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  • 1
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 9, No. Supplement_2 ( 2022-12-15)
    Kurzfassung: How to start optimal antibiotic therapy before the results of cultures and antimicrobial susceptibility tests are available? Here, we use the law of total probability to present a probabilistic approach based on antibiograms of bacterial isolates from healthcare and community-acquired infections to optimizing empiric antibiotic therapy. Methods Data on the microbiology of healthcare and community-acquired infections were analyzed from hospitals in Belo Horizonte, a three million inhabitants city from Brazil. Healthcare infections were defined by the National Healthcare Safety Network (NHSN)/CDC protocols. Only data obtained from infections with positive culture, both hospital and community, were considered. The success rate of an antibiotic (ATB) regimen, considering just one drug individually (monotherapy), was calculated by Law of Total Probability (Fig 1). In this sense, if a microorganism has not been tested for a specific antimicrobial, then, by definition, it was considered an antibiotic failure. For a regimen with more than one antibiotic, if the microorganism is sensitive to one of them, then it was considered a success of the scheme. For calculating the success probability of two or three antimicrobials A, B, and C, simultaneously (Fig 2), i.e., P(A and B) or P(A and B and C), the sensitivity to an antimicrobial was considered independent of sensitivity to any other. Then, P(A and B) = P(A) * P(B), and P(A and B and C) = P(A)*P(B) *P(C). Figure 1– Law of total probability: success rate of an antibiotic considering just one drug individually (monotherapy).Figure 2– Probability of the union of two events, success of ATB A or ATB B, and union of three events, success of ATB A or ATB B or ATB C. Results Microbiologic data from hospital acquired infections (HAI) and community-acquired infections (CAI) are analyzed once a year. Empiric antibiotic therapy to HAI were proposed for urinary tract infections (UTI), bloodstream infections (BSI), and pneumonia (Figures 2 and 3). Empiric antibiotic therapy to community-acquired infections were developed for UTI, pneumonia, gastrointestinal system infection, bone and joint infection, and skin and soft tissue infection. Figure 3– Success rate of each antibiotic alone, considering just one drug individually (monotherapy): analysis of hospital-acquired pneumonia.Figure 4– Success rate of one, two or three antibiotics: analysis of hospital-acquired pneumonia.Fig 5- Probability of the antimicrobial regimen being successful in treating an infection according to the length of stay at hospital. Conclusion We presented here a probabilistic approach to empiric antibiotic therapy. The next step is to validate all proposed regimens, that can be used to improve the success likelihood of empiric antibiotic decision making. Disclosures All Authors: No reported disclosures.
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2022
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 6, No. Supplement_2 ( 2019-10-23), p. S857-S857
    Kurzfassung: The Centers for Disease Control and Prevention (CDC) proposed standard definitions for acquired resistance in bacterias. Resistant bacteria were categorized as multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR). This study describes the incidence of Gram-negative MDR, XDR and PDR in 12 private and adult intensive care units (ICU’s) from Belo Horizonte, Minas Gerais, the sixth most populated city in Brazil, with approximately 3 million inhabitants. Methods Data were collected between January/2013 to December/2017 from 12 ICU’s. The hospitals used prospective healthcare-associated infections (HAI) surveillance protocols, in accordance to the CDC. Antimicrobial resistance from six Gram-negatives, causing nosocomial infections, were evaluated: Acinetobacter sp., Klebsiella sp., Proteus sp., Enterobacter sp., Escherichia coli, and Pseudomonas sp.. We computed the three categories of drug-resistance (MDR+XDR+PDR) to define benchmarks for the resistance rate of each Gram-negative evaluated. Benchmarks were defined as the superior limits of 95% confidence interval for the resistance rate. Results After a 5 year surveillance, 6,242 HAI strains were tested: no pandrug-resistant bacteria (PDR) was found. Acinetobacter sp. was the most resistant Gram-negative: 206 strains from 1,858 were XDR (11%), and 1,638 were MDR (88%). Pseudomonas sp.: 41/1,159 = 3.53% XDR; 180/1,159 = 15.53% MDR. Klebsiella sp.: 2/1,566 = 0,1% XDR; 813/1,566 = 52% MDR. Proteus sp.: 0/507 = 0% XDR; 163/507 = 32% MDR. Enterobacter sp.: 0/471 = 0% XDR; 148/471 = 31% MDR. Escherichia coli: 0/681 = 0% XDR; 157/681 = 23% MDR. Benchmarks for the global resistance rate of each Gram-negative (MDR+XDR+PDR): Acinetobacter sp. = 92%; Klebsiella sp. = 62%; Proteus sp. = 40%; Enterobacter sp. = 48%; Escherichia coli = 33%; Pseudomonas sp. = 30%. Conclusion This study has calculated the incidence of Gram-negative MDR, XDR and PDR, and found a higher incidence of MDR Acinetobacter sp., with an 88% multiresistance rate. Henceforth, developing countries healthcare institutions must be aware of an increased risk of infection by Acinetobacter sp.. Benchmarks have been defined, and can be used as indicators for healthcare assessment. Disclosures All authors: No reported disclosures.
