LEILA SUEMI HARIMA
Índice h a partir de 2011
5
Projetos de Pesquisa
Unidades Organizacionais
PAHC, Hospital das Clínicas, Faculdade de Medicina - Médico
FMUSP, Hospital das Clínicas, Faculdade de Medicina - Médico
FMUSP, Hospital das Clínicas, Faculdade de Medicina - Médico
16 resultados
Resultados de Busca
Agora exibindo 1 - 10 de 16
- Carbapenem-resistant Enterobacteriaceae in patients admitted to the emergency department: prevalence, risk factors, and acquisition rate(2017) SALOMAO, M. C.; GUIMARAES, T.; DUAILIBI, D. F.; PERONDI, M. B. M.; LETAIF, L. S. H.; MONTAL, A. C.; ROSSI, F.; CURY, A. P.; DUARTE, A. J. S.; LEVIN, A. S.; BOSZCZOWSKI, I.Background: Carbapenem-resistant Enterobacteriaceae (CRE) have been reported worldwide and are associated with high mortality rates. Intestinal colonization acts as a reservoir and fosters exchange of resistance mechanisms. Aim: To investigate the prevalence of patients harbouring CRE on hospital admission, risk factors associated, and the acquisition rate within the emergency department (ED). Methods: This was a cross-sectional survey with 676 patients consecutively admitted to the ED study during the months of May to July 2016. A questionnaire was performed and rectal swabs were collected from patients on admission, for culture and for multiplex real-time polymerase chain reaction (PCR). If the patient was hospitalized for more than one week in the ED, samples were taken again to determine the acquisition rate of CRE. Findings: Forty-six patients were colonized; all positive PCR were Klebsiella pneumoniae carbapenemase. The acquisition rate was 18%. Previous exposure to healthcare in the last year, liver disease, and use of antibiotics in the last month were risk factors for colonization. Six patients with no previous exposure to healthcare were CRE-colonized on admission, suggesting transmission of CRE within the community. Conclusion: Screening of high-risk patients on admission to the ED is a strategy to early identify CRE carriage and may contribute to control CRE dissemination.
- Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic(2024) RITTO, Ana Paula; ARAUJO, Adriana Ladeira de; CARVALHO, Carlos Roberto Ribeiro de; SOUZA, Heraldo Possolo De; FAVARETTO, Patricia Manga e Silva; SABOYA, Vivian Renata Boldrim; GARCIA, Michelle Louvaes; KULIKOWSKI, Leslie Domenici; KALLAS, Esper Georges; PEREIRA, Antonio Jose Rodrigues; COBELLO JUNIOR, Vilson; SILVA, Katia Regina; ABDALLA, Eidi Raquel Franco; SEGURADO, Aluisio Augusto Cotrim; SABINO, Ester Cerdeira; RIBEIRO JUNIOR, Ulysses; FRANCISCO, Rossana Pulcineli Vieira; MIETHKE-MORAIS, Anna; LEVIN, Anna Sara Shafferman; SAWAMURA, Marcio Valente Yamada; FERREIRA, Juliana Carvalho; SILVA, Clovis Artur; MAUAD, Thais; GOUVEIA, Nelson da Cruz; LETAIF, Leila Suemi Harima; BEGO, Marco Antonio; BATTISTELLA, Linamara Rizzo; DUARTE, Alberto Jose da Silva; SEELAENDER, Marilia Cerqueira Leite; MARCHINI, Julio; FORLENZA, Orestes Vicente; ROCHA, Vanderson Geraldo; MENDES-CORREA, Maria Cassia; COSTA, Silvia Figueiredo; CERRI, Giovanni Guido; BONFA, Eloisa Silva Dutra de Oliveira; CHAMMAS, Roger; BARROS FILHO, Tarcisio Eloy Pessoa de; BUSATTO FILHO, GeraldoIntroduction The COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.Methods At the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.Results Over the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020-2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.Discussion Research is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.
- Setting up hospital care provision to patients with COVID-19: lessons learnt at a 2400-bed academic tertiary center in SAo Paulo, Brazil(2020) PERONDI, Beatriz; MIETHKE-MORAIS, Anna; MONTAL, Amanda C.; HARIMA, Leila; SEGURADO, Aluisio C.As of August 30, 2020, Brazil ranked second among countries with the highest number of COVID-19 cases, with the city of SAo Paulo as the national epidemic epicenter. Local public healthcare institutions were challenged to respond to a fast-growing hospital demand, reengineering care provision to optimize clinical outcomes and minimize intra-hospital coronavirus infection. In this paper we describe how the largest public hospital complex in Latin America faced this unprecedented burden, managing severe COVID-19 cases while sustaining specialized care to patients with other conditions. In our strategic plan a 900 bed hospital was exclusively designated for COVID-19 care and continuity of care to those not infected with coronavirus ensured in other inpatient facilities. After 152 days, 4241 patients with severe COVID-19 were hospitalized, 70% of whom have already been discharged, whereas the remaining Institutes of the complex successfully maintained high complexity inpatient and urgent/emergency care to non-COVID-19 patients. (C) 2020 Sociedade Brasileira de Infectologia.
