Microvascular Inflammation of Kidney Allografts and Clinical Outcomes.

BACKGROUND: The heterogeneous clinical presentation of graft microvascular inflammation poses a major challenge to successful kidney transplantation. The effect of microvascular inflammation on allograft outcomes is unclear. METHODS: We conducted a cohort study that included kidney-transplant recipients from more than 30 transplantation centers in Europe and North America who had undergone allograft biopsy between 2004 and 2023. We integrated clinical and pathological data to classify biopsy specimens according to the 2022 Banff Classification of Renal Allograft Pathology, which includes two new diagnostic categories: probable antibody-mediated rejection and microvascular inflammation without evidence of an antibody-mediated response. We then assessed the association between the newly recognized microvascular inflammation phenotypes and allograft survival and disease progression. RESULTS: A total of 16,293 kidney-transplant biopsy specimens from 6798 patients were assessed. We identified the newly recognized microvascular inflammation phenotypes in 788 specimens, of which 641 were previously categorized as specimens with no evidence of rejection. As compared with patients without rejection, the hazard ratio for graft loss was 2.1 (95% confidence interval [CI], 1.5 to 3.1) among patients with microvascular inflammation without evidence of an antibody-mediated response and 2.7 (95% CI, 2.2 to 3.3) among patients with antibody-mediated rejection. Patients with a diagnosis of probable antibody-mediated rejection had a higher risk of graft failure beyond year 5 after biopsy than those without rejection (hazard ratio, 1.7; 95% CI, 0.8 to 3.5). Patients with a diagnosis of either newly recognized microvascular inflammation phenotype had a higher risk of progression of transplant glomerulopathy during follow-up than patients without microvascular inflammation. CONCLUSIONS: Microvascular inflammation in kidney allografts includes distinct phenotypes, with various disease progression and allograft outcomes. Our findings support the clinical use of additional rejection phenotypes to standardize diagnostics for kidney allografts. (Funded by OrganX. ClinicalTrials.gov number, NCT06496269.).

A Unique Advantage of a 24-hour Surgical Delay in Autologous Breast Reconstruction.

The surgical delay technique can be used effectively in autologous breast reconstruction when there is unfavorable flap vascular anatomy or when the reconstruction necessitates a larger volume of donor tissue to obtain optimal results. The length of time between surgically delaying the flap to pedicle division and inset of the flap often varies based on surgeon preference but is typically approximately a week or longer. The authors present a case in which a 24-hour surgical delay was successfully used to augment deep inferior epigastric perforator flaps for autologous reconstruction. This technique is beneficial as it does not allow time for scarring and adhesions to develop between stages and allows for both stages to be performed in the same hospital admission.

A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients.

In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies.

Biologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration-time curve (AUC0-672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL.

An automated histological classification system for precision diagnostics of kidney allografts.

For three decades, the international Banff classification has been the gold standard for kidney allograft rejection diagnosis, but this system has become complex over time with the integration of multimodal data and rules, leading to misclassifications that can have deleterious therapeutic consequences for patients. To improve diagnosis, we developed a decision-support system, based on an algorithm covering all classification rules and diagnostic scenarios, that automatically assigns kidney allograft diagnoses. We then tested its ability to reclassify rejection diagnoses for adult and pediatric kidney transplant recipients in three international multicentric cohorts and two large prospective clinical trials, including 4,409 biopsies from 3,054 patients (62.05% male and 37.95% female) followed in 20 transplant referral centers in Europe and North America. In the adult kidney transplant population, the Banff Automation System reclassified 83 out of 279 (29.75%) antibody-mediated rejection cases and 57 out of 105 (54.29%) T cell-mediated rejection cases, whereas 237 out of 3,239 (7.32%) biopsies diagnosed as non-rejection by pathologists were reclassified as rejection. In the pediatric population, the reclassification rates were 8 out of 26 (30.77%) for antibody-mediated rejection and 12 out of 39 (30.77%) for T cell-mediated rejection. Finally, we found that reclassification of the initial diagnoses by the Banff Automation System was associated with an improved risk stratification of long-term allograft outcomes. This study demonstrates the potential of an automated histological classification to improve transplant patient care by correcting diagnostic errors and standardizing allograft rejection diagnoses.ClinicalTrials.gov registration: NCT05306795 .

