International nosocomial infection control consortium (INICC) report, data summary of 36 countries, for 2004-2009

The results of a surveillance study conducted by the International Nosocomial Infection Control Consortium (INICC) from January 2004 through December 2009 in 422 intensive care units (ICUs) of 36 countries in Latin America, Asia, Africa, and Europe are reported. During the 6-year study period, using Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN; formerly the National Nosocomial Infection Surveillance system [NNIS]) definitions for device-associated health care-associated infections, we gathered prospective data from 313,008 patients hospitalized in the consortium's ICUs for an aggregate of 2,194,897 ICU bed-days. Despite the fact that the use of devices in the developing countries' ICUs was remarkably similar to that reported in US ICUs in the CDC's NHSN, rates of device-associated nosocomial infection were significantly higher in the ICUs of the INICC hospitals; the pooled rate of central line-associated bloodstream infection in the INICC ICUs of 6.8 per 1,000 central line-days was more than 3-fold higher than the 2.0 per 1,000 central line-days reported in comparable US ICUs. The overall rate of ventilator-associated pneumonia also was far higher (15.8 vs 3.3 per 1,000 ventilator-days), as was the rate of catheter-associated urinary tract infection (6.3 vs. 3.3 per 1,000 catheter-days). Notably, the frequencies of resistance of Pseudomonas aeruginosa isolates to imipenem (47.2% vs 23.0%), Klebsiella pneumoniae isolates to ceftazidime (76.3% vs 27.1%), Escherichia coli isolates to ceftazidime (66.7% vs 8.1%), Staphylococcus aureus isolates to methicillin (84.4% vs 56.8%), were also higher in the consortium's ICUs, and the crude unadjusted excess mortalities of device-related infections ranged from 7.3% (for catheter-associated urinary tract infection) to 15.2% (for ventilator-associated pneumonia). Copyright © 2012 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved

Impact of a multidimensional infection control strategy on catheter-associated urinary tract infection rates in the adult intensive care units of 15 developing countries: findings of the International Nosocomial Infection Control Consortium (INICC)

Salomao, Reinaldo/0000-0003-1149-4598; alvarez Moreno, carlos Arturo/0000-0001-5419-4494; Medeiros, Eduardo A/0000-0002-6205-259X; Leblebicioglu, Hakan/0000-0002-6033-8543; Yalcin, Ata Nevzat/0000-0002-7243-7354; Barahona G., Nayide/0000-0003-3559-6900; Mitrev, Zan/0000-0001-7859-8821; Kanj, Souha/0000-0001-6413-3396; Unal, Necmettin/0000-0002-9440-7893; Matta, Lorena/0000-0002-5215-3215WOS: 000309340400006PubMed: 22711598We aimed to evaluate the impact of a multidimensional infection control strategy for the reduction of the incidence of catheter-associated urinary tract infection (CAUTI) in patients hospitalized in adult intensive care units (AICUs) of hospitals which are members of the International Nosocomial Infection Control Consortium (INICC), from 40 cities of 15 developing countries: Argentina, Brazil, China, Colombia, Costa Rica, Cuba, India, Lebanon, Macedonia, Mexico, Morocco, Panama, Peru, Philippines, and Turkey. We conducted a prospective before-after surveillance study of CAUTI rates on 56,429 patients hospitalized in 57 AICUs, during 360,667 bed-days. The study was divided into the baseline period (Phase 1) and the intervention period (Phase 2). In Phase 1, active surveillance was performed. In Phase 2, we implemented a multidimensional infection control approach that included: (1) a bundle of preventive measures, (2) education, (3) outcome surveillance, (4) process surveillance, (5) feedback of CAUTI rates, and (6) feedback of performance. The rates of CAUTI obtained in Phase 1 were compared with the rates obtained in Phase 2, after interventions were implemented. We recorded 253,122 urinary catheter (UC)-days: 30,390 in Phase 1 and 222,732 in Phase 2. In Phase 1, before the intervention, the CAUTI rate was 7.86 per 1,000 UC-days, and in Phase 2, after intervention, the rate of CAUTI decreased to 4.95 per 1,000 UC-days [relative risk (RR) 0.63 (95 % confidence interval [CI] 0.55-0.72)], showing a 37 % rate reduction. Our study showed that the implementation of a multidimensional infection control strategy is associated with a significant reduction in the CAUTI rate in AICUs from developing countries.Foundation to Fight against Nosocomial InfectionsThe authors declare that they did not receive any personal funding, and the funding for the activities carried out at the INICC headquarters were provided by the corresponding author, Victor D. Rosenthal, and Foundation to Fight against Nosocomial Infections. The authors state that they do not have any conflicts of interest to declare. Every hospital's Institutional Review Board agreed to the study protocol, and patient confidentiality was protected by codifying the recorded information, making it only identifiable to the infection control team (ICT)

