Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Optimal Cut-Off Value for Homeostasis Model Assessment (HOMA) Index of Insulin-Resistance in a Population of Patients Admitted Electively in a Portuguese Cardiology Ward

INTRODUCTION: Insulin resistance is the pathophysiological key to explain metabolic syndrome. Although clearly useful, the Homeostasis Model Assessment index (an insulin resistance measurement) hasn't been systematically applied in clinical practice. One of the main reasons is the discrepancy in cut-off values reported in different populations. We sought to evaluate in a Portuguese population the ideal cut-off for Homeostasis Model Assessment index and assess its relationship with metabolic syndrome. MATERIAL AND METHODS: We selected a cohort of individuals admitted electively in a Cardiology ward with a BMI < 25 Kg/m2 and no abnormalities in glucose metabolism (fasting plasma glucose < 100 mg/dL and no diabetes). The 90th percentile of the Homeostasis Model Assessment index distribution was used to obtain the ideal cut-off for insulin resistance. We also selected a validation cohort of 300 individuals (no exclusion criteria applied). RESULTS: From 7 000 individuals, and after the exclusion criteria, there were left 1 784 individuals. The 90th percentile for Homeostasis Model Assessment index was 2.33. In the validation cohort, applying that cut-off, we have 49.3% of individuals with insulin resistance. However, only 69.9% of the metabolic syndrome patients had insulin resistance according to that cut-off. By ROC curve analysis, the ideal cut-off for metabolic syndrome is 2.41. Homeostasis Model Assessment index correlated with BMI (r = 0.371, p < 0.001) and is an independent predictor of the presence of metabolic syndrome (OR 19.4, 95% CI 6.6 - 57.2, p < 0.001). DISCUSSION: Our study showed that in a Portuguese population of patients admitted electively in a Cardiology ward, 2.33 is the Homeostasis Model Assessment index cut-off for insulin resistance and 2.41 for metabolic syndrome. CONCLUSION: Homeostasis Model Assessment index is directly correlated with BMI and is an independent predictor of metabolic syndrome.

Environment and Health in Children Day Care Centres (ENVIRH) - Study Rationale and Protocol

Background: Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects. Methods --- study design: This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children’s respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected. Results: In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children). Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children’s health. A building ventilation model will also be developed. Discussion: This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

Curcumin Inhibits Gastric Inflammation Induced by Helicobacter Pylori Infection in a Mouse Model

Helicobacter pylori (H. pylori) infection triggers a sequence of gastric alterations starting with an inflammation of the gastric mucosa that, in some cases, evolves to gastric cancer. Efficient vaccination has not been achieved, thus it is essential to find alternative therapies, particularly in the nutritional field. The current study evaluated whether curcumin could attenuate inflammation of the gastric mucosa due to H. pylori infection. Twenty-eight C57BL/6 mice, were inoculated with the H. pylori SS1 strain; ten non-infected mice were used as controls. H. pylori infection in live mice was followed-up using a modified 13C-Urea Breath Test (13C-UBT) and quantitative real-time polymerase chain reaction (PCR). Histologically confirmed, gastritis was observed in 42% of infected non-treated mice at both 6 and 18 weeks post-infection. These mice showed an up-regulation of the expression of inflammatory cytokines and chemokines, as well as of toll-like receptors (TLRs) and MyD88, at both time points. Treatment with curcumin decreased the expression of all these mediators. No inflammation was observed by histology in this group. Curcumin treatment exerted a significant anti-inflammatory effect in H. pylori-infected mucosa, pointing to the promising role of a nutritional approach in the prevention of H. pylori induced deleterious inflammation while the eradication or prevention of colonization by effective vaccine is not available.

Numerical Evaluation of Ventilation Performance in Children Day Care Centres

Modelling of ventilation is strongly dependent on the physical characteristics of the building of which precise evaluation is a complex and time consuming task. In the frame of a research project, two children day care centres (CDCC) have been selected in order to measure the envelope air permeability, the flow rate of mechanical ventilation systems and indoor and outdoor temperature. The data obtained was used as input to the computer code CONTAM for ventilation simulations. The results obtained were compared with direct measurements of ventilation flow from short term measurements with CO2 tracer gas and medium term measurements with perfluorocarbon tracer (PFT) gas decay method. After validation, in order to analyse the main parameters that affect ventilation, the model was used to predict the ventilation rates for a wide range of conditions. The purpose of this assessment was to find the best practices to improve natural ventilation. A simple analytical method to predict the ventilation flow rate of rooms is also presented. The method is based on the estimation of wind effect on the room through the evaluation of an average factor and on the assessment of relevant cross section of gaps and openings combined in series or in parallel. It is shown that it may be applied with acceptable accuracy for this type of buildings when ventilation is due essentially to wind action.

Acute Kidney Injury after Pediatric Cardiac Surgery: Risk Factors and Outcomes. Proposal for a Predictive Model

Objectives: To characterize the epidemiology and risk factors for acute kidney injury (AKI) after pediatric cardiac surgery in our center, to determine its association with poor short-term outcomes, and to develop a logistic regression model that will predict the risk of AKI for the study population. Methods: This single-center, retrospective study included consecutive pediatric patients with congenital heart disease who underwent cardiac surgery between January 2010 and December 2012. Exclusion criteria were a history of renal disease, dialysis or renal transplantation. Results: Of the 325 patients included, median age three years (1 day---18 years), AKI occurred in 40 (12.3%) on the first postoperative day. Overall mortality was 13 (4%), nine of whom were in the AKI group. AKI was significantly associated with length of intensive care unit stay, length of mechanical ventilation and in-hospital death (p<0.01). Patients’ age and postoperative serum creatinine, blood urea nitrogen and lactate levels were included in the logistic regression model as predictor variables. The model accurately predicted AKI in this population, with a maximum combined sensitivity of 82.1% and specificity of 75.4%. Conclusions: AKI is common and is associated with poor short-term outcomes in this setting. Younger age and higher postoperative serum creatinine, blood urea nitrogen and lactate levels were powerful predictors of renal injury in this population. The proposed model could be a useful tool for risk stratification of these patients.