Clinical Prediction Model for Antibody-Mediated Rejection: A Strategy to Minimize Surveillance Endomyocardial Biopsies After Heart Transplantation.

BACKGROUND: In heart transplantation, antibody-mediated rejection (AMR) is a major contributor to patient morbidity and mortality. Multiple routine endomyocardial biopsies (EMB) remain the gold standard to detect AMR, but this invasive procedure suffers from many limitations. We aimed to develop and validate an AMR risk model to improve individual risk stratification of AMR. METHODS: Heart recipients from 2 referral transplant centers, Cedars-Sinai (US) and Pitié-Salpêtrière (France), were included from 2012 to 2019. Database included detailed clinical, immunologic, imaging, and histological parameters. The US cohort was randomly distributed in a derivation (2/3) and in a test set (1/3). The primary end point was biopsy-proven AMR. A mixed effect logistic regression model with a random intercept was applied to identify variables independently associated with AMR. Simulation analyzes were performed. RESULTS: The US and French cohorts comprised a total of 1341 patients, representing 12 864 EMB. Overall, 490 AMR episodes were diagnosed (3.8% of EMB). Among the 26 potential determinants of AMR, 5 variables showed independent association: time post-transplant (P<0.001), pretransplant sensitizing event (P=0.001), circulating donor-specific anti-human leukocyte antigen antibody (P=0.001), graft dysfunction (P=0.004), and prior history of definite AMR (P<0.001). In the US test set, the calibration and the discrimination of the model were accurate (area under the curve, 0.79 [95% CI, 0.78-0.81]). Those results were confirmed in the external validation cohort (area under the curve, 0.78 [95% CI, 0.77-0.79]) and reinforced by various sensitivity analyses. The model also showed good performance to predict overall cause of rejection. Simulation models revealed that the AMR risk model could safely reduce the number of EMB. CONCLUSIONS: Our results support the use of the AMR risk model as a clinical decision tool to minimize the number of routine EMB after heart transplantation.

Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.

BACKGROUND: Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predictions of survival using updates of clinical data. METHODS: In this observational study, we used data from adult recipients of kidney transplants from 18 academic transplant centres in Europe, the USA, and South America, and a cohort of patients from six randomised controlled trials. The development cohort comprised patients from four centres in France, with all other patients included in external validation cohorts. To build deeply phenotyped cohorts of transplant recipients, the following data were collected in the development cohort: clinical, histological, immunological variables, and repeated measurements of estimated glomerular filtration rate (eGFR) and proteinuria (measured using the proteinuria to creatininuria ratio). To develop a dynamic prediction system based on these clinical assessments and repeated measurements, we used a Bayesian joint models-an artificial intelligence approach. The prediction performances of the model were assessed via discrimination, through calculation of the area under the receiver operator curve (AUC), and calibration. This study is registered with ClinicalTrials.gov, NCT04258891. FINDINGS: 13 608 patients were included (3774 in the development cohort and 9834 in the external validation cohorts) and contributed 89 328 patient-years of data, and 416 510 eGFR and proteinuria measurements. Bayesian joint models showed that recipient immunological profile, allograft interstitial fibrosis and tubular atrophy, allograft inflammation, and repeated measurements of eGFR and proteinuria were independent risk factors for allograft survival. The final model showed accurate calibration and very high discrimination in the development cohort (overall dynamic AUC 0·857 [95% CI 0·847-0·866]) with a persistent improvement in AUCs for each new repeated measurement (from 0·780 [0·768-0·794] to 0·926 [0·917-0·932]; p<0·0001). The predictive performance was confirmed in the external validation cohorts from Europe (overall AUC 0·845 [0·837-0·854]), the USA (overall AUC 0·820 [0·808-0·831]), South America (overall AUC 0·868 [0·856-0·880]), and the cohort of patients from randomised controlled trials (overall AUC 0·857 [0·840-0·875]). INTERPRETATION: Because of its dynamic design, this model can be continuously updated and holds value as a bedside tool that could refine the prognostic judgements of clinicians in everyday practice, hence enhancing precision medicine in the transplant setting. FUNDING: MSD Avenir, French National Institute for Health and Medical Research, and Bettencourt Schueller Foundation.

COVID-19 pandemic and worldwide organ transplantation: a population-based study.

