Clinical pathway intervention compliance and effectiveness when used in the treatment of patients with severe sepsis and septic shock at an Intensive Care Unit in Spain

The purpose of this quasi-experimental study was to assess levels of compliance with the intervention bundles contained in a clinical pathway used in the treatment of patients with severe sepsis and septic shock, and to analyze the pathway’s impact on survival and duration of hospital stays. We used data on 125 patients in an Intensive Care Unit, divided into a control group (N=84) and an intervention group (N=41). Levels of compliance increased from 13.1% to 29.3% in 5 resuscitation bundle interventions and from 14.3% to 22% in 3 monitoring bundle interventions. In-hospital mortality at 28 days decreased by 11.2% and the duration of hospital stay was reduced by 5 days. Although compliance was low, the intervention enhanced adherence to the instructions given in the clinical pathway and we observed a decline in mortality at 28 days and shorter hospital stays.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.