18 results
Impact of Each Component of a Ventilator Bundle on Preventing Ventilator-Associated Pneumonia and Lower Respiratory Infection
- Rafaela Pinho, Luciana Tanure, Jussara Pessoa, Leonardo Santos, Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s259-s260
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- October 2020
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Background: Ventilator-associated lower respiratory infections (LRIs) and pneumonia (VAP) are important healthcare-associated infections and are among the leading causes of death worldwide. Prevention of these infections are often based on care bundles. We investigated the incidence of VAP+LRI and the preventive efficacy of each component of our ventilator bundle. Methods: Our ventilator bundle includes 6 components that are daily checked by an infection control practitioner. These 6 evidence-based practices were implemented in 3 ICUs from a general tertiary-care private hospital in Belo Horizonte City (Brazil): (1) daily oral care with chlorhexidine; (2) elevate the head of the bed to between 30 and 45; (3) avoid scheduled ventilator circuit change; (4) monitor cuff pressure; (5) use subglottic secretion drainage; and (6) daily sedation interruption and daily assessment of readiness to extubate. VAP and ventilator-LRI definitions were obtained from the CDC NHSN. The impact of adherence rate to items in the ventilator bundle (%) on the incidence rate of VAP+LRI was assessed using linear regression and scatterplot analyses. Results: Between January 2018 and April 2019, 1,888 ventilator days were observed in the 3 ICUs, with 42 VAP and LRI events, an overall incidence rate of 22.2 cases per 1,000 ventilator days. After September 2018, the infection control service started a campaign to increase the ventilator bundle compliance (Fig. 1). Adherence rates to all 6 bundle components increased between January–August 2018 and September 2018–April 2019 from 25% to 55% for daily oral care, from 34% to 79% for elevating the head of the bed, 28% to 86% for avoiding scheduled ventilator circuit change, from 32% to 83% for cuff pressure monitoring, from 32% to 83% for subglottic secretion drainage, and from 33% to 85% for daily sedation interruption. PAV and LRI incidence decreased from 41 to 16 in ICU A, from 22 to 14 in ICU B and from 24 to 18 in ICU C. The impact of each bundle component was identified by linear regression, calculating the percentage of PAV+LRI incidence rate that is explained by bundle item adherence (r2) and correlation coefficient (r): daily sedation interruption (r2 = 48%; r = 0.69; P = .004) (Fig. 2), cuff pressure monitorization (r2 = 0.3721; r = 0.61; P = .016), subglottic secretion drainage (r2 = 36%; r = 0.60; P = .017), avoidance of scheduled ventilator circuit change (r2 = 34%; r = 0.58; P = .023), daily oral care (r2 = 25%; r = 0.50; P = .050), and elevate the head of the bed (r2 = 25%; r = 0.48; P = .067). Conclusions: The impact of each bundle component on preventing PAV+LRI was identified by the study. An educational intervention performed by the infection control service increased the adherence to the ventilator bundle, and the PAV and LRI incidence decreased.
Funding: None
Disclosures: None
Liberal and Restrictive Blood Transfusion Strategies in Orthopedic Surgery: Risk Factors for Surgical Site Infection
- Hoberdan Pereira, Marcelo Perucci, Lucas de Lima, Daniel Bodour, Laura Vieira, Rafael Teixeira, Braulio Couto, Antônio Andrade
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s313-s314
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- October 2020
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Background: The identification of risk factors for infections in surgical patients with lower-limb fractures and blood transfusions has increased in recent years. Surgical site infections (SSIs) increase hospitalization, care costs, and patient suffering. Correction surgery for lower-limb fractures and blood transfusion is quite common between surgical procedures. The aim of this study was to describe the relationship between blood transfusion and SSI in patients undergoing orthopedic surgery on lower limbs. Methods: We conducted a prospective cohort study to identify risk factors for SSI in blood transfused patients undergoing fracture repair in lower-limb surgeries between February 2017 and May 2019 in 2 reference tertiary-care hospitals in Belo Horizonte, a city of 3 million people in Brazil. Data regarding patient characteristics, surgical procedures, blood transfusions, and surgical infections were collected. Patient characterization was performed by calculating the absolute and relative frequencies of categorical variables and calculating mean, median, minimum, maximum, standard deviation, and coefficient of variation for quantitative variables. The incidence of surgical site infection, the risk of postoperative hospital death, and the total length of hospital stay were calculated by point estimates and 95% confidence intervals identified by statistical tests of bilateral hypotheses, considering the level of significance of 5%. A multivariate analysis (logistic regression) was performed to identify SSI risk factors. Results: Patients who had an indication for blood transfusion (n = 38) but who did not receive blood (n = 4) had significantly lower hemoglobin, comparing discharge with admission, than the group who received blood. Intraoperative transfusion was a risk factor for SSI (OR, 4.7) (Fig. 1). Among the 205 patients with no indication for transfusion, 98 received blood even without the indication: there was no difference in hemoglobin outcome when discharge and admission were compared, and the 98 patients were exposed to unnecessary risk. Regarding restrictive versus liberal transfusion strategies, there were differences in the variables, age (P = .000), duration of surgery (P = .003), number of comorbidities (P = .000), body mass index (BMI) (P = .027), previous hemoglobin (P = .000), and high hemoglobin (P = .000), considering the transfusion practice employed (Fig. 2). Conclusions: The indications for and definition of protocols and careful evaluation of blood transfusion are critical to avoid infectious complications in orthopedic patients with lower-limb fractures.