BACKGROUND: Preliminary data suggest that COVID-19 has reduced access to solid organ transplantation. However, the global consequences of the COVID-19 pandemic on transplantation rates and the effect on waitlisted patients have not been reported. We aimed to assess the effect of the COVID-19 pandemic on transplantation and investigate if the pandemic was associated with heterogeneous adaptation in terms of organ transplantation, with ensuing consequences for waitlisted patients. METHODS: In this population-based, observational, before-and-after study, we collected and validated nationwide cohorts of consecutive kidney, liver, lung, and heart transplants from 22 countries. Data were collected from Jan 1 to Dec 31, 2020, along with data from the same period in 2019. The analysis was done from the onset of the 100th cumulative COVID-19 case through to Dec 31, 2020. We assessed the effect of the pandemic on the worldwide organ transplantation rate and the disparity in transplant numbers within each country. We estimated the number of waitlisted patient life-years lost due to the negative effects of the pandemic. The study is registered with ClinicalTrials.gov, NCT04416256. FINDINGS: Transplant activity in all countries studied showed an overall decrease during the pandemic. Kidney transplantation was the most affected, followed by lung, liver, and heart. We identified three organ transplant rate patterns, as follows: countries with a sharp decrease in transplantation rate with a low COVID-19-related death rate; countries with a moderate decrease in transplantation rate with a moderate COVID-19-related death rate; and countries with a slight decrease in transplantation rate despite a high COVID-19-related death rate. Temporal trends revealed a marked worldwide reduction in transplant activity during the first 3 months of the pandemic, with losses stabilising after June, 2020, but decreasing again from October to December, 2020. The overall reduction in transplants during the observation time period translated to 48 239 waitlisted patient life-years lost. INTERPRETATION: We quantified the impact of the COVID-19 pandemic on worldwide organ transplantation activity and revealed heterogeneous adaptation in terms of organ transplantation, both at national levels and within countries, with detrimental consequences for waitlisted patients. Understanding how different countries and health-care systems responded to COVID-19-related challenges could facilitate improved pandemic preparedness, notably, how to safely maintain transplant programmes, both with immediate and non-immediate life-saving potential, to prevent loss of patient life-years. FUNDING: French national research agency (INSERM) ATIP Avenir and Fondation Bettencourt Schueller.

Transthyretin-mediated protein and peptide oligomerization for enhanced target clustering.

Advances in cancer research have led to the development of new therapeutics with significant and durable responses such as immune checkpoint inhibitors. More recent therapies aim to stimulate anti-tumor immune responses by targeting the tumor necrosis factor (TNF) receptors, however this approach has been shown to require clustering of receptors in order to achieve a significant response. Here we present a perspective on using transthyretin, a naturally occurring serum protein, as a drug delivery platform to enable cross-linking independent clustering of targets. TTR forms a stable homo-tetramer with exposed termini that make TTR a highly versatile platform for generating multimeric antibody fusions to enable enhanced target clustering. Fusions with antibodies or Fabs targeting TRAILR2 were shown to have robust cytotoxic activity in vitro and in vivo in colorectal xenograft models demonstrating that TTR is a highly versatile, stable, therapeutic fusion platform that can be used with antibodies, Fabs and other bioactive fusion partners and has broad applications in oncology and infectious disease research.

A Sos proteomimetic as a pan-Ras inhibitor.

Aberrant Ras signaling is linked to a wide spectrum of hyperproliferative diseases, and components of the signaling pathway, including Ras, have been the subject of intense and ongoing drug discovery efforts. The cellular activity of Ras is modulated by its association with the guanine nucleotide exchange factor Son of sevenless (Sos), and the high-resolution crystal structure of the Ras-Sos complex provides a basis for the rational design of orthosteric Ras ligands. We constructed a synthetic Sos protein mimic that engages the wild-type and oncogenic forms of nucleotide-bound Ras and modulates downstream kinase signaling. The Sos mimic was designed to capture the conformation of the Sos helix-loop-helix motif that makes critical contacts with Ras in its switch region. Chemoproteomic studies illustrate that the proteomimetic engages Ras and other cellular GTPases. The synthetic proteomimetic resists proteolytic degradation and enters cells through macropinocytosis. As such, it is selectively toxic to cancer cells with up-regulated macropinocytosis, including those that feature oncogenic Ras mutations.

