Molecular Diversity of Clinically Stable Human Kidney Allografts

Development and external validation of a predictive model for prolonged length of hospital stay in elderly patients undergoing lumbar fusion surgery: comparison of three predictive models

PURPOSE

This study aimed to develop a predictive model for prolonged length of hospital stay (pLOS) in elderly patients undergoing lumbar fusion surgery, utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree") and random forest machine-learning algorithms.

METHODS

This study was a retrospective review of a prospective Geriatric Lumbar Disease Database. The primary outcome measure was pLOS, which was defined as the LOS greater than the 75th percentile. All patients were grouped as pLOS group and non-pLOS. Three models (including logistic regression, single-classification tree and random forest algorithms) for predicting pLOS were developed using training dataset and internal validation using testing dataset. Finally, online tool based on our model was developed to assess its validity in the clinical setting (external validation).

RESULTS

The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97[55.4%] female). Multivariate logistic analyses revealed that older age (odds ratio [OR] 1.06, p < 0.001), higher BMI (OR 1.08, p = 0.002), number of fused segments (OR 1.41, p < 0.001), longer operative time (OR 1.02, p < 0.001), and diabetes (OR 1.05, p = 0.046) were independent risk factors for pLOS in elderly patients undergoing lumbar fusion surgery. The single-classification tree revealed that operative time ≥ 232 min, delayed ambulation, and BMI ≥ 30 kg/m2 as particularly influential predictors for pLOS. A random forest model was developed using the remaining 14 variables. Intraoperative EBL, operative time, delayed ambulation, age, number of fused segments, BMI, and RBC count were the most significant variables in the final model. The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.71 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. The nomogram was developed, and the C-index of external validation for PLOS was 0.69 (95% CI, 0.65-0.76).

CONCLUSION

This investigation produced three predictive models for pLOS in elderly patients undergoing lumbar fusion surgery. The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our predictive model could inform physicians about elderly patients with a high risk of pLOS after surgery.

The Optimization of Renal Graft Preservation Temperature to Mitigate Cold Ischemia-Reperfusion Injury in Kidney Transplantation

Renal transplantation is the preferred treatment for patients with end-stage renal disease. The current gold standard of kidney preservation for transplantation is static cold storage (SCS) at 4 °C. However, SCS contributes to renal ischemia-reperfusion injury (IRI), a pathological process that negatively impacts graft survival and function. Recent efforts to mitigate cold renal IRI involve preserving renal grafts at higher or subnormothermic temperatures. These temperatures may be beneficial in reducing the risk of cold renal IRI, while also maintaining active biological processes such as increasing the expression of mitochondrial protective metabolites. In this review, we discuss different preservation temperatures for renal transplantation and pharmacological supplementation of kidney preservation solutions with hydrogen sulfide to determine an optimal preservation temperature to mitigate cold renal IRI and enhance renal graft function and recipient survival.

Progress in kidney transplantation: The role for systems immunology

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

Through the Looking Glass: Unraveling the Stage-Shift of Acute Rejection in Renal Allografts

Sub-optimal sensitivity and specificity in current allograft monitoring methodologies underscore the need for more accurate and reflexive immunosurveillance to uncover the flux in alloimmunity between allograft health and the onset and progression of rejection. QSant—a urine based multi-analyte diagnostic test—was developed to profile renal transplant health and prognosticate injury, risk of evolution, and resolution of acute rejection. Q-Score—the composite score, across measurements of DNA, protein and metabolic biomarkers in the QSant assay—enables this risk prognostication. The domain of immune quiescence—below a Q-Score threshold of 32—is well established, based on published AUC of 98% for QSant. However, the trajectory of rejection is variable, given that causality is multi-factorial. Injury and subtypes of rejection are captured by the progression of Q-Score. This publication explores the clinical utility of QSant across the alloimmunity gradient of 32–100 for the early diagnosis of allograft injury and rejection.

