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1.
Front Transplant ; 3: 1305152, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38993755

RESUMO

Introduction: Coronavirus disease 2019 (COVID-19) poses an important risk of morbidity and of mortality, in patients after solid organ transplantation. Recommendations have been issued by various transplantation societies at the national and European level to manage the immunosuppressive (IS) regimen upon admission to intensive care unit (ICU). Method: The aim of this study was to evaluate the adequacy of IS regimen minimization strategy in kidney transplant recipients hospitalized in an ICU for severe COVID-19, in relation to the issued recommendations. Results: The immunosuppressive therapy was minimized in all patients, with respectively 63% and 59% of the patients meeting the local and european recommendations upon admission. During ICU stay, IS was further tapered leading to 85% (local) and 78% (european) adequacy, relative to the guidelines. The most frequent deviation was the lack of complete withdrawal of mycophenolic acid (22%). Nevertheless, the adequacy/inadequacy status was not associated to the ICU- or one-year-mortality. Discussion: In this single-center cohort, the only variable associated with a reduction in mortality was vaccination, emphasizing that the key issue is immunization prior to infection, not restoration of immunity during ICU stay.

2.
Nephrol Dial Transplant ; 38(12): 2786-2798, 2023 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-37197910

RESUMO

BACKGROUND: Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management. METHODS: We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings. RESULTS: The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses. CONCLUSION: We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.


Assuntos
Aprendizado Profundo , Transplante de Rim , Vasculite , Humanos , Capilares/patologia , Inteligência Artificial , Rim/patologia , Inflamação/patologia , Vasculite/patologia , Biópsia , Rejeição de Enxerto/patologia
3.
Nephrol Dial Transplant ; 38(7): 1741-1751, 2023 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-36792061

RESUMO

BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin A nephropathy (IgAN), it has a wide interobserver variability between specialized pathologists and others. Therefore we trained and evaluated a tool using a neural network to automate the MEST-C grading. METHODS: Biopsies of patients with IgAN were divided into three independent groups: the Training cohort (n = 42) to train the network, the Test cohort (n = 66) to compare its pixel segmentation to that made by pathologists and the Application cohort (n = 88) to compare the MEST-C scores computed by the network or by pathologists. RESULTS: In the Test cohort, >73% of pixels were correctly identified by the network as M, E, S or C. In the Application cohort, the neural network area under the receiver operating characteristics curves were 0.88, 0.91, 0.88, 0.94, 0.96, 0.96 and 0.92 to predict M1, E1, S1, T1, T2, C1 and C2, respectively. The kappa coefficients between pathologists and the network assessments were substantial for E, S, T and C scores (kappa scores of 0.68, 0.79, 0.73 and 0.70, respectively) and moderate for M score (kappa score of 0.52). Network S and T scores were associated with the occurrence of the composite survival endpoint (death, dialysis, transplantation or doubling of serum creatinine) [hazard ratios 9.67 (P = .006) and 7.67 (P < .001), respectively]. CONCLUSIONS: This work highlights the possibility of automated recognition and quantification of each element of the MEST-C classification using deep learning methods.


Assuntos
Aprendizado Profundo , Glomerulonefrite por IGA , Humanos , Glomerulonefrite por IGA/patologia , Taxa de Filtração Glomerular , Diálise Renal , Automação , Biópsia
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