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2019
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S502-S503
    Kurzfassung: In the present study we determined the prevalence of antibiotic resistance in the most common organisms causing healthcare-associated infections in tertiary-care hospitals in Belo Horizonte, a 3,000,000 inhabitants city from Brazil. Methods Microbiology data of hospital acquired infections (HAI) defined by the National Healthcare Safety Network (NHSN)/CDC protocols of seven general hospitals were analyzed: three public institutions, two philanthropic, and two private hospitals. Samples from different topographies were plate in an ideal culture medium and after growth, the microorganisms were identified by standard biochemical and microbiological methods, using the VITEK 2 compact system (Biomerieux), which allows the simultaneous identification of Gram-positive and Gram bacteria -negative and combine the identification and TSA results in a single report. Six hospitals used automated methods and one institution used manual method for antimicrobial susceptibility testing. Results Samples of seven Gram-negative and two Gram-positive bacteria collected between Dec/2019-Nov/2020 from HAI isolates were analyzed: 565 Klebsiella, 293 Escherichia coli, 153 Proteus, 403 Pseudomonas, 275 Acinetobacter, 174 Serratia, 153, 361 Staphylococcus aureus, and 176 Enterococcus. Antibiotic resistance profile of each strain is summarized in Figures 1, 2, and 3. Resistance profile: Klebsiella, E. coli, Proteus. ATB profile: Pseudomonas, Acinetobacter, Serratia. ATB profile: Enterobacter, S. aureus, Enterococcus . Conclusion Benchmarks for antibiotic resistance in the most common organisms causing healthcare-associated infections were defined, and can be used as indicators for healthcare assessment, specially in developing countries institutions. Disclosures All Authors: No reported disclosures
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2021
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 6, No. Supplement_2 ( 2019-10-23), p. S449-S449
    Kurzfassung: Exploratory laparotomy surgery is abdominal operations not involving the gastrointestinal tract or biliary system. The objective of our study is to answer three questions: (a) What is the risk of surgical site infection (SSI) after exploratory abdominal surgery? (b) What is the impact of SSI in the hospital length of stay and hospital mortality? (c) What are risk factors for SSI after exploratory abdominal surgery? Methods A retrospective cohort study assessed meningitis and risk factors in patients undergoing exploratory laparotomy between January 2013 and December 2017 from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the National Healthcare Safety Network (NHSN)/CDC procedure-associated protocols for routine SSI surveillance. 26 preoperative and operative categorical and continuous variables were evaluated by univariate and multivariate analysis (logistic regression). Outcome variables: Surgical site infection (SSI), hospital death, hospital length of stay. Variables were analyzed using Epi Info and applying statistical two-tailed test hypothesis with significance level of 5%. Results A sample of 6,591 patients submitted to exploratory laparotomy was analyzed (SSI risk = 4.3%): Hospital length of stay in noninfected patients (days): mean = 16, median = 6, std. dev. = 30; hospital stay in infected patients: mean = 32, median = 22, std. dev. = 30 (P 〈 0.001). The mortality rate in patients without infection was 14% while hospital death of infected patients was 20% (P = 0.009). Main risk factors for SSI: ügeneral anesthesia (SSI = 4.9%, relative risk – RR = 2.8, P 〈 0.001); preoperative hospital length of stay more than 4 days (SSI=3.9%, RR=1.8, P = 0.003); wound class contaminated or dirty (SSI = 5.4%, RR = 1.5, P = 0.002); duration of procedure higher than 3 hours (SSI = 7.1%, RR = 2.1, P 〈 0.001); after trauma laparotomy (SSI = 7.8%, RR = 1.9, P = 0.001). Conclusion We identified patients at high risk of surgical site infection after exploratory laparotomy: trauma patients from contaminated or dirty wound surgery, submitted to a procedure with general anesthesia that last more than 3 hours have 13% SSI. Patients without any of these four risk factors have only 1.2% SSI. Disclosures All authors: No reported disclosures.
