Construction and evaluation of a risk prediction model of hypoglycemia risk in emergency intensive care unit patients
10.3761/j.issn.0254-1769.2023.23.003
- VernacularTitle:急诊重症监护室患者低血糖风险预测模型的构建及验证
- Author:
Mengyuan QIAO
1
;
Haiyan WANG
;
Mengzhen QIN
Author Information
1. 832000 新疆维吾尔自治区石河子市 石河子大学医学院护理系
- Keywords:
Emergency Intensive Care Unit;
Hypoglycemia;
Risk Factors;
Prediction Model;
Nomograms;
Nursing Care
- From:
Chinese Journal of Nursing
2023;58(23):2835-2842
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and validate a risk prediction model of hypoglycemia in emergency intensive care unit(EICU)patients.Methods A retrospective study was conducted among 2093 EICU patients in a department of a tertiary A hospital in Urumqi from January to December 2022,as research subjects.Univariate analysis and logistic regression analysis were used to determine the risk factors for hypoglycemia,and R software was used to establish a nomogram prediction model.The area urder the receiver operator characteristic(ROC)curve was used to test the model differentiation,and the Hosmer-Lemeshow test was used to test the goodness of fit of the model.The risk prediction model was validated by the prospective study with inclusion of 699 EICU patients admitted to the same hospital from January to March 2023.Results The model variables included whether hypoglycemia occurred in the past year,acute physiology and chronic health evaluation Ⅱ score at admission,coefficient of variation of blood glucose,history of renal disease,history of diabetes,insulin treatment,and serum creatinine.The Hosmer-Lemeshow test of the model was P=0.497;the area urder the ROC curve was 0.820(95％CI:0.794～0.847);the best cutoff value was 0.495;the sensitivity was 0.856;the specificity was 0.751.The model validation results showed that the Hosmer-Lemeshow test P=0.537;the area urder the ROC curve was 0.859(95％CI:0.819～0.898);the best cutoff value was 0.597;the sensitivity was 0.840;the specificity was 0.757.Conclusion The established nomogram prediction model helps clinical staff to screen patients at high risk of hypoglycemia and provides a reference for optimizing the management of hypoglycemia in EICU patients.
