Antimicrobial use, incidence, etiology and resistance patterns in bacteria causing ventilator-associated pneumonia in a clinical-surgical intensive care unit

INTRODUCTION : Antimicrobial resistance is an increasing threat in hospitalized patients, and inappropriate empirical antimicrobial therapy is known to adversely affect outcomes in ventilator-associated pneumonia (VAP). The aim of this study was to evaluate antimicrobial usage, incidence, etiology, and antimicrobial resistance trends for prominent nosocomial pathogens causing ventilator-associated pneumonia in a clinical-surgical intensive care unit (ICU). METHODS : Gram-negative bacilli and Staphylococcus aureus causing VAP, as well as their antimicrobial resistance patterns and data on consumption (defined daily dose [DDD] per 1,000 patient days) of glycopeptides, extended-spectrum cephalosporins, and carbapenems in the unit were evaluated in two different periods (A and B). RESULTS: Antimicrobial use was high, mainly of broad-spectrum cephalosporins, with a significant increase in the consumption of glycopeptides (p < 0.0001) and carbapenems (p < 0.007) in period B. For Acinetobacter baumannii and members of the Enterobacteriaceae family, 5.27- and 3.06-fold increases in VAPs, respectively, were noted, and a significant increase in resistance rates was found for imipenem-resistant A. baumannii (p = 0.003) and third-generation cephalosporins-resistant Enterobacteriaceae (p = 0.01) isolates in this same period. CONCLUSIONS: Our results suggest that there is a link between antibiotics usage at institutional levels and resistant bacteria. The use of carbapenems was related to the high rate of resistance in A. baumannii and therefore a high consumption of imipenem/meropenem could play a major role in selective pressure exerted by antibiotics in A. baumannii strains.

Invasive candidosis: contrasting the perceptions of infectious disease physicians and intensive care physicians

Introduction We analyze how infectious disease physicians perceive and manage invasive candidosis in Brazil, in comparison to intensive care unit specialists. Methods A 38-question survey was administered to 56 participants. Questions involved clinicians' perceptions of the epidemiology, diagnosis, treatment and prophylaxis of invasive candidosis. P < 0.05 was considered statistically significant. Results The perception that candidemia not caused by Candida albicans occurs in less than 10% of patients is more commonly held by intensive care unit specialists (p=0.018). Infectious disease physicians almost always use antifungal drugs in the treatment of patients with candidemia, and antifungal drugs are not as frequently prescribed by intensive care unit specialists (p=0.006). Infectious disease physicians often do not use voriconazole when a patient's antifungal treatment has failed with fluconazole, which also differs from the behavior of intensive care unit specialists (p=0.019). Many intensive care unit specialists use fluconazole to treat candidemia in neutropenic patients previously exposed to fluconazole, in contrast to infectious disease physicians (p=0.024). Infectious disease physicians prefer echinocandins as a first choice in the treatment of unstable neutropenic patients more frequently than intensive care unit specialists (p=0.013). When candidemia is diagnosed, most infectious disease physicians perform fundoscopy (p=0.015), whereas intensive care unit specialists usually perform echocardiograms on all patients (p=0.054). Conclusions This study reveals a need to better educate physicians in Brazil regarding invasive candidosis. The appropriate management of this disease depends on more drug options being available in our country in addition to global coverage in private and public hospitals, thereby improving health care.

Acute kidney injury in a tropical country: a cohort study of 253 patients in an infectious diseases intensive care unit

Introduction: Acute kidney injury (AKI) is a frequent and potentially fatal complication in infectious diseases. The aim of this study was to investigate the clinical aspects of AKI associated with infectious diseases and the factors associated with mortality. Methods: This retrospective study was conducted in patients with AKI who were admitted to the intensive care unit (ICU) of a tertiary infectious diseases hospital from January 2003 to January 2012. The major underlying diseases and clinical and laboratory findings were evaluated. Results: A total of 253 cases were included. The mean age was 46±16 years, and 72% of the patients were male. The main diseases were human immunodeficiency virus (HIV) infection, HIV/acquired immunodeficiency syndrome (AIDS) (30%), tuberculosis (12%), leptospirosis (11%) and dengue (4%). Dialysis was performed in 70 cases (27.6%). The patients were classified as risk (4.4%), injury (63.6%) or failure (32%). The time between AKI diagnosis and dialysis was 3.6±4.7 days. Oliguria was observed in 112 cases (45.7%). The Acute Physiology and Chronic Health Evaluation (APACHE) II scores were higher in patients with HIV/AIDS (57±20, p-value=0.01) and dengue (68±11, p-value=0.01). Death occurred in 159 cases (62.8%). Mortality was higher in patients with HIV/AIDS (76.6%, p-value=0.02). A multivariate analysis identified the following independent risk factors for death: oliguria, metabolic acidosis, sepsis, hypovolemia, the need for vasoactive drugs, the need for mechanical ventilation and the APACHE II score. Conclusions: AKI is a common complication in infectious diseases, with high mortality. Mortality was higher in patients with HIV/AIDS, most likely due to the severity of immunosuppression and opportunistic diseases.