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2019
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Fundacao Sociedade Brasileira de Pediatria ; 1999
    In:  Jornal de Pediatria Vol. 75, No. 5 ( 1999-9-15), p. 361-6
    In: Jornal de Pediatria, Fundacao Sociedade Brasileira de Pediatria, Vol. 75, No. 5 ( 1999-9-15), p. 361-6
    Materialart: Online-Ressource
    ISSN: 0021-7557
    Sprache: Englisch
    Verlag: Fundacao Sociedade Brasileira de Pediatria
    Publikationsdatum: 1999
    ZDB Id: 2105628-6
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
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    Scientific Research Publishing, Inc. ; 2016
    In:  Surgical Science Vol. 07, No. 02 ( 2016), p. 58-64
    In: Surgical Science, Scientific Research Publishing, Inc., Vol. 07, No. 02 ( 2016), p. 58-64
    Materialart: Online-Ressource
    ISSN: 2157-9407 , 2157-9415
    Sprache: Unbekannt
    Verlag: Scientific Research Publishing, Inc.
    Publikationsdatum: 2016
    ZDB Id: 2616896-0
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S499-S499
    Kurzfassung: A research focused on surgical site infection (SSI) was performed in patients undergoing cardiac pacemaker implantation surgery. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, in this case the Multilayer Perceptron (MLP). Methods Data were collected from five hospitals in the city of Belo Horizonte (more than 3,000,000 inhabitants), between July 2016 and June 2018, on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved in the search. All data used in the analysis during their routine SSI surveillance procedures were collected. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% and 75% for testing, 35% and 25% for validation). They were compared by measuring AUC (Area Under the Curve - from 0 to 1) presented for each of the configurations. Results From 1394, 572 records were: 21% of deaths and 2.4% patients had SSI; from the confirmed SSI cases, approximately 64.3% had sites classified as “clean”; length of hospital stay ranged from 0 to 175 days (from 1 to 70 days); the average age is 67 years. The prediction power of SSI, the experiments achieved from 0.409 to 0.722. Conclusion Despite the considerable loss rate of more than 65% of the database samples due to the presence of noise, it was possible to have a relevant sampling for the profile evaluation of Belo Horizonte hospitals. Moreover, for the predictive process, although some configurations reached 0.722. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.nois.org.br ), two mobile application were developed: one for monitoring the patient in the hospital and the other for monitoring after hospital discharge. Disclosures All Authors: No reported disclosures
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2021
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
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    Oxford University Press (OUP) ; 2021
    In:  Open Forum Infectious Diseases Vol. 8, No. Supplement_1 ( 2021-12-04), p. S303-S304
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S303-S304
    Kurzfassung: The COVID-19 pandemic created the most severe global education disruption in history. According to UNESCO, at the peak of the crisis over 1.6 billion learners in more than 190 countries were out of school. After one year, half of the world’s student population is still affected by full or partial school closures. Here we investigated whether or not it is possible to build a multivariate score for dynamic school decision-making specially in scenarios without population-scale RT-PCR tests. Methods Normality rate is based on a COVID-19 risk matrix (Table 1). Total score (TS) is obtained by summing the risk scores for COVID-19, considering the six parameters of the pandemic in a city. The COVID-19 Normality Rate (CNR) is obtained by linear interpolation in such a way that a total score of 30 points is equivalent to a 100% possibility of normality and, in a city with only six total points would have zero percent chance of returning to normality: CNR = (TS – 6)/24 (%). The criteria for opening and closing schools can be defined based on the percentages of return to normality (Table 2). Table 1. Limits for each parameter of the risk matrix and "normality" scores in relation to COVID-19: the lower the risk, higher is the “normality” score. Table 2. Criteria for opening and closing schools in a city according to the COVID-19 Normality Rate. Results at June 3rd, 2021, we evaluated all 5,570 Brazilian cities (Figure 1): 2,708 cities (49%) with COVID-19 normality rate less than 50% (full schools closure), 2,223 cities (40%) with normality rate between 50% and 70% (in-person learning only for 5 years and 8 months-old children), 583 with normality rate between 71% and 80% (in-person learning extended to children age 12 years and less), 583 cities (1%) with normality rate between 81% to 90% (in-person learning extended to the student population age 18 years), and just one city with 92% COVID-19 normality rate (in-person learning extended to all the student population). We calculated the COVID-19 normality rate between January and May, 2021, in four countries: Brazil, USA, UK, and Italy (Figure 2). At Jun, 3rd, 2021, percentage of people fully vaccinated in Brazil varied from 0% to 69%, an average of 11%. Figure 1. COVID-19 Normality Rate in 5,570 cities in Brazil, Jun/03/2021. Figure 2. COVID-19 Normality Rate between January and May, 2021: comparison among Brazil, USA, UK, and Italy. Conclusion COVID-19 vaccination programs take several months to implement. Besides fully vaccination of the population, it is important to check if people became really safe from the virus. The COVID-19 Normality Rate is a double check multivariate score that can be used as a criteria for optimal time to return to in-person learning safely. Disclosures All Authors: No reported disclosures
