目的：探讨医院获得性细菌性脑膜炎（hospital-acquired bacterial meningitis， HABM）患者预后不良的危险因素，并构建预测其发生的列线图模型。方法：连续纳入上海交通大学医学院附属第六人民医院2013年1月至2020年12月收治的110例医院获得性细菌性脑膜炎患者，根据出院时是否死亡将其分为死亡组（n=22）和生存组（n=88），同时随机将110例患者分为建模组（n=77）和验证组（n=33）。应用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）回归及多因素Logistic回归筛选HABM患者预后不良的危险因素，并基于危险因素构建列线图模型，采用受试者操作特征（receiver operating characteristic curve，ROC）曲线的曲线下面积（area under curve，AUC）评估模型区分度，用校准曲线评估模型内部一致性情况。结果：基于LASSO回归共筛选出脑脊液（cerebro-spinal fluid，CSF）微生物培养革兰染色阳性、血常规中性粒细胞计数升高、降钙素原升高、CSF蛋白含量升高、凝血酶原时间缩短、血培养阳性、腰池引流史这7个因素，建立了HABM预后不良列线图预测模型，建模组及验证组ROC曲线的AUC分别为0.931、0.862，校准图显示校准曲线与理想曲线吻合较好，具有良好的拟合优度。结论：本研究构建危险因素列线图模型对住院HABM患者死亡具有较好的预测性、一致性和临床实用性，有助于临床医生对HABM患者预后不良的发生风险进行初步评估。

Objective To explore the risk factors for poor prognosis in patients with hospital-acquired bacterial me-ningitis (HABM) and to establish a nomogram model to predict its occurrence. Methods A total of 110 patients with HABM admitted to Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 1, 2013 to December 31, 2020 were consecutively enrolled. Based on survival status at discharge, they were divided into a death group (n=22) and a survival group (n=88). Subsequently, 110 patients were randomly divided into a training cohort (n=77) and a validation cohort (n=33). The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify risk factors for poor prognosis in patients with HABM. A nomogram model was constructed based on these risk factors. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the model discrimination, and the calibration curve was used to evaluate the internal consistency of the model. Results Based on the LASSO regression, seven factors were identified: gram-positive staining of microorga-nisms in cerebro-spinal fluid (CSF) culture, elevated neutrophil count on routine blood tests, elevated procalcitonin, elevated CSF protein, decreased prothrombin time, positive blood culture, and history of lumbar drainage. A nomogram prediction model for poor prognosis in HABM patients was established. The areas under the ROC curves for the training cohort and the validation cohort were 0.931 and 0.862, respectively. The calibration plots demonstrated that the calibration curves showed good agreement with the ideal curves, indicating an excellent goodness of fit. Conclusions The risk factor-based nomogram model established in this study demonstrates good predictability, consistency, and clinical applicability for predicting mortality in hospitalized HABM patients, supporting clinicians in the preliminary assessment of the risk of poor prognosis.