Prior activation state shapes the microglia response to antihuman TREM2 in a mouse model of Alzheimer's disease.

Triggering receptor expressed on myeloid cells 2 (TREM2) sustains microglia response to brain injury stimuli including apoptotic cells, myelin damage, and amyloid ß (Aß). Alzheimer's disease (AD) risk is associated with the TREM2R47H variant, which impairs ligand binding and consequently microglia responses to Aß pathology. Here, we show that TREM2 engagement by the mAb hT2AB as surrogate ligand activates microglia in 5XFAD transgenic mice that accumulate Aß and express either the common TREM2 variant (TREM2CV) or TREM2R47H scRNA-seq of microglia from TREM2CV-5XFAD mice treated once with control hIgG1 exposed four distinct trajectories of microglia activation leading to disease-associated (DAM), interferon-responsive (IFN-R), cycling (Cyc-M), and MHC-II expressing (MHC-II) microglia types. All of these were underrepresented in TREM2R47H-5XFAD mice, suggesting that TREM2 ligand engagement is required for microglia activation trajectories. Moreover, Cyc-M and IFN-R microglia were more abundant in female than male TREM2CV-5XFAD mice, likely due to greater Aß load in female 5XFAD mice. A single systemic injection of hT2AB replenished Cyc-M, IFN-R, and MHC-II pools in TREM2R47H-5XFAD mice. In TREM2CV-5XFAD mice, however, hT2AB brought the representation of male Cyc-M and IFN-R microglia closer to that of females, in which these trajectories had already reached maximum capacity. Moreover, hT2AB induced shifts in gene expression patterns in all microglial pools without affecting representation. Repeated treatment with a murinized hT2AB version over 10 d increased chemokines brain content in TREM2R47H-5XFAD mice, consistent with microglia expansion. Thus, the impact of hT2AB on microglia is shaped by the extent of TREM2 endogenous ligand engagement and basal microglia activation.

Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation.

Although the gold standard of monitoring kidney transplant function relies on glomerular filtration rate (GFR), little is known about GFR trajectories after transplantation, their determinants, and their association with outcomes. To evaluate these parameters we examined kidney transplant recipients receiving care at 15 academic centers. Patients underwent prospective monitoring of estimated GFR (eGFR) measurements, with assessment of clinical, functional, histological and immunological parameters. Additional validation took place in seven randomized controlled trials that included a total of 14,132 patients with 403,497 eGFR measurements. After a median follow-up of 6.5 years, 1,688 patients developed end-stage kidney disease. Using unsupervised latent class mixed models, we identified eight distinct eGFR trajectories. Multinomial regression models identified seven significant determinants of eGFR trajectories including donor age, eGFR, proteinuria, and several significant histological features: graft scarring, graft interstitial inflammation and tubulitis, microcirculation inflammation, and circulating anti-HLA donor specific antibodies. The eGFR trajectories were associated with progression to end stage kidney disease. These trajectories, their determinants and respective associations with end stage kidney disease were similar across cohorts, as well as in diverse clinical scenarios, therapeutic eras and in the seven randomized control trials. Thus, our results provide the basis for a trajectory-based assessment of kidney transplant patients for risk stratification and monitoring.

Macropinocytosis as a Key Determinant of Peptidomimetic Uptake in Cancer Cells.

Peptides and peptidomimetics represent the middle space between small molecules and large proteins-they retain the relatively small size and synthetic accessibility of small molecules while providing high binding specificity for biomolecular partners typically observed with proteins. During the course of our efforts to target intracellular protein-protein interactions in cancer, we observed that the cellular uptake of peptides is critically determined by the cell line-specifically, we noted that peptides show better uptake in cancer cells with enhanced macropinocytic indices. Here, we describe the results of our analysis of cellular penetration by different classes of conformationally stabilized peptides. We tested the uptake of linear peptides, peptide macrocycles, stabilized helices, ß-hairpin peptides, and cross-linked helix dimers in 11 different cell lines. Efficient uptake of these conformationally defined constructs directly correlated with the macropinocytic activity of each cell line: high uptake of compounds was observed in cells with mutations in certain signaling pathways. Significantly, the study shows that constrained peptides follow the same uptake mechanism as proteins in macropinocytic cells, but unlike proteins, peptide mimics can be readily designed to resist denaturation and proteolytic degradation. Our findings expand the current understanding of cellular uptake in cancer cells by designed peptidomimetics and suggest that cancer cells with certain mutations are suitable mediums for the study of biological pathways with peptide leads.

