Martin Wawruch1, Gejza Wimmer2, Jan Murin3, Martina Paduchova4, Tomas Tesar5, Lubica Hlinkova6, Peter Slavkovsky7, Lubomira Fabryova8,9, Emma Aarnio10,11. 1. Institute of Pharmacology and Clinical Pharmacology, Faculty of Medicine, Comenius University, Sasinkova 4, 811 08, Bratislava, Slovakia. martin.wawruch@gmail.com. 2. Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia. 3. 1st Department of Internal Medicine, Faculty of Medicine, Comenius University, Bratislava, Slovakia. 4. Department of Angiology, Health Centre, Trnava, Slovakia. 5. Department of Organisation and Management of Pharmacy, Faculty of Pharmacy, Comenius University, Odbojarov 10, 832 32, Bratislava, Slovakia. tesar@fpharm.uniba.sk. 6. General Health Insurance Company, Bratislava, Slovakia. 7. Institute of Pathophysiology, Faculty of Medicine, Comenius University, Bratislava, Slovakia. 8. Department of Diabetes and Metabolic Disorders, MetabolKLINIK, Bratislava, Slovakia. 9. Biomedical Research Centre, Slovak Academy of Sciences, Bratislava, Slovakia. 10. Institute of Biomedicine, University of Turku, Turku, Finland. 11. School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
Abstract
BACKGROUND AND OBJECTIVES: Secondary prevention of peripheral arterial disease includes administration of statins regardless of the patient's serum cholesterol level. Our study aimed to identify patient-associated risk factors for statin non-persistence and comparison of the explanatory power of models based on clusters of patient-associated characteristics. METHODS: Our study cohort (n = 8330) was assembled from the database of the largest health insurance provider in the Slovak Republic. Statin users aged ≥ 65 years in whom peripheral arterial disease was diagnosed during 2012 were included. Patients were followed for 5 years; those with a treatment gap period of at least 6 months without statin prescription were classified as "non-persistent". The risk factors for non-persistence were identified within six models (sociodemographic, cardiovascular events, comorbid conditions, statin-related characteristics, cardiovascular co-medication and full model) using Cox regression. The explanatory power of models was assessed using Harrell's C-index. RESULTS: At the end of the follow-up, 35.7% of patients were found to be non-persistent. The full model had the highest explanatory power (C = 0.632). Female sex, atorvastatin and rosuvastatin as initially administered statins, being a new statin user and an increasing co-payment were associated with an increased risk for non-persistence. Increasing age, history of ischaemic stroke, diabetes mellitus, general practitioner as index prescriber, increasing overall number of medications and co-administration of certain cardiovascular co-medications were associated with a lower likelihood for non-persistence. CONCLUSIONS: Patients identified as high risk for non-persistence require special attention aimed at the improvement of their persistence with statin treatment.
BACKGROUND AND OBJECTIVES: Secondary prevention of peripheral arterial disease includes administration of statins regardless of the patient's serum cholesterol level. Our study aimed to identify patient-associated risk factors for statin non-persistence and comparison of the explanatory power of models based on clusters of patient-associated characteristics. METHODS: Our study cohort (n = 8330) was assembled from the database of the largest health insurance provider in the Slovak Republic. Statin users aged ≥ 65 years in whom peripheral arterial disease was diagnosed during 2012 were included. Patients were followed for 5 years; those with a treatment gap period of at least 6 months without statin prescription were classified as "non-persistent". The risk factors for non-persistence were identified within six models (sociodemographic, cardiovascular events, comorbid conditions, statin-related characteristics, cardiovascular co-medication and full model) using Cox regression. The explanatory power of models was assessed using Harrell's C-index. RESULTS: At the end of the follow-up, 35.7% of patients were found to be non-persistent. The full model had the highest explanatory power (C = 0.632). Female sex, atorvastatin and rosuvastatin as initially administered statins, being a new statin user and an increasing co-payment were associated with an increased risk for non-persistence. Increasing age, history of ischaemic stroke, diabetes mellitus, general practitioner as index prescriber, increasing overall number of medications and co-administration of certain cardiovascular co-medications were associated with a lower likelihood for non-persistence. CONCLUSIONS:Patients identified as high risk for non-persistence require special attention aimed at the improvement of their persistence with statin treatment.
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