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Objective: To derive and validate a clinical prediction model to estimate the risk of liver disease diagnosis following liver function tests (LFTs) and to convert the model to a simplified scoring tool for use in primary care. Design: Population-based observational cohort study of patients in Tayside Scotland identified as having their LFTs performed in primary care and followed for 2 years. Biochemistry data were linked to secondary care, prescriptions and mortality data to ascertain baseline characteristics of the derivation cohort. A separate validation cohort was obtained from 19 general practices across the rest of Scotland to externally validate the final model. Setting: Primary care, Tayside, Scotland. Participants: Derivation cohort: LFT results from 310 511 patients. After exclusions (including: patients under 16 years, patients having initial LFTs measured in secondary care, bilirubin >35 μmol/L, liver complications within 6 weeks and history of a liver condition), the derivation cohort contained 95 977 patients with no clinically apparent liver condition. Validation cohort: after exclusions, this cohort contained 11 653 patients. Primary and secondary outcome measures: Diagnosis of a liver condition within 2 years. Results: From the derivation cohort (n=95 977), 481 (0.5%) were diagnosed with a liver disease. The model showed good discrimination (C-statistic=0.78). Given the low prevalence of liver disease, the negative predictive values were high. Positive predictive values were low but rose to 20-30% for high-risk patients. Conclusions: This study successfully developed and validated a clinical prediction model and subsequent scoring tool, the Algorithm for Liver Function Investigations (ALFI), which can predict liver disease risk in patients with no clinically obvious liver disease who had their initial LFTs taken in primary care. ALFI can help general practitioners focus referral on a small subset of patients with higher predicted risk while continuing to address modifiable liver disease risk factors in those at lower risk. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to examine community antibiotic prescribing across a complete geographical area for people with a positive COVID-19 test across three pandemic waves, and to examine health and demographic factors associated with antibiotic prescribing., Design: A population-based study using administrative data., Setting: A complete geographical region within Scotland, UK., Participants: Residents of two National Health Service Scotland health boards with SARS-CoV-2 virus test results from 1 February 2020 to 31 March 2022 (n=184 954). Individuals with a positive test result (n=16 025) had data linked to prescription and hospital admission data ±28 days of the test, general practice data for high-risk comorbidities and demographic data., Outcome Measures: The associations between patient factors and the odds of antibiotic prescription in COVID-19 episodes across three pandemic waves from multivariate binary logistic regression., Results: Data included 768 206 tests for 184 954 individuals, identifying 16 240 COVID-19 episodes involving 16 025 individuals. There were 3263 antibiotic prescriptions ±28 days for 2395 episodes. 35.6% of episodes had a prescription only before the test date, 52.3% of episodes after and 12.1% before and after. Antibiotic prescribing reduced over time: 20.4% of episodes in wave 1, 17.7% in wave 2 and 12.0% in wave 3. In multivariate logistic regression, being female (OR 1.31, 95% CI 1.19 to 1.45), older (OR 3.02, 95% CI 2.50 to 3.68 75+ vs <25 years), having a high-risk comorbidity (OR 1.45, 95% CI 1.31 to 1.61), a hospital admission ±28 days of an episode (OR 1.58, 95% CI 1.42 to 1.77) and health board region (OR 1.14, 95% CI 1.03 to 1.25, board B vs A) increased the odds of receiving an antibiotic., Conclusion: Community antibiotic prescriptions in COVID-19 episodes were uncommon in this population and likelihood was associated with patient factors. The reduction over pandemic waves may represent increased knowledge regarding COVID-19 treatment and/or evolving symptomatology., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)