Funding: None
Disclosures: None
Automated Prediction of Surgical Site Infection Coronary Artery Bypass (CABG) Grafting Surgery
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Aline Castro de Almeida, Filipe Batista do Amaral, Guilherme Brangioni Januário, Maria Luiza Neves Caldeira, Miriam Alice Guerra, Rayane Thamires Oliveira Moraes
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s135
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- October 2020
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Background: In 5 hospitals located in Belo Horizonte city (>3,000,000 inhabitants) a focused survey on surgical site infection (SSI) was performed in patients undergoing CABG surgery. We statistically evaluated such incidences to enable study of the prediction power of SSI through pattern recognition algorithms, in this case the multilayer perceptron (MLP) artificial neural networks. Methods: Data were collected between July 2016 and June 2018 on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. We collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which uses the SACIH (Automated Hospital Infection Control System) software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the collected database for use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, 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% or 75% for testing and 35% or 25% for validation). They were compared by measuring the AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: From 666 initial data, only 278 were able for analysis. We obtained the following statistics: 9.35% manifested SSIs; length of stay varied from 1 to 119 days, with ~40% staying between 10 and 19 days; 15.1% of the patients died. Regarding the prediction power of SSI, the experiments have a maximum value of 0.713. Conclusions: Despite the considerable loss rate of >50% of the database samples due to the presence of noise, it was possible to have a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some configurations had results equal to 0.5, others reached 0.713, which indicates that the automated SSI monitoring framework for patients undergoing coronary artery bypass grafting surgery is promising. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available at www.sacihweb.com), a mobile application was developed.
Funding: None
Disclosures: None
Investigation of Surgical Site Infection Outbreak Among Neurosurgical Patients
- Luísa Ramos, Jussara Pessoa, Leonardo Santos, Carlos Starling, Braulio Couto
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s306
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- October 2020
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Background: The infection control service of a private hospital in Belo Horizonte, Brazil, performs continuous surveillance of surgical patients according to the CDC NHSN protocols. In a routine analysis of the neurosurgical service, we identified a subtle increase in the incidence of surgical site infection (SSI): in 5 months (June–October 2018), 6 patients developed an SSI. From January 2017 until May 2018, there were no cases of infection in neurosurgery, which led us to suspect an outbreak. Methods: A cohort study was used to investigate the factors associated with risk of SSI. We investigated the following variables: ASA score, number of hospital admissions, age, preoperative hospital length of stay, duration of surgery, wound class, general anesthesia, emergency, trauma, prosthesis, surgical procedures, surgeon. Furthermore, 9 key steps were followed to investigate the outbreak: case definition (step 1), search for new SSI cases (step 2); confirmation of the outbreak (step 3); analysis of SSI cases by London Protocol (step 4); analysis of the cohort data (step 5); inspections in the surgical ward (step 6); qualitative and quantitative reports sent to the neurosurgical departments (step 7); continuing with active surveillance (stage 8); announcement of research findings (step 9). Results: The outbreak was confirmed: SSI incidence in the pre-epidemic period (January–May 2018) was 0 of 218 (0%); in the epidemic period (June–October 2018), SSI incidence was 6 of 94 (6.4%) (P < .001). We identified 3 SSI etiologic agents: 2 Klebsiella pneumoniae, 2 S. aureus, and 1 Serratia marcescens. It was unlikely that there was a common source for the outbreak. We identified the following risk factors: second or third hospital admissions (RR, 3.7; P = .041), and preoperative hospital length of stay: SSI patients (4.3±5.7 days) versus control patients (0.7 ± 2.1 days) (P = .048). None of the surgeons presented an SSI rate significantly different from each other. We used the London protocol to identify antibiotic prophylaxis failures in most cases. Conclusions: New cases of infections can be prevented if the length of preoperative hospital stay becomes as short as possible and, most importantly, if antibiotic prophylaxis does not fail.
Funding: None
Disclosures: None
Cobweb Chart for Infection Rates, Infectometer, and Outbreak Alert System: Real-Time Systems for Summarizing Nosocomial Data
- Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s171-s172
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- October 2020
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Background: Reporting nosocomial surveillance data can be difficult because the quantity of statistics, graphics, tables, and numeric data may confuse people. Another issue related to feedback regarding healthcare infections rate is that gaps exist between collecting data, the analysis, and implementation of actions based on the information produced. Even when a statistical process control chart (SPC) is used, it is interpreted retrospectively. Here, we present 3 epidemiological tools: (1) a cobweb chart for infection rates, (2) the infectometer, and (3) an outbreak alert system. Methods: For the cobweb chart, the first step is to choose how many and which infection rates will be summarized. Thereafter, all infection rates, respective benchmarks, endemic level, and actual values are placed in a spreadsheet. Although each infection rate has different units (eg, %, rates per 100 discharges, and/or rates per 1,000 denominator days), when we compare the respective endemic level and actual rate with the benchmark, dimensionless quantities are generated for each indicator, making it possible to build the cobweb graph. Using the infectometer for calculations, we (1) built an SPC chart for each infection or microorganism; (2) estimated the average month and standard deviation of the infection cases, excluding outlier data, and (3) calculated the monthly expected incidence, assuming that nosocomial infection occurrence follows a normal distribution. If the supposition of normal distribution fails, a percentile method is used. The outbreak alert system predicts outbreaks using the infectometer parameters, the last month’s observed infection cases, and a Poisson model for predicting the chance of new cases of each infection above monthly expected incidence. Results: With the adapted radar chart, we can report many infection rates in only 1 chart (Fig. 1). The SPC charts for infection rates, stratified by all the types of healthcare infections or by microorganism, can be built, and the infectometer can then be produced, showing weekly and monthly expected cases of an endemic condition. The outbreak alert system is presented as a speedometer that is analyzed at the beginning of each month (Fig. 2). Conclusions: The idea behind the cobweb chart for infection rate method is to report all infection rates in only 1 graph. With the infectometer, it is not necessary to wait until the end of the month to analyze the surveillance data; the analysis becomes prospective and timely. The outbreak alert system brings the future to the present, showing the risk of an outbreak.
