Sterile necrotizing and non-necrotizing granulomas in a heart transplant patient with history of PTLD: A unique finding

Piening, Nicholas; Saxena, Saurabh; Muñoz-Abraham, Armando Salim; Osei, Hector; Fitzpatrick, Colleen; Greenspon, José

doi:10.1016/j.ijscr.2019.05.054

Cited by 3 publications

(3 citation statements)

References 30 publications

(41 reference statements)

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“…[ (e.g., lung cancer, liver cancer) varied by regions and ages, suggesting the presence of ethnicity-based and age-based differences. As the most common cause of mortality in patients with solid organ transplant, 46 we observed that the incidence of posttransplant lymphoproliferative disorders (PTLD) increased significantly in lung transplantation and the rate not influenced by age and region. Several mechanisms may explain the increased cancer risk for heart or lung transplant recipients.…”

Section: Publication Bias Analysismentioning

confidence: 89%

Cancer risk in heart or lung transplant recipients: A comprehensive analysis of 21 prospective cohorts

et al. 2020

Cancer Medicine

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Section: Publication Bias Analysismentioning

confidence: 89%

Cancer risk in heart or lung transplant recipients: A comprehensive analysis of 21 prospective cohorts

et al. 2020

Cancer Medicine

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“…TB granulomas’ crucial constituents include neutrophils, macrophages, and lymphocytes. Necrotic granulomas house a significant number of neutrophils and eosinophils [ 37 ]. Our research indicates that a lower PNR is associated with a higher STB risk score, aligning with the previously mentioned results.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

Huang,

Zhuo,

Liu

et al. 2024

Biomol Biomed

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This study focused on the development and validation of a diagnostic model to differentiate between spinal tuberculosis (STB) and pyogenic spondylitis (PS). We analyzed a total of 387 confirmed cases, out of which 241 were diagnosed with STB and 146 were diagnosed with PS. These cases were randomly divided into a training group (n = 271) and a validation group (n = 116). Within the training group, four machine learning (ML) algorithms (least absolute shrinkage and selection operator [LASSO], logistic regression analysis, random forest, and support vector machine recursive feature elimination [SVM-RFE]) were employed to identify distinctive variables. These specific variables were then utilized to construct a diagnostic model. The model’s performance was subsequently assessed using the receiver operating characteristic (ROC) curves and the calibration curves. Finally, internal validation of the model was undertaken in the validation group. Our findings indicate that PS patients had an average platelet-to-neutrophil ratio (PNR) of 277.86, which was significantly higher than the STB patients’ average of 69.88. The average age of PS patients was 54.71 years, older than the 48 years recorded for STB patients. Notably, the neutrophil-to-lymphocyte ratio (NLR) was higher in PS patients at 6.15, compared to the 3.46 NLR in STB patients. Additionally, the platelet volume distribution width (PDW) in PS patients was 0.2, compared to 0.15 in STB patients. Conversely, the mean platelet volume (MPV) was lower in PS patients at an average of 4.41, whereas STB patients averaged 8.31. Hemoglobin (HGB) levels were lower in PS patients at an average of 113.31 compared to STB patients' average of 121.64. Furthermore, the average red blood cell (RBC) count was 4.26 in PS patients, which was less than the 4.58 average observed in STB patients. After evaluation, seven key factors were identified using the four ML algorithms, forming the basis of our diagnostic model. The training and validation groups yielded area under the curve (AUC) values of 0.841 and 0.83, respectively. The calibration curves demonstrated a high alignment between the nomogram-predicted values and the actual measurements. The decision curve indicated optimal model performance with a threshold set between 2% and 88%. In conclusion, our model offers healthcare practitioners a reliable tool to efficiently and precisely differentiate between STB and PS, thereby facilitating swift and accurate diagnoses.

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“…4 There are unique considerations in the transplant setting that make the approach to the differential diagnosis for granulomatous disease more challenging. Graft-versus-host disease, 12 posttransplant lymphoproliferative disease, 13 and cord colitis syndrome 14 have all been associated with tissue granulomas in transplant patients. There is also risk of drug-induced renal, pulmonary, and hepatic granulomatous inflammation from immunosuppressive therapy and prophylactic antimicrobial agents.…”

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confidence: 99%

Clinicopathologic Features of Tissue Granulomas in Transplant Recipients: A Single Center Study in a Nontuberculosis Endemic Region

Nussbaum¹,

Patel

Assi

et al. 2020

Archives of Pathology &Amp; Laboratory Medicine

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Context.— There is a paucity of literature about tissue granulomas in transplant patients. Objective.— To characterize the clinicopathologic features of granulomas in this population and develop a clinically judicious approach to their evaluation. Design.— We performed chart reviews of solid organ and allogeneic hematopoietic stem cell transplant recipients at Yale New Haven Hospital to identify patients with granulomas on biopsy obtained pathologic specimens. Pretransplant and posttransplant specimens were included. Data points included demographics, clinical presentation, epidemiologic risk factors, biopsy indication, location and timing, immunosuppression, histopathology, microbiology, and associated clinical diagnosis. Granuloma-related readmissions and mortality were recorded at 1, 3, and 12 months. Results.— Biopsy proven granulomas were identified in 56 of 2139 (2.6%) patients. Of 56, 16 (29%) were infectious. Common infectious etiologies were bartonellosis (n = 3) and cytomegalovirus hepatitis (n = 3). Tuberculosis was not identified. Clinical symptoms prompted tissue biopsy in 27 of 56 (48.2%) cases while biopsies were obtained for evaluation of incidental findings or routine disease surveillance in 29 of 56 (51.8%). Presence of symptoms was significantly associated with infectious etiologies; 11 of 27 (40.7%) symptomatic patients compared with 5 of 29 (17.2%) asymptomatic patients had infectious causes. One death from granulomatous cryptogenic organizing pneumonia occurred. In pretransplant asymptomatic patients, no episodes of symptomatic disease occurred posttransplantation. Conclusions.— Granulomas were uncommon in a large transplant population; most were noninfectious but presence of symptoms was associated with infectious etiologies. Granulomas discovered pretransplant without clear infectious etiology likely do not require prolonged surveillance after transplantation. Symptomatology and epidemiologic risks factors should guide extent of microbiologic evaluation.

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Sterile necrotizing and non-necrotizing granulomas in a heart transplant patient with history of PTLD: A unique finding

Cited by 3 publications

References 30 publications

Cancer risk in heart or lung transplant recipients: A comprehensive analysis of 21 prospective cohorts

Cancer risk in heart or lung transplant recipients: A comprehensive analysis of 21 prospective cohorts

Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms

Clinicopathologic Features of Tissue Granulomas in Transplant Recipients: A Single Center Study in a Nontuberculosis Endemic Region

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