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Predictive Value of Geriatric Nutritional Risk Index in Patients With Coronary Artery Disease: A Meta-Analysis

ABSTRACT: Background: Low Geriatric Nutritional Risk Index has been identified as an index of impaired nutritional state. The objective of the meta-analysis was to assess the association of the Geriatric Nutritional Risk Index (GNRI) with adverse outcomes in patients with coronary artery disease (CAD). Methods: Relevant studies were identified by comprehensively searching PubMed and Embase databases in May 2021. Studies assessing the association of GNRI with all-cause mortality or major adverse cardiovascular events (MACEs) in patients with CAD were included. The predictive value of GNRI was summarized by pooling multivariable adjusted risk ratios (RR) with 95% confidence intervals (CI) per GNRI point decrease or the lowest vs. the highest GNRI group. Results: A total of eight studies involving 9277 patients with CAD were analyzed. Meta-analysis showed that the lowest GNRI was associated with a higher risk of all-cause mortality (RR 2.10; 95% CI 1.68–2.63) and MACEs (RR 2.84; 95% CI 1.56–5.16), respectively. Furthermore, per point decrease in GNRI was associated with 8 and 10% additional risk of all-cause mortality and MACEs. Subgroup analysis indicated that the value of low GNRI in predicting all-cause mortality was not affected by subtype of patients or follow-up duration. Conclusion: Low GNRI score at baseline was associated with a higher risk of all-cause mortality and cardiovascular events in patients with CAD. The nutritional state estimated by the GNRI score could provide important predictive information in patients with CAD.

SUBMITTER: Fan Y

PROVIDER: S-EPMC8511313 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMalnutrition is a hallmark of frailty, is common among elderly patients, and is a predictor of poor outcomes in patients with severe symptomatic aortic stenosis (AS). The Geriatric Nutritional Risk Index (GNRI) is a simple and well-established screening tool to predict the risk of morbidity and mortality in elderly patients. In this study, we evaluated whether GNRI may be used in the risk stratification and management of patients undergoing transcatheter aortic valve replacement (TAVR).MethodsPatients with symptomatic severe AS (n = 953) who underwent transfemoral TAVR at the University Hospital Schleswig-Holstein Kiel, Germany, between 2010 and 2019 (development cohort) were divided into two groups: normal GNRI ≥ 98 (no nutrition-related risk; n = 618) versus low GNRI < 98 (at nutrition-related risk; n = 335). The results were validated in an independent (validation) cohort from another high-volume TAVR centre (n = 977).ResultsThe low-GNRI group had a higher proportion of female patients (59.1% vs. 52.1%), higher median age (82.9 vs. 81.8 years), prevalence of atrial fibrillation (50.4% vs. 40.0%), median logistic EuroSCORE (17.5% vs. 15.0%) and impaired left ventricular function (<35%: 10.7% vs. 6.8%), lower median estimated glomerular filtration rate (50 vs. 57 mL/min/1.73 m2 ) and median albumin level (3.5 vs. 4.0 g/dL) compared with the normal-GNRI group. Among peri-procedural complications, Acute Kidney Injury Network (AKIN) Stage 3 was more common in the low-GNRI group (3.6% vs. 0.6%, p = 0.002). After a mean follow-up of 21.1 months, all-cause mortality was significantly increased in the low-GNRI group compared with the normal-GNRI group (p < 0.001). This was confirmed in the validation cohort (p < 0.001). Low GNRI < 98 was identified as an independent risk factor for all-cause mortality (hazard ratio 1.44, 95% CI 1.01-2.04, p = 0.043). Other independent risk factors included albumin level < median of 4.0 g/dL, high-sensitive troponin T in the highest quartile (> 45.0 pg/mL), N-terminal pro-B-type natriuretic peptide in the highest quartile (> 3595 pg/mL), grade III-IV tricuspid regurgitation, pulmonary arterial hypertension, life-threatening bleeding, AKIN Stage 3 and disabling stroke.ConclusionsLow GNRI score was associated with an increased risk of all-cause mortality in patients undergoing TAVR, implying that this vulnerable group may benefit from improved preventive measures.

Project description:BACKGROUND:Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). METHODS:Patients with suspected CAD (n?=?203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. FINDINGS:A total of 196 patients with analyzable protein levels (n?=?332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79?±?0·01, outperforming prediction with generally available clinical characteristics (AUC?=?0·65?±?0·04, p?<?0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC?=?0·85?±?0·05), again outperforming prediction with generally available characteristics (AUC?=?0·70?±?0·04, p?<?0·05). INTERPRETATION:Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.

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Predictive Value of Geriatric Nutritional Risk Index in Patients With Coronary Artery Disease: A Meta-Analysis

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