Sentence examples for variability with mortality from inspiring English sources

Adequate adjustment for confounding by disease severity is needed to estimate the association of hemoglobin level variability with mortality risk.

The association of glycemic variability with mortality could be independent of critical illness, a covariate, or both.

Finally, the association of hypoglycemia with mortality was cumulative to the associations of hyperglycemia and increased glycemic variability with mortality [ 20].

Bagshaw et al. [ 26] reported an association between both hypoglycaemia and BG variability with mortality during the first 24 hours of ICU stay.

That the multivariate relationships of both mean potassium and potassium variability with mortality were as marked as those observed by others for glucose [ 11] could be explained in at least two ways.

In an analysis of the African American Study of Kidney Disease (AASK) trial, McMullan, et al. found an association between visit-to-visit BP variability with all-cause mortality and cardiovascular mortality (HR comparing highest with lowest tertile of visit-to-visit BP variability: 2.82 (95 % CI: 1.14 6.95) and 4.91 (95 % CI: 1.12 21.50), respectively) [ 20].

In this range, mortality in patients with high glycemic variability (fourth quartile with mortality of 30.1%) was 5.1 times greater than in patients with low glycemic variability (first quartile with mortality of 5.9%) [ 21].

Blood glucose variability is associated with mortality and is independent of hypoglycemia, disease severity, and comorbidities.

Since glucose variability is associated with mortality [ 6], it is unlikely that this contributes to the higher mortality in the lower glucose strata.

Hirshberg and colleagues [ 18] assessed the association of the variability of glycemia with mortality and morbidity in critically ill children admitted for more than 1 day in a pediatric ICU.

Although this observation may be partly the result of stochastic variability, the associations with mortality were all the more striking given the relatively small number of days with species data in each county (range 243 395), because most time-series studies have > 1,000 days of data (HEI 2003).