Presenting Turkish Inappropriate Medication Use in the Elderly (TIME) Criteria Set in Turkish

[Abstract Not Available

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques

Objective: This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. Methods: Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. Results: The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (-0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (-0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (-0.12), glucose (-0.12), hemoglobin A1c (-0.12), uric acid (-0.09) and creatinine (-0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. Conclusion: Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models

Presenting Turkish Inappropriate Medication use in the elderly (TIME) criteria set in Turkish

Older adults are mostly exposed to polypharmacy and inappropriate medication use (IMU) due to the increasing incidence of chronic diseases and geriatric syndromes with aging. Polypharmacy and IMU use are well-known risk factors for adverse drug reactions (1,2)

Turkish inappropriate medication use in the elderly (TIME) criteria to improve prescribing in older adults: TIME-to-STOP/TIME-to-START

Key summary pointsAim To meet the current need in different European countries for improving prescribing in older adults, we aimed to create an update screening tool getting origin from the two user friendly criterion sets: the STOPP/STARTv2 criteria and CRIME criteria. Findings Based on thorough literature review, 55 criteria were added, 17 criteria were removed, and 60 criteria were modified. As a result, 153 TIME criteria composed of 112 TIME-to-STOP and 41 TIME-to-START criteria were introduced. Message TIME criterion set is an update screening tool reported from Eastern Europe that included experts from geriatrics and other specialties frequently giving care to older adults and some additional practical explanations for clinical use. Purpose To improve prescribing in older adults, criterion sets have been introduced from different countries. While current criterion sets are useful to some extent, they do not meet the need in some European countries. Turkish inappropriate medication use in the elderly (TIME) criteria was planned to meet this need. Methods In phase 1, the user friendly sets: STOPP/START version2 and CRIME criteria were combined. National experts composed of geriatricians and non-geriatricians were invited to review and comment. In phase 2, thorough literature review was performed and reference-based revisions, omissions, and additions were made. Explanatory additions were added to some criteria to improve application in practice. In phase 3, all working group members reviewed the criteria/explanations and agreed on the final content. Results Phase 1 was performed by 49 expert academicians between May and October 2016. Phase 2 was performed by 23 working group academicians between October 2016 and November 2018 and included face-to-face interviews between at least two geriatrician members and one criterion-related specialist. Phase 3 was completed between November 2018-March 2019 with review and approval of all criteria by working group academicians. As a result, 55 criteria were added, 17 criteria were removed, and 60 criteria were modified from the first draft. A total of 153 TIME criteria composed of 112 TIME-to-STOP and 41 TIME-to-START criteria were introduced. Conclusion TIME criteria is an update screening tool that differs from the current useful tools by the interactive study of experts from geriatrics and non-geriatrics, inclusion of practical explanations for some criteria and by its eastern European origin. TIME study respectfully acknowledges its roots from STOPP/START and CRIME criteria. Studies are needed whether it would lead improvements in older adults' health

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

BackgroundAdverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population.MethodsA multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases.FindingsSubstantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism.InterpretationOur findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term.FundingNone