Analysis of the risk factors of acute gastrointestinal injury above grade II in elderly patients with severe pneumonia
10.3760/cma.j.cn431274-20220724-00720
- VernacularTitle:老年重症肺炎患者发生Ⅱ级以上急性胃肠功能损伤的预测模型
- Author:
Jingjing ZHAO
1
;
Jing WANG
;
Zhihang HU
;
Yuan ZHAN
;
Liqun ZOU
;
Li YAO
Author Information
1. 合肥市第二人民医院（安徽医科大学附属合肥医院）重症医学科，合肥 230011
- Keywords:
Aged;
Severe pneumonia;
Acute gastrointestinal injury;
Models, statistical
- From:
Journal of Chinese Physician
2023;25(4):560-564
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a prediction model of acute gastrointestinal injury (AGI) above grade II in elderly patients with severe pneumonia, and to evaluate and validate the model internally.Methods:A retrospective analysis was performed on 268 patients aged >65 years with severe pneumonia admitted to the Second People′s Hospital of Hefei from June 2019 to May 2022 (207 cases in the training set and 61 cases in the verification set). Sixteen indicators, including age, sex, underlying disease, pneumonia Severity index (PSI) score, dosage of sedative and analgesic drugs, and mechanical ventilation time of all patients were collected. After logistic regression analysis in the training set, a model was established to predict AGI above grade Ⅱ in elderly patients with severe pneumonia. Receiver operating characteristic (ROC) curve was drawed and correction curve was used to evaluate the reliability of the model. The model was internally validated by validation set data.Results:Among 207 patients with severe pneumonia in the training set, 50 patients developed AGI above grade Ⅱ during treatment. The prediction model was established by logistic regression analysis as follows: When L=Sequential Organ Failure Assessment (SOFA)×0.181+ PSI score×0.066+ propofol dosage×0.607+ reifentanil dosage×1.187, L>19.288, it can be considered that patients with severe pneumonia have a 93.24% chance of developing grade Ⅱ or above AGI. The ROC curve showed that the model was well differentiated, AUC=0.960. H-L test indicated (χ 2=7.39, P=0.496>0.05) the model fit was good. The sensitivity and specificity of the model were 82.00% and 96.82% respectively. AUC=94.58% (sensitivity 81.25%, specificity 93.33%), H-L test indicated ( χ 2=4.51, P=0.808>0.05) the prediction accuracy was 90.16%. Conclusions:The prediction model for AGI after severe pneumonia in elderly patients can be used clinically to help predict the occurrence of AGI in elderly patients with multiple injuries.
