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PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .
Subject(s)
Algorithms , Hospitals , Humans , Cohort Studies , Length of Stay , Machine LearningABSTRACT
BACKGROUND: Primary retroperitoneal sarcoma (RPS) comprises over 70 histologic subtypes, yet there are limited studies that have developed prognostic nomograms for RPS patients to predict overall survival (OS) and cancer-specific survival (CSS). The objective of this study was to construct prognostic nomograms for predicting OS and CSS in RPS patients. METHODS: We identified a total of 1166 RPS patients from the Surveillance, Epidemiology and End Results (SEER) database, and an additional 261 cases were collected from a tertiary cancer center. The study incorporated various clinicopathological and epidemiologic features as variables, and prediction windows for overall survival (OS) and cancer-specific survival (CSS) were set at 3, 5, and 7 years. Multivariable Cox models were utilized to develop the nomograms, and variable selection was performed using a backward procedure based on the Akaike Information Criterion. To evaluate the performance of the nomograms in terms of calibration and discrimination, we used calibration plots, coherence index, and area under the curve. FINDINGS: The study included 818 patients in the development cohort, 348 patients in the internal validation cohort, and 261 patients in the external validation cohort. The backward procedure selected the following variables: age, French Federation of Cancer Centers Sarcoma Group (FNCLCC) grade, pre-/postoperative chemotherapy, tumor size, primary site surgery, and tumor multifocality. The validation results demonstrated that the nomograms had good calibration and discrimination, with C-indices of 0.76 for OS and 0.81 for CSS. Calibration plots also showed good consistency between the predicted and actual survival rates. Furthermore, the areas under the time-dependent receiver operating characteristic curves for the 3-, 5-, and 7-year OS (0.84, 0.82, and 0.78, respectively) and CSS (0.88, 0.88, and 0.85, respectively) confirmed the accuracy of the nomograms. INTERPRETATION: Our study developed accurate nomograms to predict OS and CSS in patients with RPS. These nomograms have important clinical implications and can assist healthcare providers in making informed decisions regarding patient care and treatment options. They may also aid in patient counseling and stratification in clinical trials.
ABSTRACT
BACKGROUND: The murine double minute 2 (MDM2) gene is a crucial factor in the development and progression of various cancer types. Multiple rigorous scientific studies have consistently shown its involvement in tumorigenesis and cancer progression in a wide range of cancer types. However, a comprehensive analysis of the role of MDM2 in human cancer has yet to be conducted. METHODS: We used various databases, including TIMER2.0, TCGA, GTEx and STRING, to analyze MDM2 expression and its correlation with clinical outcomes, interacting genes and immune cell infiltration. We also investigated the association of MDM2 with immune checkpoints and performed gene enrichment analysis using DAVID tools. RESULTS: The pan-cancer MDM2 analysis found that MDM2 expression and mutation status were observably different in 25 types of cancer tissue compared with healthy tissues, and prognosis analysis showed that there was a significant correlation between MDM2 expression and patient prognosis. Furthermore, correlation analysis showed that MDM2 expression was correlated with tumor mutational burden, microsatellite instability and drug sensitivity in certain cancer types. We found that there was an association between MDM2 expression and immune cell infiltration across cancer types, and MDM2 inhibitors might enhance the effect of immunotherapy on breast cancer, bladder cancer and ovarian cancer. CONCLUSIONS: The first systematic pan-cancer analysis of MDM2 was conducted, and it demonstrated that MDM2 was a reliable prognostic biomarker and was closely related to cancer immunity, providing a potential immunotherapeutic target for breast cancer, bladder cancer and ovarian cancer.
