Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure

Khanam, Sayma Sabrina; Choi, Eunhee; Son, Jung Woo; Lee, Junwon; Youn, Young Jin; Yoon, Junghan; Lee, Seung Hwan; Kim, Jang Young; Ahn, Sung Gyun; Ahn, Min Soo; Kang, Seok Min; Baek, Sang Hong; Jeon, Eun Seok; Kim, Jae Joong; Cho, Myeong Chan; Chae, Shung Chull; Oh, Byung Hee; Choi, Dong Ju; Yoo, Byung Su

doi:10.1371/journal.pone.0206380

Cited by 51 publications

(53 citation statements)

References 43 publications

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“…The GWTG-HF and MAGGIC scores are well validated conventional models for risk stratification of AHF patients. [23–26] The previous validation studies have reported that the AUC of the GWTG-HF scores for predicting in-hospital mortality of patients with AHF was 0.71–0.76. [24,25] Furthermore, the AUC of the MAGGIC score for predicting 1–3-year mortality of patients with AHF in previous studies was 0.73–0.74, implying moderate accuracy for predicting the mortality of patients with AHF.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

et al. 2019

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Aims This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). Methods and results 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876–0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group( p <0.001). Conclusion DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

et al. 2019

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“…In ROC analyses, the risk factors LVEF or MAGGIC score added less predictive power to NT‐proBNP than MR‐proADM, although the difference is small for the MAGGIC score (online supplementary Figures S2 and S3 ). Another study on Asian patients reported only a negligible increase in discrimination (AUC) between survival and 1‐year mortality when NT‐proBNP or BNP was used in addition to the MAGGIC score …”

Section: Discussionmentioning

confidence: 99%

“…Another study on Asian patients reported only a negligible increase in discrimination (AUC) between survival and 1-year mortality when NT-proBNP or BNP was used in addition to the MAGGIC score. 31 Nevertheless, none of these biomarkers, despite sound pathophysiological concepts, was shown to be useful in the guidance of . HF therapy.…”

Section: Biomarkersmentioning

confidence: 99%

Biomarker guidance allows a more personalized allocation of patients for remote patient management in heart failure: results from the TIM‐HF2 trial

Möckel

Koehler

Anker

et al. 2019

European J of Heart Fail

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Aims The TIM‐HF2 study showed less days lost due to unplanned cardiovascular hospitalization or all‐cause death and improved survival in patients randomly assigned to remote patient management (RPM) instead of standard of care. Methods and results This substudy explored whether the biomarkers mid‐regional pro‐adrenomedullin (MR‐proADM) and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) could be used to identify low‐risk patients unlikely to benefit from RPM, thereby allowing more efficient allocation of the intervention. For 1538 patients of the trial (median age 73 years, interquartile range 64–78 years, 30% female), baseline biomarkers were used to select subpopulations recommended for RPM with various safety endpoints (100%, 98%, 95% sensitivity), and efficacy of RPM was assessed. Both biomarkers were strongly associated with events. The primary endpoint of lost days increased from 1.0% (1.4%) in the lowest to 17.3% (17.6%) in the highest quintile of NT‐proBNP (MR‐proADM). After combining biomarkers to identify patients recommended for RPM with 95% sensitivity, in the most efficient scenario (excluding 27% of patients; NT‐proBNP < 413.7 pg/mL and MR‐proADM < 0.75 nmol/L), the effect of RPM on patients was highly similar to the original trial (ratio of lost days: 0.78, hazard ratio for all‐cause death: 0.68). Number needed to treat for all‐cause death was lowered from 28 to 21. Rates of emergencies and telemedical efforts were significantly lower among patients not recommended for RPM. Biomarker guidance would have saved about 150 h effort/year per 100 patients of the eligible population. Conclusions The combined use of MR‐proADM and NT‐proBNP may allow safe, more precise, effective and cost‐saving allocation of patients with heart failure to RPM and warrants further prospective studies.

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“…BNP can represent powerful biological effects, such as natriuresis, vasodilation, myocardial apoptosis inhibition, and modulation of immune and inflammatory responses of cardiac injury [ 12 – 14 ]. Some prior studies suggest that BNP can be used as a biomarker for prognosis in patients with HF, and it also participates in both occurrence and development of T2DM and ischemic cardiomyopathy [ 15 , 16 ]. What's more, in diabetic patients, BNP can be used for screening the absence of left ventricular dysfunction [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Potential Molecular Mechanism of the NPPB Gene in Postischemic Heart Failure with and without T2DM

Guan

Yin

Deng

et al. 2020

BioMed Research International

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Background. This study is aimed at investigating natriuretic peptide B (NPPB) coexpression genes and their pathways involved in heart failure (HF) among patients both with and without type 2 diabetes mellitus (T2DM). Methods. The microarray dataset GSE26887, containing 19 postischemic HF patients’ peripheral blood samples (7 with T2DM and 12 without T2DM), was examined to detect the genes coexpressed with NPPB using the corr.test function in the R packet. Furthermore, using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of the coexpression genes. The modules and hub genes of the PPI network were then identified using the Cytoscape software. Results. In patients with T2DM, a total of 41 biological processes (BP), 20 cellular components (CC), 13 molecular functions (MF), and 41 pathways were identified. Furthermore, a total of 61 BPs, 16 CCs, 13 MFs, and 22 pathways in patients without T2DM were identified. In both groups of patients, 17 BPs, 10 CCs, 6 MFs, and 13 pathways were enriched. We also identified 173 intersectional coexpression genes (63 positively, 106 negatively, and 4 differently coexpressed in patients with and without T2DM, respectively) in both types of patients, which were enriched in 16 BPs, 8 CCs, 3 MFs, and 8 KEGG pathways. Moreover, the PPI network (containing 237 edges and 170 nodes) with the top module significantly enriched in 4 BPs (tricarboxylic acid metabolic process, citrate metabolic process, tricarboxylic acid cycle, and aerobic respiration) and 3 pathways (citrate cycle, malaria parasite metabolic pathway, and AGE-RAGE signaling pathway in diabetic complications) was constructed. DECR1, BGN, TIMP1, VCAN, and CTCF are the top hub genes. Conclusions. Our findings may elucidate the functions and roles of the NPPB gene in patients with postischemic HF and facilitate HF management.

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Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure

Cited by 51 publications

References 43 publications

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

Biomarker guidance allows a more personalized allocation of patients for remote patient management in heart failure: results from the TIM‐HF2 trial

Potential Molecular Mechanism of the NPPB Gene in Postischemic Heart Failure with and without T2DM

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