Usefulness of midregional proadrenomedullin to predict poor outcome in patients with community acquired pneumonia.

midregional proadrenomedullin (MR-proADM) is a prognostic biomarker in patients with community-acquired pneumonia (CAP). We sought to confirm whether MR-proADM added to Pneumonia Severity Index (PSI) improves the potential prognostic value of PSI alone, and tested to what extent this combination could be useful in predicting poor outcome of patients with CAP in an Emergency Department (ED).Consecutive patients diagnosed with CAP were enrolled in this prospective, single-centre, observational study. We analyzed the ability of MR-proADM added to PSI to predict poor outcome using receiver operating characteristic (ROC) curves, logistic regression and risk reclassification and comparing it with the ability of PSI alone. The primary outcome was "poor outcome", defined as the incidence of an adverse event (ICU admission, hospital readmission, or mortality at 30 days after CAP diagnosis).226 patients were included; 33 patients (14.6%) reached primary outcome. To predict primary outcome the highest area under curve (AUC) was found for PSI (0.74 [0.64-0.85]), which was not significantly higher than for MR-proADM (AUC 0.72 [0.63-0.81, p > 0.05]). The combination of PSI and MR-proADM failed to improve the predictive potential of PSI alone (AUC 0.75 [0.65-0.85, p=0.56]). Ten patients were appropriately reclassified when the combined PSI and MR-proADM model was used as compared with the model of PSI alone. Net reclassification improvement (NRI) index was statistically significant (7.69%, p = 0.03) with an improvement percentage of 3.03% (p = 0.32) for adverse event, and 4.66% (P = 0.02) for no adverse event.MR-proADM in combination with PSI may be helpful in individual risk stratification for short-term poor outcome of CAP patients, allowing a better reclassification of patients compared with PSI alone

ROC curves for predicting adverse event.

<p>Fig 3a. ROC curves for different biomarkers and PSI. Fig 3b. ROC curves for the PSI & MR-proADM prediction model compared to PSI</p

Kaplan-Meier survival curves at 90-days mortality according to MR-proADM quartiles.

<p>Kaplan-Meier survival curves at 90-days mortality according to MR-proADM quartiles.</p

Multivariate predictive models of adverse event and 90-day mortality.

<p>MaxM: Maximum Model: includes significant independent variables in the univariate analysis with AUC higher than 0.7.</p><p>OR: Odds Ratio and HR: Hazard Ratio.</p><p>CI 95%: confidence interval of 95%.</p><p>p: level of statistical significance.</p><p>AIC: Akaike Information Criterion (better fit of the model when AIC lower).</p><p>McFadden´s and Atkinson R2: proportion of uncertainty data explained by the model.</p><p>Calibration χ2 (p value): Hosmer and Lemeshow test.</p><p>Multivariate predictive models of adverse event and 90-day mortality.</p

Baseline characteristics of the NACURG cohort.

<p>Differences between patients who died and those who survived were assessed by Cox regression survival analysis for independent continuous variables, and a Kaplan-Meyer survival curve with log-rank tests for independent categorical variables. Differences between patients with or without adverse event were assessed by the Student t test or the non-parametric Mann-Whitney U test for continuous variables and the [χ²] test or the Fisher exact test for dichotomous categorical variables.</p><p>*p: degree of statistical significance.</p><p>**Lactate levels only available for 122 patients (54%) and not therefore included in the multivariate analysis.</p><p>***The percentage of readmissions out of the total number of patients discharged (221; 4 patients died while in hospital and 1 was still inpatient at 30 days).</p><p>Relationship between different independent variables and adverse event and 90-day mortality after consulting the Emergency Department.</p

Patients included in the study.

<p>Patients included in the study.</p

MR-proADM and CAP severity.

<p>Fig 2a. Relationship between MR-proADM and severity as established by the PSI. Analysis performed with the Jonckheere-Terpstra trend test. Tau b: Kendall’s rank correlation. p: level of statistical significance. Fig 2b. MR-proADM levels according to hospital admission, bacteremia, ICU admission, hospital readmission and 30-day mortality.</p

ROC curves for predicting 90-day mortality.

<p>Fig 5a. ROC curves for different biomarkers and PSI. Fig 5b. ROC curves for the PSI & MR-proADM prediction model compared to PSI.</p

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved

International Nosocomial Infection Control Consortium report, data summary of 50 countries for 2010-2015: Device-associated module

•We report INICC device-associated module data of 50 countries from 2010-2015.•We collected prospective data from 861,284 patients in 703 ICUs for 3,506,562 days.•DA-HAI rates and bacterial resistance were higher in the INICC ICUs than in CDC-NHSN's.•Device utilization ratio in the INICC ICUs was similar to CDC-NHSN's. Background: We report the results of International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2010-December 2015 in 703 intensive care units (ICUs) in Latin America, Europe, Eastern Mediterranean, Southeast Asia, and Western Pacific. Methods: During the 6-year study period, using Centers for Disease Control and Prevention National Healthcare Safety Network (CDC-NHSN) definitions for device-associated health care-associated infection (DA-HAI), we collected prospective data from 861,284 patients hospitalized in INICC hospital ICUs for an aggregate of 3,506,562 days. Results: Although device use in INICC ICUs was similar to that reported from CDC-NHSN ICUs, DA-HAI rates were higher in the INICC ICUs: in the INICC medical-surgical ICUs, the pooled rate of central line-associated bloodstream infection, 4.1 per 1,000 central line-days, was nearly 5-fold higher than the 0.8 per 1,000 central line-days reported from comparable US ICUs, the overall rate of ventilator-associated pneumonia was also higher, 13.1 versus 0.9 per 1,000 ventilator-days, as was the rate of catheter-associated urinary tract infection, 5.07 versus 1.7 per 1,000 catheter-days. From blood cultures samples, frequencies of resistance of Pseudomonas isolates to amikacin (29.87% vs 10%) and to imipenem (44.3% vs 26.1%), and of Klebsiella pneumoniae isolates to ceftazidime (73.2% vs 28.8%) and to imipenem (43.27% vs 12.8%) were also higher in the INICC ICUs compared with CDC-NHSN ICUs. Conclusions: Although DA-HAIs in INICC ICU patients continue to be higher than the rates reported in CDC-NSHN ICUs representing the developed world, we have observed a significant trend toward the reduction of DA-HAI rates in INICC ICUs as shown in each international report. It is INICC's main goal to continue facilitating education, training, and basic and cost-effective tools and resources, such as standardized forms and an online platform, to tackle this problem effectively and systematically