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 构建与验证回肠造口术后造口周围皮炎发生的列线图预测模型。方法 回顾性分析广州市中山大学附属第六医院2022年7月—2023年7月收治的441例回肠造口术患者资料。通过单因素Logistic回归确定影响因素，基于训练队列建立列线图模型，并采用受试者操作特征（ROC）曲线和校准曲线分别评估模型的区分度与校准能力，同时通过决策曲线分析（DCA）评估临床实用性。在内部验证队列进行模型性能验证。结果 单因素Logistic回归确定6个危险因素，最终选取其中5个（是否糖尿病、造口定位、是否接受造口护理宣教、造口皮肤凹陷、性别）构建预测模型。训练队列中，模型校准曲线显示良好拟合优度（Hosmer and Lemeshow检验P = 0.976），ROC曲线下面积为0.672；决策曲线证实模型具有临床应用价值，预测模型在验证集上有类似结果。结论 本研究建立的5参数列线图模型具有良好的区分度、校准能力和临床实用价值，可为造口周围皮炎的预测提供量化工具。

       Objective To establish a predictive nomogram for peristomal dermatitis after ileostomy．Methods This study analyzed 441 patients who underwent ileostomy at a third-class premium hospital．The risk factors of peristomal dermatitis were determined by univariate binary Logistics regression．Then a predictive nomogram was established with the risk factors in the training cohort．The discrimination and calibration ability of the nomogram was demonstrated by receiver operating characteristic curves and calibration curves，respectively．Besides，a testing cohort was utilized to validate the nomogram model．To reflect the clinical utility of the model，we also performed decision curve analysis．Results A total of six parameters was determined as the risk factors of peristomal dermatitis．Finally，five of them（whether diabetes，stoma location，whether to receive stoma care education，stoma skin depression，gender）were chosen to establish the nomogram．The calibration curve showed favorable calibration ability in the training cohort with a Hosmer and Lemeshow goodness of fit test P-value of 0.976 and the area under the curve of the nomogram is 0.672．The decision curve showed nomogram had potential clinical utility．These results were consistent with the testing cohort．Conclusions The nomogram established by five parameters was capable of predicting the occurrence of peristomal dermatitis with favorable discrimination and calibration ability and shows potential clinical utility．

       目的 评估使用炎症、免疫及营养指标对于胃癌根治术后发生腹膜转移的预测作用，并建立适用于术前风险评估的预测模型。方法 回顾性收集2022—2024年苏州市立医院85例晚期胃癌患者的临床资料，按术后是否发生腹膜转移分为转移组44例与非转移组41例。通过分析比较两组间的营养参数［血清白蛋白、纤维蛋白原及预后营养指数（PNI）］、炎症指标［中性粒细胞计数、C反应蛋白、中性粒细胞与淋巴细胞比值（NLR）及全身免疫炎症指数（SII）］、肿瘤标志物以及免疫功能指标（淋巴细胞计数、IL-2、IL-6、TNF-α和IFN-γ）等。使用单因素及多因素Logistic回归分析确定独立预测因子，并构建预测模型，采用Bootstrap法内部验证，计算校正后的曲线下面积及收缩因子，以优化模型性能。结果 多因素分析结果显示，IL-6（OR=1.116，95%CI：1.020~1.224）与CA125（OR=1.016，95%CI：1.006~1.026）为术后腹膜转移的独立预测因素，构建的预测模型区分能力良好，初始AUC为0.801（95%CI：0.702~0.900），经Bootstrap校正后AUC为0.797，模型校准度理想，具有良好的临床稳健性。结论 基于术前IL-6和CA125水平的预测模型，可用于评估胃癌患者根治术后发生腹膜转移的风险。该模型可在术前阶段有效识别高风险病例，为临床制定个体化辅助治疗方案及随访策略提供关键参考，并具有改善患者预后的潜在价值。

