Katinka J Nauta1, Feikje Groenhof2, Jan Schuling2, Jacqueline G Hugtenburg3, Hein P J van Hout4, Flora M Haaijer-Ruskamp5, Petra Denig5. 1. University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 2. Department of General Practice, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 3. Department of Clinical Pharmacology and Pharmacy, VU University Medical Center, Amsterdam, The Netherlands. 4. Department of General Practice and Elderly Care Medicine, VU University Medical Center, EMGO+ Institute, Amsterdam, The Netherlands. 5. Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Abstract
PURPOSE: The STOPP/START criteria are increasingly used to assess prescribing quality in elderly patients at practice level. Our aim was to test computerized algorithms for applying these criteria to a medical record database. METHODS: STOPP/START criteria-based computerized algorithms were defined using Anatomical-Therapeutic-Chemical (ATC) codes for medication and International Classification of Primary Care (ICPC) codes for diagnoses. The algorithms were applied to a Dutch primary care database, including patients aged ≥65 years using ≥5 chronic drugs. We tested for associations with patient characteristics that have previously shown a relationship with the original STOPP/START criteria, using multivariate logistic regression models. RESULTS: Included were 1187 patients with a median age of 75 years. In total, 39 of the 62 STOPP and 18 of the 26 START criteria could be converted to a computerized algorithm. The main reasons for inapplicability were lack of information on the severity of a condition and insufficient covering of ICPC-codes. We confirmed a positive association between the occurrence of both the STOPP and the START criteria and the number of chronic drugs (adjusted OR ranging from 1.37, 95% CI 1.04-1.82 to 3.19, 95% CI 2.33-4.36) as well as the patient's age (adjusted OR for STOPP 1.30, 95% CI 1.01-1.67; for START 1.73, 95% CI 1.35-2.21), and also between female gender and the occurrence of STOPP criteria (adjusted OR 1.41, 95% CI 1.09-1.82). CONCLUSION: Sixty-five percent of the STOPP/START criteria could be applied with computerized algorithms to a medical record database with ATC-coded medication and ICPC-coded diagnoses.
PURPOSE: The STOPP/START criteria are increasingly used to assess prescribing quality in elderly patients at practice level. Our aim was to test computerized algorithms for applying these criteria to a medical record database. METHODS: STOPP/START criteria-based computerized algorithms were defined using Anatomical-Therapeutic-Chemical (ATC) codes for medication and International Classification of Primary Care (ICPC) codes for diagnoses. The algorithms were applied to a Dutch primary care database, including patients aged ≥65 years using ≥5 chronic drugs. We tested for associations with patient characteristics that have previously shown a relationship with the original STOPP/START criteria, using multivariate logistic regression models. RESULTS: Included were 1187 patients with a median age of 75 years. In total, 39 of the 62 STOPP and 18 of the 26 START criteria could be converted to a computerized algorithm. The main reasons for inapplicability were lack of information on the severity of a condition and insufficient covering of ICPC-codes. We confirmed a positive association between the occurrence of both the STOPP and the START criteria and the number of chronic drugs (adjusted OR ranging from 1.37, 95% CI 1.04-1.82 to 3.19, 95% CI 2.33-4.36) as well as the patient's age (adjusted OR for STOPP 1.30, 95% CI 1.01-1.67; for START 1.73, 95% CI 1.35-2.21), and also between female gender and the occurrence of STOPP criteria (adjusted OR 1.41, 95% CI 1.09-1.82). CONCLUSION: Sixty-five percent of the STOPP/START criteria could be applied with computerized algorithms to a medical record database with ATC-coded medication and ICPC-coded diagnoses.
Authors: Dee Mangin; Jennifer Lawson; Jessica Cuppage; Elizabeth Shaw; Katalin Ivanyi; Amie Davis; Cathy Risdon Journal: Ann Fam Med Date: 2018-11 Impact factor: 5.166
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Authors: Bastiaan T G M Sallevelt; Corlina J A Huibers; Jody M J Op Heij; Toine C G Egberts; Eugène P van Puijenbroek; Zhengru Shen; Marco R Spruit; Katharina Tabea Jungo; Nicolas Rodondi; Olivia Dalleur; Anne Spinewine; Emma Jennings; Denis O'Mahony; Ingeborg Wilting; Wilma Knol Journal: Drugs Aging Date: 2021-12-08 Impact factor: 3.923