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：分析急性有机磷农药中毒（AOPP）引发缺血缺氧性脑病预后相关因素，建立相关的预后预测模型。方法：回顾性分析90例（33例预后不良、57例预后良好）AOPP致HIE患者（2022年3月～2025年8月）的临床资料、中毒指标和血清学指标，独立危险因素用Logistic回顾分析筛选，并构建预后不良预测模型，采用ROC工具对模型效能进行验证。结果：Logistic 回归分析显示，年龄≥60岁、重度中毒、中毒至就诊时间、LAC水平、CHE水平、CRP水平及NSE水平均为患者预后不良的独立危险因素（P＜0.05）；AUC、灵敏度、特异度为0.943、90.91%、87.72%。结论：高龄、中毒程度高及中毒至就诊时间长等因素可导致AOPP致HIE患者出现不良结局，据此构建风险预测模型可有效预测预后不良的发生风险。

To determine the key impacting factors for hypoxic ischemic encephalopathy (HIE) caused by acute organophosphorus pesticide poisoning (AOPP) and build a prediction model. Methods: The clinical data, poisoning indicators and serological indicators of 90 patients (33 cases with poor prognosis and 57 cases with good prognosis) with HIE caused by AOPP (from March 2022 to Aug 2025) were analyzed. Independent risk factors were screened using logistic retrospective analysis, and a poor prognosis prediction model was constructed. The model efficiency was verified by the receiver operating curve (ROC). Results: Logistic regression analysis showed that age ≥ 60 years, severe poisoning, time from poisoning to treatment, LAC level, CHE level, CRP level, and NSE level were all risk factors for the prognosis in patients (P < 0.05). The AUC, sensitivity, and specificity were 0.943, 90.91%, and 87.72%.Conclusion: Factors such as advanced age, high degree of poisoning, and long time from poisoning to treatment can lead to adverse outcomes in patients with HIE caused by AOPP. Based on this, building a risk prediction model can effectively predict the risk of poor prognosis.

       目的  探讨结肠镜下息肉切除术后复发的危险因素，并基于机器学习算法构建复发风险预警模型，为防治对策提供依据。方法  回顾性收集2018年9月—2023年9月六安市人民医院1 058例初次行无痛结肠镜下息肉切除术患者的临床资料，使用单因素和多因素Logistic回归分析筛选复发危险因素。采用7∶3随机抽样分为训练集和验证集，分别通过决策树、贝叶斯及Logistic回归算法构建预测模型，并以受试者工作特征曲线（ROC）曲线下面积（AUC）、灵敏度、特异度等指标来评估模型效能。结果  单因素分析显示，性别、吸烟、代谢综合征、息肉数量、息肉位置、山田分型、组织病理学类型、切除方式、复查时间、肠息肉直径、手术时间是复发的危险因素（P＜0.05）。多因素分析显示，性别、代谢综合征、息肉数量、息肉直径、肠息肉位置、山田分型、组织学病理类型、切除方式、手术时间均是结肠息肉内镜下切除术后复发的危险因素。模型评估显示，决策树算法、贝叶斯算法、Logistic回归算法的ROC曲线下面积（AUC）分别为0.849、0.818、0.811；灵敏度分别为85.14%、81.62%、79.43%；特异度分别为81.69%、79.45%、74.18%；约登指数分别为0.534、0.551、0.573；95%CI分别为0.810~0.876、0.794~0.860、0.782~0.850；决策树算法模型效能最佳，Logistic回归算法的性能最差。结论  性别、代谢综合征、肠息肉特征（数量、直径、位置等）是术后复发的关键危险因素。决策树模型在风险预测中表现最优，可为临床制定个体化随访策略提供参考。

       Objective  To explore the  risk factors for  recurrence after painless colonoscopic polypectomy and construct a recurrence risk warning model based on machine learning algorithms to provide evidence for prevention and treatment strategies．Methods  A retrospective analysis was conducted on clinical data from 1 058 patients who underwent their first painless colonoscopy-guided polypectomy at our hospital between September 2018 and September 2023．Univariate and multivariate Logistic  regression analyses were performed to identify recurrence risk factors．The dataset was randomly divided into training and validation sets using a 7∶3 ratio．Prediction models were constructed using decision tree，Bayesian，and Logistic regression algorithms，and their performance was evaluated using metrics such as the area under the receiver operating characteristic curve（AUC），sensitivity，specificity，and others．Results  Univariate analysis revealed that gender，smoking，metabolic syndrome，number of polyps，polyp location，Yamada classification，histopathological type，resection method，follow-up time，polyp diameter，and operation duration were risk factors for recurrence（P＜0.05）．Multivariate analysis identified gender，metabolic syndrome，number of polyps，polyp diameter，polyp location，Yamada classification，histopathological type，resection method，and operation duration as independent risk factors for recurrence after endoscopic polypectomy．Model evaluation showed AUC values of 0.849，0.818，and 0.811 for the decision tree，Bayesian，and Logistic regression algorithms，respectively．Sensitivity values were 85.14%，81.62%，and 79.43%；specificity values were 81.69%，79.45%，and 74.18%；Youden’s indices were 0.534，0.551，and 0.573；and 95% confidence intervals（CIs）were 0.810–0.876，0.794–0.860，and 0.782–0.850，respectively．The  decision tree algorithm demonstrated the best predictive performance，while the Logistic regression algorithm performed the least favorably．Conclusions  Gender，metabolic syndrome，and polyp characteristics（number，diameter，location，etc．）are key  risk factors for recurrence after polypectomy．The decision tree algorithm exhibited optimal predictive efficacy，offering valuable insights for developing individualized follow-up strategies in clinical practice．

