RESUMEN
BACKGROUND: The UK introduced financial incentives for management of atrial fibrillation (AF) in 2006, after which there was an increase in the proportion of patients with AF diagnosed as resolved. Removal of incentives in Scotland provides a natural experiment to investigate the effects of withdrawal of an incentive on diagnosis of resolved AF. AIM: To investigate the effects of introduction and withdrawal of financial incentives on the diagnosis of resolved AF. DESIGN & SETTING: Cohort study in a large database of UK primary care records, before and after introduction of incentives in April 2006 in Scotland, England, and Northern Ireland, and their withdrawal in April 2016 in Scotland. METHOD: Interrupted time-series analysis of monthly rates of resolved AF from January 2000-September 2019. RESULTS: A total of 251 526 adult patients with AF were included, of whom 14 674 were diagnosed as resolved AF. In April 2006 there were similar shift-changes in rates of resolved AF per 1000 in England 1.55 (95% confidence interval [CI] = 1.11 to 2.00) and Northern Ireland 1.54 (95% CI = 0.91 to 2.18), and a smaller increase in Scotland 0.79 (95% CI = 0.04 to 1.53). There were modest downward post-introduction trends in all countries. After Scotland's withdrawal of the incentive in April 2016 there was a small, statistically non-significant, downward shift in rate of resolved AF per 1000 (0.39 [95% CI = -3.21 to 2.42]) and no change in post-removal trend. CONCLUSION: Introduction of a financial incentive coincided with an increase in resolved AF but no evidence was found that its withdrawal led to a reduction.
RESUMEN
OBJECTIVES: Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score. DESIGN: Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection. SETTING: Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. PARTICIPANTS: Patients with COVID-19 admitted to UHB January-August 2020 were included. MAIN OUTCOME MEASURES: Death and ITU admission within 28 days of admission. RESULTS: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785). CONCLUSIONS: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.