Purpose: This study aims to develop and validate a prediction model for non-operative, epidermal growth factor receptor (EGFR)-positive, locally advanced elderly esophageal cancer (LAEEC).
Methods: A total of 80 EGFR-positive LAEEC patients were included in the study. All patients underwent radiotherapy, while 41 cases received icotinib concurrent systemic therapy. A nomogram was established using univariable and multivariable Cox analyses. The model's efficacy was assessed through area under curve (AUC) values, receiver operating characteristic (ROC) curves at different time points, time-dependent AUC (tAUC), calibration curves, and clinical decision curves. Bootstrap resampling and out-of-bag (OOB) cross-validation methods were employed to verify the model's robustness. Subgroup survival analysis was also conducted.
Results: Univariable and multivariable Cox analyses revealed that icotinib, stage, and ECOG score were independent prognostic factors for LAEEC patients. The AUCs of model-based prediction scoring (PS) for 1-, 2-, and 3-year overall survival (OS) were 0.852, 0.827, and 0.792, respectively. Calibration curves demonstrated that the predicted mortality was consistent with the actual mortality. The time-dependent AUC of the model exceeded 0.75, and the internal cross-validation calibration curves showed good agreement between predicted and actual mortality. Clinical decision curves indicated that the model had a substantial net clinical benefit within a threshold probability range of 0.2 to 0.8. Model-based risk stratification analysis demonstrated the model's excellent ability to distinguish survival risk. Further subgroup analyses showed that icotinib significantly improved survival in patients with stage III and ECOG score of 1 (HR 0.122, P<0.001).
Conclusions: Our nomogram model effectively predicts the overall survival of LAEEC patients, and the benefits of icotinib were found in the clinical stage III population with good ECOG scores.
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http://dx.doi.org/10.3389/fonc.2023.1097907 | DOI Listing |
Front Immunol
January 2025
School of Nursing, Zunyi Medical University, Zunyi, China.
Background: Most patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Medical Laboratory, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
Background: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.
Methods: A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024.
Int J Cardiol Heart Vasc
February 2025
Department of Cardiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225000, China.
Background: Thrombolysis in Myocardial Infarction (TIMI) risk score in patients with ST-segment elevation myocardial infarction (STEMI) is associated with major adverse cardiovascular events (MACE). This study aimed to develop a prediction model based on the TIMI risk score for MACE in STEMI patients after percutaneous coronary intervention (PCI).
Methods: We conducted a retrospective data analysis on 290 acute STEMI patients admitted to the Affiliated Hospital of Yangzhou University from January 2022 to June 2023 and met the inclusion criteria.
Pak J Med Sci
January 2025
Zhuqing Ji Department of Medicine Oncology, The Affiliated Huai'an 1st People's Hospital of Nanjing Medical University, Huaian, Jiangsu Province 223300, P.R. China.
Objective: To explore the risk factors associated with postoperative atrial fibrillation (POAF) after off-pump coronary artery bypass grafting (OPCABG) and to construct a nomogram predictive model.
Methods: In this retrospective cohort study, clinical data of 193 patients who received OPCABG in Huai'an First People's Hospital Affiliated to Nanjing Medical University from June 2021 to November 2023 were retrospectively analyzed. Based on the established diagnosis of POAF, patients were divided into the POAF group (n=75) and the non-POAF group (n=118).
Front Genet
January 2025
Department of Oncology, The Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.
Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a dismal prognosis. Treatment outcomes exhibit substantial variability across patients, underscoring the urgent need for robust predictive models to effectively estimate survival probabilities and therapeutic responses in PDAC.
Methods: Metabolic and immune-related genes exhibiting differential expression were identified using the TCGA-PDAC and GTEx datasets.
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