Funding: None
Disclosures: None
Risk Factors for Surgical Site Infection After Orthopedic Trauma Surgery: A Two-Year Prospective Multicenter Analysis
- Milena Reis Abreu, Larissa Paiva, Tamires Costa Mendes, Barbara Cristiny Maia, Ana Luiza Rodrigues, cia Moreira, Victor de Souza, Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s377-s378
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- October 2020
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Background: Trauma is defined by the NHSN as “blunt or penetrating traumatic injury.” Therefore, if the surgery was performed because of a recent fall, for example, then it is a trauma surgery. Here, we investigated which preoperative and operative parameters are associated with surgical site infection (SSI) after orthopedic trauma surgery. Objective: We aimed to answer 3 main questions: What is the risk of wound infection for patients undergoing trauma surgery? What are the main etiologic agents of SSI after trauma surgery? And what are the risk factors associated with SSI after trauma surgery? Methods: This prospective multicenter cohort study included 2,035 patients undergoing trauma surgery between July 2016 and June 2018 in 4 hospitals in Belo Horizonte, Brazil. Outcome variables were SSI, hospital mortality, and length of hospital stay. The following preoperative and operative parameters were evaluated: age, length of hospital stay before surgery, duration of surgery, number of professionals at surgery, number of hospital admissions, surgical wound classification, American Society of Anesthesiologists (ASA) preoperative assessment score, type of surgery (elective, emergency), general anesthesia (yes, no), trauma surgery (yes, no), and the 3-point prediction Nosocomial Infections Surveillance (NNIS) risk index. Results: The overall estimated SSI risk was 2.8% (95% CI, 2.0%–3.6%). Hospital mortality risk after trauma surgery was 3.4% (95% CI, 2.8%–4.4%). Hospital length of stay parameters in noninfected patients were as follows: mean, 8 days; median, 3 days; SD, 12 days. Hospital length of stay parameters in infected patients were mean, 30 days; median, 23 days; with SD, 31 days. The parameters for hospital stay in infected patients were mean, 10 days; median, 3 days, and SD, 15.9 (P < .001). Trauma orthopedic surgery lasting >2 hours was associated with approximately twice the risk (RR, 2.2) of developing an SSI compared to ≤2 hours of surgery: 27 of 739 (3.7%) versus 21 of 1,290 (1.6%), respectively, (P = .005) (Fig. 1). The NNIS risk index predicts the risk of SSI after trauma surgery (P = .003): 13 of 737 SSIs (1.8%) had an NNIS risk index of 0; 20 of 736 SSIs (2.7%) had an NNIS risk index of 1; 8 of 211 SSIs (3.8%) had an NNIS risk index of 2; and 2 of 11 SSIs (18.2%) had an NNIS risk index of 3 (Fig. 2). Conclusions: We identified intrinsic risk factors for SSI after orthopedic trauma surgery. The identification of the actual SSI incidence after trauma surgery in developing country hospitals and associated risk factors may support actions to minimize the complications caused by SSI.
Funding: None
Disclosures: None
Prediction of Surgical Risk in General Surgeries: Process Optimization Through Support Vector Machine (SVM) Algorithm
- Flávio Souza, Braulio Couto, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Gabriele Maria Braga, Jéssica Angelina Teixeira, Renata Carvalho Santos, Julia Maria Campos Martins, Karla Silvia de Sousa, Douglas Nascimento de Souza, Gustavo Barros Alves, Vladimir Alexei Rodrigues Rocha
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s355-s356
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- October 2020
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Background: In 5 hospitals in Belo Horizonte (population, 3 million) between July 2016 and June 2018, a survey was performed regarding surgical site infection (SSI). We statistically evaluated SSI incidents and optimized the power to predict SSI through pattern recognition algorithms based on support vector machines (SVMs). Methods: Data were collected on SSIs at 5 different hospitals. The hospital infection control committees (CCIHs) of the hospitals collected all data used in the analysis during their routine SSI surveillance procedures; these data were sent to the NOIS (Nosocomial Infection Study) Project. NOIS uses SACIH software (an automated hospital infection control system) to collect data from hospitals that participate voluntarily in the project. In the NOIS, 3 procedures were performed: (1) a treatment of the database collected for use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of SVM with a nonlinear separation process varying in configurations including kernel function (Laplace, Radial Basis, Hyperbolic Tangent and Bessel) and the k-fold cross-validation–based resampling process (ie, the use of data varied according to the amount of folders that cross and combine the evaluated data, being k = 3, 5, 6, 7, and 10). The data were compared by measuring the area under the curve (AUC; range, 0–1) for each of the configurations. Results: From 13,383 records, 7,565 were usable, and SSI incidence was 2.0%. Most patients were aged 35–62 years; the average duration of surgery was 101 minutes, but 76% of surgeries lasted >2 hours. The mean hospital length of stay without SSI was 4 days versus 17 days for the SSI cases. The survey data showed that even with a low number of SSI cases, the prediction rate for this specific surgery was 0.74, which was 14% higher than the rate reported in the literature. Conclusions: Despite the high noise index of the database, it was possible to sample relevant data for the evaluation of general surgery patients. For the predictive process, our results were >0.50 and were 14% better than those reported in the literature. However, the database requires more SSI case samples because only 2% of positive samples unbalanced the database. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed (available at www.sacihweb.com).