Nosocomial infections in a neonatal intensive care unit during 16 years: 1997-2012

Introduction Surveillance of nosocomial infections (NIs) is an essential part of quality patient care; however, there are few reports of National Healthcare Safety Network (NHSN) surveillance in neonatal intensive care units (NICUs) and none in developing countries. The purpose of this study was to report the incidence of NIs, causative organisms, and antimicrobial susceptibility patterns in a large cohort of neonates admitted to the NICU during a 16-year period. Methods The patients were followed 5 times per week from birth to discharge or death, and epidemiological surveillance was conducted according to the NHSN. Results From January 1997 to December 2012, 4,615 neonates, representing 62,412 patient-days, were admitted to the NICU. The device-associated infection rates were as follows: 17.3 primary bloodstream infections per 1,000 central line-days and 3.2 pneumonia infections per 1,000 ventilator-days. A total of 1,182 microorganisms were isolated from sterile body site cultures in 902 neonates. Coagulase-negative staphylococci (CoNS) (34.3%) and Staphylococcus aureus (15.6%) were the most common etiologic agents isolated from cultures. The incidences of oxacillin-resistant CoNS and Staphylococcus aureus were 86.4% and 28.3%, respectively. Conclusions The most important NI remains bloodstream infection with staphylococci as the predominant pathogens, observed at much higher rates than those reported in the literature. Multiresistant microorganisms, especially oxacillin-resistant staphylococci and gram-negative bacilli resistant to cephalosporin were frequently found. Furthermore, by promoting strict hygiene measures and meticulous care of the infected infants, the process itself of evaluating the causative organisms was valuable.

Evaluation of the predictive indices for candidemia in an adult intensive care unit

INTRODUCTION: To evaluate predictive indices for candidemia in an adult intensive care unit (ICU) and to propose a new index. METHODS: A prospective cohort study was conducted between January 2011 and December 2012. This study was performed in an ICU in a tertiary care hospital at a public university and included 114 patients staying in the adult ICU for at least 48 hours. The association of patient variables with candidemia was analyzed. RESULTS: There were 18 (15.8%) proven cases of candidemia and 96 (84.2%) cases without candidemia. Univariate analysis revealed the following risk factors: parenteral nutrition, severe sepsis, surgical procedure, dialysis, pancreatitis, acute renal failure, and an APACHE II score higher than 20. For the Candida score index, the odds ratio was 8.50 (95% CI, 2.57 to 28.09); the sensitivity, specificity, positive predictive value, and negative predictive value were 0.78, 0.71, 0.33, and 0.94, respectively. With respect to the clinical predictor index, the odds ratio was 9.45 (95%CI, 2.06 to 43.39); the sensitivity, specificity, positive predictive value, and negative predictive value were 0.89, 0.54, 0.27, and 0.96, respectively. The proposed candidemia index cutoff was 8.5; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.77, 0.70, 0.33, and 0.94, respectively. CONCLUSIONS: The Candida score and clinical predictor index excluded candidemia satisfactorily. The effectiveness of the candidemia index was comparable to that of the Candida score.

Characterization of newborns with nonimmune hydrops fetalis admitted to a neonatal intensive care unit

PURPOSE: To determine the incidence and characteristics of nonimmune hydrops fetalis in the newborn population. METHOD: A retrospective study of the period between 1996 and 2000, including all newborns with a prenatal or early neonatal diagnosis of nonimmune hydrops fetalis, based on clinical history, physical examination, and laboratory evaluation. The following were analyzed: prenatal follow-up, delivery type, gender, birth weight, gestational age, presence of perinatal asphyxia, nutritional classification, etiopathic diagnosis, length of hospital stay, mortality, and age at death. RESULTS: A total of 47 newborns with hydrops fetalis (0.42% of live births), 18 (38.3%) with the immune form and 29 (61.7%) with the nonimmune form, were selected for study. The incidence of nonimmune hydrops fetalis was 1 per 414 neonates. Data was obtained from 21 newborns, with the following characteristics: 19 (90.5%) were suspected from prenatal diagnosis, 18 (85.7%) were born by cesarean delivery, 15 (71.4%) were female, and 10 (47.6%) were asphyxiated. The average weight was 2665.9 g, and the average gestational age was 35 3/7 weeks; 14 (66.6%) were preterm; 18 (85.0 %) appropriate delivery time; and 3 (14.3%) were large for gestational age. The etiopathic diagnosis was determined for 62%, which included cardiovascular (19.0%), infectious (9.5%), placental (4.8%), hematologic (4.7%), genitourinary (4.8%), and tumoral causes (4.8%), and there was a combination of causes in 9.5%. The etiology was classified as idiopathic in 38%. The length of hospital stay was 26.6 ± 23.6 days, and the mortality rate was 52.4%. CONCLUSIONS: The establishment of a suitable etiopathic diagnosis associated with prenatal detection of nonimmune hydrops fetalis can be an important step in reducing the neonatal mortality rate from this condition.

