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
4 resultados
Resultados de Busca
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- 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.
- 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.
- Decontamination and re-use of surgical masks and respirators during the COVID-19 pandemic(2021) CORTES, Marina Farrel; ESPINOZA, Evelyn Patricia Sanchez; NOGUERA, Saidy Liceth Vasconez; SILVA, Aline Alves; MEDEIROS, Marion Elke Sielfeld Araya de; BOAS, Lucy Santos Villas; FERREIRA, Noely Evangelista; TOZETTO-MENDOZA, Tania Regina; MORAIS, Fernando Goncalves; QUEIROZ, Rayana Santiago de; PROENCA, Adriana Coracini Tonacio de; GUIMARAES, Thais; GUEDES, Ana Rubia; LETAIF, Leila Suemi Harima; MONTAL, Amanda Cardoso; MENDES-CORREA, Maria Cassia; JOHN, Vanderley M.; LEVIN, Anna S.; COSTA, Silvia FigueiredoObjectives: The coronavirus disease 2019 pandemic increased global demand for personal protective equipment (PPE) and resulted in shortages. The study evaluated the re-use of surgical masks and respirators by analysing their performance and safety before and after reprocessing using the following methods: oven, thermal drying, autoclave, and hydrogen peroxide plasma vapour. Methods: In total, 45 surgical masks and 69 respirators were decontaminated. Visual integrity, air permeability, burst resistance, pressure differential and particulate filtration efficiency of new and decontaminated surgical masks and respirators were evaluated. In addition, 14 used respirators were analysed after work shifts before and after decontamination using reverse transcription polymerase chain reaction (RT-PCR) and viral culturing. Finally, reprocessed respirators were evaluated by users in terms of functionality and comfort. Results: Oven decontamination (75 degrees C for 45 min) was found to be the simplest decontamination method. Physical and filtration assays indicated that all reprocessing methods were safe after one cycle. Oven decontamination maintained the characteristics of surgical masks and respirators for at least five reprocessing cycles. Viral RNA was detected by RT-PCR in two of the 14 used respirators. Four respirators submitted to viral culture were PCR-negative and culture-negative. Reprocessed respirators used in work shifts were evaluated positively by users, even after three decontamination cycles. Conclusion: Oven decontamination is a safe method for reprocessing surgical masks and respirators for at least five cycles, and is feasible in the hospital setting. (C) 2020 Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.