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

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

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.

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

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.

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

We performed a meta-analysis to determine cancer risks at multiple sites and their associations with tumor mutation burden (TMB), an index for immunogenicity, in heart or lung transplant recipients. A comprehensive search of PubMed, Web of Science, EMBASE, and Medline was conducted. Random effects models were used to calculate standardized incidence ratios (SIRs) versus the general population and to determine the risks of different cancers. Weighted linear regression (WLR) was used to analyze the associations between the SIRs and TMBs. (PROSPERO CRD42020159599). Data from 21 studies including 116,438 transplant recipients (51,173 heart transplant recipients and 65,265 lung transplant recipients) with a total follow-up of 601,330.7 person-years were analyzed. Compared with the general population, heart transplant recipients displayed a 3.13-fold higher cancer risk [SIR: 3.13; 95% confidence interval (CI): 2.38-4.13; p < 0.001]; lung transplant recipients displayed a 4.28-fold higher cancer risk [SIR: 4.28; 95% CI: 3.18-5.77; p < 0.001]. The correlation coefficients were 0.54 (p = 0.049) and 0.79 (p < 0.001) in heart and lung transplant recipients, respectively, indicating that 29% and 63% of the differences in the SIRs for cancer types might be explained by the TMBs. Our study demonstrated that both heart and lung transplant recipients displayed a higher risk of certain site-specific cancers. These findings can provide individualized guidance for clinicians for detection of cancer among heart or lung transplantation recipients. In addition, we provided evidence that increased risks of post-transplant cancers can be attributed to immunosuppression.