      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 基于SEER数据库分析三阴性乳腺癌（TNBC）的预后,并建立Cox回归临床预测模型且进行内部验证。方法 使用SEER*Stat软件（8.4.2版）筛选2010—2015年诊断为TNBC的病例,进行单因素和Cox多因素回归以及向后逐步回归分析,明确与生存相关的独立危险因素,构建预测TNBC患者3年和5年癌症特异生存（CSS）率的Nomogram图,并用受试者工作特征曲线,Harrell’s一致性指数,临床预测模型校准曲线以及决策曲线对该模型进行评估及内部验证,以评估该模型的临床预测效能。结果 共筛选出符合纳入标准的TNBC患者5 564例,按照7∶3的比例随机拆分为训练集（n=3 894）和验证集（n=1 670）。通过单因素,多因素分析显示TNM分期、放射治疗、化学治疗以及手术和其他治疗的先后顺序是与TNBC患者CSS显著相关的独立危险因素（P<0.05）。利用上述预后相关因素建立Nomogram图模型。训练集的C-index为0.731（95%CI：0.712~0.749）,验证集的C-index为0.719（95%CI：0.688~0.749）,训练集和验证集3年和5年生存ROC曲线的曲线下面积均>0.7,区分度较好,且校准曲线拟合良好。结论 TNM分期、放射治疗、化学治疗以及手术和其他治疗的先后顺序是TNBC的独立预后因素,基于此建立的Nomogram图临床预测模型区分度、准确度以及临床适用性较好,能较好地预测TNBC患者的生存预后。

Objective To analyze the prognosis of triple negative breast cancer（TNBC）based on the SEER database,and to establish a Cox regression clinical prediction model with internal validation．Methods Cases diagnosed with TNBC from 2010 to 2015 were screened using SEER*Stat software（version 8.4.2）,and univariate and Cox multifactorial regression as well as backward stepwise regression analyses were performed to identify the independent risk factors associated with survival,and to construct a clinical prediction model for predicting the three- and five-year cancer specific survival（CSV）of TNBC patients．Survival（CSS）rates of TNBC patients at 3 and 5 years,and the model was evaluated and internally validated using the ROC curve,Harrell’s consistency index（C-index）,clinical prediction model calibration curve,and decision-making curve（DCA curve）to assess the predictive efficacy of the model for clinical prediction．Results A total of 5 564 TNBC patients meeting the inclusion criteria were screened and randomly split into a training set（n=3 894）and a validation set（n=1 670）according to a 7∶3 ratio．By univariate,multivariate analysis showed that T-stage,N-stage,M-stage,radiotherapy,chemotherapy,and the sequence of surgery and other treatments were independent risk factors significantly associated with CSS in TNBC patients．The above prognostic-related factors were utilized to build a Nomogram plot model．The C-index was 0.731（95%CI：0.712-0.749）for the training set and 0.719（95%CI：0.688-0.749）for the validation set,and the areas under the curves of the 3- and 5-year survival ROC curves of both the training and validation sets were >0.7,which was a good differentiation,and the calibration curves were well-fitted．Conclusions T-stage,N-stage,M-stage,radiotherapy,chemotherapy,and the sequence of surgery and other treatments are independent prognostic factors for TNBC,and the Nomogram clinical prediction model based on this has good differentiation,accuracy,and clinical utility,and can better predict the survival prognosis of TNBC patients．

