The association between atrial fibrillation and dementia: A UK linked electronic health records cohort study.

BACKGROUND: We investigated the association between atrial fibrillation (AF) and dementia, and its subtypes (vascular-VaD, Alzheimer, mixed and rare dementia), and identified predictors for dementia in AF patients. METHODS: The analysis was based on 183,610 patients with new-onset AF and 367,220 non-AF controls in the United Kingdom between 1998 and 2016, identified in three prospectively collected, linked electronic health records sources. Time-to-event (dementia or subtypes) analyses were performed using Cox proportional hazards and weighted Cox. Sub-analyses performed: including & censoring stroke and age (median used as cut-off). RESULTS: Over a median follow-up of 2.67 years (IQR .65-6.02) for AF patients and 5.84 years for non-AF patients (IQR 2.26-11.80), incidence of dementia in the AF cohort was 2.65 per 100 person-years, compared to 2.02 in the non-AF cohort. After adjustment, a significant association was observed between AF and all-cause dementia (HR = 1.38, 95% CI: 1.31-1.45), driven by a strong association with VaD (HR = 1.55, 95% CI: 1.41-1.70). AF was also associated with mixed dementia (HR = 1.26, 95% CI: 1.01-1.56), but we could not confirm an association with Alzheimer (HR = 1.05, 95% CI: .94-1.16) and rare dementia forms (HR = 1.19, 95% CI: .90-1.56). Ischemic stroke (HR = 1.40, 95% CI: 1.26-1.56), subarachnoid haemorrhage (HR = 2.08, 95% CI: 1.47-2.96), intracerebral haemorrhage (HR = 1.95, 95% CI: 1.54-2.48) and diabetes (HR = 1.32, 95% CI: 1.24-1.41) were identified as the strongest predictors of dementia in AF patients. CONCLUSIONS: AF patients have an increased risk of dementia, independent of stroke, with highest risk of VaD. Management and prevention of the identified risk factors could be crucial to reduce the increasing burden of dementia.

What can we learn from supermarkets? An application of the Poisson distribution (order-up-to) model to improve blood culture bottle supplies.

BACKGROUND: The order-up-to inventory model is a method for identifying and maintaining the optimal product level for items that that hold value over time and have recurring demand. Typically, supermarkets utilise it to manage stock-levels of non-perishable goods. LOCAL PROBLEM: This project aimed to improve blood culture bottle supplies following reports by junior doctors of shortages when dealing with septic/unwell patients. METHODS: Data regarding blood culture bottle use was acquired from four hospitals within one trust in London (245 wards). The mathematical 'order-up-to' inventory model (an iterative Poisson distribution) was applied to the 6 months' data. INTERVENTIONS: The model found three predictable levels of demand to stock wards with no shortages in 99.3% of circumstances (based on historical data). Wards were stocked with blood culture bottles as per their required demand. RESULTS: A collection method and infrastructure was designed to implement the new policy and was applied to a London tertiary centre. A review of doctors, nurses and ward-managers found significant improvements in supply with no shortages since the model was applied. Issues with the dataset were identified for intensive therapy unit / high dependency unit. CONCLUSIONS: The 'order-up-to' inventory model provides a useful tool within hospitals for improving stock levels of blood cultures bottles and with that the satisfaction of trainees and patient safety.