Tangri, Navdeep

Artificial intelligence (AI) is rapidly transforming the delivery of kidney care through predictive analytics, machine learning, deep learning, and generative AI technologies. To meet this challenge, the American Society of Nephrology convened an AI Workgroup to provide a framework for the responsible use of AI in nephrology. The group outlines foundational principles to guide AI development: prioritizing patient benefit, ensuring clinician oversight, and advancing innovation in high-burden disease areas. Its set of foundational assumptions are grounded in the physician always being in the loop and an overarching goal to benefit patients with kidney diseases. This review provides an overview of the clinical uses of AI in nephrology and offers practical guidance for nephrologists seeking to incorporate AI into CKD and AKI management, dialysis, and transplantation care. It also highlights key challenges—such as data quality, equity, transparency, and clinical integration—that must be addressed to ensure the responsible and effective implementation of AI in kidney care.

Although Chronic Kidney Disease is common, only a relatively small proportion of individuals will reach kidney failure requiring dialysis or transplantation. Validated risk equations using routine laboratory tests have been developed that can easily be used at the bedside to help clinicians accurately predict the risk of kidney failure in their patient population, in turn informing patient-centered conversations, guiding appropriate nephrology referrals, improving the timing of dialysis treatment planning, and identifying individuals who are most likely to benefit from interventions. In this article, individuals living with kidney disease share why access to individualized prediction of kidney failure risk can help patients manage their disease and why it should be considered an essential component of kidney care.

Background Patients on home dialysis therapies experience technique failure, which is associated with morbidity and mortality. Reasons for technique failure are complex, and often related to functional decline in the patient or caregiver. Frailty is associated with an increased risk of adverse health outcomes. We investigated the impact of frailty on technique failure and mortality in a prospective cohort of patients on home dialysis therapies. Methods We collected objective (Fried criteria and Short Physical Performance Battery [SPPB]), and subjective (physician and nurse impression) measures of frailty from 109 prevalent home dialysis patients. Our primary outcome was a composite of technique failure, defined as a permanent unplanned transition (> 30 days in duration) to facility-based hemodialysis or all-cause death. The association between different frailty assessment tools and the primary composite outcome was evaluated using Cox models. Results Fried criteria and physician impression was associated with a greater than 2-fold increase in risk of our composite outcome (HR: 2.10 [95% CI 1.09 – 3.99], 2.15 [95% CI 1.15 – 4.00, respectively] in adjusted analyses. Weakness and weight loss subdomains of the Fried criteria were both associated with an increased risk of our composite outcome in adjusted analyses (HR: 2.16 [95% CI 1.23 – 3.78], 2.69 [95% CI 1.39 – 5.40], respectively). Conclusions Objective and subjective measures of frailty are associated with a more than 2-fold higher risk of technique failure or death in patients undergoing home dialysis. Assessing frailty as part of the clinical evaluation for home dialysis therapies may be useful for prognostication and clinical management.

Background and objectives Dialysis is a preference-sensitive decision where prognosis may play an important role. Although patients desire risk prediction, nephrologists are wary of sharing this information. We reviewed the performance of prognostic indices for patients starting dialysis to facilitate bedside translation. Design, setting, participants, & measurements Systematic review and meta-analysis following the PRISMA guidelines. We searched Ovid MEDLINE, Ovid Embase, Ovid Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus for eligible studies of patients starting dialysis published from inception to December 31, 2018. Selection Criteria: Articles describing validated prognostic indices predicting mortality at the start of dialysis. We excluded studies limited to prevalent dialysis patients, AKI and studies excluding mortality in the first 1–3 months. Two reviewers independently screened abstracts, performed full text assessment of inclusion criteria and extracted: study design, setting, population demographics, index performance and risk of bias. Pre-planned random effects meta-analysis was performed stratified by index and predictive window to reduce heterogeneity. Results Of 12,132 articles screened and 214 reviewed in full text, 36 studies were included describing 32 prognostic indices. Predictive windows ranged from 3 months to 10 years, cohort sizes from 46 to 52,796. Meta-analysis showed discrimination area under the curve (AUC) of 0.71 (95% confidence interval, 0.69 to 073) with high heterogeneity ( I 2 =99.12). Meta-analysis by index showed highest AUC for The Obi, Ivory, and Charlson comorbidity index (CCI)=0.74, also CCI was the most commonly used (ten studies). Other commonly used indices were Kahn-Wright index (eight studies, AUC 0.68), Hemmelgarn modification of the CCI (six studies, AUC 0.66) and REIN index (five studies, AUC 0.69). Of the indices, ten have been validated externally, 16 internally and nine were pre-existing validated indices. Limitations include heterogeneity and exclusion of large cohort studies in prevalent patients. Conclusions Several well validated indices with good discrimination are available for predicting survival at dialysis start.