目的 分析产后出血预测评分与产妇凝血指标的相关性,以及出血预测评分对阴道分娩产后出血的预测效能。方法 采用回顾性研究,纳入2021年1月—2022年12月河南科技大学第二附属医院收治的136例阴道分娩产妇,根据产后出血情况,将合并产后出血的36例患者列为病例组,其余100例列为对照组,比较两组患者的产后出血预测评分及凝血指标,经Spearman相关性系数验证产后出血预测评分结果与凝血指标的相关性,依据实际出血情况,验证产后出血预测评分、各凝血指标对产后出血的预测效能。结果 病例组患者的产后出血预测评分为（7.33±2.46）分,D-二聚体（D-D）为（2.62±0.41）mg/L,均高于对照组[（6.14±2.06）分、（2.17±0.45）mg/L],纤维蛋白原（FIB）为（4.42±1.25）g/L,低于对照组（5.23±1.16）g/L;活化部分凝血活酶时间（APTT）为（37.44±10.25）s,凝血酶原时间（PT）为（15.45±4.12）s,凝血酶时间（TT）为（16.77±4.25）s,均高于对照组[（30.11±10.12）s、（12.49±4.11）s、（13.34±4.18）s],差异具有统计学意义（P<0.05）。经Spearman相关性系数分析,产后出血预测评分与经阴道分娩产妇的D-D、APTT、PT、TT呈正相关,与FIB呈负相关。通过绘制受试者工作特征曲线（ROC）后得知,产后出血预测评分及凝血指标对产后出血均有一定预测价值,但产后出血预测评分的AUC值大于各凝血指标。结论 产后出血预测评分与产妇凝血功能指标呈正相关,将产后出血预测评分与凝血指标检测相结合能实现对产后出血的早期识别及诊断。

Objective To analyze the correlation between postpartum bleeding prediction score and maternal blood coagulation index and the prediction efficiency of postpartum bleeding in vaginal delivery．Methods This is a retrospective study．The cases were included from January 2021 to December 2022．The subjects of the study were 136 vaginal delivery mothers． According to the delivery situation,36 patients with postpartum bleeding were included in the case group,and the rest 100 patients were included in the control group．The postpartum bleeding prediction score and coagulation indicators of the two groups were compared．The correlation between postpartum bleeding prediction score and coagulation indicators was verified by Spearman correlation coefficient．According to the actual bleeding situation,verify the predictive score for postpartum bleeding and the diagnostic efficacy of various coagulation indicators on postpartum bleeding．Results According to the test,the predictive score for postpartum bleeding in the case group was（7.33±2.46）,D-dimer（D-D）was（2.62±0.41）mg/L,which were higher than those in the control group [（6.14±2.06）,（2.17±0.45）mg/L]．Fibrinogen（FIB）was（4.42±1.25）g/L,lower than the control group（5.23±1.16）g/L,activated partial thromboplastin time（APTT）was（37.44±10.25）s,prothrombin time（PT）was（15.45±4.12）s,and thrombin time（TT）was（16.77±4.25）s．Compared with the control group [（30.11±10.12）s,（12.49±4.11）s,and（13.34±4.18）s）],the above indicators were all higher（P<0.05）．Through Spearman correlation coefficient analysis,the predictive score of postpartum bleeding was positively correlated with the D-D,APTT,PT,TT,negatively correlated with the FIB of the parturient who delivered through vagina．After drawing the ROC curve,it was found that both the postpartum hemorrhage prediction score and coagulation indicators had certain predictive value for postpartum hemorrhage,but the AUC value of the postpartum hemorrhage prediction score was greater than each coagulation indicator．Conclusions The prediction score of postpartum bleeding is positively correlated with the coagulation function indicators of the parturient,combining the score and indicators can achieve early identification and diagnosis of postpartum bleeding．

胶质瘤是颅内最常见的原发性恶性肿瘤,其分级对患者治疗方式的选择和预后至关重要。尽管目前组织病理学仍是其最为可靠的分级手段,但需通过有创性手术以获取组织样本,存在一定的风险。相较之下,磁共振成像（MRI）作为一种非侵入性影像诊断工具,在胶质瘤分级中发挥着不可或缺的作用。然而,传统MRI评估受限于医师个体主观性强和可重复性差的问题,一定程度上影响了准确的分级结果。近年来,影像组学技术的崭露头角为解决上述难题开辟了新视角,通过高通量提取影像数据特征捕捉并量化肿瘤的影像学表现,避免因主观因素而导致的不确定性,协助医师更准确地评估肿瘤的恶性程度。本文对近五年来MRI影像组学在胶质瘤术前分级预测方面的相关研究进行了简要综述,旨在为相关领域研究者提供有益的参考和借鉴,以推动MRI影像组学在临床实践中的应用。

Glioma is the most common primary malignant brain tumor,and its grading is crucial for treatment decisions and prognosis．Currently,histopathology remains the gold standard for grading,but it requires invasive procedures and carries inherent risks．In contrast,magnetic resonance imaging（MRI）,a non-invasive diagnostic tool,plays an indispensable role in glioma grading．However,traditional MRI assessment is hampered by interobserver subjectivity and limited repeatability,which compromise grading accuracy．In recent years,radiomics,a burgeoning field,has offered a promising solution to address these challenges．By extracting high-dimensional imaging data features,radiomics enables the quantification of tumor radiological characteristics and elimination of subjectivity-related discrepancies．This technology assists clinicians in more precisely assessing the malignancy of gliomas．This article summarizes relevant studies in the past five years on the application of MRI radiomics in preoperative glioma grading,aiming to provide valuable insights and guidance to researchers in the field and promote the clinician implementation of MRI radiomics．