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2021
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
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    Oxford University Press (OUP) ; 2015
    In:  Bioinformatics Vol. 31, No. 8 ( 2015-04-15), p. 1267-1273
    In: Bioinformatics, Oxford University Press (OUP), Vol. 31, No. 8 ( 2015-04-15), p. 1267-1273
    Kurzfassung: Motivation: The identification of potential drug target proteins in bacteria is important in pharmaceutical research for the development of new antibiotics to combat bacterial agents that cause diseases. Results: A new model that combines the singular value decomposition (SVD) technique with biological filters composed of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of Escherichia coli (strain K12) has been created to predict potential antibiotic drug targets in the Enterobacteriaceae family. This model identified 99 potential drug target proteins in the studied family, which exhibit eight different functions and are protein-coding essential genes or similar to protein-coding essential genes of E.coli (strain K12), indicating that the disruption of the activities of these proteins is critical for cells. Proteins from bacteria with described drug resistance were found among the retrieved candidates. These candidates have no similarity to the human proteome, therefore exhibiting the advantage of causing no adverse effects or at least no known adverse effects on humans. Contact:  rita_silverio@hotmail.com. Supplementary information:  Supplementary data are available at Bioinformatics online.
    Materialart: Online-Ressource
    ISSN: 1367-4811 , 1367-4803
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2015
    ZDB Id: 1468345-3
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
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    Oxford University Press (OUP) ; 2021
    In:  Open Forum Infectious Diseases Vol. 8, No. Supplement_1 ( 2021-12-04), p. S206-S207
    In: Open Forum Infectious Diseases, Oxford University Press (OUP), Vol. 8, No. Supplement_1 ( 2021-12-04), p. S206-S207
    Kurzfassung: How to improve the high mortality rate of sepsis? The prompt identification of at-risk patients, and the interdisciplinary sepsis treatment protocol implementation are interventions that can reverse such unacceptable outcome. The objective of our study is to summarize main results of the protocol for the management of sepsis and septic shock, implemented at Biocor Instituto, a general hospital in Belo Horizonte, a 3,000,000 inhabitants city from Brazil. Methods Prospective cohort study of patients with sepsis, evaluated between May/2018-Apr/2020. Univariate and multivariate analysis by logistic regression to identify risk factors for hospital death. Results Over 28 months, 220 patients were included in sepsis protocol: 121 hospital deaths, a crude mortality = 121/220 = 55% (95%C.I. = [48%;62%]). 136 patients (62%) came from the emergency room. In 97 cases (44%) it was possible to isolate 111 microorganisms, with a predominance of Klebsiella, E.coli, and S.aureus. 75% of the cases (165) had definition of APACHE, with the absolute majority of these (88%) having APACHE between 25 and 40. Most patients (52%) received antibiotic (ATB) in 15 minutes and only 4% received ATB after 60 minutes of waiting time. In 198 patients (90%) it was possible to identify the focus of sepsis, with a predominance of pulmonary (47%), urinary (21%) and abdominal (15%). Hospital mortality varied from 30 to 62%, when the focus was pulmonary (p-value = 0.045). In univariate analysis (Figure 1), pulmonary sepsis, creatinine, lactate, and APACHE were significantly associated with hospital death. The time for ATB administration was typically close to 20 minutes, and time to receive the therapeutic antibiotic were not associated with the patient’s death. By using the logistic model (Figure 2) to assign cases of predicted hospital death for probabilities & gt;= 0.5 and controls for probabilities & lt; 0.5, the prediction model had a sensitivity of 0.68 (0.59–0.76), a specificity of 0.58 (0.48–0.67), an area under the curve of the receiver operating characteristic curve of 0.75 (0.68–0.82). There was no significant difference between observed versus expected mortality by APACHE (Figure 3). Figure 1. Univariate analysis to identify risk factors for hospital death. Figure 2. Logistic model for predicting hospital death. Figure 3. Observed X Expected/severity-adjusted mortality (APACHE). Conclusion The logistic model developed uses only creatinine and lactate data to predict suspected sepsis patients with high death risk. Disclosures All Authors: No reported disclosures
    Materialart: Online-Ressource
    ISSN: 2328-8957
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2021
    ZDB Id: 2757767-3
    Standort Signatur Einschränkungen Verfügbarkeit
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