Proteinuria frequency and subsequent renal dysfunction in bevacizumab-treated patients: a single center, retrospective, observational study.

BACKGROUND: Proteinuria is a common adverse event observed during treatment with antivascular endothelial growth factor (VEGF) antibodies. Proteinuria is a risk factor for renal dysfunction and cardiovascular complications in patients with chronic kidney disease. However, the association between anti-VEGF antibody-induced proteinuria and renal dysfunction or cardiovascular complications remains unclear. METHODS: This retrospective, observational study included patients with cancer that were treated with bevacizumab (BV) at Kyoto University Hospital (Kyoto, Japan) between January 2006 and March 2018. Adverse event rates were compared between patients who developed qualitative ≥ 2 + proteinuria and those who developed < 1 + proteinuria. Adverse events were defined as renal dysfunction (i.e., ≥ 57% decrease in the eGFR, compared to the rate at the initial treatment) and hospitalization due to BV-associated cardiovascular complications and other adverse events. RESULTS: In total, 734 patients were included in this analysis. Renal dysfunction was more common in patients with ≥ 2 + proteinuria than in those with < 1 + proteinuria (13/199, 6.5% vs. 12/535, 2.3%). Seven of these 13 patients with ≥ 2 + proteinuria had transient reversible renal dysfunction. Only four (2.0%) patients had BV-associated renal dysfunction. Of the 734 patients, six patients, 16 patients, and 13 patients were hospitalized because of the adverse events of cardiovascular complications, thromboembolisms, and cerebrovascular complications, respectively. No relationship was observed between these adverse events and proteinuria. CONCLUSION: BV treatment-induced proteinuria was not associated with renal dysfunction or other adverse events. Continuing BV with caution is a possible treatment option, even after proteinuria develops, in patients with cancer and a limited prognosis.

Annual trends of ophthalmic surgeries in Japan's super-aged society, 2014-2020: a national claims database study.

This study aimed to analyze the trends and factors influencing the number of ophthalmic surgeries in Japan using the open data from the National Database of Health Insurance Claims and Specific Health Checkups of Japan published by the Ministry of Health, Labour and Welfare. We calculated the number of cataract, glaucoma, and vitreoretinal surgeries, categorized by sex, age, and surgical type, for the fiscal years (FY) 2014 to 2020. The number of cataract surgeries remained stable at approximately 1.45 million cases from FY 2014 to 2018, increased to nearly 1.6 million cases in FY 2019, and decreased to 1.45 million cases in FY 2020. Among glaucoma surgeries, surgical treatments were increased 1.8 times over 7 years, from 33,000 to 60,000 cases. Laser treatment remained steady at around 55,000 cases from FY 2014 to 2017 and then increased to approximately 60,000 cases. The number of vitreoretinal surgeries was increased 1.2 times from FY 2014 to 2019, from 120,000 to 140,000, and decreased to 130,000 by FY 2020. Trends in ophthalmic surgeries over the past 7 years may be influenced by population aging, minimally invasive surgery, and the coronavirus disease pandemic. These findings have implications on surgical decision-making and resource allocation.

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

INTRODUCTION: Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology. METHODS: We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunoglobulin A nephropathy. We assessed the relationship between the obtained scores and clinical variables of urinary occult blood, urinary protein, serum creatinine (SCr), systolic blood pressure, and age. RESULTS: The glomeruli of the patients were classified into 12 distinct classes and 10 patches. The output of the fine-tuned CNN, which we defined as the histological scores, had significant relationships with assessed clinical variables. In addition, the clustering and visualization results suggested that the defined clusters captured important findings when evaluating renal histopathology. For the score of the patch-based cluster containing crescentic glomeruli, SCr (coefficient = 0.09, P = 0.019) had a significant relationship. CONCLUSION: The proposed approach could successfully extract features that were related to the clinical variables from the kidney biopsy images along with the visualization for interpretability. The approach could aid in the quantified evaluation of renal histopathology.

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. METHODS: We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS: Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION: Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.

Detecting Severe Incidents from Electronic Medical Records Using Machine Learning Methods.

The goal of this research was to design a solution to detect non-reported incidents, especially severe incidents. To achieve this goal, we proposed a method to process electronic medical records and automatically extract clinical notes describing severe incidents. To evaluate the proposed method, we implemented a system and used the system. The system successfully detected a non-reported incident to the safety management department.

The Clinical Effectiveness and Cost-Effectiveness of Screening for Age-Related Macular Degeneration in Japan: A Markov Modeling Study.

OBJECTIVE: To investigate the cost-effectiveness of screening and subsequent intervention for age-related macular degeneration (AMD) in Japan. METHODS: The clinical effectiveness and cost-effectiveness of screening and subsequent intervention for AMD were assessed using a Markov model. The Markov model simulation began at the age of 40 years and concluded at the age of 90 years. The first-eye and second-eye combined model assumed an annual state-transition probability, development of prodromal symptoms, choroidal neovascularization (CNV), and reduction in visual acuity. Anti-vascular-endothelial-growth-factor (anti-VEGF) intravitreal injection therapy and photodynamic therapy (PDT) were performed to treat CNV. Intake of supplements was recommended to patients who had prodromal symptoms and unilateral AMD. Data on prevalence, morbidity, transition probability, utility value of each AMD patient, and treatment costs were obtained from published clinical reports. RESULTS: In the base-case analysis, screening for AMD every 5 years, beginning at the age of 50 years, showed a decrease of 41% in the total number of blind patients. The screening program reduced the incidence of blindness more than did the additional intake of supplements. However, the incremental cost-effectiveness ratio (ICER) of screening versus no screening was 27,486,352 Japanese yen (JPY), or 259,942 US dollars (USD) per quality-adjusted life year (QALY). In the sensitivity analysis, prodromal symptom-related factors for AMD had great impacts on the cost-effectiveness of screening. The lowest ICER obtained from the best scenario was 4,913,717 JPY (46,470 USD) per QALY, which was approximately equal to the willingness to pay in Japan. CONCLUSIONS: Ophthalmologic screening for AMD in adults is highly effective in reducing the number of patients with blindness but not cost-effective as demonstrated by a Markov model based on clinical data from Japan.