Our understanding of the conformational and electrostatic determinants that underlie targeting of human leukocyte antigens (HLA) by anti-HLA alloantibodies is principally based upon in silico modelling. Here we provide a biochemical/biophysical and functional characterization of a human monoclonal alloantibody specific for a common HLA type, HLA-A*11:01. We present a 2.4 Å resolution map of the binding interface of this antibody on HLA-A*11:01 and compare the structural determinants with those utilized by T-cell receptor (TCR), killer-cell immunoglobulin-like receptor (KIR) and CD8 on the same molecule. These data provide a mechanistic insight into the paratope−epitope relationship between an alloantibody and its target HLA molecule in a biological context where other immune receptors are concomitantly engaged. This has important implications for our interpretation of serologic binding patterns of anti-HLA antibodies in sensitized individuals and thus, for the biology of human alloresponses.

The world population is aging and the prevalence of noncommunicable diseases such as diabetes, hypertension, and chronic kidney disease (CKD) will increase significantly. With advances in medical treatment and public health, the human lifespan continues to outpace the health span in such a way that the last decade of life is generally spent in poor health. In 2015, the World Health Organization defined healthy aging as ‘the process of developing and maintaining the functional ability that enables wellbeing in older age.’ CKD is increasingly being recognized as a model of accelerated aging and is associated with physical performance decline, cognitive decline, falls and fractures, poor quality of life, loss of appetite, and inflammation. Frailty and dementia are the final pathways and key determinants of disability and mortality independent of underlying disease. CKD, dementia, and frailty share a triangular relationship with synergistic actions and have common risk factors wherein CKD accelerates frailty and dementia through mechanisms such as uremic toxicity, metabolic acidosis and derangements, anorexia and malnutrition, dialysis-related hemodynamic instability, and sleep disturbance. Frailty accelerates glomerular filtration decline as well as dialysis induction in CKD and more than doubles the mortality risk. Anorexia is one of the major causes of protein-energy malnutrition, which is also prevalent in the aging population and warrants screening. Healthcare systems across the world need to have a system in place for the prevention of CKD amongst high-risk older adults, focusing on screening for poor prognostic factors amongst patients with CKD such as frailty, poor appetite, and cognitive impairment and providing necessary person-centered interventions to reverse underlying factors that may contribute to poor outcomes.

Deceased donor kidney transplantation (DDKT) is common in high income Western countries with high transplantation rates. However, the utilization of deceased organs is suboptimal in Asia, due to a multitude of factors. Coherent policies are integral to the development of DDKT programs and deterrence of commercialization, but most are still at an infancy and formative stage in Asia. This review article identifies the glass ceiling effects of social, cultural, religious, political, and technical factors hampering the progress of DDKT in Asia. Additionally, it reviews the history of policy development in different countries and describes their idiosyncratic barriers and challenges. Lastly, it discusses innovative policy measures that can be undertaken to proliferate DDKT practice and curtail commercialization. The long-term ideal is to achieve regional equity and self-sufficiency, through a shared ethos of social and ethical responsibility that transcends and resonates with the different segments of the Asian community.

Background: Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. Objective: The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. Methods: The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter’s corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. Results: The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. Conclusions: We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

Electronic health records (EHR) detect the onset of acute kidney injury (AKI) in hospitalized patients, and may identify those at highest risk of mortality and renal replacement therapy (RRT), for earlier targeted intervention. Prospective observational study to derive prediction models for hospital mortality and RRT, in inpatients aged ≥18 years with AKI detected by EHR over 1 year in a tertiary institution, fulfilling modified KDIGO criterion based on serial serum creatinine (sCr) measures. We studied 3333 patients with AKI, of 77,873 unique patient admissions, giving an AKI incidence of 4%. KDIGO AKI stages at detection were 1(74%), 2(15%), 3(10%); corresponding peak AKI staging in hospital were 61, 20, 19%. 392 patients (12%) died, and 174 (5%) received RRT. Multivariate logistic regression identified AKI onset in ICU, haematological malignancy, higher delta sCr (sCr rise from AKI detection till peak), higher serum potassium and baseline eGFR, as independent predictors of both mortality and RRT. Additionally, older age, higher serum urea, pneumonia and intraabdominal infections, acute cardiac diseases, solid organ malignancy, cerebrovascular disease, current need for RRT and admission under a medical specialty predicted mortality. The AUROC for RRT prediction was 0.94, averaging 0.93 after 10-fold cross-validation. Corresponding AUROC for mortality prediction was 0.9 and 0.9 after validation. Decision tree analysis for RRT prediction achieved a balanced accuracy of 70.4%, and identified delta-sCr ≥ 148 μmol/L as the key factor that predicted RRT. Case fatality was high with significant renal deterioration following hospital-wide AKI. EHR clinical model was highly accurate for both RRT prediction and for mortality; allowing excellent risk-stratification with potential for real-time deployment.

The current state-of-the-art technology employed to assess anti-human leukocyte antigen antibodies (Anti-HLA Ab) for donor-recipient matching and patient risk stratification in renal transplantation is the single antigen bead (SAB) assay. However, there are limitations to the SAB assay as it is not quantitative and due to variations in techniques and reagents, there is no standardization across laboratories. In this study, a structurally-defined human monoclonal alloantibody was employed to provide a mechanistic explanation for how fundamental alloantibody biology influences the readout from the SAB assay. Performance of the clinical SAB assay was evaluated by altering Anti-HLA Ab concentration, subclass, and detection reagents. Tests were conducted in parallel by two internationally accredited laboratories using standardized protocols and reagents. We show that alloantibody concentration, subclass, laboratory-specific detection devices, subclass-specific detection reagents all contribute to a significant degree of variation in the readout. We report a significant prozone effect affecting HLA alleles that are bound strongly by the test alloantibody as opposed to those bound weakly and this phenomenon is independent of complement. These data highlight the importance for establishing international standards for SAB assay calibration and have significant implications for our understanding of discordance in previous studies that have analyzed its clinical relevance.