Prognosis and Risks for Probable Chronic Lung Allograft Dysfunction: A Prospective Multicenter Study.

Community-acquired respiratory virus (CARV) infections occur frequently after lung transplantation and may adversely impact outcomes. We hypothesized that while asymptomatic carriage would not increase the risk of chronic lung allograft dysfunction (CLAD) and graft loss, severe infection would. All lung transplant cases between January 2000 and July 2013 performed at our center were reviewed for respiratory viral samples. Each isolation of virus was classified according to clinical level of severity: asymptomatic, symptomatic without pneumonia, and viral pneumonia. Multivariate Cox modeling was used to assess the impact of CARV isolation on progression to CLAD and graft loss. Four thousand four hundred eight specimens were collected from 563 total patients, with 139 patients producing 324 virus-positive specimens in 245 episodes of CARV infection. Overall, the risk of CLAD was elevated by viral infection (hazard ratio [HR], 1.64; P < 0.01). This risk, however, was due to viral pneumonia alone (HR, 3.94; P < 0.01), without significant impact from symptomatic viral infection (HR, 0.97; P = 0.94) nor from asymptomatic viral infection (HR, 0.99; P = 0.98). The risk of graft loss was not increased by asymptomatic CARV infection (HR, 0.74; P = 0.37) nor symptomatic CARV infection (HR, 1.39; P = 0.41). Viral pneumonia did, however, significantly increase the risk of graft loss (HR, 2.78; P < 0.01). With respect to CARV, only viral pneumonia increased the risk of both CLAD and graft loss after lung transplantation. In the absence of pneumonia, respiratory viruses had no impact on measured outcomes.

Human cytomegalovirus (HCMV) infection is common and often severe in lung transplant recipients (LTRs), and it is a risk factor associated with chronic lung allograft dysfunction (CLAD). The complex interplay between HCMV and allograft rejection is still unclear. Currently, no treatment is available to reverse CLAD after diagnosis, and the identification of reliable biomarkers that can predict the early development of CLAD is needed. This study investigated the HCMV immunity in LTRs who will develop CLAD. This study quantified and phenotyped conventional (HLA-A2pp65) and HLA-E-restricted (HLA-EUL40) anti-HCMV CD8+ T (CD8 T) cell responses induced by infection in LTRs developing CLAD or maintaining a stable allograft. The homeostasis of immune subsets (B, CD4T, CD8 T, NK, and γδT cells) post-primary infection associated with CLAD was also investigated. At M18 post-transplantation, HLA-EUL40 CD8 T responses were less frequently found in HCMV+ LTRs (21.7%) developing CLAD (CLAD) than in LTRs (55%) keeping a functional graft (STABLE). In contrast, HLA-A2pp65 CD8 T was equally detected in 45% of STABLE and 47.8% of CLAD LTRs. The frequency of HLA-EUL40 and HLA-A2pp65 CD8 T among blood CD8 T cells shows lower median values in CLAD LTRs. Immunophenotype reveals an altered expression profile for HLA-EUL40 CD8 T in CLAD patients with a decreased expression for CD56 and the acquisition of PD-1. In STABLE LTRs, HCMV primary infection causes a decrease in B cells and inflation of CD8 T, CD57+/NKG2C+ NK, and δ2-γδT cells. In CLAD LTRs, the regulation of B, total CD8 T, and δ2+γδT cells is maintained, but total NK, CD57+/NKG2C+ NK, and δ2-γδT subsets are markedly reduced, while CD57 is overexpressed across T lymphocytes. CLAD is associated with significant changes in anti-HCMV immune cell responses. Our findings propose that the presence of dysfunctional HCMV-specific HLA-E-restricted CD8 T cells together with post-infection changes in the immune cell distribution affecting NK and γδT cells defines an early immune signature for CLAD in HCMV+ LTRs. Such a signature may be of interest for the monitoring of LTRs and may allow an early stratification of LTRs at risk of CLAD.

Chronic lung allograft dysfunction (CLAD) limits long-term survival after lung transplantation. Of the two subtypes, restrictive allograft syndrome (RAS) is characterized by a larger lung volume decrease and worse prognosis than bronchiolitis obliterans syndrome (BOS). We used computed tomography (CT) volumetry to classify CLAD subtypes and determined their clinical impact. Adult primary lung transplants performed 2003-2015 (n=167) were retrospectively evaluated for CLAD and subclassified with CT volumetry. Lung volume decrease of<15% from baseline resulted in BOSCT-vol and ≥15% resulted in RASCT-vol diagnosis. Clinical impact of CLAD subtypes was defined, and the prognostic value of different lung function, radiological, and lung volume parameters present at the time of CLAD diagnosis were compared. CLAD affected 43% of patients and was classified with CT volumetry as BOSCT-vol in 89% and RASCT-vol in 11%. Median graft survival estimate in RASCT-vol was significantly decreased compared to BOSCT-vol (1.6vs. 9.7 years, P=.038). At CLAD onset, RASCT-vol diagnosis (P=.05), increased lung density (P=.007), and more severe FEV1 (P=.004) decline from baseline, increased graft loss risk in multivariate analysis. CT volumetry serves to identify lung transplant patients with a poor clinical outcome but should be validated in prospective trials.

