Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We collected retrospective data on demographic characteristics, blood tests, and urine tests of the patients who underwent kidney biopsy. The dataset was divided into derivation and validation cohorts, with temporal validation. We employed five machine learning models-eXtreme Gradient Boosting (XGBoost), LightGBM, Random Forest, Artificial Neural Networks, and 1 Dimentional-Convolutional Neural Network (1D-CNN)-and logistic regression, evaluating performance via the area under the receiver operating characteristic curve (AUROC) and explored variable importance through SHapley Additive exPlanations method. The study included 1268 participants, with 353 (28%) diagnosed with IgA nephropathy. In the derivation cohort, LightGBM achieved the highest AUROC of 0.913 (95% CI 0.906-0.919), significantly higher than logistic regression, Artificial Neural Network, and 1D-CNN, not significantly different from XGBoost and Random Forest. In the validation cohort, XGBoost demonstrated the highest AUROC of 0.894 (95% CI 0.850-0.935), maintaining its robust performance. Key predictors identified were age, serum albumin, IgA/C3, and urine red blood cells, aligning with existing clinical insights. Machine learning can be a valuable non-invasive tool for IgA nephropathy.

Performance of ChatGPT and Bard in self-assessment questions for nephrology board renewal.

BACKGROUND: Large language models (LLMs) have impacted advances in artificial intelligence. While LLMs have demonstrated high performance in general medical examinations, their performance in specialized areas such as nephrology is unclear. This study aimed to evaluate ChatGPT and Bard in their potential nephrology applications. METHODS: Ninety-nine questions from the Self-Assessment Questions for Nephrology Board Renewal from 2018 to 2022 were presented to two versions of ChatGPT (GPT-3.5 and GPT-4) and Bard. We calculated the correct answer rates for the five years, each year, and question categories and checked whether they exceeded the pass criterion. The correct answer rates were compared with those of the nephrology residents. RESULTS: The overall correct answer rates for GPT-3.5, GPT-4, and Bard were 31.3% (31/99), 54.5% (54/99), and 32.3% (32/99), respectively, thus GPT-4 significantly outperformed GPT-3.5 (p < 0.01) and Bard (p < 0.01). GPT-4 passed in three years, barely meeting the minimum threshold in two. GPT-4 demonstrated significantly higher performance in problem-solving, clinical, and non-image questions than GPT-3.5 and Bard. GPT-4's performance was between third- and fourth-year nephrology residents. CONCLUSIONS: GPT-4 outperformed GPT-3.5 and Bard and met the Nephrology Board renewal standards in specific years, albeit marginally. These results highlight LLMs' potential and limitations in nephrology. As LLMs advance, nephrologists should understand their performance for future applications.

Pregnancy-onset thrombotic thrombocytopenic purpura with nephrotic syndrome: a case report.

Pregnancy-onset thrombotic thrombocytopenic purpura (TTP) was reported by many obstetricians and hematologists, but less by nephrologists, and the detailed clinical course of its renal complication is not known. Here, we report a case of a 33-year-old pregnant woman who suffered from pregnancy-onset TTP with nephrotic syndrome which was controlled by the termination of pregnancy. On admission, she had periorbital and lower leg edema at 32 weeks of gestation. Her serum albumin level was 2.8 g/dL and the urine protein/creatinine ratio was 4.1 g/g Cr. Besides those, she had thrombocytopenia, hemolytic anemia, and severe deficiency of A Disintegrin-like and Metalloproteinase with Thrombospondin type 1 motifs 13 (ADAMTS-13) activity. Thus, she was diagnosed with nephrotic syndrome due to pregnancy-onset TTP. A cesarean section was performed without complications for the patient and her baby. Then, all her symptoms improved shortly. She was suspected of congenital TTP because of no ADAMTS-13 inhibitor results and the persistent deficiency of ADAMTS-13 activity even after her condition improved. Pregnancy-onset TTP can cause nephrotic syndrome. Termination of pregnancy should be considered in cases with pregnancy-onset TTP to protect kidney function.