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- Danielsson, Bengt, et al.
(författare)
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Antidepressants and antipsychotics classified with torsades de pointes arrhythmia risk and mortality in older adults - a Swedish nationwide study
- 2016
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Ingår i: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 81:4, s. 773-783
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Tidskriftsartikel (refereegranskat)abstract
- AimThe aim of the study was to examine mortality risk associated with use of antidepressants and antipsychotics classified with torsades de pointes (TdP) risk in elderly. MethodsA matched case-control register study was conducted in people 65 years and older dying outside hospital from 2008-2013 (n=286092) and matched controls (n=1430460). The association between prescription of antidepressants and antipsychotics with various TdP risk according to CredibleMeds () and all-cause mortality was studied by multivariate conditional logistic regression adjusted for comorbidity and several other confounders. ResultsUse of antidepressants classified with known or possible TdP risk, was associated with higher adjusted risk for mortality (OR 1.53, 95% CI 1.51, 1.56 and OR 1.63, 95% CI 1.61, 1.67, respectively) compared with antidepressants classified with conditional TdP risk (OR 1.25, 95% CI 1.22, 1.28) or without TdP classification (OR 0.99, 95% CI 0.94, 1.05). Antipsychotics classified with known TdP risk were associated with higher risk (OR 4.57, 95% CI 4.37, 4.78) than antipsychotics with possible risk (OR 2.58, 95% CI 2.52, 2.64) or without TdP classification (OR 2.14, 95% CI 2.03, 2.65). The following risk ranking was observed for commonly used antidepressants: mirtazapine > citalopram > sertraline > amitriptyline and for antipsychotics: haloperidol > risperidone >olanzapine > quetiapine. ConclusionThe CredibleMeds system predicted drug-associated risk for mortality in the elderly at the risk class level. Among antipsychotics, haloperidol, and among antidepressants, mirtazapine and citalopram, were associated with the highest risks. The results suggest that the TdP risk with antidepressants and antipsychotics should be taken into consideration when prescribing to the elderly.
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| 2. |
- Johnell, Kristina, et al.
(författare)
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Psychotropic drugs and the risk of fall injuries, hospitalisations and mortality among older adults
- 2017
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Ingår i: International Journal of Geriatric Psychiatry. - : Wiley. - 0885-6230 .- 1099-1166. ; 32:4, s. 414-420
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Tidskriftsartikel (refereegranskat)abstract
- ObjectiveTo investigate whether psychotropics are associated with an increased risk of fall injuries, hospitalizations, and mortality in a large general population of older adults.MethodsWe performed a nationwide matched (age, sex, and case event day) case–control study between 1 January and 31 December 2011 based on several Swedish registers (n = 1,288,875 persons aged ≥65 years). We used multivariate conditional logistic regression adjusted for education, number of inpatient days, Charlson co-morbidity index, dementia and number of other drugs.ResultsAntidepressants were the psychotropic most strongly related to fall injuries (ORadjusted: 1.42; 95% CI: 1.38–1.45) and antipsychotics to hospitalizations (ORadjusted: 1.22; 95% CI: 1.19–1.24) and death (ORadjusted: 2.10; 95% CI: 2.02–2.17). Number of psychotropics was associated with increased the risk of fall injuries, (4 psychotropics vs 0: ORadjusted: 1.53; 95% CI: 1.39–1.68), hospitalization (4 psychotropics vs 0: ORadjusted: 1.27; 95% CI: 1.22–1.33) and death (4 psychotropics vs 0: ORadjusted: 2.50; 95% CI: 2.33–2.69) in a dose–response manner. Among persons with dementia (n = 58,984), a dose–response relationship was found between number of psychotropics and mortality risk (4 psychotropics vs 0: ORadjusted: 1.99; 95% CI: 1.76–2.25).ConclusionsOur findings support a cautious prescribing of multiple psychotropic drugs to older patients. © 2016 The Authors. International Journal of Geriatric Psychiatry Published by John Wiley & Sons, Ltd.
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| 5. |
- Ripatti, Samuli, et al.
(författare)
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GENESTAT : an information portal for design and analysis of genetic association studies
- 2009
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Ingår i: European Journal of Human Genetics. - : Springer Science and Business Media LLC. - 1018-4813 .- 1476-5438. ; 17:4, s. 533-536
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Tidskriftsartikel (refereegranskat)abstract
- We present the rationale, the background and the structure for version 2.0 of the GENESTAT information portal (www.genestat.org) for statistical genetics. The fast methodological advances, coupled with a range of standalone software, makes it difficult for expert as well as non-expert users to orientate when designing and analysing their genetic studies. The ultimate ambition of GENESTAT is to guide on statistical methodology related to the broad spectrum of research in genetic epidemiology. GENESTAT 2.0 focuses on genetic association studies. Each entry provides a summary of a topic and gives links to key papers, websites and software. The flexibility of the internet is utilised for cross-referencing and for open editing. This paper gives an overview of GENESTAT and gives short introductions to the current main topics in GENESTAT, with additional entries on the website. Methods and software developers are invited to contribute to the portal, which is powered by a Wikipedia-type engine and allows easy additions and editing.
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| 7. |
- Törnblom, Michael, et al.
(författare)
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Extracting Dosage Per Day From Free-Text Medication Prescriptions
- 2016
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Ingår i: Value in Health. - : Elsevier BV. - 1098-3015 .- 1524-4733. ; 19:7, s. A393-A393
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Tidskriftsartikel (refereegranskat)abstract
- Objectives The Swedish prescribed drug register contains dose instructions as written by the physician. A challenge is to convert the text into a number of doses per day which can be used to calculate duration of treatment. The objective of this study is to compare algorithms for named entity recognition to extract dosage per day. Methods Two sequence models, Hidden Markov Model (HMM) and Conditional Random Fields (CRF), were used to predict label sequences. The HMM and CRF were compared using different measures of prediction: precision, recall, F-score and accuracy. We also evaluated how prediction was effected by including more labels and features; for CRF models we used 12 labels for both models with 2 and 11 feature types respectively, for HMM models we used 12, 15 and 18 labels respectively. Using the predicted labels, a rule-based algorithm was used to predict dosage per day. Prediction of dosage per day was evaluated using accuracy. Results Label prediction: As expected, increasing the number of labels/features increased the F-score. The CRF model with 11 feature types had a F-score of 0.989 compared to 0.972 using two feature types. The HMM model with 15 and 18 labels both achieved a F-score of 0.986 compared to 0.966 using 12 labels. In terms of precision and recall the performance of the CRF and HMM varied. Dosage prediction: The CRF model with 11 feature types achieved 97.2% accuracy. The HMM with 15 labels achieved a higher accuracy than with 18 labels (95.7% versus 95.5%). Conclusions The CRF had the highest accuracy in label and dosage per day prediction. The HMM model also had comparably high accuracy but was generally lower than the CRF. We recommend CRF over HMM for named entity recognition on prescription text; it is time efficient and predicts dosage per day with high accuracy.
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