We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic regression analysis. In the validation stage, 108 patients were included for temporal validation between January 2020 and June 2020. The effectiveness of the model was evaluated through discrimination and calibration. As a result, the prediction model contains seven risk factors (age, anesthesia method, score of mini-mental state examination, hypoxia, major hemorrhage, level of interleukin-6, and company of family members), which had an area under the receiver operating characteristics curve of 0.82 (95% confidence interval 0.76-0.88) and was stable after bootstrapping. The temporal validation resulted in an area under the curve of 0.80 (95% confidence interval 0.67-0.93). Our prediction model had excellent discrimination power in predicting postoperative delirium in elderly patients and could assist intensive care physicians with early prevention.
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http://dx.doi.org/10.1155/2021/9959077 | DOI Listing |
J Phys Chem B
January 2025
School of Chemistry, The University of New South Wales, Sydney, NSW 2052, Australia.
A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (rSCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Parul Institute of Applied Sciences, Parul University, Vadodara, India.
Background: Breast cancer remains a significant global health challenge, requiring innovative therapeutic strategies. In silico methods, which leverage computational tools, offer a promising pathway for vaccine development. These methods facilitate antigen identification, epitope prediction, immune response modelling, and vaccine optimization, accelerating the design process.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Department of Physics, Faculty of Sciences, Arak University, Arak, Iran.
Objective: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors including treatment modalities, lifestyle choices, and habits such as smoking and alcohol consumption. This study aims to establish a novel relationship using linear regression models between dose and the risk of SC, comparing different prediction methods for lung, colon, and breast cancer.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Department of Biology, Faculty of Science, University of Sistan and Baluchestan, Zahedan, Iran.
Background: LIN28, a highly conserved RNA-binding protein, regulate a wide variety of post-transcriptional cellular processes. The current study aimed to identify genetic variants of five single nucleotide polymorphisms (SNPs) in the LIN28B gene (rs221634, rs22163, rs314276, rs9404590, and rs12194974) and their association with Breast cancer.
Method: 220 patients and 230 controls were genotyped by the RFLP assay for Lin28B gene variants.
Asian Pac J Cancer Prev
January 2025
Department of Nuclear Medicine, Busan Paik Hospital, University of Inje College of Medicine, Busan, Republic of Korea.
Objective: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regional lymph node metastasis in patients with colon cancer.
Methods: This retrospective study included 193 patients diagnosed with colon cancer between January 2014 and December 2017. All patients underwent F-18 FDG PET/CT and blood test before surgery.
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