A randomized study to compare oral potassium binders in the treatment of acute hyperkalemia.

BACKGROUND: The KBindER (K+ Binders in Emergency Room and hospitalized patients) clinical trial is the first head-to-head evaluation of oral potassium binders (cation-exchange resins) for acute hyperkalemia therapy. METHODS: Emergency room and hospitalized patients with a blood potassium level ≥ 5.5 mEq/L are randomized to one of four study groups: potassium binder drug (sodium polystyrene sulfonate, patiromer, or sodium zirconium cyclosilicate) or nonspecific laxative (polyethylene glycol). Exclusion criteria include recent bowel surgery, ileus, diabetic ketoacidosis, or anticipated dialysis treatment within 4 h of treatment drug. Primary endpoints include change in potassium level at 2 and 4 h after treatment drug. Length of hospital stay, next-morning potassium level, gastrointestinal side effects and palatability will also be analyzed. We are aiming for a final cohort of 80 patients with complete data endpoints (20 per group) for comparative statistics including multivariate adjustment for kidney function, diabetes mellitus, congestive heart failure, metabolic acidosis, renin-angiotensin-aldosterone system inhibitor prescription, and treatment with other agents to lower potassium (insulin, albuterol, loop diuretics). DISCUSSION: The findings from our study will inform decision-making guidelines on the role of oral potassium binders in the treatment of acute hyperkalemia. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04585542 . Registered 14 October 2020.

AI Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Recently, AI tools have been deployed with increasing speed in educational and clinical settings. However, the use of AI by trainees across different levels of experience has not been well studied. This study investigates the impact of AI assistance on diagnostic accuracy for intracranial hemorrhage (ICH) and large vessel occlusion (LVO) by medical students (MS) and resident trainees (RT). MATERIALS AND METHODS: This prospective study was conducted between March 2023 and October 2023. MS and RT were asked to identify ICH and LVO in 100 non-contrast head CTs and 100 head CTAs, respectively. One group received diagnostic aid simulating AI for ICH only (n = 26), the other for LVO only (n = 28). Primary outcomes included accuracy, sensitivity, and specificity for ICH / LVO detection without and with aid. Study interpretation time was a secondary outcome. Individual responses were pooled and analyzed with chi-square; differences in continuous variables were assessed with ANOVA. RESULTS: 48 participants completed the study, generating 10,779 ICH or LVO interpretations. With diagnostic aid, MS accuracy improved 11.0 points (P < .001) and RT accuracy showed no significant change. ICH interpretation time increased with diagnostic aid for both groups (P < .001) while LVO interpretation time decreased for MS (P < .001). Despite worse performance in detection of the smallest vs. the largest hemorrhages at baseline, MS were not more likely to accept a true positive AI result for these more difficult tasks. Both groups were considerably less accurate when disagreeing with the AI or when supplied with an incorrect AI result. CONCLUSIONS: This study demonstrated greater improvement in diagnostic accuracy with AI for MS compared to RT. However, MS were less likely than RT to overrule incorrect AI interpretations and were less accurate, even with diagnostic aid, than the AI was by itself. ABBREVIATIONS: ICH = intracranial hemorrhage; LVO = large vessel occlusion; MS = medical students; RT = resident trainees.

Artificial Intelligence Assessment of Renal Scarring (AIRS Study).

Background: The goal of the Artificial Intelligence in Renal Scarring (AIRS) study is to develop machine learning tools for noninvasive quantification of kidney fibrosis from imaging scans. Methods: We conducted a retrospective analysis of patients who had one or more abdominal computed tomography (CT) scans within 6 months of a kidney biopsy. The final cohort encompassed 152 CT scans from 92 patients, which included images of 300 native kidneys and 76 transplant kidneys. Two different convolutional neural networks (slice-level and voxel-level classifiers) were tested to differentiate severe versus mild/moderate kidney fibrosis (≥50% versus <50%). Interstitial fibrosis and tubular atrophy scores from kidney biopsy reports were used as ground-truth. Results: The two machine learning models demonstrated similar positive predictive value (0.886 versus 0.935) and accuracy (0.831 versus 0.879). Conclusions: In summary, machine learning algorithms are a promising noninvasive diagnostic tool to quantify kidney fibrosis from CT scans. The clinical utility of these prediction tools, in terms of avoiding renal biopsy and associated bleeding risks in patients with severe fibrosis, remains to be validated in prospective clinical trials.