Risk Factors for Death in Children with Visceral Leishmaniasis

Visceral leishmaniasis (VL) is a deadly disease caused by a protozoan called Leishmania. It is transmitted to humans from infected animals by a sandfly bite. Most people actually manage to control the infection and do not get sick, while others develop a range of symptoms. VL impairs the production of blood components and causes the immune system to malfunction, thus anemia, bleeding, and bacterial infections often complicate the disease and can lead to death. To identify risk factors for death from VL, the authors studied 546 children in a referral center in Recife, Brazil. They looked at clinical history, physical examination and full blood counts on the assumption these could be easily assessed in peripheral health facilities. They found that the presence of fast breathing, jaundice, mucosal (e.g. gum) bleeding and bacterial infections would each increase the risk of death in three to four-fold. The presence of very low counts of neutrophils and platelets would increase the risk of death in three and 12-fold respectively. This knowledge can help clinicians to anticipate the use of antibiotics or transfusion of blood products in high risk patients, who would potentially benefit from transfer to centers with advanced life support facilities

Pre-hospital antibiotic treatment and mortality caused by invasive meningococcal disease, adjusting for indication bias

<p>Abstract</p> <p>Background</p> <p>Mortality from invasive meningococcal disease (IMD) has remained stable over the last thirty years and it is unclear whether pre-hospital antibiotherapy actually produces a decrease in this mortality. Our aim was to examine whether pre-hospital oral antibiotherapy reduces mortality from IMD, adjusting for indication bias.</p> <p>Methods</p> <p>A retrospective analysis was made of clinical reports of all patients (n = 848) diagnosed with IMD from 1995 to 2000 in Andalusia and the Canary Islands, Spain, and of the relationship between the use of pre-hospital oral antibiotherapy and mortality. Indication bias was controlled for by the propensity score technique, and a multivariate analysis was performed to determine the probability of each patient receiving antibiotics, according to the symptoms identified before admission. Data on in-hospital death, use of antibiotics and demographic variables were collected. A logistic regression analysis was then carried out, using death as the dependent variable, and pre-hospital antibiotic use, age, time from onset of symptoms to parenteral antibiotics and the propensity score as independent variables.</p> <p>Results</p> <p>Data were recorded on 848 patients, 49 (5.72%) of whom died. Of the total number of patients, 226 had received oral antibiotics before admission, mainly betalactams during the previous 48 hours. After adjusting the association between the use of antibiotics and death for age, time between onset of symptoms and in-hospital antibiotic treatment, pre-hospital oral antibiotherapy remained a significant protective factor (Odds Ratio for death 0.37, 95% confidence interval 0.15–0.93).</p> <p>Conclusion</p> <p>Pre-hospital oral antibiotherapy appears to reduce IMD mortality.</p

Predictive Models for the Diagnostic of Human Visceral Leishmaniasis in Brazil

Visceral leishmaniasis (VL) is a neglected tropical disease endemic to 65 countries, including Brazil, where the disease frequently occurs in remote locations and treatment is often performed on the basis of clinical suspicion. Predictive models based on scoring systems could be a helpful tool for the clinical management of VL. Based on clinical signs and symptoms, and five different serological tests of 213 patients with parasitologically confirmed (cases) and 119 with clinical suspicion of VL but with another confirmed etiology (non-cases), twelve prediction models using logistic regression and classification and regression trees (CART) for VL diagnosis were developed. The model composed of the clinical-laboratory variables and the rk39 rapid test showed the best performance in both logistic regression and CART (Sensitivity of 90.1% and specificity ranging from 97.2–97.4%). The scoring system is simple and based on the clinical-laboratory findings that are easily available in most clinical settings. The results suggest that those models might be useful in locations where access to available diagnostic methods is difficult, contributing to more efficient and more rational allocation of healthcare resources

Pathocenosis: A Holistic Approach to Disease Ecology

The History of medicine describes the emergence and recognition of infectious diseases, and human attempts to stem them. It also throws light on the role of changing environmental conditions on disease emergence/re-emergence, establishment and, sometimes, disappearance. However, the dynamics of infectious diseases is also influenced by the relationships between the community of interacting infectious agents present at a given time in a given territory, a concept that Mirko Grmek, an historian of medicine, conceptualized with the word “pathocenosis”. The spatial and temporal evolution of diseases, when observed at the appropriate scales, illustrates how a change in the pathocenosis, whether of “natural” or anthropic origin, can lead to the emergence and spread of diseases

Early Clinical Manifestations Associated with Death from Visceral Leishmaniasis

The visceral leishmaniasis (VL) is a disease potentially fatal if not diagnosed and treated opportunely. This article presents the results of the study on the manifestations identified at the time of the clinical suspicion of the VL cases. This study was conducted in Belo Horizonte, the capital of the State of Minas Gerais, located in southeastern Brazil. This study is both timely and substantive because the Belo Horizonte is an area of transmission of VL, with one of the highest VL-death proportions of Brazil. The patients with higher risk of death had at least one of the following characteristics: ≥60 years, weakness, HIV co-infection, bleeding, jaundice and other associated infections. During the period 2002–2009, 8% to 22% of the patients with VL progressed to death in Belo Horizonte, whilst the proportion in the country was much lower and varied between 5% and 9%. This study has identified vulnerable patients who are at higher risk of death from VL and who would benefit from early predictive evaluation of the prognostic. Hence, the knowledge regarding the factors associated with death may contribute for clinical management and for reduction of deaths from VL

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

BACKGROUND: Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. METHODS: The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. RESULTS: It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. CONCLUSION: The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources

Algorithms to predict cerebral malaria in murine models using the SHIRPA protocol

<p>Abstract</p> <p>Background</p> <p><it>Plasmodium berghei </it>ANKA infection in C57Bl/6 mice induces cerebral malaria (CM), which reproduces, to a large extent, the pathological features of human CM. However, experimental CM incidence is variable (50-100%) and the period of incidence may present a range as wide as 6-12 days post-infection. The poor predictability of which and when infected mice will develop CM can make it difficult to determine the causal relationship of early pathological changes and outcome. With the purpose of contributing to solving these problems, algorithms for CM prediction were built.</p> <p>Methods</p> <p>Seventy-eight <it>P. berghei</it>-infected mice were daily evaluated using the primary SHIRPA protocol. Mice were classified as CM+ or CM- according to development of neurological signs on days 6-12 post-infection. Logistic regression was used to build predictive models for CM based on the results of SHIRPA tests and parasitaemia.</p> <p>Results</p> <p>The overall CM incidence was 54% occurring on days 6-10. Some algorithms had a very good performance in predicting CM, with the area under the receiver operator characteristic (_auROC) curve ≥ 80% and positive predictive values (PV+) ≥ 95, and correctly predicted time of death due to CM between 24 and 72 hours before development of the neurological syndrome (_auROC = 77-93%; PV+ = 100% using high cut off values). Inclusion of parasitaemia data slightly improved algorithm performance.</p> <p>Conclusion</p> <p>These algorithms work with data from a simple, inexpensive, reproducible and fast protocol. Most importantly, they can predict CM development very early, estimate time of death, and might be a valuable tool for research using CM murine models.</p