       Objective To evaluate the predictive value of inflammatory，immune，and nutritional indicators for peritoneal metastasis after radical gastrectomy in gastric cancer and to establish a predictive model applicable for preoperative risk assessment．Methods Clinical data from 85 patients with advanced gastric cancer treated at Suzhou Municipal Hospital between 2022 and 2024 were retrospectively collected．Patients were divided into two groups based on postoperative peritoneal metastasis occurrence：a metastasis group（n=44）and a non-metastasis group（n=41）．Indicators analyzed and compared between groups included nutritional parameters（serum albumin，fibrinogen，PNI），inflammatory markers（neutrophil count，CRP，NLR，SII），tumor markers and immune indicators（lymphocyte count，IL-2，IL-6，TNF-α，IFN-γ）．Univariate and multivariate logistic regression analyses identified independent predictors for constructing the prediction model．Internal validation was performed using 1 000 bootstrap resamples to calculate the adjusted area under the curve（AUC）and shrinkage factor for model optimization．Results Multivariate analysis identified IL-6（OR=1.116，95%CI：1.020-1.224）and CA125（OR=1.016，95%CI：1.006-1.026） as independent predictors of postoperative peritoneal metastasis．The constructed predictive model demonstrated good discriminatory ability，with an initial AUC of 0.801（95%CI：0.702–0.900）and a bootstrap-corrected AUC of 0.797．The model exhibited ideal calibration and good clinical robustness．Conclusions This study established a predictive model based on preoperative IL-6 and CA125 levels to assess the risk of peritoneal metastasis after radical surgery in gastric cancer patients．The model effectively identifies high-risk cases preoperatively，providing critical guidance for developing individualized adjuvant treatment and follow-up strategies，with potential to improve patient prognosis．

       目的 探究1990—2023年中国人群下背痛（LBP）疾病负担，并预测2024—2040年LBP疾病负担发展趋势，为公共卫生政策制定提供数据支撑。方法 基于GBD2023数据库，采用Joinpoint模型、分解分析（decomposition analysis）量化我国LBP负担变化趋势，最后通过ARIMA（autoregressive integrated moving average model）模型预测未来趋势。结果 2023年我国LBP患病人数达9 532.40万人，发病人数为4 138.36万人，伤残调整生命年（DALYs），虽然患病人数、发病人数及DALYs绝对数较1990年显著增长，但年龄标准化率（ASR）均呈显著下降趋势（AAPC依次为-0.90%、-0.84%、-0.89%）。分解分析表明，人口年龄结构是我国LBP疾病负担增长的主要正向驱动因素，流行病学趋势变化对患病率和DALYs起关键抑制作用，且存在明显性别差异。ARIMA模型预测显示，2024—2040年男性和女性LBP相关ASR将持续下降，但我国女性LBP负担仍高于男性。结论 我国LBP负担沉重，受人口结构、性别差异、行为与职业等多方面因素影响，未来需通过分人群、分职业精准干预及多部门协同，持续降低疾病负担。

     Objective To explore the characteristics of disease burden of low back pain（LBP） in the Chinese population from 1990 to 2023，and predict its development trend from 2024 to 2040，so as to provide data support for the formulation of public health policies．Methods Based on the Global Burden of Disease Study 2023（GBD 2023） database，the Joinpoint model and decomposition analysis were adopted to quantify the changing trend of LBP burden in China．The autoregressive integrated moving average（ARIMA） model was used to predict the future trend，with the data for prediction stratified by gender and age group．Results In 2023，the number of LBP patients in China reached 95.3240 million，the number of incident cases was 41.3836 million，and the disability-adjusted life years（DALYs） were 10.6359 million person-years．Although the absolute numbers of prevalent cases，incident cases，and DALYs increased significantly compared with 1990，the age-standardized rates（ASRs） all showed a significant downward trend（average annual percentage changes were -0.90%，-0.84%，and -0.89%，respectively）．Decomposition analysis revealed that population age structure was the primary positive driving factor for the growth of LBP disease burden in China，while changes in epidemiological trends played a key inhibitory role in prevalence and DALYs，with significant gender differences．ARIMA model prediction indicated that the LBP-related ASRs would continue to decrease in both males and females from 2024 to 2040，but the LBP burden in females would remain higher than that in males in China．Conclusions The baseline burden of LBP in China is heavy，affected by multiple factors such as population structure，gender differences，behavioral and occupational factors．In the future，targeted interventions for specific populations and occupations，as well as multi-sectoral collaboration，are needed to continuously reduce the disease burden．

