The effect of diabetes on abdominal aortic aneurysm growth - updated systematic review and meta-analysis.

Studies have shown that diabetes mellitus is associated with a reduced prevalence and growth of abdominal aortic aneurysms (AAA). Establishing the factors that influence AAA growth will enable us to risk stratify patients and potentially optimise management. We aimed to provide an updated systematic review and meta-analysis that would inform more targeted screening practices based on patient demographics. MEDLINE, EMBASE, and DARE were searched using the Ovid interface and PubMed search engine. Studies were deemed eligible if they compared the growth rate of AAA between diabetic and non-diabetic populations. The mean difference (MD) and 95% confidence internal (CI) was used for data synthesis. Twenty-four studies from 20 articles with a total of 10,121 participants were included in our meta-analysis. An overall negative effect was shown between AAA growth and diabetes, with an annual mean effect of -0.25 mm/year (95% CI -0.35, -0.15; I2 = 73%). Our meta-analysis, which is larger and scientifically more robust compared to previous analyses, has confirmed that diabetes reduces the growth of AAA by approximately 0.25 mm a year compared to non-diabetic populations. This could have significant implications for AAA screening practices.

Predicting Readmission to Intensive Care After Cardiac Surgery Within Index Hospitalization: A Systematic Review.

Readmission to the cardiac intensive care unit after cardiac surgery has significant implications for both patients and healthcare providers. Identifying patients at risk of readmission potentially could improve outcomes. The objective of this systematic review was to identify risk factors and clinical prediction models for readmission within a single hospitalization to intensive care after cardiac surgery. PubMed, MEDLINE, and EMBASE databases were searched to identify candidate articles. Only studies that used multivariate analyses to identify independent predictors were included. There were 25 studies and five risk prediction models identified. The overall rate of readmission pooled across the included studies was 4.9%. In all 25 studies, in-hospital mortality and duration of hospital stay were higher in patients who experienced readmission. Recurring predictors for readmission were preoperative renal failure, age >70, diabetes, chronic obstructive pulmonary disease, preoperative left ventricular ejection fraction <30%, type and urgency of surgery, prolonged cardiopulmonary bypass time, prolonged postoperative ventilation, postoperative anemia, and neurologic dysfunction. The majority of readmissions occurred due to respiratory and cardiac complications. Four models were identified for predicting readmission, with one external validation study. As all models developed to date had limitations, further work on larger datasets is required to develop clinically useful models to identify patients at risk of readmission to the cardiac intensive care unit after cardiac surgery.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.