Elevation of serum ferritin levels for predicting a poor outcome in hospitalized patients with influenza infection

OBJECTIVES

There is increasing evidence that ferritin is a key marker of macrophage activation, but its potential role in influenza infection remains unexplored. Our aim was to assess whether hyperferritinaemia (ferritin ≥500 ng/mL) could be a marker of poor prognosis in hospitalized patients with confirmed influenza A infection.

METHODS

We prospectively recruited all hospitalized adult patients who tested positive for the influenza A rRT-PCR assay performed on respiratory samples in two consecutive influenza periods (2016-17 and 2017-18). Poor outcome was defined as the presence of at least one of the following: respiratory failure, admission to the intensive care unit, or in-hospital mortality.

RESULTS

Among 494 patients, 68 (14%) developed poor outcomes; 112 patients (23%) had hyperferritinaemia (39/68, 57% in the poor-outcome group versus 73/426, 17% in the remaining patients, p < 0.0001). Median serum ferritin levels were significantly higher in the subgroup of patients with poor outcomes (609 ng/mL, range 231-967 versus 217 ng/mL, range 140-394, p < 0.0001). In multivariate analysis, hyperferritinaemia was associated with a five-fold increase in the odds ratio of developing poor outcome. After adjusting for classic influenza risk factors, ferritin remained as a significant predictive factor in all exploratory models. Ferritin levels had a good discriminative capacity with an area under the ROC curve of 0.72 (95% confidence interval (CI) 0.65-0.8, p < 0.001) and an overall diagnostic accuracy for predicting poor outcome of 79.3% (95%CI 75.4-82.7%).

CONCLUSIONS

Serum ferritin may discriminate a subgroup of patients with influenza infection who have a higher risk of developing a poor outcome.

Energy consumption as a predictor test of the durability of a biological tissue employed in cardiac bioprosthesis

The mechanical behavior of the young bull pericardium in a fatigue test has been studied. This material is a similar tissue to those used in valve leaflet construction for a cardiac bioprosthesis. The consumed energy on each test was evaluated and afterwards used as a predictor of the biomaterial strength. Two-hundred and nine samples were tested to cyclical fatigue. The cut-off point to determine the sample quality was whether or not they resisted at least 4500 cycles. Only 22 samples withstood over that point (10.52%). The samples were classified according to their fatigue behavior in excellent, undefined and unsuitable. By using as a reference the consumed energy in the first 25 cycles, we could distinguish correctly (between 93.2 and 96.1%) the unsuitable material and most of the excellent (between 78.1 and 95.2%). From the rejected material 77% was really detachable and from the accepted, only 50% was excellent, with an equal methodology. The receiver operating characteristics curve was employed to establish decision levels when selecting samples, being 0.85 the best area (theoretical maximum value of 1). It is concluded that the energy wasted is a good predictor of the strength of the tissue. More than 90% of the unsuitable material and 50% of the excellent material (5% of all the material) is detected with this method.