       肺癌作为全球恶性肿瘤中发病率和死亡率较高的一种，手术是其主要治疗手段。然而，肺切除手术常引起肺功能下降，影响患者术后功能恢复及生活质量。准确预测术后肺功能对制定个体化手术方案、降低并发症风险及改善患者预后具有重要意义。目前，研究者已开发出多种结合临床指标、影像学参数及生物标志物的预测模型，用于评估肺癌患者术后肺功能变化。然而，现有模型在准确性、特异性、标准化及临床推广方面仍存在诸多挑战。本文系统综述肺癌术后肺功能的影响因素，并对现有预测模型进行总结与评价，以期为患者术前评估、手术方案选择及术后管理提供参考，进而为改善治疗效果与患者生活质量提供理论依据。

       Lung cancer is a kind of malignant tumor with high morbidity and mortality in the world，and surgery is the main treatment．However，pneumonectomy is often accompanied by the loss of lung function，which seriously affects the recovery of lung function and the  quality of life．Accurate prediction of postoperative pulmonary function is of great significance for formulating individualized surgical plans，reducing the risk of complications and improving the prognosis of patients．At present，researchers have developed a variety of predictive models that combine clinical indicators，imaging parameters，and biomarkers to evaluate postoperative lung function changes in patients with lung cancer．However，there are still many challenges in the accuracy，specificity，standardization and clinical promotion of existing models．This paper systematically reviews the influencing factors of lung function after lung cancer surgery，and summarizes and evaluates the existing prediction models，in order to provide a reference for preoperative evaluation，surgical plan selection and postoperative management of patients，and then provide theoretical basis for improving treatment effect and quality of life of patients．

      目的   基于决策树构建老年患者吞咽障碍预警模型。方法  采用便利取样法对宁夏银川市宁夏回族自治区人民医院老年科住院的200例老年患者进行调查。结果  200例老年患者中，吞咽障碍发生率为40.5%。依据是否发生吞咽障碍将其患者分为两组，两组患者在性别、年龄、文化程度、职业、医保类型、家庭年收入、日常生活能力、衰弱、抑郁、营养、体质指数（BMI）比较（χ ² 值分别为13.321、4.064、31.944、36.695、18.230、19.681、52.509、10.253、20.456、9.070、9.483），差异均有统计学意义（均P＜0.05）。决策树模型筛选出老年患者吞咽障碍的影响因素主要有自理能力、职业、文化程度和抑郁，决策树模型受试者工作特征曲线下面积为0.862，灵敏度为79.8%，特异度为79.0%，P＜0.001。结论  基于自理能力、职业、文化程度和抑郁构建的决策树模型，能有效预测老年患者吞咽障碍风险。

       Objective  To construct a swallowing disorder warning model for elderly patients based on decision tree．Methods  Convenience  sampling was  used to  study  200  elderly  patients  admitted to the  geriatric  department  of  a tertiary comprehensive hospital in Yinchuan，Ningxia．Results  Among 200 elderly patients，the incidence of swallowing disorders was 40.5%．The two groups of patients were compared in terms of gender，age，education level，occupation，medical insurance type，annual family income，daily living ability，frailty，depression，nutrition，and BMI（χ ²  values were 13.321，4.064，31.944，36.695，18.230，19.681，52.509，10.253，20.456，9.070，9.483，respectively），and the  differences were  statistically significant（all P＜0.05）．The decision tree model identified the main influencing factors of swallowing disorders in elderly patients as self-care ability，occupation，education level，and depression．The Receiver Operating Characteristic curve of the decision tree model had an area under the curve of 0.862，sensitivity of 79.8%，and specificity of 79.0%，P＜0.001．Conclusions  A decision tree model based on self-care ability，occupation，education level，and depression can effectively predict the  risk of swallowing disorders in elderly patients．