Covalent Targeting of Ras G12C by Rationally Designed Peptidomimetics.

Protein-protein interactions (PPIs) play a critical role in fundamental biological processes. Competitive inhibition of these interfaces requires compounds that can access discontinuous binding epitopes along a large, shallow binding surface area. Conformationally defined protein surface mimics present a viable route to target these interactions. However, the development of minimal protein mimics that engage intracellular targets with high affinity remains a major challenge because mimicry of a portion of the binding interface is often associated with the loss of critical binding interactions. Covalent targeting provides an attractive approach to overcome the loss of noncovalent contacts but have the inherent risk of dominating noncovalent contacts and increasing the likelihood of nonselective binding. Here, we report the iterative design of a proteolytically stable α3ß chimeric helix mimic that covalently targets oncogenic Ras G12C as a model system. We explored several electrophiles to optimize preferential alkylation with the desired C12 on Ras. The designed lead peptide modulates nucleotide exchange, inhibits activation of the Ras-mediated signaling cascade, and is selectively toxic toward mutant Ras G12C cancer cells. The relatively high frequency of acquired cysteines as missense mutations in cancer and other diseases suggests that covalent peptides may offer an untapped therapeutic approach for targeting aberrant protein interactions.

Identification and Characterization of Trajectories of Cardiac Allograft Vasculopathy After Heart Transplantation: A Population-Based Study.

BACKGROUND: Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient mortality. Little is known about the prototypes of CAV trajectories at the population level. We aimed to identify the different evolutionary profiles of CAV and to determine the respective contribution of immune and nonimmune factors in CAV development. METHODS: Heart transplant recipients were from 4 academic centers (Pitié-Salpêtrière and Georges Pompidou Hospital, Paris, Katholieke Universiteit Leuven, and Cedars-Sinai, Los Angeles; 2004-2016). Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessments of clinical, histological, and immunologic parameters. The main outcome was a prediction for CAV trajectory. We identified CAV trajectories by using unsupervised latent class mixed models. We then identified the independent predictive variables of the CAV trajectories and their association with mortality. RESULTS: A total of 1301 patients were included (815 and 486 in the European and US cohorts, respectively). The median follow-up after transplantation was 6.6 (interquartile range, 4-9.1) years with 4710 coronary angiographies analyzed. We identified 4 distinct profiles of CAV trajectories over 10 years. The 4 trajectories were characterized by (1) patients without CAV at 1 year and nonprogression over time (56.3%), (2) patients without CAV at 1 year and late-onset slow CAV progression (7.6%), (3) patients with mild CAV at 1 year and mild progression over time (23.1%), and (4) patients with mild CAV at 1 year and accelerated progression (13.0%). This model showed good discrimination (0.92). Among candidate predictors assessed, 6 early independent predictors of these trajectories were identified: donor age (P<0.001), donor male sex (P<0.001), donor tobacco consumption (P=0.001), recipient dyslipidemia (P=0.009), class II anti-human leukocyte antigen donor-specific antibodies (P=0.004), and acute cellular rejection ≥2R (P=0.028). The 4 CAV trajectories manifested consistently in the US independent cohort with similar discrimination (0.97) and in different clinical scenarios, and showed gradients for overall-cause mortality (P<0.001). CONCLUSIONS: In a large multicenter and highly phenotyped prospective cohort of heart transplant recipients, we identified 4 CAV trajectories and their respective independent predictive variables. Our results provide the basis for a trajectory-based assessment of patients undergoing heart transplantation for early risk stratification, patient monitoring, and clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04117152.

Development of BioRad NGC and GE ÄKTA pure systems for highly automated three column protein purification employing tandem affinity, buffer exchange and size exclusion chromatography.

