{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Gao C"],"pubmed_abstract":["To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases an"],"journal":["Scientific reports"],"pagination":["8183"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11893776"],"repository":["biostudies-literature"],"pubmed_title":["Risk factors and an optimized prediction model for urosepsis in diabetic patients with upper urinary tract stones."],"pmcid":["PMC11893776"],"pubmed_authors":["Qiu J","Liu J","Liu M","Wang D","Gao C"],"additional_accession":[]},"is_claimable":false,"name":"Risk factors and an optimized prediction model for urosepsis in diabetic patients with upper urinary tract stones.","description":"To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases an","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-06T22:38:58.553Z","creation":"2025-04-04T02:54:15.239Z"},"accession":"S-EPMC11893776","cross_references":{"pubmed":["40065041"],"doi":["10.1038/s41598-025-91787-2"]}}