Gotowa bibliografia na temat „Circulo Israelita (São Paulo, Brazil)”

ABSTRACT Vascular punctures are often necessary in critically ill patients. They are secure, but not free of complications. Ultrasonography enhances safety of the procedure by decreasing puncture attempts, complications and costs. This study reviews important publications and the puncture technique using ultrasound, bringing part of the experience of the intensive care unit of the Hospital Israelita Albert Einstein, São Paulo (SP), Brazil, and discussing issues that should be considered in future studies.

A few studies have performed to evaluate the temperature variation influences over on the stroke rates in Brazil. METHOD: 176 medical records of inpatients were analyzed after having had a stroke between 2004 and 2006 at Hospital Israelita Albert Einstein. The temperature preceding the occurrence of the symptoms was recorded, as well as the temperature 6, 12 and 24 hours before the symptoms in 6 different weather substations, closest to their houses in São Paulo. RESULTS: Strokes occurred more frequently after a variation of 3ºC between 6 and 24 hours before the symptoms. There were most hospitalizations between 23-24ºC. CONCLUSION: Incidence of stroke on these patients was increased after a variation of 3º Celsius within 24 hours before the ictus. The temperature variations could be an important factor in the occurrence of strokes in this population.

By using the example of Jewish immigration to São Paulo in the 1930s and 1940s and analysing the history of the Congregação Israelita Paulista (CIP) under the leadership of Fritz Pinkuss, this article shows how emotions were used in different ways. Such an approach gives new insight into the complexity of migration history. The Brazilian government under Gétulio Vargas openly embraced emotional mobilisation against ‘Semites’ and ‘foreigners’, and in so doing wanted to introduce a new understanding of the nation and secure their political influence. At the same time, Pinkuss also used emotions in his communal policies to establish a new religious union, a new form of inclusion and solidarity in the Jewish community. By transferring German Jewish traditions to Brazil and emphasising their flexibility, Pinkuss not only created a new emotional bond, but also laid the ground for integration of the émigré community into Brazilian society.

Abstract Objective The present study is a systematic review of the literature to assess whether the presence of endometriosis determines or contributes to adverse obstetric outcomes. Data Sources The present work was carried out at the Hospital Israelita Albert Einstein, São Paulo, state of São Paulo, Brazil, in accordance to the PRISMA methodology for systematic reviews. A review of the literature was performed using PubMed, Web of Science and Scopus databases. The keywords used were: pregnancy outcome, pregnancy complications, obstetrical complications, obstetrics, obstetric outcomes and endometriosis. The survey was further completed by a manually executed review of cross-referenced articles, which was last performed on November 30, 2018. Selection of studies The survey disclosed a total of 2,468 articles, published from May 1946 to October 2017. A total of 18 studies were selected to be further classified according to their quality and relevance. Data Collection The Newcastle–Ottawa Quality Assessment Scale was used for classification. Five studies of greater impact and superior evidence quality and 13 studies of moderate evidence quality were selected. We analyzed the studies for the characteristics of their patients plus how endometriosis was diagnosed and their respective obstetric outcomes taking into account their statistical relevance. Data Synthesis Analyses of the higher impact and better quality studies have shown high incidence of preterm birth and placenta previa in patients with endometriosis. Conclusion Placenta previa and preterm birth are the most statistically significant outcomes related to endometriosis, as indicated by our systematic review. The present information is useful to alert obstetricians and patients about possible unfavorable obstetric outcomes.

Coronavirus, also known as COVID-19, was first detected in Wuhan, China, in December 2019. It is a family of viruses ranging from the common cold to severe acute respiratory syndrome (SARS). The symptoms of such a virus are similar to those of a cold or seasonal allergies. Like other respiratory viruses, it is mainly transmitted through airborne droplets when coughing or sneezing. Therefore, the recognition of COVID-19 requires careful laboratory analysis, and the reduction of recognition resources is a major challenge. On 11 March, 2020, the World Health Organization (WHO) declared COVID-19, caused by SARS-CoV-2, a pandemic, as there had been an exponential increase in cases worldwide, and demand for intensive beds and related structures had far exceeded existing capacity. The first examples of this are the regions of Italy. Brazil registered the first case of SARS-CoV-2 on 02/26/2020. Transmission of the virus in this country shifted very quickly from imported cases to local and, finally, community missions, with the Brazilian federal government announcing national community transmission on 03/20/2020. As of March 23, in the state of São Paulo with a population of about 12 million people, where the Israelita Albert Einstein Hospital is located, 477 cases of the disease and 30 related deaths were registered, and on March 27, there were already 1223 cases of COVID-19 with 68 concomitant deaths. To slow the spread of the virus in the state of São Paulo, quarantines and social distancing measures were introduced. One of the motivations for this challenge is the fact that, in the context of an extensive healthcare system with the possible limitation of SARS-CoV-2 testing, it is not practical to test every case, and test results can only be used in testing the target subpopulation. The study objective is to build a model based on machine learning that can predict the detection of SARS-CoV-2 from medical data. For this, various classification models of machine learning are compared, and the best one to predict coronaviruses is determined. The comparison is based on individuals in class 1, i.e., those with a positive test. Therefore, it is required to determine the machine learning model with the best response and F1 score for class 1.Materials and Methods. An open-source data set from the Israelita Albert Einstein Hospital in São Paulo, Brazil, was taken as a basis. The following machine learning models were used for the study: RandomForests (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and AdaBoost (AB), as well as the 10-time cross-validation technique. Some machine learning performance measures, such as accuracy, recall, and F1 score were evaluated.Results. Out of a total of 5,644 people tested during the COVID-19 pandemic, 5,086 people tested negative and 558 people tested positive. At the same time, support for machine vectors showed the best results in detecting coronavirus with a recall of 75 % and an F1 score of 60 % compared to models: Random drill, KNN, LR, AB, and DT.Discussion and Conclusions. It was found that when using AB algorithms, greater accuracy is achieved, but the stability of the LSVM algorithm is higher. Therefore, it can be recommended as a useful tool for detecting COVID-19.

