A nomogram model for predicting malnutrition after a tracheotomy
10.3760/cma.j.issn.0254-1424.2024.03.002
- VernacularTitle:气管切开患者营养不良预测模型的构建及评估
- Author:
Ang CAI
1
;
Junfeng YANG
;
Ruyao LIU
;
Le WANG
;
Yi LI
;
Liugen WANG
;
Heping LI
;
Xi ZENG
Author Information
1. 郑州大学第一附属医院康复医学科，郑州 450000
- Keywords:
Tracheotomy;
Malnutrition;
Risk factors;
Prediction models;
Nomograms
- From:
Chinese Journal of Physical Medicine and Rehabilitation
2024;46(3):199-204
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the risk factors for malnutrition after a tracheotomy and to construct a predictive model useful for its prevention through early intervention.Methods:Clinical data describing 440 tracheotomy patients were subjected to a retrospective analysis. The variables examined were age, sex, etiology, Glasgow Coma Score (GCS), activities of daily living (ADL) score, age-corrected Charlson comorbidity index (aCCI), food intake, swallowing function, incidence of infections, as well as any history of diabetes mellitus, hypertension, smoking or alcohol consumption. Patients identified as being at risk of malnutrition (NRS-2002≥3) were screened using the Nutritional Risk Screening tool (NRS-2002) and the European Society of Clinical Nutrition and Metabolism′s ESPEN2015 criteria. The subjects were thus categorized into a malnutrition group of 343 and a control group of 97. Unifactorial and multifactorial logistic regression analyses were performed, and stepwise regression was applied to include the factors found significant in the unifactorial analysis into the multifactorial logistic regression analysis, and to construct a column-line graph prediction model. The clinical utility of the model was assessed by applying the receiver operator characteristics (ROC) curves, calibration plots and decision curve analysis (DCA).Results:Of the 440 persons studied, 343 (78%) were malnourished. The multivariate logistic regression analysis showed that pulmonary infection, dysphagia, low GCS score and high aCCI score were significant risk factors for malnutrition after a tracheotomy. A prediction nomograph was constructed. After fitting and correcting, the area under the curve (AUC) of the prediction model′s ROC curve was 0.911, the specificity was 80.4%, and the sensitivity was 91.3%. That was significantly higher than the AUCs for pulmonary infection (0.809), dysphagia (0.697), aCCI (0.721) and GCS (0.802). Bootstrap self-sampling was used to verify the model internally. After 1000 samples the average absolute error between the predicted risk and the actual risk was 0.013, indicating good prediction ability. The DCA results demonstrated that the model has substantial clinical applicability across a range of nutritional interventions, particularly for threshold probability values ranging from 0 to 0.96.Conclusion:Pulmonary infection, dysphagia, low GCS score, and high aCCI score are risk factors for malnutrition among tracheotomy patients. The nomogram model constructed in this study has good predictive value for the occurrence of malnutrition among such patients.