       目的 汇总分析肝硬化患者消化道出血风险预测模型，为今后模型的建立和优化提供参考。方法   系统检索中国知网、维普、PubMed数据库在2025年4月22日前公开发表的所有肝硬化患者消化道出血风险预测模型，按纳入标准筛选文献，对最终纳入文章分析摘录并系统汇总，包括模型特征、危险因素及模型预测评估效果等信息。结果   共检索3 603篇预测模型相关研究论文，最终纳入30篇，其中中国27篇、韩国1篇、印度1篇、埃及1篇。22项研究收集了肝硬化病因，其中病毒性肝病最多（72.94%，2 922/4 006），药物性肝病及非酒精性脂肪性肝病最少（均为0.02%，1/4 006）。在研究类型上，有28篇单中心研究，2篇为多中心研究，其中有12个模型未进行验证，只有1个模型进行了外部验证，其余模型只进行了内部验证，曲线下面积（AUC）范围0.680~0.994。根据模型纳入因素特点，分为血常规指标、凝血指标、生化指标、影像学指标、复合指标、其他指标共6种，其中纳入因素最多为影像学指标，最少为凝血指标。在纳入危险因素中，第1位为门静脉直径，第2位为血小板计数，第3位为血红蛋白水平及脾脏硬度，所有因素中与脾脏相关的指标最多。结论   肝硬化患者消化道出血风险预测模型研究质量有待提升，影像学指标应用最广，脾脏相关指标重要性突出，门静脉直径、血小板计数、血红蛋白水平及脾脏硬度为最常用的危险预测因素。

       Objective  To  summarize and analyze the  prediction models for gastrointestinal  bleeding  risk in  patients with cirrhosis，providing references for the establishment and optimization of future models．Methods  A systematic search was conducted in CNKI，VIP，and PubMed for all published prediction models for gastrointestinal bleeding risk in patients with cirrhosis before April 22，2025．Articles were screened according to the inclusion criteria，and the finally included articles were analyzed and summarized，including model characteristics，risk factors，and model prediction evaluation effects．Results  A total of 3 603 related research papers on prediction models were initially retrieved，and 30 were finally included，with 27 from China，one from South Korea，one from India，and one from Egypt．Among the 22 studies that collected the etiology of cirrhosis，viral hepatitis was the most common（72.94%，2 922/4 006），while drug-induced liver disease and non-alcoholic fatty liver disease were the least common（0.02%，1/4 006）．In terms of study type，28 were single-center studies and two were multicenter studies．Among them，12 models were not validated，only one model was externally validated，and the rest were only internally validated，with an area under the curve range of 0.680-0.994．According to the characteristics of the factors included in the models，they were divided into six types of indicators：blood routine，coagulation，biochemistry，imaging，composite，and others，among which imaging indicators were the most common and coagulation indicators were the least．In the included risk factors，the first was portal vein diameter，the second was platelets count，and the third was hemoglobin level and spleen stiffness，with the most factors related to the spleen．Conclusions  The quality of studies on prediction models for gastrointestinal bleeding risk in cirrhosis patients needs to be improved．Imaging indicators are the most widely used，and spleen-related indicators are of prominent importance，with portal vein diameter，platelets count，hemoglobin level，and spleen stiffness being the most commonly used risk prediction factors．

      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   通过机器学习方法构建脓毒症谵妄患者30 d死亡的预测模型，并识别关键预测因子。方法   采用基于医疗信息集成重症监护数据库（Medical Information Mart for Intensive Care IV）的回顾性队列研究方法，boruta筛选重要特征，并通过决策树，K近邻，LightGBM，随机森林，支持向量机，XGBoost构建模型进行分析，通过ROC曲线下面积进行评估，利用F1分数、召回率、精确率、特异度、灵敏度和阳性预测值比较模型表现。结果  XGBoost模型在训练集和验证集中的ROC曲线下面积分别为0.906和0.762，表明该模型具有良好的预测能力，入院年龄、红细胞分布宽度和白细胞计数是最重要的预测因子。结论   基于机器学习的脓毒症谵妄患者预后预测模型展现出良好的预测效能，为临床早期干预提供了重要参考依据。