Prevalence rates of infection in intensive care units of a tertiary teaching hospital

OBJECTIVE: To determine the prevalence rates of infections among intensive care unit patients, the predominant infecting organisms, and their resistance patterns. To identify the related factors for intensive care unit-acquired infection and mortality rates. DESIGN: A 1-day point-prevalence study. SETTING:A total of 19 intensive care units at the Hospital das Clínicas - University of São Paulo, School of Medicine (HC-FMUSP), a teaching and tertiary hospital, were eligible to participate in the study. PATIENTS: All patients over 16 years old occupying an intensive care unit bed over a 24-hour period. The 19 intensive care unit s provided 126 patient case reports. MAIN OUTCOME MEASURES: Rates of infection, antimicrobial use, microbiological isolates resistance patterns, potential related factors for intensive care unit-acquired infection, and death rates. RESULTS: A total of 126 patients were studied. Eighty-seven patients (69%) received antimicrobials on the day of study, 72 (57%) for treatment, and 15 (12%) for prophylaxis. Community-acquired infection occurred in 15 patients (20.8%), non- intensive care unit nosocomial infection in 24 (33.3%), and intensive care unit-acquired infection in 22 patients (30.6%). Eleven patients (15.3%) had no defined type. The most frequently reported infections were respiratory (58.5%). The most frequently isolated bacteria were Enterobacteriaceae (33.8%), Pseudomonas aeruginosa (26.4%), and Staphylococcus aureus (16.9%; [100% resistant to methicillin]). Multivariate regression analysis revealed 3 risk factors for intensive care unit-acquired infection: age > 60 years (p = 0.007), use of a nasogastric tube (p = 0.017), and postoperative status (p = 0.017). At the end of 4 weeks, overall mortality was 28.8%. Patients with infection had a mortality rate of 34.7%. There was no difference between mortality rates for infected and noninfected patients (p=0.088). CONCLUSION: The rate of nosocomial infection is high in intensive care unit patients, especially for respiratory infections. The predominant bacteria were Enterobacteriaceae, Pseudomonas aeruginosa, and Staphylococcus aureus (resistant organisms). Factors such as nasogastric intubation, postoperative status, and age ³60 years were significantly associated with infection. This study documents the clinical impression that prevalence rates of intensive care unit-acquired infections are high and suggests that preventive measures are important for reducing the occurrence of infection in critically ill patients.

Ethical aspects in the management of the terminally ill patient in the pediatric intensive care unit

OBJECTIVE: To identify the prevalence of management plans and decision-making processes for terminal care patients in pediatric intensive care units. METHODOLOGY: Evidence-based medicine was done by a systematic review using an electronic data base (LILACS, 1982 through 2000) and (MEDLINE, 1966 through 2000). The key words used are listed and age limits (0 to 18 years) were used. RESULTS: One hundred and eighty two articles were found and after selection according to the exclusion/inclusion criteria and objectives 17 relevant papers were identified. The most common decisions found were do-not-resuscitation orders and withdrawal or withholding life support care. The justifications for these were "imminent death" and "unsatisfatory quality of life". CONCLUSION: Care management was based on ethical principles aiming at improving benefits, avoiding harm, and when possible, respecting the autonomy of the terminally ill patient.

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Combining analytical methodologies for the identification of Aspergillus section Flavi strains isolated from Brazilian agricultural commodities

The use of chemical analysis of microbial components, including proteins, became an important achievement in the 80’s of the last century to the microbial identification. This led a more objective microbial identification scheme, called chemotaxonomy, and the analytical tools used in the field are mainly 1D/2D gel electrophoresis, spectrophotometry, high-performance liquid chromatography, gas chromatography, and combined gas chromatography-mass spectrometry. The Edman degradation reaction was also applied to peptides sequence giving important insights to the microbial identification. The rapid development of these techniques, in association with knowledge generated by DNA sequencing and phylogeny based on rRNA gene and housekeeping genes sequences, boosted the microbial identification to an unparalleled scale. The recent results of mass spectrometry (MS), like Matrix-Assisted Laser Desorption/Ionisation Time-of-Flight (MALDI-TOF), for rapid and reliable microbial identification showed considerable promise. In addition, the technique is rapid, reliable and inexpensive in terms of labour and consumables when compared with other biological techniques. At present, MALDI-TOF MS adds an additional step for polyphasic identification which is essential when there is a paucity of characters or high DNA homologies for delimiting very close related species. The full impact of this approach is now being appreciated when more diverse species are studied in detail and successfully identified. However, even with the best polyphasic system, identification of some taxa remains time-consuming and determining what represents a species remains subjective. The possibilities opened with new and even more robust mass spectrometers combined with sound and reliable databases allow not only the microbial identification based on the proteome fingerprinting but also include de novo specific proteins sequencing as additional step. These approaches are pushing the boundaries in the microbial identification field.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.