Funding: None
Disclosures: None
Artificial Neural Networks Applied to Prediction to Assess the Likelihood of Surgical Site Infection in Different Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Vladimir Alexei Rodrigues Rocha, Ana Luiza de Oliveira Rocha, Breno Henrique Colares Silva, Bruna Stella Vieira do Nascimento, Carolina Nunes Dutra, Luiza Pedrosa Gomes, Maria Clara Vilaça, Julia D. O. Matias, Laís L. de Araújo, Luaan S. Rossati, Layna R. Polidoro
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s129
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- October 2020
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Background: Based on data obtained from hospitals in the city of Belo Horizonte (population ~3,000,000), we evaluated relevant factors such as death, age, duration of surgery, potential for contamination and surgical site infection, plastic surgery, and craniotomy. The possibility of predicting surgical site infection (SSI) was then analyzed using pattern recognition algorithms based on MLP (multilayer perceptron). Methods: Data were collected by the hospital infection control committees (CCIHs) in hospitals in Belo Horizonte between 2016 and 2018. The noisy records were filtered, and the occurrences were analyzed. Finally, the predictive power of SSI of 5 types MLP was evaluated experimentally: momentum, backpropagation standard, weight decay, resilient propagation, and quick propagation. The model used 3, 5, 7, and 10 neurons in the occult layer and with resamples varied the number of records for testing (65% and 75%) and for validation (35% and 25%). Comparisons were made by measuring the AUC (area under the curve (range, 0–1). Results: From 1,096 records of craniotomy, 289 were usable for analysis. Moreover, 16% died; averaged age was 56 years (range, 40–65); mean time of surgery was 186 minutes (range, 95–250 minutes); the number of hospitalizations ranged from 1 (90.6%) to 8 (0.3%). Contamination among these cases was rated as follows: 2.7% contaminated, 23.5% potentially contaminated, 72.3% clean. The SSI rate reached 4%. The prediction process in AUCs ranged from 0.7 to 0.994. In plastic surgery, from 3,693 records, 1,099 were intact, with only 1 case of SSI and no deaths. The average age for plastic surgery was 41 years (range, 16–91); the average time of surgery was 218.5 minutes (range, 19–580 minutes); the number of hospitalizations ranged from 1 (77.4%) to 6 times (0.001%). Contamination among these cases was rated as follows: 27.90% potential contamination, 1.67% contaminated, and 0.84% infected. The prediction process ranged in AUCs from 0.2 to 0.4. Conclusions: We identified a high noise index in both surgeries due to subjectivity at the time of data collection. The profiles of each surgery in the statistical analyses were different, which was reflected in the analyzed structures. The MLP for craniotomy surgery demonstrated relevant predictive power and can guide intelligent monitoring software (available in www.sacihweb.com). However, for plastic surgeries, MLPs need more SSI samples to optimize outcomes. To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.
Disclosures: None
Funding: None
Pattern Recognition Algorithms for Predicting Surgical Site Infection in Abdominal Hysterectomy
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Amanda Martins Fagundes, Beatriz Viana Ferreira Escalda, Isabela Marques de Souza, Laura Ferraz de Vasconcelos, Maria Eduarda Rodrigues Medeiros, Thais Azevedo de Almeida
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s344-s345
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- October 2020
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Background: This research represents an experiment based in surgical site infection (SSI) to patients undergoing abdominal hysterectomy surgery procedures in hospitals in Belo Horizonte, (population, 3 million). We statistically evaluated such incidences and studied the SSI prediction power of pattern recognition algorithms, the artificial neural networks based in multilayer perceptron (MLP). Methods: Between July 2016 and June 2018, data on SSI were collected by the hospital infection control committees (CCIH) of the 3 hospitals involved in the research. They collected all data used in the analysis during their routine SSI surveillance procedures. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH (ie, automated hospital infection control system software) to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed for SSI prediction: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 types of MLP (ie, backpropagation standard, momentum, resilient propagation, weight decay, and quick propagation). MLPs were tested with 3, 5, 7, and 10 hidden-layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring area under the curve (AUC; range, 0–1) presented for each of the configurations. Results: From 1,166 records collected, only 665 records were enabled for analysis. Regarding statistical data: the average duration of surgery was 100 minutes (range, 31–180); patients were aged 41–49 years; the SSI rate was low (only 10 cases); the average length of stay was 2 days; and there were no deaths among the cases. Moreover, 29% of the operative sites were contaminated and 57% were potentially contaminated, revealing a high rate of potential contamination in the operative sites. The prediction process achieved 0.995. Conclusions: Despite the noise in the database, it was possible to obtain a relevant sampling to evaluate the profile of hospitals in Belo Horizonte. In addition, for the predictive process, although some settings achieved AUC results of 0.5, others achieved and AUC of 0.995, indicating the promise of the automated SSI monitoring framework for abdominal hysterectomy surgery (available in www.sacihweb.com). To optimize data collection and to enable other hospitals to use the SSI prediction tool, a mobile application was developed.
