Immature platelet fraction as predictive index of sepsis

Introduction The incidence of sepsis is reported around 37% in European ICUs [1]. The mortality rate depends on the severity of organ failure, up to 65% if four or more organs are involved. Multiple organ failure (MOF) is due to microcirculatory dysfunction with microthrombosis resulting from coagulation disorders including platelets’ activation. An early diagnosis should identify the microcirculatory dysfunction before MOF became clinically evident. The diagnosis of sepsis is commonly based on clinical criteria, pathogen identifi cation and use of markers like procalcitonin (PCT) and C-reactive protein (PCR) associated with infection. The aim of our study is to evaluate whether the routine measurement of immature platelet fraction (IPF), considered a precocious marker of platelet production, is associated with sepsis and its severity and/or whether it could be used as a predicting marker of sepsis. Methods We enrolled 66 consecutive patients admitted to the ICU, dividing them into two groups: septic (n = 44) and no septic (n = 22). The severity of sepsis was evaluated. The exclusion criterion was a platelet count <150,000/mm3. Blood count, coagulation, PCR, PCT, and IPF were collected every day. Results The IPF values between septic (4.6 ± 3.1) and no septic patients (3.3 ± 1.5) did not diff er (P = 0.16). No correlation was found between IPF values and the severity of septic condition (no sepsis 11.7 ± 10.1; sepsis 14.3 ± 10.5; severe sepsis 10.5 ± 9.1; septic shock 19.5 ± 12.4; P = 0.3). When we considered only subjects who did not have sepsis at the ICU admission we found that patients who developed sepsis during the recovery had IPF values higher than patients who did not develop sepsis (Table 1). Conclusions From our results IPF cannot be considered a marker of sepsis. Conversely it could be used as predictive index of sepsis because it can identify patients who will develop sepsis. References 1. Vincent et al.: Sepsis in European intensive care units: results of the SOAP study. Intensive Care Med 2006, 34:344-353

On the inferential implications of decreasing weight structures in mixture models

Bayesian estimation of nonparametric mixture models strongly relies on available representations of discrete random probability measures. In particular, the order of the mixing weights plays an important role for the identifiability of component-specific parameters which, in turn, affects the convergence properties of posterior samplers. The geometric process mixture model provides a simple alternative to models based on the Dirichlet process that effectively addresses these issues. However, the rate of decay of the mixing weights for this model may be too fast for modeling data with a large number of components. The need for different decay rates arises. Some variants of the geometric process featuring different decay behaviors, while preserving the decreasing structure, are presented and investigated. An asymptotic characterization of the number of distinct values in a sample from the corresponding mixing measure is also given, highlighting the inferential implications of different prior specifications. The analysis is completed by a simulation study in the context of density estimation. It shows that by controlling the decaying rate, the mixture model is able to capture data with a large number of components

Early pathological gambling in co-occurrence with semantic variant primary progressive aphasia: A case report

We have comprehensively documented a case of semantic variant of primary progressive aphasia (sv-PPA) presenting with early-onset pathological gambling (PG). While a growing number of studies have shown the presence of behavioral alterations in patients with sv-PPA, PG has been observed only in the behavioral variant of frontotemporal dementia (bv-FTD). To date, no case of PG with the co-occurrence of prominent semantic deficits at the onset of the disease has been reported in the literature. Impulse disorders at onset may wrongly lead to a misdiagnosis (ie, psychiatric disorders). Therefore, a wider characterization of cognitive/aphasia symptoms in patients presenting impulse disorders and predominant language dysfunctions is recommended

The prognostic importance of chronic end-stage diseases in geriatric patients admitted to 163 Italian ICUs

BACKGROUND: The number of elderly patients undergoing major surgical interventions and then needing admission to intensive care unit (ICU) grows steadily. We investigated this issue in a cohort of 232,278 patients admitted in five years (2011-2015) to 163 Italian general ICUs. METHODS: Surgical patients older than 75 registered in the GiViTI MargheritaPROSAFE project were analyzed. The impact on hospital mortality of important chronic conditions (severe COPD, NYHA class IV, dementia, end-stage renal disease, cirrhosis with portal hypertension) was investigated with two prognostic models developed yearly on patients staying in the ICU less or more than 24 hours. RESULTS: 44,551 elderly patients (19.2%) underwent emergency (47.3%) or elective surgery (52.7%). At least one severe comorbidity was present in 14.6% of them, yielding a higher hospital mortality (32.4%, vs. 21.1% without severe comorbidity). In the models for patients staying in the ICU 24 hours or more, cirrhosis, NYHA class IV, and severe COPD were constant independent predictors of death (adjusted odds ratios [ORs] range 1.67-1.97, 1.54-1.91, and 1.34-1.50, respectively), while dementia was statistically significant in four out of five models (adjusted ORs 1.23-1.28). End-stage renal disease, instead, never resulted to be an independent prognostic factor. For patients staying in the ICU less than 24 hours, chronic comorbidities were only occasionally independent predictors of death. CONCLUSIONS: Our study confirms that elderly surgical patients represent a relevant part of all ICUs admissions. About one of seven bear at least one severe chronic comorbidity, that, excluding end-stage renal disease, are all strong independent predictors of hospital death

Generalized Bayesian Record Linkage and Regression with Exact Error Propagation

Record linkage (de-duplication or entity resolution) is the process of merging noisy databases to remove duplicate entities. While record linkage removes duplicate entities from such databases, the downstream task is any inferential, predictive, or post-linkage task on the linked data. One goal of the downstream task is obtaining a larger reference data set, allowing one to perform more accurate statistical analyses. In addition, there is inherent record linkage uncertainty passed to the downstream task. Motivated by the above, we propose a generalized Bayesian record linkage method and consider multiple regression analysis as the downstream task. Records are linked via a random partition model, which allows for a wide class to be considered. In addition, we jointly model the record linkage and downstream task, which allows one to account for the record linkage uncertainty exactly. Moreover, one is able to generate a feedback propagation mechanism of the information from the proposed Bayesian record linkage model into the downstream task. This feedback effect is essential to eliminate potential biases that can jeopardize resulting downstream task. We apply our methodology to multiple linear regression, and illustrate empirically that the "feedback effect" is able to improve the performance of record linkage.Comment: 18 pages, 5 figure

A New Strategy for Treatment of a Congenital Arteriovenous Fistula of the Neck. Case Report

AbstractCongenital arteriovenous fistulas (AVF) without associated vascular malformations are uncommon. Only a very few cases of AVF have been reported in the neck. We describe our findings in a patient with AVF treated by a combined vascular and endovascular approach

Association between structural connectivity and generalized cognitive spectrum in alzheimer’s disease

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7)