Experimental investigation of perturbation Monte-Carlo based derivative estimation for imaging low-scattering tissue

Experimental results for imaging the low-scattering tissue phantoms based on the derivative estimation through perturbation Monte- Carlo (pMC) method are presented. It is proven that pMC-based methods give superior reconstructions compared to diffusion-based reconstruction methods. An easy way to estimate the Jacobian using analytical expression obtained from perturbation Monte-Carlo method is employed. Simulation studies on the same objects, considered in the experiment, are performed and corresponding results are found to be in reasonable agreement with the experimental studies. It is shown that inter-parameter cross talk in diffusion based methods lead to false results for the low-scattering tissue, where as the pMC-based method gives accurate results

Experimental investigation of perturbation Monte-Carlo based derivative estimation for imaging low-scattering tissue

Experimental results for imaging the low-scattering tissue phantoms based on the derivative estimation through perturbation Monte-Carlo (pMC) method are presented. It is proven that pMC-based methods give superior reconstructions compared to diffusion-based reconstruction methods. An easy way to estimate the Jacobian using analytical expression obtained from perturbation Monte-Carlo method is employed. Simulation studies on the same objects, considered in the experiment, are performed and corresponding results are found to be in reasonable agreement with the experimental studies. It is shown that inter-parameter cross talk in diffusion based methods lead to false results for the low-scattering tissue, where as the pMC-based method gives accurate results

Locating Human Faces in a Cluttered Scene

In this paper, we present two new schemes for finding human faces in a photograph. The first scheme adopts a distribution-based model approach to face-finding. Distributions of the face and the face-like manifolds are approximated using higher order statistics (HOS) by deriving a series expansion of the density function in terms of the multivariate Gaussian and the Hermite polynomials in an attempt to get a better approximation to the unknown original density function. An HOS-based data clustering algorithm is then proposed to facilitate the decision process. The second scheme adopts a hidden Markov model (HMM) based approach to the face-finding problem. This is an unsupervised scheme in which face-to-nonface and nonface-to-face transitions are learned by using an HMM. The HMM learning algorithm estimates the HMM parameters corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. We present experimental results on the performance of both schemes. A training data base of face images was constructed in the laboratory. The performances of both the proposed schemes are found to be quite good when measured with respect to several standard test face images

Locating human faces in a cluttered scene

In this paper, we present two new schemes for finding human faces in a photograph. The first scheme adopts a distribution-based model approach to face-finding. Distributions of the face and the face-like manifolds are approximated using higher order statistics (HOS) by deriving a series expansion of the density function in terms of the multivariate Gaussian and the Hermite polynomials in an attempt to get a better approximation to the unknown original density function. An HOS-based data clustering algorithm is then proposed to facilitate the decision process. The second scheme adopts a hidden Markov model (HMM) based approach to the face-finding problem. This is an unsupervised scheme in which face-to-nonface and nonface-to-face transitions are learned by using an HMM. The HMM learning algorithm estimates the HMM parameters corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. We present experimental results on the performance of both schemes. A training data base of face images was constructed in the laboratory. The performances of both the proposed schemes are found to be quite good when measured with respect to several standard test face images