Immune Checkpoint Inhibitors-Associated Diabetes and Ketoacidosis Were Found in FDA Adverse Event Reporting System: A Real-World Evidence Database Study.

OBJECTIVE: To investigate the risk of developing diabetes and ketoacidosis in clinical patients with immune checkpoint inhibitors (ICIs). METHODS: We looked in the FDA Adverse Event Reporting System for reports of ICIs-associated diabetes mellitus (DM) and ketoacidosis between January 2004 and March 2022. We explored the signals using fourfold table-based proportional imbalance algorithms. Patient characteristics, country distribution, and outcomes of adverse reactions were described. Kruskal-Wallis test was used to compare the time of onset and prognosis of adverse reactions. RESULTS: A total of 2110 reports of ICIs-related DM were included in the study. The largest number of reports was from Japan (752, 35.64%), followed by the United States and France (624, 29.57%; 183, 8.67%). Seven drugs detected signals of DM and ketoacidosis according to 4 proportional imbalance algorithms: nivolumab, pembrolizumab, cemiplimab, dostarlimab, atezolizumab, avelumab, and durvalumab. Diabetes and ketoacidosis generally occurred early in the course of ICIs treatment, the median time to event onset was 144.5 (interquartile range 27-199) days. ICIs-related diabetes and ketoacidosis events resulted in 934 major medical events (44.3%), 524 hospitalizations (24.8%), 60 life-threatening events (2.8%), 42 deaths (2.0%), and 39 disability events (1.8%). CONCLUSION: The study reveals the risk and characteristics of diabetes and ketoacidosis associated with ICIs, which may provide evidence for postmarketing evaluation. Careful consideration should be given to the possibility of an increased risk of diabetes and ketoacidosis after using ICIs, and careful monitoring for diabetes and ketoacidosis is recommended.

Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.

BACKGROUND: The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud. METHODS: A group of 482 patients were studied in this perspective work. The 3D facial data were obtained using a 3D camera and represented as a 3D facial points cloud. A DL model, PointNet++, was trained and evaluated using the points cloud as inputs and classified the malnutrition states. The performance was evaluated with the area under the receiver operating characteristic curve, accuracy, specificity, sensitivity, and F1 score. RESULTS: Among the 482 patients, 150 patients (31.1%) were diagnosed as having moderate malnutrition and 54 patients (11.2%) as having severe malnutrition. The DL model achieved the performance with an area under the receiver operating characteristic curve of 0.7240 ± 0.0416. CONCLUSION: The DL model achieved encouraging performance in accurately classifying nutrition states based on a points cloud of 3D facial information of patients with malnutrition.

Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study.

Background: Prompt diagnosis of malnutrition and appropriate interventions can substantially improve the prognosis of patients with cancer; however, it is difficult to unify the tools for screening malnutrition risk. 3D imaging technology has been emerging as an approach to assisting in the diagnosis of diseases, and we designed this study to explore its application value in identifying the malnutrition phenotype and evaluating nutrition status. Methods: Hospitalized patients treating with maintenance chemotherapy for advanced malignant tumor of digestive system were recruited from the Department of Oncology, whose NRS 2002 score > 3. Physical examination and body composition data of patients at risk for malnutrition were analyzed by physicians trained to complete a subjective global assessment. The facial depression index was recognized using the Antera 3D® system, temporal and periorbital depression indexes were acquired using the companion software Antera Pro. This software captures quantitative data of depression volume, affected area, and maximum depth of temporal and periorbital concave areas. Results: A total of 53 inpatients with malnutrition-related indicators were included. The volume of temporal depression was significantly negatively correlated with upper arm circumference (r = -0.293, p = 0.033) and calf circumference (r = -0.285, p = 0.038). The volume and affected area of periorbital depression were significantly negatively correlated with fat mass index (r = -0.273, p = 0.048 and r = -0.304, p = 0.026, respectively) and percent body fat (r = -0.317, p = 0.021 and r = -0.364, p = 0.007, respectively). The volume and affected area of temporal depression in patients with muscle loss phenotype (low arm circumference/low calf circumference/low handgrip strength/low fat-free mass index) were significantly higher than those in patients without muscle loss. Moreover, patients with fat mass loss phenotype (low fat mass index) showed a significant increase in the volume and affected area of periorbital depression. Conclusion: The facial temporal region, and periorbital depression indicators extracted by 3D image recognition technology were significantly associated with the phenotype of malnutrition-related muscle and fat loss and showed a trend of grade changes in the population of different subjective global assessment nutritional classifications.