Urinary Biomarkers for Kidney Allograft Injury

The current standard of serum creatinine and biopsy to monitor allograft health has many limitations. The most significant drawback of the current standard is the lack of sensitivity and specificity to allograft injuries, which are diagnosed only after significant damage to the allograft. Thus, it is of critical need to identify a biomarker that is sensitive and specific to the early detection of allograft injuries. Urine, as the direct renal ultrafiltrate that can be obtained noninvasively, directly reflects intrarenal processes in the allograft at greater accuracy than analysis of peripheral blood. We review transcriptomic, metabolomic, genomic, and proteomic discovery-based approaches to identifying urinary biomarkers for the noninvasive detection of allograft injuries, as well as the use of urine cell-free DNA in the QSant urine assay as a sensitive surrogate for the renal allograft biopsy for rejection diagnosis.

Allorecognition and the spectrum of kidney transplant rejection

Detection of mismatched human leukocyte antigens by adaptive immune cells is considered as the main cause of transplant rejection, leading to either T-cell mediated rejection or antibody-mediated rejection. This canonical view guided the successful development of immunosuppressive therapies and shaped the diagnostic Banff classification for kidney transplant rejection that is used in clinics worldwide. However, several observations have recently emerged that question this dichotomization between T-cell mediated rejection and antibody-mediated rejection, related to heterogeneity in the serology, histology, and prognosis of the rejection phenotypes. In parallel, novel insights were obtained concerning the dynamics of donor-specific anti-human leukocyte antigen antibodies, the immunogenicity of donor-recipient non-human leukocyte antigen mismatches, and the autoreactivity against self-antigens. Moreover, the potential of innate allorecognition was uncovered, as exemplified by natural killer cell-mediated microvascular inflammation through missing self, and by the emerging evidence on monocyte-driven allorecognition. In this review, we highlight the gaps in the current classification of rejection, provide an overview of the expanding insights into the mechanisms of allorecognition, and critically appraise how these could improve our understanding and clinical approach to kidney transplant rejection. We argue that consideration of the complex interplay of various allorecognition mechanisms can foster a more integrated view of kidney transplant rejection and can lead to improved risk stratification, targeted therapies, and better outcome after kidney transplantation.

Integrated Analysis of Prognostic Genes Associated With Ischemia-Reperfusion Injury in Renal Transplantation

Background

Ischemia–reperfusion injury (IRI) remains an inevitable and major challenge in renal transplantation. The current study aims to obtain deep insights into underlying mechanisms and seek prognostic genes as potential therapeutic targets for renal IRI (RIRI).

Methods

After systematically screening the Gene Expression Omnibus (GEO) database, we collected gene expression profiles of over 1,000 specimens from 11 independent cohorts. Differentially expressed genes (DEGs) were identified by comparing allograft kidney biopsies taken before and after reperfusion in the discovery cohort and further validated in another two independent transplant cohorts. Then, graft survival analysis and immune cell analysis of DEGs were performed in another independent renal transplant cohort with long-term follow-ups to further screen out prognostic genes. Cell type and time course analyses were performed for investigating the expression pattern of prognostic genes in more dimensions utilizing a mouse RIRI model. Finally, two novel genes firstly identified in RIRI were verified in the mouse model and comprehensively analyzed to investigate potential mechanisms.

Results

Twenty DEGs upregulated in the process of RIRI throughout different donor types (living donors, cardiac and brain death donors) were successfully identified and validated. Among them, upregulation of 10 genes was associated with poor long-term allograft outcomes and exhibited strong correlations with prognostic immune cells, like macrophages. Furthermore, certain genes were found to be only differentially expressed in specific cell types and remained with high expression levels even months after RIRI in the mouse model, which processed the potential to serve as therapeutic targets. Importantly, two newly identified genes in RIRI, Btg2 and Rhob, were successfully confirmed in the mouse model and found to have strong connections with NF-κB signaling.

Conclusions

We successfully identified and validated 10 IRI-associated prognostic genes in renal transplantation across different donor types, and two novel genes with crucial roles in RIRI were recognized for the first time. Our findings offered promising potential therapeutic targets for RIRI in renal transplantation.