目的 建立一种有效的Nomogram模型，用以评估新生儿耐碳青霉烯类肺炎克雷伯菌（CRKP）肠道定植风险，运用建立的Nomogram模型得出新生儿CRKP具体的定植概率大小，采取个体化的预防策略，减少定植的发生，降低新生儿CRKP继发感染的可能性。 方法 选取入院后48 h新生儿进行直肠拭子/大便培养及药敏鉴定为CRKP定植患者187例，在同期入院的经检测为非CRKP定植患儿中随机抽取187例为非定植组，共374例进行回顾性研究。通过R语言（R 4.2.1）的caret软件包对374例总样本按建模组∶验证组=3∶1进行随机分组。模型建立：对建模组数据运用R 4.2.1的glmnet包进行LASSO回归分析确定最终用于建模的预测因素，rms软件包进行Nomogram模型的构建。模型验证：R 4.2.1 pROC和rms软件包对建模组和验证组数据分别进行一致性指数（Cindex）、受试者工作特征曲线（ROC）及其曲线下面积（AUC）、校准曲线对Nomogram模型的效能进行内部验证和外部验证。 结果 运用LASSO回归分析，从研究的35个可能影响新生儿CRKP定植的项目中确定了8个预测因子，分别是性别、剖宫产、母乳喂养、鼻胃管、灌肠、碳青霉烯类、益生菌和总住院时间。使用这8个预测变量构建的Nomogram模型显示出中等预测能力，ROC曲线下面积为建模组0.835、验证组0.800。Hos-mer-Lemeshow检验显示，预测概率与实际概率高度一致（建模组，P = 0.678 > 0.05；验证组，P = 0.208 > 0.05）显示出非常好的拟合度。 结论 在Nomogram模型中引入性别、剖宫产、母乳喂养、鼻胃管、灌肠、使用碳青霉烯类抗生素、口服益生菌、总住院时间可以提高其预测新生儿CRKP定植风险的能力。根据Nomogram模型预测的定植概率的大小不同，采取个体化的预防措施，减少新生儿CRKP定植的发生，降低新生儿CRKP继发感染的发生率。

Objective To establish a Nomogram model for assessing the risk of intestinal colonization by Carbapenem?Resistant Klebsiella pneumoniae （CRKP） to determine the specific probability of colonization and adopt individualized prevention strategies for the purpose of reducing the occurrence of colonization and secondary infection of neonatal CRKP. Methods A total of 187 neonates hospitalized between January 2021 and October 2022 and diagnosed with CRKP colonization by rectal swab/fecal culture as well drug sensitivity identification 48 h after admission were assigned to the CRKP group. Another 187 neonates without non?CRKP colonization during the same period were set as the non?CRKP group. All the data of the two groups were used for a retrospective analysis. The caret package in R 4.2.1 was used to randomly divide the 374 cases into the model group and validation group at a ratio of 3∶1. Then the glmnet package in R 4.2.1 was used to conduct a LASSO regression analysis over the data from the model group to determine the predictive factors for modeling and the rms software package was used to build a Nomogram model. The pROC and rms packages in R 4.2.1 were used to examine the data， analyzing the consistency indexes （Cindex）， receiver operating characteristic curves （ROC）， and area under the curves （AUC） and performing the internal and external validation of the efficacy of the Nomogram model via the calibration curves. Results LASSO regression analysis determined eight predictors from the 35 factors probably affecting neonatal CRKP colonization： gender， cesarean section， breastfeeding， nasogastric tube， enema， carbapenems， probiotics， and hospital stay. The Nomogram model constructed using these eight predictors as variables could predict CRKP colonization to a moderate extent， with the area under the ROC curve of 0.835 and 0.800 in the model and validation group， respectively. The Hos?mer?Lemeshow test showed that the predicted probability was highly consistent with the actual probability （the modeling group： P = 0.678 > 0.05； the validation group： P = 0.208 > 0.05）， presenting a higher degree of fitting. Conclusion The Nomogram model containing such variables as gender， cesarean section， breastfeeding， nasogastric tube， enema， carbapenems， probiotics， and hospital stay is more effective in predicting the risk of neonatal CRKP colonization. Therefore， preventive measures should be individualized based on the colonization probability predicted by the Nomogram model in order to keep neonates from CRKP colonization and reduce the incidence of secondary CRKP infections among them.