Affinity purification, such as Protein A (ProA) followed by size exclusion chromatography (SEC) remains a popular method to obtain research scale proteins. With the need for higher throughput protein production increasing for discovery research, there is substantial interest in the automation of complex protein purification processes, which often start with a ProA step followed by SEC. However, the harsh elution conditions from ProA based chromatography can destabilize some proteins resulting in particulates, which in turn can cause column fouling and potential cross-contamination of subsequent purifications. We modified both Bio Rad NGC and ÄKTA Pure systems to run a three-column process (ProA to buffer exchange to SEC) enabling automated tandem affinity to SEC purification while minimizing the risk of SEC column fouling and subsequent cross-contamination. The intervening buffer exchange column, unlike the final SEC column, can be rapidly regenerated using harsh methods between runs, and these automated systems are capable of processing up to six samples per day without user intervention.

A Single-Institution Case Series of Outpatient Same-Day Mastectomy: Implementation of a Quality Improvement Project and Initiative for Enhanced Recovery After Surgery.

Background: National data demonstrate a trend toward outpatient same-day mastectomy. The possible drivers of this change include the costs related to hospital admission and effective management of postoperative pain. We retrospectively analyzed our single-institution experience with outpatient same-day mastectomy that incorporates a multimodal pain management regimen. Methods: We retrospectively reviewed the medical records of patients who underwent same-day mastectomy at a single academic hospital. All patients received a multimodal, perioperative pain management regimen consisting of the intraoperative administration of 1,000 mg of intravenous (IV) acetaminophen and 30 mg of IV ketorolac, combined with the operating surgeon performing a 4- to 5-level, midaxillary, intercostal nerve block using liposomal bupivacaine. All patients were discharged with a prescription for acetaminophen with codeine, along with options for nonnarcotic alternatives as needed for pain. Results: We reviewed the data on 72 patients who underwent mastectomies: 11 (15.3%) bilateral and 61 (84.7%) unilateral. The average age was 57 years, and average body mass index was 30 kg/m2. The average length of stay of 4 to 6 hours was a marked reduction compared to a 23-hour observational period or an inpatient hospital stay. The average follow-up was 20.1 weeks. Five patients presented to the emergency department (ED) within the 30-day postoperative period, with 2 patients (2.8%) requiring readmission to the hospital for non-pain-related issues. The other 3 patients (4.2%) were evaluated for specific pain-related issues but did not require admission and were discharged home from the ED. Conclusion: Our data support outpatient same-day mastectomy incorporating a multimodal, perioperative pain management regimen as a safe and feasible treatment option. Potential additional benefits may include decreased oral opioid use and cost savings for the hospital.

Archetype Analysis Identifies Distinct Profiles in Renal Transplant Recipients with Transplant Glomerulopathy Associated with Allograft Survival.

BACKGROUND: Transplant glomerulopathy, a common glomerular lesion observed after kidney transplant that is associated with poor prognosis, is not a specific entity but rather the end stage of overlapping disease pathways. Its heterogeneity has not been precisely characterized to date. METHODS: Our study included consecutive kidney transplant recipients from three centers in France and one in Canada who presented with a diagnosis of transplant glomerulopathy (Banff cg score ≥1 by light microscopy), on the basis of biopsies performed from January of 2004 through December of 2014. We used an unsupervised archetype analysis of comprehensive pathology findings and clinical, immunologic, and outcome data to identify distinct groups of patients. RESULTS: Among the 8207 post-transplant allograft biopsies performed during the inclusion period, we identified 552 biopsy samples (from 385 patients) with transplant glomerulopathy (incidence of 6.7%). The median time from transplant to transplant glomerulopathy diagnosis was 33.18 months. Kidney allograft survival rates at 3, 5, 7, and 10 years after diagnosis were 69.4%, 57.1%, 43.3%, and 25.5%, respectively. An unsupervised learning method integrating clinical, functional, immunologic, and histologic parameters revealed five transplant glomerulopathy archetypes characterized by distinct functional, immunologic, and histologic features and associated causes and distinct allograft survival profiles. These archetypes showed significant differences in allograft outcomes, with allograft survival rates 5 years after diagnosis ranging from 88% to 22%. Based on those results, we built an online application, which can be used in clinical practice on the basis of real patients. CONCLUSIONS: A probabilistic data-driven archetype analysis approach applied in a large, well defined multicenter cohort refines the diagnostic and prognostic features associated with cases of transplant glomerulopathy. Reducing heterogeneity among such cases can improve disease characterization, enable patient-specific risk stratification, and open new avenues for archetype-based treatment strategies and clinical trials optimization.