A real-world pharmacovigilance analysis of FDA adverse event reporting system database for upadacitinib

Objective: To mine the adverse drug event (ADE) signals of upadacitinib based on the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to provide a reference for the safe clinical use of the drug.Methods: The ADE data for upadacitinib from Q1 2004 to Q1 2023 in the FAERS database were retrieved, and data mining was performed using the reporting odds ratio and proportional reporting ratio.Results: A total of 21,213 ADE reports for the primary suspect drug upadacitinib were obtained, involving 444 ADEs. Patients aged ≥60 years (21.48%) and female (70.11%) patients were at a higher risk of ADEs with upadacitinib. After data cleaning, 182 ADE signals from 19 system organ classes (SOCs) were obtained. Six of these SOCs that occurred more frequently and were not mentioned in the drug labeling information included renal and urinary system (1.09%), reproductive and breast diseases (1.14%), ear and labyrinth disorders (0.57%), psychiatric disease (0.57%), blood and lymphatic system disorders (0.57%), and endocrine disorders (0.57%). The top ten most frequent ADE signals reported for upadacitinib were mainly related to: infections and infestations (7), investigations (2), and skin and subcutaneous tissue disorders (1). The top 10 ADEs in signal intensity ranking were lip neoplasm, ureteral neoplasm, eczema herpeticum, vulvar dysplasia, mediastinum neoplasm, eosinopenia, herpes zoster cutaneous disseminated, eye ulcer, acne cystic, and Moraxella infection. The top 10 high-frequency events leading to serious adverse events were urinary tract infection (2.74%), herpes zoster (1.63%), diverticulitis (1.19%), bronchitis (0.68%), nasopharyngitis (0.68%), localised infection (0.66%), nephrolithiasis (0.66%), pulmonary thrombosis (0.66%), blood cholesterol increased (0.55%), and Pneumocystis jirovecii pneumonia (0.53%).Conclusion: Clinicians should be vigilant to upadacitinib-induced events in systems not covered in the drug labeling information and to new and highly signaled ADEs to ensure the safe and effective use of upadacitinib

Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application

Table1_A real-world pharmacovigilance analysis of FDA adverse event reporting system database for upadacitinib.DOCX

Objective: To mine the adverse drug event (ADE) signals of upadacitinib based on the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to provide a reference for the safe clinical use of the drug.Methods: The ADE data for upadacitinib from Q1 2004 to Q1 2023 in the FAERS database were retrieved, and data mining was performed using the reporting odds ratio and proportional reporting ratio.Results: A total of 21,213 ADE reports for the primary suspect drug upadacitinib were obtained, involving 444 ADEs. Patients aged ≥60 years (21.48%) and female (70.11%) patients were at a higher risk of ADEs with upadacitinib. After data cleaning, 182 ADE signals from 19 system organ classes (SOCs) were obtained. Six of these SOCs that occurred more frequently and were not mentioned in the drug labeling information included renal and urinary system (1.09%), reproductive and breast diseases (1.14%), ear and labyrinth disorders (0.57%), psychiatric disease (0.57%), blood and lymphatic system disorders (0.57%), and endocrine disorders (0.57%). The top ten most frequent ADE signals reported for upadacitinib were mainly related to: infections and infestations (7), investigations (2), and skin and subcutaneous tissue disorders (1). The top 10 ADEs in signal intensity ranking were lip neoplasm, ureteral neoplasm, eczema herpeticum, vulvar dysplasia, mediastinum neoplasm, eosinopenia, herpes zoster cutaneous disseminated, eye ulcer, acne cystic, and Moraxella infection. The top 10 high-frequency events leading to serious adverse events were urinary tract infection (2.74%), herpes zoster (1.63%), diverticulitis (1.19%), bronchitis (0.68%), nasopharyngitis (0.68%), localised infection (0.66%), nephrolithiasis (0.66%), pulmonary thrombosis (0.66%), blood cholesterol increased (0.55%), and Pneumocystis jirovecii pneumonia (0.53%).Conclusion: Clinicians should be vigilant to upadacitinib-induced events in systems not covered in the drug labeling information and to new and highly signaled ADEs to ensure the safe and effective use of upadacitinib.</p

Table2_A real-world pharmacovigilance analysis of FDA adverse event reporting system database for upadacitinib.DOCX

