CESAR HIGA NOMURA
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/44 - Laboratório de Ressonância Magnética em Neurorradiologia, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/44 - Laboratório de Ressonância Magnética em Neurorradiologia, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina - Líder
8 resultados
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- COVID-19 pneumonia: what is the role of imaging in diagnosis?(2020) ARAUJO-FILHO, Jose de Arimateia Batista; SAWAMURA, Marcio Valente Yamada; COSTA, Andre Nathan; CERRI, Giovanni Guido; NOMURA, Cesar Higa
- Chronic lung lesions in COVID-19 survivors: predictive clinical model(2022) CARVALHO, Carlos Roberto Ribeiro; CHATE, Rodrigo Caruso; SAWAMURA, Marcio Valente Yamada; GARCIA, Michelle Louvaes; LAMAS, Celina Almeida; CARDENAS, Diego Armando Cardona; LIMA, Daniel Mario; SCUDELLER, Paula Gobi; SALGE, Joao Marcos; NOMURA, Cesar Higa; GUTIERREZ, Marco AntonioObjective This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. Design This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO(2)), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO(2), FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. Setting A tertiary hospital in Sao Paulo, Brazil. Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged >= 18 years. Primary outcome measure A predictive clinical model for lung lesion detection on chest CT. Results There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO(2), FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO(2) and FVC (sensitivity, 0.85 +/- 0.08; specificity, 0.70 +/- 0.06; F1-score, 0.79 +/- 0.06 and area under the curve, 0.80 +/- 0.07). Conclusion A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.
- Thoracic tomographic manifestations in symptomatic respiratory patients with COVID-19(2020) FARIAS, Lucas de Pádua Gomes de; STRABELLI, Daniel Giunchetti; FONSECA, Eduardo Kaiser Ururahy Nunes; LOUREIRO, Bruna Melo Coelho; NOMURA, Cesar Higa; SAWAMURA, Márcio Valente YamadaAbstract China was the epicenter for the novel coronavirus disease (COVID-19), which quickly spread to other Asian countries and later to Western countries; subsequently, COVID-19 was categorized as a pandemic by the World Health Organization. Diagnosis primarily depends on viral detection in respiratory samples; however, available kits are limited, lack high sensitivity, and have a long turnaround time for providing results. In this scenario, computed tomography has emerged as an efficient and available high-sensitivity method, allowing radiologists to readily recognize findings related to COVID-19. The objective of this article is to demonstrate the main tomographic findings in symptomatic respiratory patients with COVID-19 to assist medical professionals during this critical moment.
- Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study(2023) CARVALHO, Carlos Roberto Ribeiro; LAMAS, Celina Almeida; CHATE, Rodrigo Caruso; SALGE, Joao Marcos; SAWAMURA, Marcio Valente Yamada; ALBUQUERQUE, Andre L. P. de; JR, Carlos Toufen; LIMA, Daniel Mario; GARCIA, Michelle Louvaes; SCUDELLER, Paula Gobi; NOMURA, Cesar Higa; GUTIERREZ, Marco Antonio; BALDI, Bruno GuedesBackgroundCoronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity. MethodsA prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO(2) evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion. ResultsIn total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78 +/- 0.02;specificity,0.79 +/- 0.01;F1-score,0.78 +/- 0.02;positive predictive rate,0.78 +/- 0.02; accuracy,0.78 +/- 0.02; and area under the curve,0.83 +/- 0.01). ConclusionICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.
- Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazil ian experience(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; ASSUNCAO JUNIOR, Antonildes Nascimento; ARAUJO-FILHO, Jose De Arimateia Batista; FERREIRA, Lorena Carneiro; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; CHATE, Rodrigo Caruso; CERRI, Giovanni Guido; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaOBJECTIVE: To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes. METHODS: Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RTPCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay RESULTS: A total of 457 patients with a mean age of 57 +/- 15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of X50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of X50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of X50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001). CONCLUSION: Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of X50% might be associated with a higher risk of mortality.
