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．

目的 基于随机森林方法构建甲状腺功能减退（简称甲减）患病风险预测模型。方法 从MIMIC-IV数据库纳入5 735名甲减患者为病例组,4 803名非甲减患者为对照组,基于随机森林模型进行建模。同时利用逻辑回归、贝叶斯正则化神经网络、XGBoost作为比较模型。最后用准确率、F1分数、精确率、召回率、特异性以及AUC值评价四个机器学习模型性能。结果 随机森林模型准确率为0.85,F1分数为0.84,精确率为0.84,召回率为0.84,特异性为0.86,AUC值为0.91。在该模型中,促甲状腺激素、年龄、绝对淋巴细胞计数、血液中红细胞数、中性白细胞、性别、碱性磷酸酶、丙氨酸氨基转移酶、嗜酸性粒细胞绝对计数、尿素氮为甲减患者诊断重要性排前10的指标。结论 采用随机森林方法构建的甲减患病预测模型为甲减的早期诊断有潜在应用价值。

Objective To construct a risk prediction model for hypothyroidism based on the random forest model．Methods A total of 5 735 hypothyroidism patients were included from the MIMIC-IV database as the case group, and 4 803 non-hypothyroidism patients were included as the control group．Random forest models were constructed for both groups, and logistic regression, Bayesian regularized neural network, and XGBoost were used as comparative models．The performance of the four machine learning models was evaluated using accuracy, F1 score, precision, recall, specificity, and AUC value．Results The random forest model had an accuracy of 0.85, an F1 score of 0.84, a precision of 0.84, a recall of 0.84, a specificity of 0.86, and an AUC value of 0.91．In this model, thyroid-stimulating hormone, age, absolute lymphocyte count, red blood cell count in blood, neutrophil, gender, alkaline phosphatase, aspartate aminotransferase, absolute eosinophil count, and blood urea nitrogen were the top 10 indicators for diagnosing hypothyroidism patients．Conclusions The hypothyroidism disease prediction model constructed using the random forest method has potential application value for the early diagnosis of hypothyroidism．

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.