Funding: None
Disclosures: None
How to Convince People and Get Adherence to Hand Hygiene Practices? The Success of Ozires, the Humanoid Robot!
- Braulio Couto, Amanda Machado, Ana Clara Barbosa, , , , , Bruna Mendes, Maria da Glória Nogueira, Maria Luiza Peixoto, André Alvim, Jeruza Romaniello, Adriana Alves, Bruno Batista, Luciana Covello, Carlos Starling
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- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s254-s255
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- October 2020
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Background: Our team has been fighting nosocomial infections since 1991. During our journey, we often ask why people do not wash their hands! Semmelweiss discovered in the 1840s that handwashing prevented deaths from puerperal sepsis, but we still need to convince healthcare workers about hand hygiene. One answer is that washing hands is an unsophisticated gesture, without any technology, so people just do not do it. How can we improve compliance with hand hygiene? We imagined a robot in our team to remind people to wash their hands. Then, in 2016 we met Meccanoid, a US$200 toy robot: a 4-foot-tall programmable humanoid robot with voice recognition capabilities. We made adaptions in the robot (mini-projector + audio amplifier + alcohol dispenser + spy camera), and we gave him a name (Ozires) and a purpose: He became a professor who teaches healthcare workers how, when, and why wash their hands! Here, we describe the multimodal strategy centered around Ozires. Methods: The multimodal strategy consists of 7 key elements: (1) the robot, accompanied by a infection control practitioner, performs audio and video lectures about hand hygiene techniques, motivational videos, data feedback; (2) the robot’s wood copies with sound alert with motion detector for hand hygiene are spread out in the whole hospital; (3) fridge magnet with robot prints (gifts for patients and healthcare professionals); (4) app for hand hygiene monitoring (Hands Clean); (5) adherence rates by professional category and individual feedback; (6) patient empowerment for hand hygiene; and (7) sound alert for hand hygiene in the patient room’s door. Results: After the insertion of Ozires in 3 ICUs of hospital A (pilot study), the hand hygiene (HH) rate increased from ~36%, between January and July 2016, to ~68% between August 2016 and October 2019. At hospital B, Ozires started his lectures in May 2018, throughout the hospital. Hand hygiene adherence increased from 23% between July and December 2017 to 60% between June 2018 and October 2019. In the 3 months before this multimodal strategy was implemented in hospital C (June–August 2019), and the mean rate of hand hygiene was 65%. With the robot, the hand hygiene rate increased to 94% (September–October 2019). Conclusions: The multimodal strategy centered around the robot Ozires works! Hand hygiene compliance increased significantly after the interventions. People listen the robot much more attentively than to their human colleagues, and healthcare worker behavior changed! We need to go further improve the program, but it is sustainable. Finally, we succeeded in convincing people to improve their hand hygiene practices.
Funding: None
Disclosures: None
Effect of Short-Term Carbapenem Restriction on Antimicrobial Susceptibility of Resistant Gram-Negative Bacilli in an ICU
- Mariana Melo, Raquel Bandeira, lio de Castro Giselle Dias, Braulio Couto
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- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s200-s201
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- October 2020
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Background: Carbapenem-resistant GNB infections are a serious public health problem worldwide, particularly due to the high mortality associated with them and the low number of therapeutic options. One approach to this challenge is the development of antimicrobial stewardship programs. Objective: We evaluated the impact of a carbapenem restriction program on reducing of bacterial resistance in an intensive care unit (ICU). Methods: A retrospective study conducted in 2 phases in the 80-bed ICU of an acute-care public hospital in Minas Gerais, Brazil. The preintervention phase lasted 16 months (January 2018–April 2019) and the second phase (carbapenem restriction), after the intervention, lasted 4 months (May–August 2019). The intervention was defined as carbapenem-sparing and the use of meropenem was authorized in 3 situations: (1) treatment of serious infections documented by extended-spectrum β-lactamase–producing Enterobacteriacea (ESBL); (2) therapeutic failure with the use of another antimicrobial; and (3) infectious disease recommendation. Data were obtained through consultation of electronic medical records and microbiological results, as standardized by the CLSI, for patients with a >48-hour stay in the ICU and who met the criteria for healthcare-associated infection (HAI) according to the CDC NHSN definition. Results: Before the intervention, on average, 50 cultures were obtained with positive results for multidrug-resistant GNB–MER-GNB (SD, 12.2) and in the intervention phase, this number was 31 cultures (SD, 12.8; P = .010). Average carbapenem consumption decreased significantly with corresponding increase in cefepime consumption in the same period (Fig. 1). The ATB (DDD per 1,000 patient days) before the intervention for carbapenems was 110.6 (SD, 97.1) and for cefepime was 8.2 (SD, 5.9). In the intervention phase, the ATB for carbapenems was 44.7 (SD, 38.5; P = .015) and for cefepime it was 32.0 (SD, 20.3; P < .001). In terms of multidrug resistance rate, before the intervention, 95 of 149 of Acinetobacter (64%) were resistant and during the intervention, 13 of 30 Acinetobacter (43%) were resistant (P = .043). Other GNB (Klebsiella, Proteus, Escherichia coli, and Pseudomonas) reduced the resistance rate, but without statistical significance. We observed a reduction in the HAI rate per MDR-GNB (Fig. 2): before the intervention, it was 22.7 (SD, 5.5) and during the intervention phase it was 16.5 (SD, 7.7; P = .07), although this change did not reach statistical significance. Nevertheless, the ICU Klebsiella infection rate did significantly decrease; it was 5.5 (SD, 1.9) before the intervention and 2.4 (SD, 1.8) after the intervention (P = .009). Conclusions: Short-term carbapenem restriction may be an effective strategy to reduce the incidence of carbapenem-resistant GNB infections in the ICU. The scarce arsenal available for the treatment of MDR-GNB and the high mortality rate justify the growing need for stewardship programs in Brazilian ICUs.
