald L Castelino; Luke R Bereznicki Journal: PLoS One Date: 2016-10-31 Impact factor: 3.240
Authors: Hannah De Schutter; Julie Hias; Laura Hellemans; Karolien Walgraeve; Jos Tournoy; Peter Verhamme; Peter Sinnaeve; Rik Willems; Walter Droogné; Christophe Vandenbriele; Lucas Van Aelst; Thomas Vanassche; Lorenz Van der Linden Journal: Eur Geriatr Med Date: 2022-10-14 Impact factor: 3.269