Summary: Bleeding is a frequent complication after allogeneic haematopoietic stem cell transplantation (HSCT) and may affect survival. The purpose of this study was to determine the incidence and risk factors for life‐threatening bleeding after HSCT by retrospective evaluation of 491 allogeneic HSCT recipients. With a median follow‐up of 33 months, 126 out of 491 allogeneic HSCT recipients experienced a haemorrhagic event (25·7%) and 46 patients developed a life‐threatening bleeding episode (9·4%). Pulmonary and gastrointestinal bleeding were the most common sites for life‐threatening bleeding, followed by central nervous system. In multivariate analyses, the presence of severe thrombocytopenia after day +28 and the development of grade III–IV acute graft‐versus‐host disease (GVHD) or thrombotic microangiopathy (TMA) retained their association with life‐threatening bleeding events. The overall survival at 3 years among patients without bleeding was 67·1% for only 17·1% for patients with life‐threatening bleeding (P < 0·001). In conclusion, life‐threatening bleeding is a common complication after allogeneic HSCT. Prolonged severe thrombocytopenia, acute grade III–IV GVHD and TMA were associated with its development.
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.
1 October 2015 in Medicina Intensiva (English Edition)
In tonal music, musical tension is strongly associated with musical expression, particularly with expectations and emotions. Most listeners are able to perceive musical tension subjectively, yet musical tension is difficult to be measured objectively, as it is connected with musical parameters such as rhythm, dynamics, melody, harmony, and timbre. Musical tension specifically associated with melodic and harmonic motion is called tonal tension. In this article, we are interested in perceived changes of tonal tension over time for chord progressions, dubbed tonal tension profiles. We propose an objective measure capable of capturing tension profile according to different tonal music parameters, namely, tonal distance, dissonance, voice leading, and hierarchical tension. We performed two experiments to validate the proposed model of tonal tension profile and compared against Lerdahl’s model and MorpheuS across 12 chord progressions. Our results show that the considered four tonal parameters contribute differently to the perception of tonal tension. In our model, their relative importance adopts the following weights, summing to unity: dissonance (0.402), hierarchical tension (0.246), tonal distance (0.202), and voice leading (0.193). The assumption that listeners perceive global changes in tonal tension as prototypical profiles is strongly suggested in our results, which outperform the state-of-the-art models.