Objectives: The purpose of this study was to investigate the baseline independent risk factors for predicting 6-month mortality of patients with anti-melanoma differentiation-associated gene 5 (anti-MDA5)-positive dermatomyositis (DM) and develop a matrix prediction model formed by these risk factors.
Methods: The hospitalized patients with DM who completed at least 6-month follow-up were recruited as a derivation cohort. The primary exposure was defined as positive anti-MDA5 at the baseline. The primary outcome was all-cause 6-month mortality after enrollment. A matrix prediction model was developed in the derivation cohort, and another published cohort was used for external validation.
Results: In derivation cohort, 82 patients with DM were enrolled (mean age of onset 50 ± 11 years and 63% women), with 40 (49%) showing positive anti-MDA5. Gottron sign/papules (OR: 5.135, 95%CI: 1.489-17.708), arthritis (OR: 5.184, 95%CI: 1.455-18.467), interstitial lung disease (OR: 7.034, 95%CI: 1.157-42.785), and higher level of C4 (OR: 1.010, 95%CI: 1.002-1.017) were the independent associators with positive anti-MDA5 in patients with DM. Patients with anti-MDA5-positive DM had significant higher 6-month all-cause mortality than those with anti-MDA5-negative (30 vs. 0%). Among the patients with anti-MDA5-positive DM, compared to the survivors, non-survivors had significantly advanced age of onset (59 ± 6 years vs. 46 ± 9 years), higher rates of fever (75 vs. 18%), positive carcinoma embryonic antigen (CEA, 75 vs. 14%), higher level of ferritin (median 2,858 ug/L . 619 ug/L, all < 0.05). A stepwise multivariate Cox regression showed that ferritin ≥1,250 μg/L (HR: 10.4, 95%CI: 1.8-59.9), fever (HR: 11.2, 95%CI: 2.5-49.9), and positive CEA (HR: 5.2, 95%CI: 1.0-25.7) were the independent risk factors of 6-month mortality. A matrix prediction model was built to stratify patients with anti-MDA5-positive DM into different subgroups with various probabilities of 6-month mortality risk. In an external validation cohort, the observed 6-month all-cause mortality was 78% in high-risk group, 43% in moderate-risk group, and 25% in low-risk group, which shows good accuracy of the model.
Conclusion: Baseline characteristics such as fever, ferritin ≥1,250 μg/L, and positive CEA are the independent risk factors for 6-month all-cause mortality in patients with anti-MDA5-positive DM. A novel matrix prediction model composed of these three clinical indicators is first proposed to provide a chance for the exploration of individual treatment strategies in anti-MDA5-positive DM subgroups with various probabilities of mortality risk.
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http://dx.doi.org/10.3389/fmed.2022.860798 | DOI Listing |
Sci Rep
December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
View Article and Find Full Text PDFEnviron Technol
December 2024
Univ Rennes, CNRS, ISCR (Institut des Sciences Chimiques de Rennes) - UMR 6226, Rennes, France.
A natural water sampled after a sand filtration step and spiked with four organic micropollutants (metolachlor ESA, metolachlor NOA, desethylatrazine and metaldehyde) was treated by a loose nanofiltration membrane. The Steric, Electric, and Dielectric model (SEDE model) was then used to predict the separation performance of the membrane towards the various ions and micropollutants in the water matrix in order to study the transport mechanism of ions and micropollutants through the membrane. The SEDE model was found to satisfactorily predict the rejection sequences of inorganic anions and cations, as well as neutral (desethylatrazine and metaldehyde) and charged (metolachlor ESA and metolachlor NOA) micropollutants.
View Article and Find Full Text PDFJ Cosmet Dermatol
January 2025
Centre Médical Laser Palaiseau, Palaiseau, France.
Introduction: Single-nucleotide polymorphisms (SNPs) represent a significant genetic variation influencing individual responses to cosmetic dermatology treatments. SNP profiling offers a pathway to personalized skincare by enabling practitioners to predict patient outcomes, customize interventions, and mitigate risks.
Background: The integration of genetic insights into dermatology has gained traction, with SNP analysis revealing predispositions in skin characteristics, such as collagen degradation, pigmentation, and inflammatory responses.
BMC Health Serv Res
December 2024
NuMIQ Research Focus Area, School of Nursing Science, North-West University, 11 Hoffman Street, Potchefstroom, South Africa.
Background: The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the specific factors leading to these issues is critical for adequately supporting nurses and informing policymakers.
View Article and Find Full Text PDFFront Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
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