       Objective  To construct a  30-day mortality  prediction model for  patients with  sepsis-associated  delirium using machine learning methods and identify key predictive factors．Methods  A  retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database．Important features were selected using the Boruta algorithm，and models including Decision Tree，K-Nearest Neighbors，LightGBM，Random Forest，Support Vector Machine，and XGBoost were constructed and analyzed．Model performance was evaluated using the area under the reciver operater characteristic（ROC）curve（AUC），along with F1 score，recall，precision，specificity，sensitivity，and positive predictive value．Results  The XGBoost model demonstrated strong predictive performance，with AUC values of 0.906 in the training set and 0.762 in the test set．Key predictors identified included admission age，red blood cell distribution width，and white blood cell count．Conclusions  The machine learning-based prediction model for sepsis-associated delirium prognosis exhibits robust predictive efficacy，providing a valuable tool for early clinical intervention．

目的 构建并验证机械通气患儿肠内营养支持发生误吸的风险预测模型。方法 回顾性分析中山市博爱医院2021年3月—2023年3月儿童重症监护病房330例行机械通气并进行肠内营养的患儿临床资料,通过二元Logistic回归,获取机械通气患儿肠内营养支持发生误吸的预测因素,绘制列线图模型,并进行模型评价及验证。结果 330例机械通气患儿中,104例患儿发生误吸、226例未发生误吸。两组患儿在意识状态、机械通气方式、管饲量、胃残留量、胃管置入深度、促胃动力药、镇静剂等方面对比差异具有统计学意义（P<0.05）。二元Logistic结果显示,胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂是机械通气患儿肠内营养支持发生误吸的影响因素（P<0.05）。建模组AUC为0.810（95%CI：0.760~0.860）,Hosmer-Lemesh结果显示,χ²=3.245,P=0.846;外部验证组AUC为0.873（95%CI：0.831~0.914）,Hosmer-Lemesh结果显示,χ²=3.567,P=0.875。建模组和训练组DCA曲线大部分落于Y=0上方。建模组与外部验证组校准曲线均与参考曲线高度贴合,预测概率与实际概率接近,校准度良好。结论 基于胃残留量、机械通气方式、管饲量、意识状态、胃管置入深度、促胃动力药、镇静剂等7项指标构建的风险预测模型具有一定的临床价值,可作为医护人员识别肠内营养机械通气误吸高危患儿的工具。

Objective To establish and verify the risk prediction model of enteral nutritional aspiration in children with mechanical ventilation．Methods The clinical data of 330 children who underwent mechanical ventilation and enteral nutrition in the PICU of Zhongshan Boai Hospital from March 2021 to March 2023 were retrospectively analyzed．The independent predictive factors of enteral nutrition support aspiration in children with mechanical ventilation were obtained by binary Logistic regression,and the nomographic model was drawn,and the model was evaluated and verified. Results Among 330 children with mechanical ventilation,104 had aspiration and 226 did not．There were statistically significant differences between the two groups in consciousness state,mechanical ventilation mode,tube feeding amount,gastric residual amount,gastric tube insertion depth,gastric motivity drugs,sedatives,etc.（P<0.05）．Binary Logistic results showed that gastric residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motonics and sedatives were the influential factors of enteral nutritional aspiration in children with mechanical ventilation（P<0.05）．The AUC of the modeling group was 0.810（95%CI：0.760-0.860）,and the Hosmer-Lemesh result showed that χ²=3.245,P=0.846．The AUC of the external verification group was 0.873（95%CI：0.831-0.914）,and the Hosmer-Lemesh result showed that χ²=3.567,P=0.875．The DCA curves of modeling group and training group mostly were above Y=0．The calibration curves of the modeling group and the external verification group are highly fit to the reference curves,and the prediction probability was close to the actual probability,and the calibration degree was good．Conclusion sThe risk prediction model based on 7 indexes,including stomach residual amount,mechanical ventilation mode,tube feeding amount,state of consciousness,depth of gastric tube insertion,gastric motivity drug and sedative,with certain clinical value,and can be used as a tool for medical staff to identify children at high risk of enteral nutritional mechanical aspiration．