基于膈肌超声心脏参数血气指标构建重症肺炎脱机拔管失败的nomogram预测模型OACSTPCD
Construction of a Nomogram Prediction Model for Failure of Extubation in Severe Pneumonia Based on Diaphragmatic Ultrasound Cardiac Parameters and Blood Gas Indices
目的:探究膈肌超声、心脏参数、血气指标联合预测重症肺炎脱机拔管失败价值,并构建nomogram预测模型,以期为临床早期针对性制定干预方案提供参考.方法:选取2021 年3 月至2023 年12 月贵州医科大学附属医院重症肺炎患者 210 例,作为研究对象,将研究对象随机分为 70%(147 例)训练集及30%(63 例)验证集,统计患者一般资料,采用Lasso-Logistic回归方程筛选重症肺炎脱机拔管失败预测因子,构建nomogram预测模型,采用受试者工作特征曲线(ROC)、决策曲线(DCA)、校准曲线分析预测模型效能.结果:训练集、验证集中,病例组(脱机拔管失败)与对照组(脱机拔管成功)年龄、机械通气时间、APACHEⅡ评分、ICU入住时间、CRP/ALB、心脏参数、膈肌超声参数、PaO2、PaCO2、P/F、PA-aO-2、心肺基础疾病史比较差异有统计学意义(P<0.05);Logistic回归方程显示,年龄、DTF、DE、E/A、PaO2、PaCO2、CRP/ALB、心肺疾病基础史均是重症肺炎脱机拔管失败影响因素(P<0.05);经R语言软件可视化处理得到重症肺炎脱机拔管失败nomogram预测模型,nomogram 预测模型在训练集、验证集中AUC分别为0.866(95%CI:0.801～0.930)、0.917(95%CI:0.853～0.982,校准曲线接近于 48 度参考线,预测点分布均匀,DCA曲线在0.35～0.8 区间内,nomogram 预测模型在训练集、验证集中能取得最大获益.结论:基于膈肌超声、心脏参数、血气指标构建重症肺炎脱机拔管失败的nomogram预测模型可用于临床早期预测脱机拔管风险,以针对性制定相应干预方案,改善预后.
Objective:To explore the value of combined phrenic ultrasonography,cardiac parameters,and blood gas indexes in predicting the failure of offline extubation of severe pneumonia,and to construct a no-mogram prediction model for early clinical intervention.Methods:A total of 210 patients with severe pneumo-nia in the Affiliated Hospital of Guizhou Medical University from March 2021 to December 2023 were selected as the study subjects,and the study subjects were randomly divided into a training set(70%,147 cases)and a verification set(30%,63 cases).The general information of the patients was analyzed.The lasso-Logistic regression equation was used to screen the predictors of offline extubation failure for severe pneumonia,and a nomogram prediction model was constructed.The receiver operating characteristic curve(ROC),decision curve(DCA),and calibration curve were used to analyze the efficacy of the model.Results:In the training set and the validation set,the differences in age,mechanical ventilation time,APACHE Ⅱ score,ICU stay,CRP/ALB,cardiac parameters,diaphragm ultrasound parameters,PaO2,PaCO2,P/F,PA-aO-2,and his-tory of underlying cardiopulmonary disease were statistically significant when comparing the case group(failed extubation off the machine)with the control group(successful extubation off the machine)(P<0.05);Logis-tic regression equation showed that age,DTF,DE,E/A,PaO2,PaCO2,CRP/ALB,and basic history of cardiopulmonary disease were all influencing factors for the failure of offline extubation of severe pneumonia(P<0.05).The nomogram prediction model for offline extubation failure for severe pneumonia was obtained by visualization using R language software.The AUC of the nomogram prediction model was 0.866(95%CI:0.801-0.930)in the training set and 0.917(95%CI:0.853-0.982)in the verification set,respectively.The calibration curve was close to the 48° reference line,the prediction points were evenly distributed,and the DCA curve was within the range of 0.35～0.8.The nomogram prediction model could obtain the greatest benefits in both the training set and the verification set.Conclusion:This nomogram prediction model based on diaphragm ultrasound,heart parameters,and blood gas indexes can be used to predict the risk of offline extu-bation of severe pneumonia at an early stage.Accordingly,appropriate intervention plans can be made and the prognosis is improved.
韦卫琴;胡晓纯;房东海;张运铎;代传扬;张燕;周永芳
贵州医科大学附属医院,贵州 贵阳 550004
重症肺炎脱机拔管膈肌超声心脏参数血气指标nomogram预测模型
Severe pneumoniaOff-line tube extractionDiaphragm ultrasoundCardiac pa-rametersBlood gas indexNomogram prediction model
《河北医学》 2024 (009)
1519-1525 / 7
2021年贵州省科教青年英才培训工程项目,[编号:黔省专合字(2021)260号]
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