基于入院指标的细菌性肝脓肿液化成熟度贝叶斯统计预测模型构建OA北大核心CSTPCD

Background and Aims:The liquefaction degree of abscesses is a crucial factor affecting the early treatment,invasive drainage,and prognosis of patients with pyogenic liver abscesses(PLA).Effectively diagnosing PLA early and providing timely assessment and treatment are focal challenges in clinical practice.Currently,the diagnostic and treatment strategies both at home and abroad rely on enhanced CT scans,MRI examinations,and surgical conditions to determine the nature of abscesses,and there is a lack of rapid means to determine abscess characteristics.This study was conducted to construct a predictive model for the liquefaction maturity of PLA using routine admission examination indexes and the Bayesian statistical method to provide a scientific basis for the early diagnosis and treatment of PLA. Methods:Data of 116 PLA patients admitted to the Fifth Affiliated Hospital of Xinjiang Medical University between January 2018 and December 2022 were collected.Patients were classified into a complete liquefied group(59 cases)and an incomplete liquefied group(57 cases)based on the abscess maturity confirmed by enhanced CT and surgical conditions.Comparison was made between the two groups regarding routine admission examination indexes and clinical characteristics.The original data was subjected to binary classification,and after screening,variables with significant diagnostic values were identified.The Bayesian statistical method was employed to establish a predictive model for the liquefaction degree of PLA.The model was validated using 23 PLA patients admitted to the Fifth Affiliated Hospital of Xinjiang Medical University from January 2023 to November 2023,and the ROC curve was generated to evaluate the model's predictive performance. Results:Screening results revealed that factors such as onset time,white blood cell count,neutrophil count,neutrophil percentage,neutrophil-to-lymphocyte ratio,platelet count,procalcitonin,alanine aminotransferase,and plain CT values were significantly associated with the liquefaction degree of PLA(all P＜0.05).ROC curve validation demonstrated that the Bayesian statistical predictive model based on these variables had a sensitivity of 90.0％,specificity of 84.6％,and accuracy of 87.3％. Conclusion:The constructed Bayesian statistical predictive model for the liquefaction degree of PLA can effectively and rapidly determine the nature of abscesses.It can be used in the early stages of the disease when PLA is not excluded based on routine examination indicators at admission and clinical features with good sensitivity and specificity.