Differential effect of Charlson index and its components on in-hospital mortality and health costs

Introduction: The Charlson index (Charlson, 1987) is a commonly used comorbidity index in outcome studies. Still, the use of different weights makes its calculation cumbersome, while the sum of its components (comorbidities) is easier to compute. In this study, we assessed the effects of 1) the Charlson index adapted for the Swiss population and 2) the sum of its components (number of comorbidities, maximum 15) on a) in-hospital deaths and b) cost of hospitalization. Methods: Anonymous data was obtained from the administrative database of the department of internal medicine of the Lausanne University Hospital (CHUV). All hospitalizations of adult (>=18 years) patients occurring between 2003 and 2011 were included. For each hospitalization, the Charlson index and the number of comorbidities were calculated. Analyses were conducted using Stata. Results: Data from 32,741 hospitalizations occurring between 2003 and 2011 was analyzed. On bivariate analysis, both the Charlson index and the number of comorbidities were significantly and positively associated with in hospital death. Conversely, multivariate adjustment for age, gender and calendar year using Cox regression showed that the association was no longer significant for the number of comorbidities (table). On bivariate analysis, hospitalization costs increased both with Charlson index and with number of comorbidities, but the increase was much steeper for the number of comorbidities (figure). Robust regression after adjusting for age, gender, calendar year and duration of hospital stay showed that the increase in one comorbidity led to an average increase in hospital costs of 321 CHF (95% CI: 272 to 370), while the increase in one score point of the Charlson index led to a decrease in hospital costs of 49 CHF (95% CI: 31 to 67). Conclusion: Charlson index is better than the number of comorbidities in predicting in-hospital death. Conversely, the number of comorbidities significantly increases hospital costs.

Sparse regularization for fiber ODF reconstruction: from the suboptimality of ℓ2 and ℓ1 priors to ℓ0.

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a large variety of methods have been recently proposed to shorten the acquisition times. Among them, spherical deconvolution approaches have gained a lot of interest for their ability to reliably recover the intra-voxel fiber configuration with a relatively small number of data samples. To overcome the intrinsic instabilities of deconvolution, these methods use regularization schemes generally based on the assumption that the fiber orientation distribution (FOD) to be recovered in each voxel is sparse. The well known Constrained Spherical Deconvolution (CSD) approach resorts to Tikhonov regularization, based on an ℓ(2)-norm prior, which promotes a weak version of sparsity. Also, in the last few years compressed sensing has been advocated to further accelerate the acquisitions and ℓ(1)-norm minimization is generally employed as a means to promote sparsity in the recovered FODs. In this paper, we provide evidence that the use of an ℓ(1)-norm prior to regularize this class of problems is somewhat inconsistent with the fact that the fiber compartments all sum up to unity. To overcome this ℓ(1) inconsistency while simultaneously exploiting sparsity more optimally than through an ℓ(2) prior, we reformulate the reconstruction problem as a constrained formulation between a data term and a sparsity prior consisting in an explicit bound on the ℓ(0)norm of the FOD, i.e. on the number of fibers. The method has been tested both on synthetic and real data. Experimental results show that the proposed ℓ(0) formulation significantly reduces modeling errors compared to the state-of-the-art ℓ(2) and ℓ(1) regularization approaches.