Background: This study aimed to investigate the level of guideline adherence for cardiometabolic health monitoring for patients prescribed antipsychotic medicines in UK primary care.
Methods: In this population-based retrospective open cohort study, we used dataset of patients from the IQVIA Medical Research Data (IMRD) database between 1st January 2003 to 31st December 2018. Clinical Read codes were used to identify a cohort of adult patients with a diagnosis of Schizophrenia and at least four prescriptions of an anti-psychotic medication within 12 months of diagnosis. We then extracted data in relation to monitoring of cardiometabolic parameters (body compositions, lipids, and glucose outcomes) at baseline, then at six weeks, 12 weeks, and then 12 months. The frequency of outcome monitoring was described using descriptive statistics.
Findings: A total of 11,435 patients were eligible and of them (n = 9707; 84·8%) were prescribed second-generation antipsychotics (SGAs). Only a small portion of the cohort (≈2·0%) received complete monitoring (at time points) for certain outcomes. Just over half the patients (n = 6599, 52%) had evidence of any cardiometabolic baseline testing for any of the study outcomes and the high majority had at least one abnormal lab value at baseline (n = 4627, 96·7%).
Interpretation: In UK primary care, cardiometabolic monitoring practices among patients prescribed antipsychotics remain suboptimal. There is a need to promote guideline adherence to prevent adverse outcomes in antipsychotic users.
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http://dx.doi.org/10.1016/j.comppsych.2023.152419 | DOI Listing |
Pharmazie
December 2024
Department of Hospital Pharmaceutics, School of Pharmacy, Showa University, Tokyo, Japan.
This study aimed to determine the risk of emergency admission by ambulance in patients taking potentially inappropriate medications (PIMs). We included 273,932 patients aged over 75 years of age admitted between January 1, 2019, and December 31, 2019, using the Japan Medical Data Center medical insurance database containing anonymized patient data. We excluded patients without a history of admission.
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Maccabi Healthcare Services, Tel Aviv, 6812509, Israel.
Urinary tract infections (UTIs) often prompt empiric outpatient antibiotic prescriptions, risking mismatches. This study evaluates the impact of "UTI Smart-Set" (UTIS), an AI-driven decision-support tool, on prescribing patterns and mismatches in a large outpatient organization. UTIS integrates machine learning forecasts of antibiotic resistance, patient data, and guidelines into a user-friendly order set for UTI management.
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Growing evidence of the benefits of home ventilatory support in patients with chronic respiratory failure along with technological advances in ventilators have enabled their use in overly complex situations, shaping a new scenario for physicians. This has further given rise to new challenges related to their incorporation into current medical practice. However, this evolution needs to be coupled with knowledge and skills of physicians who are willing to prescribe Home Mechanical Ventilation (HMV), in order to prevent them from making inappropriate choices or adjustments that may ultimately have ethical and legal implications.
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†Division of Geriatrics, Department of Internal Medicine, Marmara University Medical School, Pendik, Istanbul, Turkey.
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Health Sciences North Research Institute, Sudbury, ON, Canada.
Background: Opioid Agonist Treatment (OAT) is the most effective intervention for opioid use disorder (OUD), but retention has decreased due to increasingly potent drugs like fentanyl. This cohort can be used retrospectively to observe trends in service utilization, healthcare integration, healthcare costs and patient outcomes. It also facilitates the design of observational studies to mimic a prospective design.
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