Objective We investigated the clinical features and the potential risk factors of malnutrition in patients with chronic heart failure(CHF),and constructed the risk prediction model of malnutrition.Methods A total of459 CHF patients admitted between January 2023 and July 2024 were classified into a normal nutrition group and a malnutrition group based on the geriatric Nutrition Risk Index(GNRI)score upon admission.The patient-related data were gathered,and single-variable and multi-variable logistic analyses were first carried out to identify the risk factors associated with the malnutrition risk.Subsequently,the stepwise regression approach was employed to define the inclusion criteria and construct a malnutrition nomogram model for CHF patients.The diagnostic efficacy and calibration of this model were appraised using the ROC curve and calibration curve,and its clinical utility was assessed via the clinical decision curve.A P value less than 0.05 signified statistically significant differences.Results Anxiety(OR=1.1902,95%CI:1.0217～1.3865),urea nitrogen(OR=1.4842,95%CI:1.1187～1.9691),low body weight(OR=0.8463,95%CI:0.7852～0.9121),and low albumin(OR=0.0467,95%CI:0.0172～0.1268)were risk factors for malnutrition.The optimal model inclusion factors were selected by stepwise regression,including:Body weight,7 items of Generalized Anxiety Disorder Scale(GAD-7),urea nitrogen,uric acid,albumin,total cholesterol,high density lipoprotein cholesterol(HDL-L),low density lipoprotein cholesterol(LDL-L),D-dimer.The area under the ROC curve(AUC)of the column chart model based on the above factors is 0.996(95%CI:0.971～0.978),with a sensitivity of 97.8%and a specificity of 97.1%.The C-index validated internally in the calibration curve was 0.824.The calibration chart and validation results demonstrate good consis-tency and applicability.Conclusion The column chart prediction model created in this study based on nine factors including body weight,GAD-7,urea nitrogen,uric acid,albumin,total cholesterol,HDL-L,LDL-L,and D-dimer had good calibration and prediction performance,and had good clinical practicality,which was helpful for clinicians to make diagnosis and treatment decisions for malnutrition in CHF patients.

Objective We investigated the clinical features and the potential risk factors of malnutrition in patients with chronic heart failure(CHF),and constructed the risk prediction model of malnutrition.Methods A total of459 CHF patients admitted between January 2023 and July 2024 were classified into a normal nutrition group and a malnutrition group based on the geriatric Nutrition Risk Index(GNRI)score upon admission.The patient-related data were gathered,and single-variable and multi-variable logistic analyses were first carried out to identify the risk factors associated with the malnutrition risk.Subsequently,the stepwise regression approach was employed to define the inclusion criteria and construct a malnutrition nomogram model for CHF patients.The diagnostic efficacy and calibration of this model were appraised using the ROC curve and calibration curve,and its clinical utility was assessed via the clinical decision curve.A P value less than 0.05 signified statistically significant differences.Results Anxiety(OR=1.1902,95%CI:1.0217～1.3865),urea nitrogen(OR=1.4842,95%CI:1.1187～1.9691),low body weight(OR=0.8463,95%CI:0.7852～0.9121),and low albumin(OR=0.0467,95%CI:0.0172～0.1268)were risk factors for malnutrition.The optimal model inclusion factors were selected by stepwise regression,including:Body weight,7 items of Generalized Anxiety Disorder Scale(GAD-7),urea nitrogen,uric acid,albumin,total cholesterol,high density lipoprotein cholesterol(HDL-L),low density lipoprotein cholesterol(LDL-L),D-dimer.The area under the ROC curve(AUC)of the column chart model based on the above factors is 0.996(95%CI:0.971～0.978),with a sensitivity of 97.8%and a specificity of 97.1%.The C-index validated internally in the calibration curve was 0.824.The calibration chart and validation results demonstrate good consis-tency and applicability.Conclusion The column chart prediction model created in this study based on nine factors including body weight,GAD-7,urea nitrogen,uric acid,albumin,total cholesterol,HDL-L,LDL-L,and D-dimer had good calibration and prediction performance,and had good clinical practicality,which was helpful for clinicians to make diagnosis and treatment decisions for malnutrition in CHF patients.