Funding: None
Disclosures: None
Hospital Infections by Stenotrophomonas maltophilia: Results in Five Years of Multicentric Study
- Luciana Tanure, Rafaela Pinho, Mayra de Oliveira, Daniela Ribeiro, Jose A. Ferreira, Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s250-s251
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- October 2020
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Background:Stenotrophomonas maltophilia is an emerging pathogen responsible for high morbidity and mortality rates. Hospital infections caused by this bacteria, especially in intensive care centers, are concerning for the health system, given that the microorganism is multidrug resistant to most antimicrobials available. Objective: Therefore, the present study is built from an analysis of the variables related to nosocomial infections caused by S. maltophilia in hospitals in Brazil, to display points of major concern. Methods: We used the data collected by the Infection Prevention and Control Service to clarify the incidence rate of Stenotrophomonas maltophilia in Brazilian hospitals as well as the gross lethality of these infections and the profiles of infected patients. We collected and analyzed epidemiological data from 10 hospitals in Brazil for the period July 2014 to June 2019 according to the CDC NHSN protocol. Results: In 5 years, 93 Stenotrophomonas maltophilia infections were diagnosed in the hospitals analyzed. Overall, 61 occurred in men (66%) and 32 occurred in women (34%). Furthermore, 47 cases (51%) occurred in adult ICUs; 19 cases (20%) followed zascular surgery; 9 (10%) cases occurred in the neonatal ICU; 7 (8%) cases were from the medical clinic; and 11 (12%) were from other clinics. The incidence rate was 1.2 cases for 10,000 hospitalizations, ranging from 0.0 to 2.8 (Fig. 1). Patients’ ages ranged from 0 to 90 years, with a mean of 55 years (SD, 26 years) and a median of 64 years. Time between admission and diagnosis of infection was 1 to 102 days, with a mean of 24 days (SD, 21 days) and a median of 17 days. The gross lethality for S. maltophilia infection was 43 of 93 (46%) (95% CI, 35.8%–56.9%). The frequencies of specific infections were as follows (Fig. 2): pneumonia, 26 (28%); tracheobronchitism, 22 (24%); primary bloodstream infection, 18 (19%); skin and soft-tissue infection, 13 (14%); local infection, 7 (8%); vascular access infection, 3 (3%); urinary tract infection, 2 (2%); gastrointestinal infection, 1 (1%); and eye, nose, throat, and mouth infections, 1 (1%). Conclusions:Stenotrophomonas maltophilia infection is a rare and highly lethal event that usually occurs after 2 weeks of hospitalization. The most affected region is the respiratory tract, with a higher incidence in patients aged >60 years or in the ICU. Early and accurate investigations of multiresistant microorganisms in a hospital setting are needed to reduce patient morbidity and mortality.
Funding: None
Disclosures: None
Automated Risk Analysis of Surgical Site Infection in Hip Arthroplasty Surgeries
- Flávio Souza, Braulio Couto, Felipe Leandro Andrade da Conceição, Gabriel Henrique Silvestre da Silva, Igor Gonçalves Dias, Rafael Vieira Magno Rigueira, Gustavo Maciel Pimenta, Maurilio Martins, Julio Cesar Mendes, Ana Flavia Viana Quintão, Camila Vieira Brandão, Débora Martins Borges, Eduarda Muzzi Torres Lage, Luiza da Conceição Sabadini, Sabrina de Almeida Lopes
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s135-s136
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- October 2020
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Background: In 7 hospitals in Belo Horizonte, a city with >3,000,000 inhabitants, a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing arthroplasty surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through pattern recognition algorithms, the MLPs (multilayer perceptron). Methods: Data were collected on SSI by the hospital infection control committees (CCIHs) of the hospitals involved in the research. All data used in the analysis during their routine SSI surveillance procedures were collected. The information was forwarded to the NOIS (Nosocomial Infection Study) Project, which used SACIH automated hospital infection control system software to collect data from a sample of hospitals participating voluntarily in the project. After data collection, 3 procedures were performed: (1) a treatment of the database collected for the use of intact samples; (2) a statistical analysis on the profile of the hospitals collected; and (3) an assessment of the predictive power of 5 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% or 75% for testing and 35% or 25% for validation). The results were compared by measuring AUC (area under the curve; range, 0–1) presented for each of the configurations. Results: Of 1,246 records, 535 were intact for analysis. We obtained the following statistics: the average surgery time was 190 minutes (range, 145–217 minutes); the average age of the patients was 67 years (range, 9–103); the prosthetic implant index was 98.13%; the SSI rate was 1.49%, and the death rate was 1.21%. Regarding the prediction power, the maximum prediction power was 0.744. Conclusions: Despite the considerable loss rate of almost 60% of the database samples due to the presence of noise, it was possible to perform relevant sampling for the profile evaluation of hospitals in Belo Horizonte. For the predictive process, some configurations have results that reached 0.744, which indicates the usefulness of the structure for automated SSI monitoring for patients undergoing hip arthroplasty surgery. To optimize data collection and to enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com ), a mobile application was developed.