- Evaluation of the RSNA and CORADS classifications for COVID-19 on chest computed tomography in the Brazilian population(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; GARCIA, Jose Vitor Rassi; GAMA, Victor Arcanjo Almeida; FERREIRA, Lorena Carneiro; CHATE, Rodrigo Caruso; ASSUNCAO JUNIOR, Antonildes Nascimento; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaOBJECTIVE: To determine the correlation between the two tomographic classifications for coronavirus disease (COVID-19), COVID-19 Reporting and Data System (CORADS) and Radiological Society of North America Expert Consensus Statement on Reporting Chest Computed Tomography (CT) Findings Related to COVID-19 (RSNA), in the Brazilian population and to assess the agreement between reviewers with different experience levels. METHODS: Chest CT images of patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-positive COVID-19 were categorized according to the CORADS and RSNA classifications by radiologists with different levels of experience and who were initially unaware of the RT-PCR results. The inter- and intra-observer concordances for each of the classifications were calculated, as were the concordances between classifications. RESULTS: A total of 100 patients were included in this study. The RSNA classification showed an almost perfect inter-observer agreement between reviewers with similar experience levels, with a kappa coefficient of 0.892 (95% confidence interval [CI], 0.788-0.995). CORADS showed substantial agreement among reviewers with similar experience levels, with a kappa coefficient of 0.642 (95% CI, 0.491-0.793). There was inter-observer variation when comparing less experienced reviewers with more experienced reviewers, with the highest kappa coefficient of 0.396 (95% CI, 0.255-0.588). There was a significant correlation between both classifications, with a Kendall coefficient of 0.899 (p<0.001) and substantial intra-observer agreement for both classifications. CONCLUSION: The RSNA and CORADS classifications showed excellent inter-observer agreement for reviewers with the same level of experience, although the agreement between less experience reviewers and the reviewer with the most experience was only reasonable. Combined analysis of both classifications with the first RT-PCR results did not reveal any false-negative results for detecting COVID-19 in patients.
- Chest computed tomography in the diagnosis of COVID-19 in patients with false negative RT-PCR(2021) FONSECA, Eduardo Kaiser Ururahy Nunes; FERREIRA, Lorena Carneiro; LOUREIRO, Bruna Melo Coelho; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; QUEIROZ, Gabriel Abrantes de; GARCIA, Jose Vitor Rassi; TEIXEIRA, Renato de Freitas; GAMA, Victor Arcanjo Almeida; CHATE, Rodrigo Caruso; ASSUNCAO JUNIOR, Antonildes Nascimento; SAWAMURA, Marcio Valente Yamada; NOMURA, Cesar HigaObjective: To evaluate the role of chest computed tomography in patients with COVID-19 who presented initial negative result in reverse transcriptase-polymerase chain reaction (RT-PCR). Methods: A single-center, retrospective study that evaluated 39 patients with negative RT-PCR for COVID-19, who underwent chest computed tomography and had a final clinical or serological diagnosis of COVID-19. The visual tomographic classification was evaluated according to the Consensus of the Radiological Society of North America and software developed with artificial intelligence for automatic detection of findings and chance estimation of COVID-19. Results: In the visual tomographic analysis, only one of them (3%) presented computed tomography classified as negative, 69% were classified as typical and 28% as indeterminate. In the evaluation using the software, only four (about 10%) had a probability of COVID-19 <25%. Conclusion: Computed tomography can play an important role in management of suspected cases of COVID-19 with initial negative results in RT-PCR, especially considering those patients outside the ideal window for sample collection for RT-PCR.
- Imaging findings in COVID-19 pneumonia(2020) FARIAS, Lucas de Padua Gomes de; FONSECA, Eduardo Kaiser Ururahy Nunes; STRABELLI, Daniel Giunchetti; LOUREIRO, Bruna Melo Coelho; NEVES, Yuri Costa Sarno; RODRIGUES, Thiago Potrich; CHATE, Rodrigo Caruso; NOMURA, Cesar Higa; SAWAMURA, Marcio Valente Yamada; CERRI, Giovanni GuidoThe coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan city and was declared a pandemic in March 2020. Although the virus is not restricted to the lung parenchyma, the use of chest imaging in COVID-19 can be especially useful for patients with moderate to severe symptoms or comorbidities. This article aimed to demonstrate the chest imaging findings of COVID-19 on different modalities: chest radiography, computed tomography, and ultrasonography. In addition, it intended to review recommendations on imaging assessment of COVID-19 and to discuss the use of a structured chest computed tomography report. Chest radiography, despite being a low-cost and easily available method, has low sensitivity for screening patients. It can be useful in monitoring hospitalized patients, especially for the evaluation of complications such as pneumothorax and pleural effusion. Chest computed tomography, despite being highly sensitive, has a low specificity, and hence cannot replace the reference diagnostic test (reverse transcription polymerase chain reaction). To facilitate the confection and reduce the variability of radiological reports, some standardizations with structured reports have been proposed. Among the available classifications, it is possible to divide the radiological findings into typical, indeterminate, atypical, and negative findings. The structured report can also contain an estimate of the extent of lung involvement (e.g., more or less than 50% of the lung parenchyma). Pulmonary ultrasonography can also be an auxiliary method, especially for monitoring hospitalized patients in intensive care units, where transfer to a tomography scanner is difficult.