Leonardo Spatola1, Silvia Finazzi2, Albania Calvetta2, Francesco Reggiani2, Emanuela Morenghi3, Silvia Santostasi2, Claudio Angelini2, Salvatore Badalamenti2, Giacomo Mugnai4. 1. Division of Nephrology and Hemodialysis, Humanitas Clinical and Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy. Leonardo.spatola@humanitas.it. 2. Division of Nephrology and Hemodialysis, Humanitas Clinical and Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy. 3. Division of Biostatistics, Humanitas Clinical and Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy. 4. Electrophysiology and Cardiac Pacing Unit, Ospedale Civile di Mirano, via Luigi Mariutto 76, 30035, Mirano, VE, Italy.
Abstract
BACKGROUND: Malnutrition is an important risk factor for cardiovascular mortality in hemodialysis (HD) patients. However, current malnutrition biomarkers seem unable to accurately estimate the role of malnutrition in predicting cardiovascular risk. Our aim was to investigate the role of the Subjective Global Assessment-Dialysis Malnutrition Score (SGA-DMS) compared to two well-recognized comorbidity scores-Charlson Comorbidity Index (CCI) and modified CCI (excluding age-factor) (mCCI)-in predicting cardiovascular events in HD patients. METHODS: In 86 maintenance HD patients followed from June 2015 to June 2017, we analyzed biohumoral data and clinical scores as risk factors for cardiovascular events (acute heart failure, acute coronary syndrome and stroke). Their impact on outcome was investigated by linear regression, Cox regression models and ROC analysis. RESULTS: Cardiovascular events occurred in 26/86 (30%) patients during the 2-year follow-up. Linear regression showed only age and dialysis vintage to be positively related to SGA-DMS: B 0.21 (95% CI 0.01; 0.30) p 0.05, and B 0.24 (0.09; 0.34) p 0.02, respectively, while serum albumin, normalized protein catabolic rate (nPCR) and dialysis dose (Kt/V) were negatively related to SGA-DMS: B - 1.29 (- 3.29; - 0.81) p 0.02; B - 0.08 (- 1.52; - 0.35) p 0.04 and B - 2.63 (- 5.25; - 0.22) p 0.03, respectively. At Cox regression analysis, SGA-DMS was not a risk predictor for cardiovascular events: HR 1.09 (0.9; 1.22), while both CCI and mCCI were significant predictors: HR 1.43 (1.13; 1.87) and HR 1.57 (1.20; 2.06) also in Cox adjusted models. ROC analysis reported similar AUCs for CCI and mCCI: 0.72 (0.60; 0.89) p 0.00 and 0.70 (0.58; 0.82) p 0.00, respectively, compared to SGA-DMS 0.56 (0.49; 0.72) p 0.14. CONCLUSIONS: SGA-DMS is not a superior and significant prognostic tool compared to CCI and mCCI in assessing cardiovascular risk in HD patients, even it allows to appraise both malnutrition and comorbidity status.
BACKGROUND:Malnutrition is an important risk factor for cardiovascular mortality in hemodialysis (HD) patients. However, current malnutrition biomarkers seem unable to accurately estimate the role of malnutrition in predicting cardiovascular risk. Our aim was to investigate the role of the Subjective Global Assessment-Dialysis Malnutrition Score (SGA-DMS) compared to two well-recognized comorbidity scores-Charlson Comorbidity Index (CCI) and modified CCI (excluding age-factor) (mCCI)-in predicting cardiovascular events in HDpatients. METHODS: In 86 maintenance HDpatients followed from June 2015 to June 2017, we analyzed biohumoral data and clinical scores as risk factors for cardiovascular events (acute heart failure, acute coronary syndrome and stroke). Their impact on outcome was investigated by linear regression, Cox regression models and ROC analysis. RESULTS: Cardiovascular events occurred in 26/86 (30%) patients during the 2-year follow-up. Linear regression showed only age and dialysis vintage to be positively related to SGA-DMS: B 0.21 (95% CI 0.01; 0.30) p 0.05, and B 0.24 (0.09; 0.34) p 0.02, respectively, while serum albumin, normalized protein catabolic rate (nPCR) and dialysis dose (Kt/V) were negatively related to SGA-DMS: B - 1.29 (- 3.29; - 0.81) p 0.02; B - 0.08 (- 1.52; - 0.35) p 0.04 and B - 2.63 (- 5.25; - 0.22) p 0.03, respectively. At Cox regression analysis, SGA-DMS was not a risk predictor for cardiovascular events: HR 1.09 (0.9; 1.22), while both CCI and mCCI were significant predictors: HR 1.43 (1.13; 1.87) and HR 1.57 (1.20; 2.06) also in Cox adjusted models. ROC analysis reported similar AUCs for CCI and mCCI: 0.72 (0.60; 0.89) p 0.00 and 0.70 (0.58; 0.82) p 0.00, respectively, compared to SGA-DMS 0.56 (0.49; 0.72) p 0.14. CONCLUSIONS: SGA-DMS is not a superior and significant prognostic tool compared to CCI and mCCI in assessing cardiovascular risk in HDpatients, even it allows to appraise both malnutrition and comorbidity status.
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