Funding: None
Disclosures: None
Quality of Hospital Infection Control Programs in Low- and Middle-income Countries: A National Survey
- André Alvim, Gazzinell Gazzinell, Braulio Couto
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s363
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- October 2020
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Background: One of the strategies to reduce healthcare-associated infections (HAIs) and promote the quality of disease prevention and control actions is the creation of a hospital infection control program. This program is a set of deliberately and systematically developed actions aimed toward reducing the incidence and severity of infections to the maximum extent possible. In Brazil, studies on the subject still need to be improved; they focus on structural and process assessments, especially the survey of continuing education indicators as a quality requirement for the prevention of HAIs. The organizational context does not contribute to the success of the program, and difficulties remain in implementing recommendations and in implementing patient safety policies. Objective: To analyze hospital infection control programs in relation to quality components. Methods: This cross-sectional epidemiological study was conducted in health services located in the 5 official regions of Brazil: Midwest, Northeast, North, Southeast, and South. To select the study sites, nonprobabilistic sampling using the snowball technique was used. The potential study population consisted of 114 hospital infection control services. Health professionals responded to the structured instrument sent electronically via e-mail, and other health services near their locality, until reaching a national proportion. We used the “Hospital Infection Control Program Evaluation Questionnaire”; it consists of 36 multiple-choice questions. This tool was validated by 96 expert judges using the Cronbach’s alpha test (0.82) and the content validity index (0.88). A data analysis was performed using the multivariate principal component analysis technique (PCA). Results: Overall, 13 PCA components (Fig. 1) were used to build a score for measuring the performance of the hospital infection control program (ie, IQPC score). The Southern region had the best performance of the hospital infection control program (mi = 1.50; P = .02) (Fig. 2), private administration (mi = 0.45; P = .05), of hospitals that contained 300 beds or (mi = 1.38; P < .01), hospitals that used the NHSN criterion for HAI surveillance (mi = 2.12; P < .01), and those who searched prospective activity as a surveillance method (mi = 0.51; P < .01). Conclusions: The quality of nosocomial infection control programs still needs to be improved among health services, highlighting the need to invest in small, publicly managed hospitals that use retrospective active surveillance methods.
Funding: None
Disclosures: None
Etiology, Incidence, and Risk Factors for Meningitis after Ventriculoperitoneal Shunt Procedures: A Multicenter Study
- Danilo Silva, Henrique Couto, Hoberdan Pereira, Gregory Lauar Souza, Andressa Silveira, Handerson Dias Duarte de Carvalho, Fernando Bracarense, Fernanda de Carvalho, Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s221-s222
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- October 2020
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Background: The ventriculoperitoneal shunt is the main procedure used for to treat communicating hydrocephalus. Surgical site infection associated with the shunt device is the most common complication and a cause of morbidity and mortality of related to the treatment. We sought to answer 3 questions: (1) What is the risk of meningitis after ventricular shunt operations? (2) What are the risk factors for meningitis? (3) What are the main microorganisms causing meningitis? Methods: We conducted a retrospective cohort study of patients undergoing ventricular shunt operations between July 2015 and June 2018 from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the CDC NHSN. Our sample size was 926, and we evaluated 26 preoperative and operative variables by univariate and multivariate analysis. Our outcome variables of interest were meningitis and hospital death. Results: In total, 71 cases of meningitis were diagnosed (risk, 7.7%; 95% CI, 6.1%–9.6%). The mortality rate among patients without infection was 10%, whereas hospital mortality of infected patients was 13% (P = .544). The 3 main risk factors for meningitis after ventricular shunt were identified by logistic regression model: age <2 years (OR, 3.20; P < .001), preoperative hospital stay >4 days (OR, 2.02; P = .007) and >1 surgical procedure, in addition to ventricular shunt (OR, 3.23; P = .043). Almost 1 of 3 of all patients was <2 years old (290, 31%). Also, 430 patients had >4 preoperative days (46%). Patients aged ≥2 years who underwent surgery 4 days after hospital admission had an increased risk of meningitis, from 4% to 6% (P = .140). If a patient <2 years old underwent surgery 4 or more days after hospital admission, the risk of meningitis increased from 9% to 18% (P = .026; Fig. 1). We built a risk index using the number of main risk factors based on a logistic regression model (0, 1, 2 or 3; Fig. 2). Conclusions:We identified 2 intrinsic risk factors for meningitis after ventricular shunt, age <2 years and multiple surgical procedures, and 1 extrinsic risk factor, the preoperative length of hospital stay.
