Neurospine
Department of Neurosurgery, Stanford University, Stanford, CA, USA.
Published: June 2024
Objective: Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors.
Methods: The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004-2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation.
Results: This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p < 0.001), while also projecting an average cost savings of 50% more than the LACE index.
Conclusion: Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224744 | PMC |
http://dx.doi.org/10.14245/ns.2347340.670 | DOI Listing |
Acta Biomater
January 2025
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America; Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia 30322, United States of America. Electronic address:
Pro-tumoral M2 tumor-associated macrophages (TAMs) play a critical role in the tumor immune microenvironment (TIME), making them an important therapeutic target for cancer treatment. Approaches for imaging and monitoring M2 TAMs, as well as tracking their changes in response to tumor progression or treatment are highly sought-after but remain underdeveloped. Here, we report an M2-targeted magnetic resonance imaging (MRI) probe based on sub-5 nm ultrafine iron oxide nanoparticles (uIONP), featuring an anti-biofouling coating to prevent non-specific macrophage uptake and an M2-specific peptide ligand (M2pep) for active targeting of M2 TAMs.
View Article and Find Full Text PDFSpine J
January 2025
Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha 410008, China. Electronic address:
Background: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. The application of AI-driven large language models (LLMs) shows great potential for improving the accuracy of this differential diagnosis.
Purpose: To evaluate the performance of various machine learning models and ChatGPT-4 in distinguishing between STB and ST.
Discov Oncol
January 2025
Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.
Methods: This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM.
Glioblastoma Multiforme (GBM) is the most prevalent and highly malignant form of adult brain cancer characterized by poor overall survival rates. Effective therapeutic modalities remain limited, necessitating the search for novel treatments. Neurodevelopmental pathways have been implicated in glioma formation, with key neurodevelopmental regulators being re- expressed or co-opted during glioma tumorigenesis.
View Article and Find Full Text PDFTher Clin Risk Manag
January 2025
Department of Oncology, Gaoxin Branch of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.
Background: The relationship between molecular phenotype and prognosis in high-grade gliomas (WHO III and IV, HGG) treated with radiotherapy and chemotherapy is not fully understood and needs further exploration.
Methods: The HGG patients following surgery and treatment with radiotherapy and chemotherapy. Univariate and multivariate Cox analyses were used to assess the independent prognostic factors.
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