Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides

Sawano, Mitsuaki; Shiraishi, Yasuyuki; Kohsaka, Shun; Nagai, Toshiyuki; Goda, Ayumi; Mizuno, Atsushi; Sujino, Yasumori; Nagatomo, Yuji; Kohno, Takashi; Anzai, Toshihisa; Fukuda, Keiichi; Yoshikawa, Tsutomu

doi:10.1002/ehf2.12278

Cited by 82 publications

(70 citation statements)

References 34 publications

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“…Numerous risk scores have been developed to predict mortality in patients with HF . These tools generally do not have the discriminatory power of MARKER‐HF.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous risk scores have been developed to predict mortality in patients with HF. [3][4][5][6][7][8][9][10][11][12][13] performance of previous risk prediction models has often been ascribed to the heterogeneity of patients with HF. However, their modest performance is more likely due to the failure of the statistical methods used in their design to capture prognostically important multi-dimensional correlations between the state of the patient and their outcome.…”

Section: Discussionmentioning

confidence: 99%

“…The ability to accurately assess outcomes in patients with HF, however, has proven to be a difficult task. Although a number of tools, including biomarkers, risk scores and their combination have been developed for this purpose, most have achieved only modest success, particularly when they are employed in HF populations other than those from which the score was derived . For instance, the Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score achieved a C‐statistic for the area under the curve (AUC) of 0.74 for predicting mortality risk in a large cohort of patients followed in the Swedish Heart Failure Registry, but performed less well in patients on a transplant waiting list and in a large population of ambulatory HF patients in the US where the C‐statistics were 0.69 and 0.70, respectively .…”

Section: Introductionmentioning

confidence: 99%

“…The ability to accurately assess outcomes in patients with HF, however, has proven to be a difficult task. Although a number of tools, including biomarkers, 1,2 risk scores [3][4][5][6][7][8][9][10] and their combination [11][12][13] have been developed for this purpose, most waiting list and in a large population of ambulatory HF patients in the US where the C-statistics were 0.69 and 0.70, respectively. 16,18 The results with previous HF risk scores are likely due to several causes including dependence on variables that are not universally available and temporal dispersion in the collection of variables so that the state of the patient at a discrete point in the course of their disease is not captured.…”

Section: Introductionmentioning

confidence: 99%

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Improving risk prediction in heart failure using machine learning

Adler

Voors

Klein

et al. 2019

European J of Heart Fail

170

111

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Background Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi‐dimensional interactions. Methods and results We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high‐risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations. Conclusions Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging.

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“…Numerous risk scores have been developed to predict mortality in patients with HF . These tools generally do not have the discriminatory power of MARKER‐HF.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving risk prediction in heart failure using machine learning

Adler

Voors

Klein

et al. 2019

European J of Heart Fail

170

111

View full text Add to dashboard Cite

show abstract

“…One of the most important predictors that was present in all three models was the log of NT‐proBNP. This is an important variable that not only is associated with prediction of all‐cause death, the composite of CV death and HF hospitalization, but HF hospitalization alone . While it has been well known that NT‐proBNP has important prognostic information, the confirmation in this high‐risk population independent of ethnicity and race, many co‐morbidities and the congestion score, provides additional important information to the clinician .…”

Section: Discussionmentioning

confidence: 99%

Clinical factors related to morbidity and mortality in high‐risk heart failure patients: the GUIDE‐IT predictive model and risk score

O’Connor

Fiuzat

Mulder

et al. 2019

European J of Heart Fail

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Background Most heart failure (HF) risk scores have been derived from cohorts of stable HF patients and may not incorporate up to date treatment regimens or deep phenotype characterization that change baseline risk over the short‐ and long‐term follow‐up period. We undertook the current analysis of participants in the GUIDE‐IT (Guiding Evidence‐Based Therapy Using Biomarker Intensified Treatment) trial to address these limitations. Methods and results The GUIDE‐IT study randomized 894 high‐risk patients with HF and reduced ejection fraction (≤ 40%) to biomarker‐guided treatment strategy vs. usual care. We performed risk modelling using Cox proportional hazards models and analysed the relationship between 35 baseline clinical factors and the primary composite endpoint of cardiovascular (CV) death or HF hospitalization, the secondary endpoint of all‐cause mortality, and the exploratory endpoint of 90‐day HF hospitalization or death. Prognostic relationships for continuous variables were examined and key predictors were identified using a backward variable selection process. Predictive models and risk scores were developed. Over a median follow‐up of 15 months, the cumulative number of HF hospitalizations and CV deaths was 328 out of 894 patients (Kaplan–Meier event rate 34.5% at 12 months). Frequency of all‐cause deaths was 143 out of 894 patients (Kaplan–Meier event rate 12.2% at 12 months). Outcomes for the primary and secondary endpoints between strategy arms of the study were similar. The most important predictor that was present in all three models was the baseline natriuretic peptide level. Hispanic ethnicity, low sodium and high heart rate were present in two of the three models. Other important predictors included the presence or absence of a device, New York Heart Association class, HF duration, black race, co‐morbidities (sleep apnoea, elevated creatinine, ischaemic heart disease), low blood pressure, and a high congestion score. Conclusion Risk models using readily available clinical information are able to accurately predict short‐ and long‐term CV events and may be useful in optimizing care and enriching patients for clinical trials. Clinical trial registration: http://ClinicalTrials.gov ID number NCT01685840.

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Clinical significance of spleen stiffness in patients with acute decompensated heart failure

et al. 2020

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Aims Congestive splenomegaly is a classic sign of organ congestion in acute decompensated heart failure (ADHF). Shear wave elastography (SWE) allows the measurement of spleen stiffness (SS). We hypothesized that SS could quantify the severity of splenic congestion and predict adverse events in ADHF. Methods and Results This study included two cohorts: a haemodynamic cohort (62 HF patients) and an outcome cohort (115 ADHF patients). SS was measured by two‐dimensional SWE on the same day of right heart catheterization in the haemodynamic cohort. Right atrial pressure (RAP) independently correlated with SS (β = 0.32, P = 0.002). SS was measured in the outcome cohort before discharge. The 115 patients were divided into three groups on the basis of the tertile value of SS. The third tertile SS group had a higher prevalence of severe tricuspid regurgitation, higher N‐terminal B‐type natriuretic peptide (NT pro‐BNP), and larger right ventricular diastolic diameter, than had the first tertile group and the second tertile group. During a median follow‐up period of 105 (77–135) days, adverse events occurred in 25 patients (one death and 24 rehospitalizations for HF). The third tertile SS group had a significantly higher rate of adverse events (P < 0.001). A higher SS was independently associated with adverse events after adjusting for conventional validated risk score, liver function test, liver stiffness, and estimated RAP. Conclusions The degree of SS at discharge can be used as a marker of residual splenic congestion, which is predictive of adverse events in patients with ADHF.

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Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides

Cited by 82 publications

References 34 publications

Improving risk prediction in heart failure using machine learning

Improving risk prediction in heart failure using machine learning

Clinical factors related to morbidity and mortality in high‐risk heart failure patients: the GUIDE‐IT predictive model and risk score

Clinical significance of spleen stiffness in patients with acute decompensated heart failure

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