There is a concern around the world of an increasing caesarean section rate. It was estimated that between 2010 and 2015, caesarean section rates increased by almost 50%. There are several implications for this, considering that caesarean sections are associated with higher costs and worse clinical outcomes. In this context, several interventions have been considered to increase vaginal delivery rates, including the Adequate Childbirth Project (PPA) in Brazil. This study aimed to verify the impact of the strategies adopted internally in the Hospital Israelita Albert Einstein (HIAE) located in São Paulo, Brazil, regarding the reduction of caesarean sections and their perinatal results. Actions to support our study were implemented in two phases based on the PPA schedule. These actions involved three axes: a multidisciplinary team, pregnant women and facility improvements. All pregnant women admitted for childbirth at the HIAE between 2014 and 2019 were included in this study. The overall rate of vaginal delivery in this study population and among primiparous women and the percentage of admissions to the neonatal intensive care unit (NICU) were analysed in three periods: before the implementation of PPA actions (period A), after the first phase of the project (period B) and after its second phase (period C). The results showed an increase in the average vaginal delivery rate from 23.57% in period A to 27.88% in period B, and to 30.06% in period C (AxB, p<0.001; BxC, p=0.004). There was a decrease in the average of NICU admissions over the periods (period A 19.22%, period B 18.71% and period C 13.22%); a significant reduction was observed when periods B and C (p<0.001) were compared.

ABSTRACT Objective To evaluate the epidemiological profile and overall survival of a large population of elderly individuals diagnosed with solid tumors in a tertiary hospital. Methods This retrospective study included patients aged >65 years, diagnosed with solid tumors between January 2007 and December 2011, at Hospital Israelita Albert Einstein, São Paulo, Brazil. The medical records were reviewed to obtain information about clinical variables and overall survival. Results A total of 806 patients were identified, and 58.4% were male. Mean age was 74 years (65 to 99 years). The most common types were prostate (22%), colorectal (21%), breast (19%), and lung cancer (13%), followed by bladder (8%), pancreas (6%), and other types (11%). The majority of patients were diagnosed at early stage disease. After a median follow-up of 27 months (15 to 45 months), 29% of the patients (234/806) died, predominantly in the group older than 70 years. For the entire cohort, the median 2-year survival rate was 71%. Median overall survival was not reached within the study period. In a multivariate analysis, age (HR: 1.35; 95%CI: 1.25-1.45; p<0.001) and disease stage (HR: 1.93; 95%CI: 1.75-2.14; p<0.001) were independent negative predictors of poor survival. Conclusion The most prevalent tumors were prostate, colorectal, breast, and lung cancer, with the larger proportion diagnosed at initial stages, reflecting the great number of patients alive at last follow-up.

A fast and efficient diagnosis of serious infectious diseases, such as the recent SARS-CoV-2, is necessary in order to curb both the spread of existing variants and the emergence of new ones. In this regard and recognizing the shortcomings of the reverse transcription-polymerase chain reaction (RT-PCR) and rapid diagnostic test (RDT), strategic planning in the public health system is required. In particular, helping researchers develop a more accurate diagnosis means to distinguish patients with symptoms with COVID-19 from other common infections is what is needed. The aim of this study was to train and optimize the support vector machine (SVM) and K-nearest neighbors (KNN) classifiers to rapidly identify SARS-CoV-2 (positive/negative) patients through a simple complete blood test without any prior knowledge of the patient’s health state or symptoms. After applying both models to a sample of patients at Israelita Albert Einstein at São Paulo, Brazil (solely for two examined groups of patients’ data: “regular ward” and “not admitted to the hospital”), it was found that both provided early and accurate detection, based only on a selected blood profile via the statistical test of dependence (ANOVA test). The best performance was achieved by the improved SVM technique on nonhospitalized patients, with precision, recall, accuracy, and AUC values reaching 94%, 96%, 95%, and 99%, respectively, which supports the potential of this innovative strategy to significantly improve initial screening.

Objective: To determine the incidence and the rate of red blood cell alloimmunization in polytransfused patients. Methods: A polytransfused patient was defined as having received at least 6 units of red cell concentrates during a 3-month period. The records of all patients (n = 12,904) who had received red blood cell units were examined retrospectively by searching the computer database at Hospital Israelita Albert Einstein in São Paulo, Brazil, over a 6-year period, between 2003 and 2009. Results: During this time, 77,049 red cell concentrate transfusions were performed in 12,904 patients. There were 3,044 polytransfused patients, 227 of whom (7.5%) presented with irregular erythrocyte antibodies. The prevalence of alloantibody specificity was: Anti-E>anti-D>anti-K>anti-C>anti-Dia>anti-c>anti-Jka>anti-S in 227 polytransfused patients. We found combinations of alloantibodies in 79 patients (34.8%), and the most common specificities were against the Rh and/or Kell systems. These antibodies show clinical significance, as they can cause delayed hemolytic transfusion reactions and perinatal hemolytic disease. About 20% of the patients showed an IgG autoantibody isolated or combined with alloantibodies. Interestingly, a high incidence of antibodies against low frequency antigens was detected in this study, mainly anti-Dia. Conclusion: Polytransfused patients have a high probability of developing alloantibodies whether alone or combined with autoantibodies and antibodies against low frequency antigens. Transfusion of red blood cells with a phenotype-compatible with RH (C, E, c), K, Fya, and Jka antigens is recommended for polytransfused patients in order to prevent alloimmunization and hemolytic transfusion reactions.