RODRIGO CARUSO CHATE
Índice h a partir de 2011
7
Projetos de Pesquisa
Unidades Organizacionais
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/65, Hospital das Clínicas, Faculdade de Medicina
LIM/65, Hospital das Clínicas, Faculdade de Medicina
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- Computed tomography in hypersensitivity pneumonitis: main findings, differential diagnosis and pitfalls(2018) DIAS, Olivia Meira; BALDI, Bruno Guedes; PENNATI, Francesca; ALIVERTI, Andrea; CHATE, Rodrigo Caruso; SAWAMURA, Marcio Valente Yamada; CARVALHO, Carlos Roberto Ribeiro de; ALBUQUERQUE, Andre Luis Pereira deIntroduction: Hypersensitivity pneumonitis (HP) is a disease with variable clinical presentation in which inflammation in the lung parenchyma is caused by the inhalation of specific organic antigens or low molecular weight substances in genetically susceptible individuals. Alterations of the acute, subacute and chronic forms may eventually overlap, and the diagnosis based on temporality and presence of fibrosis (acute/inflammatory HP vs. chronic HP) seems to be more feasible and useful in clinical practice. Differential diagnosis of chronic HP with other interstitial fibrotic diseases is challenging due to the overlap of the clinical history, and the functional and imaging findings of these pathologies in the terminal stages.Areas covered: This article reviews the essential features of HP with emphasis on imaging features. Moreover, the main methodological limitations of high-resolution computed tomography (HRCT) interpretation are discussed, as well as new perspectives with volumetric quantitative CT analysis as a useful tool for retrieving detailed and accurate information from the lung parenchyma.Expert commentary: Mosaic attenuation is a prominent feature of this disease, but air trapping in chronic HP seems overestimated. Quantitative analysis has the potential to estimate the involvement of the pulmonary parenchyma more accurately and could correlate better with pulmonary function results.
- Texture-based classification of lung disease patterns in chronic hypersensitivity pneumonitis and comparison to clinical outcomes(2021) PENNATI, F.; ALIBONI, L.; ANTONIAZZA, A.; BERETTA, D.; DIAS, O.; BALDI, B. G.; SAWAMURA, M.; CHATE, R. C.; CARVALHO, C. R. R. De; ALBUQUERQUE, A.; ALIVERTI, A.Computer-aided detection algorithms applied to CT lung imaging have the potential to objectively quantify pulmonary pathology. We aim to develop an automatic classification method based on textural features able to classify healthy and pathological patterns on CT lung images and to quantify the extent of each disease pattern in a group of patients with chronic hypersensitivity pneumonitis (cHP), in comparison to pulmonary function tests (PFTs). 27 cHP patients were scanned via high resolution CT (HRCT) at full-inspiration. Regions of interest (ROIs) were extracted and labeled as normal (NOR), ground glass opacity (GGO), reticulation (RET), consolidation (C), honeycombing (HB) and air trapping (AT). For each ROI, statistical, morphological and fractal parameters were computed. For automatic classification, we compared two classification methods (Bayesian and Support Vector Machine) and three ROI sizes. The classifier was therefore applied to the overall CT images and the extent of each class was calculated and compared to PFTs. Better classification accuracy was found for the Bayesian classifier and the 16x16 ROI size: 92.1 +/- 2.7%. The extent of GGO, HB and NOR significantly correlated with forced vital capacity (FVC) and the extent of NOR with carbon monoxide diffusing capacity (DLCO).
- A textural approach for quantitative CT in chronic hypersensitivity pneumonitis (cHP)(2019) PENNATI, Francesca; DIAS, Olivia; ANTONIAZZA, Alessio; BERETTA, Davide; ALIBONI, Lorenzo; BALDI, Bruno Guedes; SAWAMURA, Marcio; CHATE, Rodrigo Caruso; CARVALHO, Carlos Roberto Ribeiro De; ALBUQUERQUE, Andre; ALIVERTI, Andrea
- Swyer-James-MacLeod Syndrome: The Hyperlucent Lung(2020) FARIAS, Lucas De Padua Gomes De; FONSECA, Eduardo Kaiser Ururahy Nunes; CHATE, Rodrigo Caruso; SAWAMURA, Marcio Valente Yamada
- 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.
- COVID-19 on resonance magnetic: an incidental but important finding in times of pandemic(2020) GARCIA, Jose Vitor Rassi; FONSECA, Eduardo Kaiser Ururahy Nunes; CHATE, Rodrigo Caruso; STRABELLI, Daniel Giunchetti; FARIAS, Lucas de Padua Gomes de; LOUREIRO, Bruna Melo Coelho; FERREIRA, Lorena Carneiro; SAWAMURA, Marcio Valente Yamada
- 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.
- Rasmussen aneurysm(2021) FARIAS, L. P. G. de; FONSECA, E. K. U. N.; CHATE, R. C.; SAWAMURA, M. V. Y.
- 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.