ABSTRACTBackgroundImmunoglobulin A nephropathy (IgAN) is the most frequent recurrent disease in kidney transplant recipients and its recurrence contributes to reducing graft survival. Several variables at the time of recurrence have been associated with a higher risk of graft loss. The presence of clinical or subclinical inflammation has been associated with a higher risk of kidney graft loss, but it is not precisely known how it influences the outcome of patients with recurrent IgAN.MethodsWe performed a multicentre retrospective study including kidney transplant recipients with biopsy-proven recurrence of IgAN in which Banff and Oxford classification scores were available. ‘Tubulo-interstitial inflammation’ (TII) was defined when ‘t’ or ‘i’ were ≥2. The main endpoint was progression to chronic kidney disease (CKD) stage 5 or to death censored-graft loss (CKD5/DCGL).ResultsA total of 119 kidney transplant recipients with IgAN recurrence were included and 23 of them showed TII. Median follow-up was 102.9 months and 39 (32.8%) patients reached CKD5/DCGL. TII related to a higher risk of CKD5/DCGL (3 years 18.0% vs 45.3%, log-rank 7.588, P = .006). After multivariate analysis, TII remained related to the risk of CKD5/DCGL (HR 2.344, 95% CI 1.119–4.910, P = .024) independently of other histologic and clinical variables.ConclusionsIn kidney transplant recipients with IgAN recurrence, TII contributes to increasing the risk of CKD5/DCGL independently of previously well-known variables. We suggest adding TII along with the Oxford classification to the clinical variables to identify recurrent IgAN patients at increased risk of graft loss who might benefit from intensified immunosuppression or specific IgAN therapies.

Azithromycin prophylaxis after lung transplantation is associated with improved overall survival

Influence of Donor-Recipient Age Mismatch in Young Lung Transplant Recipients

To compare machine learning (ML) to traditional models to predict radiographic progression in patients with early axial spondyloarthritis (axSpA). We carried out a prospective French multicentric DESIR cohort study with 5 years of follow-up that included patients with chronic back pain for <3 years, suggestive of axSpA. Radiographic progression was defined as progression at the spine (increase of at least 1 point of mSASSS scores/2 years) or at the sacroiliac joint (worsening of at least one grade of the mNY score between 2 visits). Statistical analyses were based on patients without any missing data regarding the outcome and variables of interest (295 patients).Traditional modelling: we performed a multivariate logistic regression model (M1); then variable selection with stepwise selection based on Akaike Information Criterion (stepAIC) method (M2), and Least Absolute Shrinkage and Selection Operator (LASSO) method (M3).ML modelling: using "SuperLearner" package on R, we modelled radiographic progression with stepAIC, LASSO, random forest, Discrete Bayesian Additive Regression Trees Samplers (DBARTS), Generalized Additive Models (GAM), multivariate adaptive polynomial spline regression (polymars), Recursive Partitioning And Regression Trees (RPART) and Super Learner. Accuracy of these models was compared based on their 10-fold cross-validated AUC (cv-AUC). 10-fold cv-AUC for traditional models were 0.79 and 0.78 for M2 and M3, respectively. The three best models in the ML algorithms were the GAM, the DBARTS and the Super Learner models, with 10-fold cv-AUC of: 0.77, 0.76 and 0.74, respectively. Two traditional models predicted radiographic progression as good as the eight ML models tested in this population.

Sepsis is a life-threatening condition often complicated by organ dysfunction and is associated with a high mortality rate. The dysregulation of immune response, inflammation, and nutritional status are critical factors contributing to its pathogenesis. This study aimed to develop a nomogram that integrates prognostic immune-inflammatory-nutritional indicators with other clinical information to predict 28-day mortality in sepsis patients with acute respiratory distress syndrome (ARDS). Clinical data from 635 adult sepsis patients with ARDS were obtained from Shaanxi Provincial People's Hospital and randomly divided into a training set (n = 477) and a validation set (n = 158). To identify predictors of 28-day mortality in sepsis patients with ARDS, univariate analysis, and least absolute shrinkage and selection operator (LASSO) regression were utilized. Subsequently, a multivariate logistic regression model was constructed to identify independent predictors. A nomogram was then developed by integrating the selected indicators. The model's performance was assessed with respect to discrimination, calibration, and clinical utility through the use of the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). The independent predictors utilized for the construction of the nomogram included the albumin-alkaline phosphatase ratio (AAPR), albumin-bilirubin grade (ALBI), neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), and lactate-albumin ratio (LAR). Notably, the nomogram exhibited superior predictive performance, with an AUC of 0.873 in the training set and 0.837 in the validation set, as compared to the SOFA score, which showed an AUC of 0.689 in the training set and 0.684 in the validation set, for predicting 28-day mortality in sepsis patients with ARDS. The calibration plots demonstrated excellent consistency. DCA confirmed the model's clinical utility, showing a positive net benefit across a wide range of clinically relevant threshold probabilities (approximately 10% to 70%), which supports its potential to guide clinical decision-making. We have successfully developed and validated a robust nomogram that integrates seven readily accessible immune-inflammatory-nutritional indicators. This model serves as an individualized and precise tool for predicting the 28-day mortality risk in sepsis patients with acute respiratory distress syndrome (ARDS), thereby potentially enhancing early risk stratification and informing clinical decision-making.