Objective: To mine the adverse drug event (ADE) signals of upadacitinib based on the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to provide a reference for the safe clinical use of the drug.Methods: The ADE data for upadacitinib from Q1 2004 to Q1 2023 in the FAERS database were retrieved, and data mining was performed using the reporting odds ratio and proportional reporting ratio.Results: A total of 21,213 ADE reports for the primary suspect drug upadacitinib were obtained, involving 444 ADEs. Patients aged ≥60 years (21.48%) and female (70.11%) patients were at a higher risk of ADEs with upadacitinib. After data cleaning, 182 ADE signals from 19 system organ classes (SOCs) were obtained. Six of these SOCs that occurred more frequently and were not mentioned in the drug labeling information included renal and urinary system (1.09%), reproductive and breast diseases (1.14%), ear and labyrinth disorders (0.57%), psychiatric disease (0.57%), blood and lymphatic system disorders (0.57%), and endocrine disorders (0.57%). The top ten most frequent ADE signals reported for upadacitinib were mainly related to: infections and infestations (7), investigations (2), and skin and subcutaneous tissue disorders (1). The top 10 ADEs in signal intensity ranking were lip neoplasm, ureteral neoplasm, eczema herpeticum, vulvar dysplasia, mediastinum neoplasm, eosinopenia, herpes zoster cutaneous disseminated, eye ulcer, acne cystic, and Moraxella infection. The top 10 high-frequency events leading to serious adverse events were urinary tract infection (2.74%), herpes zoster (1.63%), diverticulitis (1.19%), bronchitis (0.68%), nasopharyngitis (0.68%), localised infection (0.66%), nephrolithiasis (0.66%), pulmonary thrombosis (0.66%), blood cholesterol increased (0.55%), and Pneumocystis jirovecii pneumonia (0.53%).Conclusion: Clinicians should be vigilant to upadacitinib-induced events in systems not covered in the drug labeling information and to new and highly signaled ADEs to ensure the safe and effective use of upadacitinib.</p

Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.

RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS: Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS: In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103&nbsp;=&nbsp;96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53&nbsp;=&nbsp;81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION: ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p &lt; 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p &lt; 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p &lt; 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p &lt; 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p &lt; 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009)

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes

A total of 457 patients, including 241 HR+/HER2− patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p p p = 0.031); homogeneous enhancement was mostly seen in HR+ (p p p p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009)

Table_1_Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia.XLS

ObjectiveTo establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors.MethodsThe cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool.ResultsThe area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0.874–0.923), 0.902 (95% CI: 0.876–0.924), and 0.955 (95% CI: 0.935–0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p  0.5).ConclusionMachine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.</p

Effect of respiratory muscle training in patients with stable chronic obstructive pulmonary disease: A systematic review and meta-analysis

Objectives: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder characterized by progressive airflow limitation. This meta-analysis aims to evaluate the effectiveness of respiratory muscle training (RMT) on key pulmonary function parameters, inspiratory muscle strength and quality of life in patients with stable COPD. Methods: A comprehensive search was conducted in the databases including PubMed, Cochrane, Web of Science, Embase, and ClinicalTrials.gov, from their inception to June 12, 2023. Randomized controlled trials (RCTs) evaluating the impact of RMT on stable COPD were included for meta-analysis. Results: In total, 12 RCTs involving 453 participants were included in the meta-analysis. RMT demonstrated a significant increase in maximal inspiratory pressure (PImax, MD, 95% CI: 14.34, 8.17 to 20.51, P < 0.001) but not on maximal expiratory pressure (PEmax). No significant improvement was observed in 6-Min walk test (6MWT), dyspnea, forced expiratory volume in 1 s (FEV1), forced vital capacity ratio (FVC) and quality of life between RMT and control groups. However, subgroup analysis revealed a significant negative effect of RMT alone on FEV1/FVC (MD, 95% CI: 2.59, −5.11 to −0.06, P = 0.04). When RMT was combined with other interventions, improvements in FEV1/FVC and FEV1 were found, although not statistically significant. Conclusion: RMT can effectively improve maximal inspiratory pressure in stable COPD patients, but the effect is slight in improving lung function, dyspnea and quality of life. It is recommended to combine with other treatment strategies to comprehensively improve the prognosis of COPD patients

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.

BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.Materials and methodsA total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing.ResultsThe diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets.ConclusionDiagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis