目的 构建首发脑出血患者并发卒中相关性肺炎的风险预测模型并验证模型的预测性能。方法 回顾性分析2012年1月—2022年12月广州市第一人民医院治的419例首发脑出血患者的临床资料，按照7︰3比例随机化分为训练列（293例）和验证队列（126例）。统计基于开发队列数据，采用Logistic回归模型分析首发脑出血患者并发卒中相关性肺炎的影响因素，并构建风险预测模型。基于开发队列和验证队列数据，采用校准曲线、受试者操作特征（ROC）曲线下面积和决策曲线分析模型的预测性能。结果 419例首发脑出血患者中有113例发生卒中相关性肺炎，发生率为26.97%。美国国立卫生研究院卒中量表（NIHSS）评分、吞咽困难、初始血肿体积、中性粒细胞百分比与白蛋白比值（NPAR）、中性粒细胞计数与淋巴细胞计数比值（NLR）、手术治疗、气管插管、留置胃管均是首发脑出血患者并发卒中相关性肺炎的影响因素（P＜0.05）。基于上述影响因素构建了首发脑出血患者并发卒中相关性肺炎的风险预警模型，校准曲线显示模型在开发队列和验证队列中预测卒中相关性肺炎发生率均与实际发生率相近；ROC曲线显示此模型在开发队列、验证队列中预测的曲线下面积分别为0.906（95%CI：0.867~0.937）、0.884（95%CI：0.815~0.934）；决策曲线分析显示当开发队列阈概率在3%~80%内、验证队列阈概率在2%~76%内使用此模型干预比全/无干预更有临床价值。结论 基于NIHSS评分、吞咽困难、初始血肿体积、NPAR、NLR、手术治疗、气管插管、留置胃管构建的首发脑出血患者并发卒中相关性肺炎的风险预测模型具有良好预测性能和临床应用价值。

      Objective To construct a risk prediction model for stroke associated pneumonia in patients with initial cerebral hemorrhage（ICH）and validate the predictive performance of the model．Methods A retrospective analysis was conducted on the clinical data of 419 patients with ICH admitted to our hospital from January 2012 to December 2022．They were randomly divided into a development cohort（293 cases）and a validation cohort（126 cases）according to a 7∶3 ratio．The Logistic regression model was used to analyze the influencing factors of stroke related pneumonia in patients with ICH based on the development cohort data，and a risk prediction model was constructed．Based on the development cohort data and validation cohort data，the predictive performance of the model was analyzed using calibration curves，receiver operating characteristic（ROC）curve，and decision curve analysis．Results Among 419 patients，113 developed stroke associated pneumonia，with a rate of 26.97%．The National Institutes of Health Stroke Scale（NIHSS）score，swallowing difficulties，initial hematoma volume，neutrophil percentage to albumin ratio（NPAR），neutrophil count to lymphocyte count ratio（NLR），surgical treatment，endotracheal intubation，and indwelling gastric tube were all independent influencing factors for stroke associated pneumonia in patients with ICH（P＜0.05）．Based on the above influencing factors，a risk prediction model for stroke associated pneumonia in patients with ICH was constructed．The calibration curve showed that the predicted incidence of stroke associated pneumonia by the model in both the development and validation cohorts was close to the actual incidence．The ROC curve showed that the predicted area under the curve for this model in the development cohort and validation cohort was 0.906（95%CI：0.867-0.937）and 0.884（95%CI：0.815-0.934），respectively．The decision curve analysis showed that when the threshold probability of the development cohort was between 3%-80%，and the threshold probability of the validation cohort was between 2%-76%，the intervention using this model was more clinically valuable than all/no intervention．

Conclusions The risk prediction model for stroke associated pneumonia in patients with ICH based on NIHSS score，swallowing difficulties，initial hematoma volume，NPAR，NLR，surgical treatment，tracheal intubation，and indwelling gastric tube has good predictive performance and clinical application value．

      目的 分析儿童大环内酯类耐药重症肺炎支原体肺炎（SMPP）的危险因素,构建列线图预测模型。 方法 回顾性收集2023年1月—2024年9月在广州医科大学附属番禺中心医院儿科住院治疗的1 121例大环内酯类耐药肺炎支原体肺炎患儿入院初期的临床资料。按7∶3比例将患儿资料随机分为训练集（784例）和验证集（337例）。采用R4.4.1软件使用10重交叉验证最小绝对收缩与选择算法（LASSO）回归分析进行单因素变量筛选,采用Logistics回归分析建立预测模型, 绘制可视化列线图。使用受试者操作特征曲线（ROC）, 校准曲线、Hosmer-Lemeshow（HL）检验及临床决策曲线（DCA）分别评估模型的区分度、校准度和临床使用价值。 结果 在训练集中, LASSO回归结合Logistics回归分析结果显示,院前发热时间>5.5 d、谷丙转氨酶>14.5 U/L、乳酸脱氢酶>287.5 U/L、C反应蛋白>18.65 mg/L、肺实变、合并病毒感染是大环内酯类耐药SMPP发生的危险因素（P<0.05）, 根据上述危险因素构建列线图预测模型。训练集和验证集ROC曲线下面积分别为0.847和0.822; 校准曲线和HL检验显示模型具有良好的校准度; DCA显示预测模型在风险阈值为0.05~0.95时预测性能最优。 结论 院前发热时间、谷丙转氨酶、乳酸脱氢酶、C反应蛋白、肺实变、合并病毒感染是大环内酯类耐药SMPP发生的影响因素, 基于以上因素构建的列线图模型具有较好的预测效能, 有利于早期识别耐药重症病例, 及早采取有效干预,改善患者预后。

      Objective To explore the risk factors and to construct a nomogram prediction model for severe macrolide-resistant Mycoplasma pneumoniae pneumonia（MPP）in children．Methods The clinical data during the initial admission period of 1 121 children with macrolide-resistant MPP who were hospitalized in the Department of Pediatrics of the Affiliated Panyu Central Hospital of Guangzhou Medical University from January 2023 to September 2024 were retrospectively collected．The children data were randomly divided into a training set（n=784）and a validation set（n=337）at a ratio of 7∶3．With R language software（version 4.4.1）, least absolute shrinkage and selection operator（LASSO）regression analysis with tenfold cross-validation was used to screen risk factors, Logistics regression analysis was used to establish prediction model, and a visualization of the risk variables was created using a nomogram．The receiver operating characteristic（ROC）curves, calibration curves, Hosmer-Lemeshow（HL）test and clinical decision curve analysis（DCA）were used to evaluate the discrimination, calibration and clinical application value of the model．Results In the training set, LASSO regression analysis combined with Logistics regression analysis showed that prehospital fever duration > 5.5 days, alanine aminotransferase level> 14.5 U/L, lactate dehydrogenase level> 287.5 U/L, C-reactive protein > 18.65 mg/L, lung consolidation, and co-infection with virus were risk factors for severe macrolide-resistant MPP（P<0.05）．A nomogram prediction model was constructed based on the above risk factors．The area under the ROC curves of the training set and the validation set were 0.847 and 0.822, respectively．The calibration curves and HL test showed that the model had good calibration． The DCA curves showed that the prediction model had the best prediction performance when the risk threshold was between 0.05-0.95．Conclusions Prehospital fever duration, alanine aminotransferase level, lactate dehydrogenase level, C-reactive protein level, lung consolidation and co-infection with virus were risk factors for prediction of severe macrolide-resistant MPP．The nomogram model based on the above factors had a good prediction efficiency, which was conducive to early identification of severe cases with macrolide-resistant, and taking early effective interventions to improve the prognosis．

目的 探讨老年吸入性肺炎的危险因素,建立风险预测模型,以期降低老年吸入性肺炎的发病率。方法 选取2017年8月28日—2020年 10月30日广州市第一人民医院老年病科住院治疗的老年肺炎患者205例,按照是否发生吸入性肺炎分为吸入性肺炎组和非吸入性肺炎组,对比2组患者的各项指标,分析老年吸入性肺炎的危险因素,建立风险预测模型,采用ROC曲线对模型进行预测效果检验。结果 多因素Logistic回归分析结果显示,脑梗塞、帕金森、留置胃管、长期卧床为老年吸入性肺炎的危险因素(P<0.05)。模型公式为Logit(P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀。该模型ROC曲线下面积为0.894。结论 本研究中的模型预测效果良好,可为医护人员预测老年患者发生吸入性肺炎的概率,及时采取相应的预见性护理及干预性治疗。

Objective To explore the risk factors of aspiration pneumonia in the elderly and establish the risk prediction model, in order to reduce the incidence of aspiration pneumonia in the elderly. Methods A total of 205 elderly patients with pneumonia who were hospitalized in the department of geriatrics, Guangzhou First People's Hospital from August 28, 2017 to October 30, 2020, were divided into aspiration pneumonia group and non-aspiration pneumonia group according to whether aspiration pneumonia occurred. The indicators of the two groups of patients were compared, the risk factors of aspiration pneumonia in the elderly were analyzed, the risk prediction model was established, and the prediction effect of the model was tested by receiver operating characteristic curve. Results Multivariate Logistic regression analysis showed that cerebral infarction, Parkinson's disease, indwelling nasogastric tube, and being bedridden were risk factors for aspiration pneumonia in elderly patients (P<0.05). The model formula was Logit (P)=-2.952+1.221X₂+2.417X₃+2.388X₈+1.683X₁₀. The area under receiver operating characteristic curve of this model was 0.894. Conclusion The prediction effect of the model in this study was good, which could predict the probability of aspiration pneumonia in elderly patients for medical staff, and to timely take the corresponding predictive care and interventional treatment.