Construction And Verification Of Risk Prediction Model For Prolonged Mechanical Ventilation In Patients With Aneurysmal Subarachnoid Hemorrhage

Objective:To investigate the risk factors associated with prolonged mechanical ventilation（PMV）in patients with aneurysmal subarachnoid hemorrhage,and to establish and verify a nomogram risk prediction model.To provide evidence for nurses to identify high-risk patients who are prone to prolonged mechanical ventilation,so as to make appropriate measures and shorten the duration of mechanical ventilation.Methods:This study was a retrospective study.The convenient sampling method was adopted to select patients with aneurysmal subarachnoid hemorrhage who underwent mechanical ventilation in the Neurosurgical intensive care unit of a third class hospital in Shenyang from January 1,2015 to October 31,2021 as the research objects.Using the self-designed questionnaire as the research tool,the clinical data were collected.The patients were randomly divided into modeling group（221 cases）and validation group（94 cases）at a ratio of 7:3.SPSS 25.0 and R-4.2.1 software were used to analyze the data.Univariate analysis was performed in the modeling group,and then binary Logistic regression analysis was performed.Based on the statistical relevance of the results,a prediction model for nomogram was developed in R-4.2.1,and its discrimination,calibration and clinical utility were assessed.Results:（1）This study included 315 patients,including 176 patients with prolonged mechanical ventilation,and the incidence of prolonged mechanical ventilation was55.9%;（2）In univariate analysis,the significant variables were Hunt-Hess grade（P=0.009）,pneumonia（P=0.003）,aneurysms location（P=0.151）,number of aneurysms（P=0.057）,tracheotomy（P<0.001）,sedative drugs（P=0.001）,TP（P=0.010）,ALB（P=0.130）,age（P=0.047）,GCS score（P=0.001）,WFNS grade（P=0.034）,SOFA score（P<0.001）,APACHE II score（P<0.001）,WBC（P<0.001）,Glu（P<0.001）,CK（P<0.001）,hs-c Tnl（P<0.001）,P（A-a）O₂（P<0.001）,Pa O₂/Fi O₂（P<0.001）.The statistically significant variables in multivariate analysis were age（OR=1.033,P=0.040）,sedative drugs（OR=2.481,P=0.045）,tracheotomy（OR=12.734,P<0.001）and P（A-a）O₂（OR=1.018,P=0.002）;（3）The formula of the prediction model was Y=Logit（P）=-4.578+0.033×age+0.909×sedative drugs+2.544×tracheotomy+0.017×P（A-a）O₂.A nomogram was drawn to visualize the model;（4）The discrimination,calibration,and clinical usefulness of the nomogram model were evaluated and verified in the modeling and validation groups.The area under the ROC curve of the nomogram model in the modeling and validation groups were 0.862（95%CI:0.813,0.910）and 0.804（95%CI:0.714,0.894）,and the model had good discrimination.The calibration curves of the modeling group and the validation group showed that the predicted line fitted well with the ideal line,indicating that the model fitted well.The clinical decision curve between the two groups showed that the model had good clinical usefulness.Conclusion:（1）Age,sedative drugs,tracheotomy,and P（A-a）O₂ are independent risk factors for prolonged mechanical ventilation in patients with aneurysmal subarachnoid hemorrhage;（2）The nomogram model constructed by using the above four independent risk factors has good discrimination,accuracy and clinical usefulness,which makes the application simple and quick.It can be used as a prediction tool for prolonged mechanical ventilation in patients with aneurysmal subarachnoid hemorrhage,and provide evidence for clinical medical staff to identify high-risk patients and formulate intervention measures.