The Western Australian lung transplant programme commenced in 2004 to serve the growing demand of patients with end-stage lung disease. This report summarises our 11-year experience in lung transplantation. Data on 115 consecutive patients and their respective donors transplanted between 2004 and 2015 were collected. The Kaplan-Meier method was used to estimate survival. Cox regression was used to analyse the impact of donor and recipient characteristics on survival. A total of 88 bilateral, 22 single-lung and 5 heart-lung transplants were performed in Western Australia during the first 11 years of the lung transplant programme. The most common indications for transplantation were interstitial lung disease (30.4%), cystic fibrosis (27.8%) and chronic obstructive pulmonary disease (excluding alpha-1 antitrypsin deficiency) (22.6%). Median recipient age was 50 years. Overall survival rates were 96% at 3 months, 93% at 1 year, 84% at 3 years and 70% at 5 years. Older age and higher BMI negatively impacted survival. Chronic lung allograft dysfunction was the leading cause of late mortality. Lung transplantation is a treatment option in end-stage lung disease, with annual transplant rates in Western Australia continuing to rise. Our patients enjoy survival rates that compare favourably against international standards.

Three-year results of an investigator-driven multicenter, international, randomized open-label de novo trial to prevent BOS after lung transplantation

Clinical predictors for restrictive allograft syndrome: A nested case-control study.

We consider the problem of predicting survival times of cancer patients from the gene expression profiles of their tumor samples via linear regression modeling of log-transformed failure times. The partial least squares (PLS) and least absolute shrinkage and selection operator (LASSO) methodologies are used for this purpose where we first modify the data to account for censoring. Three approaches of handling right censored data-reweighting, mean imputation, and multiple imputation-are considered. Their performances are examined in a detailed simulation study and compared with that of full data PLS and LASSO had there been no censoring. A major objective of this article is to investigate the performances of PLS and LASSO in the context of microarray data where the number of covariates is very large and there are extremely few samples. We demonstrate that LASSO outperforms PLS in terms of prediction error when the list of covariates includes a moderate to large percentage of useless or noise variables; otherwise, PLS may outperform LASSO. For a moderate sample size (100 with 10,000 covariates), LASSO performed better than a no covariate model (or noise-based prediction). The mean imputation method appears to best track the performance of the full data PLS or LASSO. The mean imputation scheme is used on an existing data set on lung cancer. This reanalysis using the mean imputed PLS and LASSO identifies a number of genes that were known to be related to cancer or tumor activities from previous studies.

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.

In the context of limited donor pool in cardiothoracic transplantation, utilization of organs from high risk donors, such as suicidal hanging donors, while ensuring safety, is under consideration. We sought to evaluate the outcomes of lung transplantations (LTx) that use organs from this group.Between January 2011 and December 2015, 265 LTx were performed at our center. Twenty-two recipients received lungs from donors after suicidal hanging (group 1). The remaining 243 transplantations were used as a control (group 2). Analysis of recipient and donor characteristics as well as outcomes was performed.No statistically significant difference was found in the donor characteristics between analyzed groups, except for higher incidence of cardiac arrest, younger age and smoking history of hanging donors (P < .001, P = .022 and P = .0042, respectively). Recipient preoperative and perioperative characteristics were comparable. Postoperatively in group 1 there was a higher incidence of extracorporeal life support (27.3 vs 9.1%, P = .019). There were no significant differences in chronic lung allograft dysfunction-free survival between group 1 and 2: 92.3 vs 94% at 1 year and 65.9 vs 75.5% at 3 years (P = .99). The estimated cumulative survival rate was also similar between groups: 68.2 vs 83.2% at 1 year and 68.2% versus 72% at 3 years (P = .3758).Hanging as a donor cause of death is not associated with poor mid-term survival or chronic lung allograft dysfunction following transplantation. These results encourage assessment of lungs from hanging donors, and their consideration for transplantation.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.