Nosocomial Bloodstream Infections in Brazilian Pediatric Patients: Microbiology, Epidemiology, and Clinical Features

Background: Nosocomial bloodstream infections (nBSIs) are an important cause of morbidity and mortality and are the most frequent type of nosocomial infection in pediatric patients.Methods: We identified the predominant pathogens and antimicrobial susceptibilities of nosocomial bloodstream isolates in pediatric patients (<= 16 years of age) in the Brazilian Prospective Surveillance for nBSIs at 16 hospitals from 12 June 2007 to 31 March 2010 (Br SCOPE project).Results: in our study a total of 2,563 cases of nBSI were reported by hospitals participating in the Br SCOPE project. Among these, 342 clinically significant episodes of BSI were identified in pediatric patients (<= 16 years of age). Ninety-six percent of BSIs were monomicrobial. Gram-negative organisms caused 49.0% of these BSIs, Gram-positive organisms caused 42.6%, and fungi caused 8.4%. the most common pathogens were Coagulase-negative staphylococci (CoNS) (21.3%), Klebsiella spp. (15.7%), Staphylococcus aureus (10.6%), and Acinetobacter spp. (9.2%). the crude mortality was 21.6% (74 of 342). Forty-five percent of nBSIs occurred in a pediatric or neonatal intensive-care unit (ICU). the most frequent underlying conditions were malignancy, in 95 patients (27.8%). Among the potential factors predisposing patients to BSI, central venous catheters were the most frequent (66.4%). Methicillin resistance was detected in 37 S. aureus isolates (27.1%). of the Klebsiella spp. isolates, 43.2% were resistant to ceftriaxone. of the Acinetobacter spp. and Pseudomonas aeruginosa isolates, 42.9% and 21.4%, respectively, were resistant to imipenem.Conclusions: in our multicenter study, we found a high mortality and a large proportion of gram-negative bacilli with elevated levels of resistance in pediatric patients.Pfizer, Inc.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Inst Oncol Pediat IOP GRAAC, São Paulo, BrazilHosp Israelita Albert Einstein, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, São Paulo, BrazilHosp 9 Julho, São Paulo, BrazilSanta Casa Porto Alegre, Porto Alegre, RS, BrazilHosp Conceicao, Porto Alegre, RS, BrazilHosp Base, Brasilia, DF, BrazilHosp Walter Cantidio, Fortaleza, Ceara, BrazilHosp Diadema, São Paulo, BrazilHosp Espanhol, Salvador, BA, BrazilHosp Coracao, Natal, RN, BrazilHosp UNIMED, Natal, RN, BrazilHosp Clin Goiania, Goiania, Go, BrazilHosp Rim & Hipertensao, São Paulo, BrazilUniv Fed Triangulo Mineiro, Uberaba, MG, BrazilVirginia Commonwealth Univ, Richmond, VA USAUniversidade Federal de São Paulo UNIFESP, São Paulo, BrazilFAPESP: 2006/57700-0Web of Scienc

Geographical Variability in the Likelihood of Bloodstream Infections Due to Gram-Negative Bacteria: Correlation with Proximity to the Equator and Health Care Expenditure (vol 9, e114548, 2014)

Hosp Univ Austral, Div Infect Dis Prevent & Infect Control Serv, Buenos Aires, DF, ArgentinaHosp Univ Austral, Microbiol Lab, Buenos Aires, DF, ArgentinaMonash Hlth, Monash Infect Dis, Clayton, Vic, AustraliaWollongong Hosp, Wollongong, NSW, AustraliaUniversidade Federal de São Paulo, Div Infect Dis, Lab Especial Microbiol Clin, São Paulo, BrazilHosp Israelita Albert Einstein, São Paulo, BrazilVirginia Commonwealth Univ, Med Ctr, Richmond, VA USAHosp Rim & Hipertensao, São Paulo, BrazilHosp Santa Casa Porto Alegre, Porto Alegre, RS, BrazilHosp Conceicao, Porto Alegre, RS, BrazilHosp Walter Cantidio, Fortaleza, Ceara, BrazilHosp Diadema, São Paulo, BrazilHosp Espanhol, Salvador, BA, BrazilHosp Clin Goiania, Goiania, Go, BrazilMt Sinai Hosp, Toronto, ON M5G 1X5, CanadaUniv Alberta, Div Infect Dis, Edmonton, AB, CanadaCairo Univ Kasr Ainy, Dar Al Fouad Hosp, Fac Med, Dept Clin Pathol, Cairo, EgyptHygeia Gen Hosp, Athens, GreeceUniv Tubingen Hosp, Internal Med, Div Infect Dis, Tubingen, GermanyTokyo Metropolitan Tama Med Ctr, Dept Infect Prevent, Tokyo, JapanAmphia Hosp Breda, Lab Microbiol & Infect Control, Breda, NetherlandsThammasat Univ Hosp, Div Infect Dis, Pathum Thani, ThailandSt John Hosp & Med Ctr, Infect Prevent & Control Dept, Grosse Pointe Woods, MI USAUniv Hosp Bern, Dept Infect Dis, CH-3010 Bern, SwitzerlandUniv Bern, Bern, SwitzerlandBarnes Jewish Hosp, St Louis, MO 63110 USAUniversidade Federal de São Paulo, Div Infect Dis, Lab Especial Microbiol Clin, São Paulo, BrazilWeb of Scienc

Development of a Risk Prediction Model for Carbapenem-Resistant Enterobacteriaceae Infection after Liver Transplantation: A Multinational Cohort Study

BACKGROUND: Patients colonized with carbapenem resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT) with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies.METHODS: Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created.RESULTS: 840 LT recipients found to be colonized with CRE before (n=203) or after (n=637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention were retained in the prediction model. Median 30 and 60-day predicted risk was 15% (IQR 11-24%) and 21% (IQR 15-33%), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (AUC 74.6, Brier index 16.3) and bootstrapped validation dataset (AUC 73.9, Brier index 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/.CONCLUSIONS: Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant

Development of a Risk Prediction Model for Carbapenem-resistant Enterobacteriaceae Infection After Liver Transplantation: A Multinational Cohort Study

Background. Patients colonized with carbapenem-resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT), with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies.Methods. Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray subdistribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created.Results. A total of 840 LT recipients found to be colonized with CRE before (n = 203) or after (n = 637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (interquartile range [IQR], 9-42) days after LT. Pre- and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical reintervention were retained in the prediction model. Median 30- and 60-day predicted risk was 15% (IQR, 11-24) and 21% (IQR, 15-33), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (area under the curve [AUC], 74.6; Brier index, 16.3) and bootstrapped validation dataset (AUC, 73.9; Brier index, 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/.Conclusions. Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant