Navegando por Palavras-chave "renal biopsy"

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    An overview on frequency of renal biopsy diagnosis in Brazil: clinical and pathological patterns based on 9617 native kidney biopsies
    (Oxford Univ Press, 2010-02-01) Polito, Maria Goretti [UNIFESP]; Ribeiro de Moura, Luiz Antonio [UNIFESP]; Kirsztajn, Gianna Mastroianni [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Background. Studies about the prevalence of renal and particularly glomerular diseases in Brazil are still scarce.Methods. We evaluated retrospectively the reports of 9,617 renal biopsies, analyzed by the same pathologist, from January 1993 to December 2007.Results. the 9,617 renal biopsies performed in subjects of all ages in native kidneys. 4,619 were primary glomerulopathies (GN), the most frequent was focal segmental glomerulosclerosis (FSGS, 24.6%), followed by membranous nephropathy (MN, 20.7%), IgA nephropathy (IgAN, 20.1%), minimal change disease (MCD, 15.5%), mesangioproliferative non IgAN (nonIgAN, 5.2%), diffuse proliferative GN (DPGN, 4.7%) and membranoproliferative GN (MPGN, 4.2%). Lupus nephritis was responsible for most cases which etiology was determined, i.e., 950 out of 2,046 cases (45.5%), followed by post infectious GN (18.9%), diabetic nephropathy (8.5%), benign and malignant nephroangiosclerosis (7.3%), haemolytic-uraemic syndrome and thrombotic thrombocytopenic purpura (HUS/TTP), amyloidosis (4.8%) and vasculitis (4.7%). There was a predominance of secondary GN in the North, mostly due to lupus nephritis (LN); FSGS was very common in Northeast (27.7%), Central (26.9%) and Southeast regions (24.1%); IgAN was most frequent in South (22.8%) and MN in North (29.6%); the total prevalence of MPGN was low, and its regional distribution has not changed along the years.Conclusion. FSGS was the most frequent primary glomerular disease, followed closely by MN and IgAN. the predominance of FSGS is in accordance with recent studies all over the world that revealed its frequency is increasing. Lupus nephritis predominated among secondary GN in most regions, a finding observed in other studies.
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    Presence of arteriolar hyalinosis in post-reperfusion biopsies represents an additional risk to ischaemic injury in renal transplant
    (Wiley-Blackwell, 2016) Matos, Ana Cristina; Câmara, Niels Olsen Saraiva [UNIFESP]; Requiao-Moura, Lucio R.; Tonato, Eduardo J.; Filiponi, Thiago C.; Souza-Durao, Marcelino, Jr.; Malheiros, Denise M.; Fregonesi, Mauricio; Borrelli, Milton; Pacheco-Silva, Alvaro [UNIFESP]
    Aim: The role of post-reperfusion biopsy findings as a predictor of early and long-term graft function and survival is still a target of research. Methods: We analyzed data from 136 post-reperfusion biopsies performed in deceased donor renal transplanted patients from November 2008 to May 2012. We analyzed the presence of acute tubular necrosis (ATN), arteriolar hyalinosis (AH), intimal thickness (IT), interstitial fibrosis (IF) and glomerulosclerosis (GS). We also analyzed the impact of donor features on the following outcomes: delayed graft function (DGF) and chronic allograft dysfunction defined as eGFR < 60mL/min at 1 year. Results: The mean donor age was 41 years, 26% of whom were extended criteria donors (ECD), 33% had hypertension and 50% had cerebral vascular accident (CVA) as the cause of death. ATN was present in 87% of these biopsies, AH in 31%, IF in 21%, IT in 27% and GS in 32%. DGF occurred in 80% and chronic allograft dysfunction was present in 53%. AHwas the only histological finding associated with DGF and chronic allograft dysfunction at 1 year. Patients with AH had a lower eGFR at 1 year than patients without it (49.8 mL/min x 64.5 mL/min, P= 0.02). In the multivariate analysis, risk variables for development of chronic graft dysfunction were male sex (odds ratio [OR] = 3.159 [CI: 1.22-8.16]
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    Reconhecimento de entidades mencionadas para auxílio na descoberta de conhecimento em laudos de biópsia renal escritos em texto livre
    (Universidade Federal de São Paulo (UNIFESP), 2014-07-30) Nicolas, Flavia Pena [UNIFESP]; Pisa, Ivan Torres Pisa [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Introduction: The health area is currently experiencing a great need for acquisition of knowledge, particularly from patient health records. This demand has caused techniques for natural language processing and text mining become indispensable resources for processing information. Objective: Thus, this study aimed to recognize named entities in renal biopsy reports, using text mining techniques supported by NLP and machine learning. Secondary objectives were to group the terms, characterizing sections of reports and create a specific vocabulary to renal biopsy area, aiming a future establishment of an ontology and support the knowledge discovery. Methods: To achieve the main goal, we used text mining techniques and tools, and we create an automatic terms recognizer based on the four vocabularies in Portuguese in the UMLS: DeCS, MedDRA, WHO and ICPC. In a complementary manner, we use techniques of machine learning and statistical analysis to classify and characterize the sections of the reports in accordance with the terms DeCS automatically recognized. Results: The recognizer was applied to the pre-processed reports, using the four vocabularies in Portuguese, also pre-processed. The best performance was achieved with DeCS while the worst was with ICPC. The number of terms that was automatically recognized was small, which was confirmed in the validation, after manual recognition of terms held for six volunteer doctors. This result is due to the scarcity of vocabularies in Portuguese, neither of which specifically covers the renal area. Conclusion: Thus, we conclude that the text mining techniques and term extraction tools were satisfactory, but because of the lack of vocabularies in Portuguese, in renal area, we couldn’t recognize a lot of terms automatically, generating differences between the terms that were automatically recognized and the terms that were recognized by doctors. Based on the intersection of these two results we create a vocabulary for renal biopsy that will be used to creating ontology and decision support systems, and assist in knowledge discovery. As complementary activities, we grouped the terms DeCS recognized in sections, using ML classifiers and we characterized the sections of reports based on the connections between DeCS terms, using statistical analyses.