Funding: None
Disclosures: None
Secular Trends in Nosocomial Carbapenem-Resistant Enterobacteriaceae (CRE): Twenty-Five Years of Surveillance in Brazilian Hospitals
- Jose Antonio Ferreira, Braulio Couto, Carlos Starling
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s383-s384
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- October 2020
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Background: Enterobacteriaceae that develop resistance to carbapenems are a family of different types of bacteria that cause hospital-acquired infections. We evaluated the incidence of nosocomial infections caused by carbapenem-resistant Enterobacteriaceae (CRE) in 13 Brazilian hospitals over 25 years from 1995 to 2019. Methods: CRE was defined as Enterobacteriaceae that is nonsusceptible to any of the a carbapenem (doripenem, meropenem, or imipenem) AND is resistant to all of the following third-generation cephalosporins: ceftriaxone, cefotaxime, and ceftazidime. Hospital-acquired infections (HAIs) were diagnosed according to the CDC NHSN protocols in 13 hospitals from Belo Horizonte, Brazil, between January 1995 to June 2019. Results: In total, 33,922 HAIs caused by Enterobacteriaceae were diagnosed in 25 years across all 13 hospitals. The percentage of CRE varied among hospitals from a minimum of 3% in hospital to a maximum of 30% in hospital E (Fig. 2). The percentage of CRE varied along time as well: for 1995–1999, 0.1% (2 of 1,414) were CRE; for 2000–2004, 0.5% (28 of 5,160) were CRE; for 2005–2009, 2.0% (160 of 8,068) were CRE; for 2010–2014, 11.1% (971 of 8,771) were CRE; and for 2015–2019, 20.2% (2,127 of 10,509) were CRE (Fig. 1). ICU patients and elderly were the most affected by CRE, which has increased lethality, compared to non-CRE Enterobacteriaceae. Conclusions: Over 25 years, CRE percentage increase from almost zero in 1995–1999, to >20% in 2015–2019.
Funding: None
Disclosures: None
Improving Water Quality Can Reduce Pyrogenic Reactions Associated With Reuse of Cardiac Catheters
- Rosemary E. Duffy, Braulio Couto, Jussara M. Pessoa, Carlos Starling, Silma Pinheiro, Michele L. Pearson, Matthew J. Arduino, Barbara J. Mattson, William R. Jarvis
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 24 / Issue 12 / December 2003
- Published online by Cambridge University Press:
- 02 January 2015, pp. 955-960
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- December 2003
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Objective:
To report the results of our preintervention investigation and subsequent 19-month three-phase intervention study designed to reduce pyrogenic reactions among patients undergoing cardiac catheterization using reprocessed catheters.
Design:A case-control study for the preintervention period and a prospective cohort study for the intervention period.
Setting:A 400-bed hospital in Belo Horizonte, Brazil.
Participants:Any patient undergoing cardiac catheterization in the hospital.
Interventions:Three intervention phases were implemented to improve the quality of the water supplied to the cardiac catheter reprocessing laboratory. Standard operating procedures for reprocessing cardiac catheters were established and reprocessing staff were trained and educated.
Results:The rate of pyrogenic reactions decreased significantly during the intervention phases, from 12.8% (159 of 1,239) in phase 1 to 5.3% (38 of 712) in phase 2 to 0.5% (4 of 769) in phase 3 (chi-square test for linear trend, 97.5; P < .001).
Conclusion:Improving water quality and using standard operating procedures for reprocessing catheters can prevent pyrogenic reactions in hospitalized patients.
Prevalence of Nosocomial Infections in General Hospitals in Belo Horizonte
- Edna Maria Rezende, Bráulio Roberto Gonçalves Marinho Couto, Carlos Ernesto Ferreira Starling, Celina Maria Módena
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 19 / Issue 11 / November 1998
- Published online by Cambridge University Press:
- 31 March 2016, pp. 872-876
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- November 1998
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OBJECTIVE:
To assess the magnitude of nosocomial infections (NI) in general hospitals of Belo Horizonte.
DESIGN:Multicenter point-prevalence study of nosocomial infections.
SETTING:All of the 11 general hospitals of Belo Horizonte that have more than 20 beds, from August 27 to October 5, 1992.
RESULTS:Of the 2,339 patients surveyed, 267 patients had 328 nosocomial infections. The global prevalence rate of NI was 14.0%, ranging from 4.6% to 27.3% in the hospitals surveyed. The most prevalent infections were found to be pneumonia and surgical-wound infections, representing 19.5% and 19.2%, respectively, of the total infections. The highest prevalence rates of NI were observed in the cardiac surgery (31.9%), pediatric (27.2%), and orthopedic (20.7%) services. The most frequently isolated microorganisms were Staphylococcus aureus, Escherichia coli, Pseudomonas species, and Klebsiella species.
CONCLUSION:The study allowed a thorough evaluation of the NI distribution profile in Belo Horizonte, Minas Gerais, Brazil, and showed it to be a serious public health problem that requires interinstitutional efforts so that effective action can be taken.