- Epidemiologic Surveillance in an academic hospital during the COVID-19 pandemic in Sao Paulo, Brazil: the key role of epidemiologic engagement in operational processes(2020) MARCILIO, Izabel; MIETHKE-MORAIS, Anna; HARIMA, Leila; MONTAL, Amanda C.; PERONDI, Beatriz; AYRES, Jose Ricardo de Carvalho Mesquita; GOUVEIA, Nelson; BONFA, Eloisa; NOVAES, Hillegonda Maria Dutilh
- Virtual visits to inpatients by their loved ones during COVID-19(2020) RIOS, Izabel Cristina; CARVALHO, Ricardo Tavares de; RUFFINI, Vitor Maia Teles; MONTAL, Amanda Cardoso; HARIMA, Leila Suemi; CRISPIM, Douglas Henrique; ARAI, Lilian; PERONDI, Beatriz; MORAIS, Anna Miethke; ANDRADE, Andrea Janaina de; BONFA, Eloisa Silva Dutra de Oliveira
- Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource management(2022) LEVIN, Anna S.; FREIRE, Maristela P.; OLIVEIRA, Maura Salaroli de; NASTRI, Ana Catharina S.; HARIMA, Leila S.; PERDIGAO-NETO, Lauro Vieira; MAGRI, Marcello M.; FIALKOVITZ, Gabriel; FIGUEIREDO, Pedro H. M. F.; SICILIANO, Rinaldo Focaccia; SABINO, Ester C.; CARLOTTI, Danilo P. N.; RODRIGUES, Davi Silva; NUNES, Fatima L. S.; FERREIRA, Joao EduardoBackground Optimal COVID-19 management is still undefined. In this complicated scenario, the construction of a computational model capable of extracting information from electronic medical records, correlating signs, symptoms and medical prescriptions, could improve patient management/prognosis. Methods The aim of this study is to investigate the correlation between drug prescriptions and outcome in patients with COVID-19. We extracted data from 3674 medical records of hospitalized patients: drug prescriptions, outcome, and demographics. The outcome evaluated was hospital outcome. We applied correlation analysis using a Logistic Regression algorithm for machine learning with Lasso and Matthews correlation coefficient. Results We found correlations between drugs and patient outcomes (death/discharged alive). Anticoagulants, used very frequently during all phases of the disease, were associated with good prognosis only after the first week of symptoms. Antibiotics very frequently prescribed, especially early, were not correlated with outcome, suggesting that bacterial infections may not be important in determining prognosis. There were no differences between age groups. Conclusions In conclusion, we achieved an important result in the area of Artificial Intelligence, as we were able to establish a correlation between concrete variables in a real and extremely complex environment of clinical data from COVID-19. Our results are an initial and promising contribution in decision-making and real-time environments to support resource management and forecasting prognosis of patients with COVID-19.
- Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records(2022) RODRIGUES, Davi Silva; NASTRI, Ana Catharina S.; MAGRI, Marcello M.; OLIVEIRA, Maura Salaroli de; SABINO, Ester C.; FIGUEIREDO, Pedro H. M. F.; LEVIN, Anna S.; FREIRE, Maristela P.; HARIMA, Leila S.; NUNES, Fatima L. S.; FERREIRA, Joao EduardoBackground COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. Methods We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clinicas (Sao Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient's outcome. Results Time series-based machine learning models are capable of predicting a COVID-19 patient's outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). Conclusions Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.
- Scarce Resource Allocation for Critically ill Patients During the COVID-19 Pandemic: A Public Health Emergency in Sao Paulo Brazil(2021) LIN, Chin An; FRANCO, Juliana Bertoldi; RIBEIRO, Sabrina Correa da Costa; DADALTO, Luciana; LETAIF, Leila Suemi Harima
- Disinfection of 3D-printed protective face shield during COVID-19 pandemic(2021) NOGUERA, Saidy Vasconez; ESPINOZA, Evelyn Patricia Sanchez; CORTES, Marina Farrel; OSHIRO, Izabel Cristina Vilela; SPADAO, Fernanda de Sousa; BRANDAO, Laura Maria Brasileiro; BARROS, Ana Natiele da Silva; COSTA, Sibeli; ALMEIDA, Bianca Leal de; SORIANO, Paula Gemignani; SALLES, Alessandra Grassi; ESCORCIO, Mirian Elizabete Marques; BARRETTI, Cristina Madeira; BAPTISTA, Fernanda Spadotto; ALVARENGA, Glaura Souza; MARINHO, Igor; LETAIF, Leila Suemi Harima; LI, Ho Ye; BACCHI, Pedro; SANTOS, Ana Rubia Guedes dos; REGADAS, Lucas Borges; BRAGA, Carlos Eduardo Lima; ZSIGMOND, Fabio; SEGURADO, Aluisio Cotrim; GUIMARAES, Thais; LEVIN, Anna Sara; BERTOLDI, Cristiane Aun; CATALANI, Luiz Henrique; ZANCUL, Eduardo de Senzi; COSTA, Silvia FigueiredoThis study assessed the disinfection using 70% ethanol; H2O2-quaternary ammonium salt mixture; 0.1% sodium hypochlorite and autoclaving of four 3D-printed face shields with different designs, visor materials; and visor thickness (0.5-0.75 mm). We also investigated their clinical suitability by applying a questionnaire to health workers (HW) who used them. Each type of disinfection was done 40 times on each type of mask without physical damage. In contrast, autoclaving led to appreciable damage.
bookPart Doenças da tireoide(2015) CARVALHO, Daniel Fiordelisio de; LETAIF, Leila Suemi Harima