Highlights
- •A risk score to predict mortality in acute heart failure was developed.
- •The newly developed 4 V-RS comprises four readily available variables.
- •The 4 V-RS performs equally to the more complex pre-existing risk models.
Abstract
Background
Risk stratification is important in patients with acute heart failure (AHF), and a
simple risk score that accurately predicts mortality is needed. The aim of this study
is to develop a user-friendly risk-prediction model using a machine-learning method.
Methods
A machine-learning-based risk model using least absolute shrinkage and selection operator
(LASSO) regression was developed by identifying predictors of in-hospital mortality
in the derivation cohort (REALITY-AHF), and its performance was externally validated
in the validation cohort (NARA-HF) and compared with two pre-existing risk models:
the Get With The Guidelines risk score incorporating brain natriuretic peptide and
hypochloremia (GWTG-BNP-Cl-RS) and the acute decompensated heart failure national
registry risk (ADHERE).
Results
In-hospital deaths in the derivation and validation cohorts were 76 (5.1 %) and 61
(4.9 %), respectively. The risk score comprised four variables (systolic blood pressure,
blood urea nitrogen, serum chloride, and C-reactive protein) and was developed according
to the results of the LASSO regression weighting the coefficient for selected variables
using a logistic regression model (4 V-RS). Even though 4 V-RS comprised fewer variables,
in the validation cohort, it showed a higher area under the receiver operating characteristic
curve (AUC) than the ADHERE risk model (AUC, 0.783 vs. 0.740; p = 0.059) and a significant improvement in net reclassification (0.359; 95 % CI, 0.10–0.67;
p = 0.006). 4 V-RS performed similarly to GWTG-BNP-Cl-RS in terms of discrimination
(AUC, 0.783 vs. 0.759; p = 0.426) and net reclassification (0.176; 95 % CI, −0.08–0.43; p = 0.178).
Conclusions
The 4 V-RS model comprising only four readily available data points at the time of
admission performed similarly to the more complex pre-existing risk model in patients
with AHF.
Graphical abstract
Graphical Abstract
Keywords
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References
- Impending epidemic - future projection of heart failure in Japan to the year 2055.Circ J. 2008; 72: 489-491https://doi.org/10.1253/circj.72.489
- Heart disease and stroke statistics-2019 update: a report from the American Heart Association.Circulation. 2019; 139: e56-e528https://doi.org/10.1161/CIR.0000000000000659
- Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals.Lancet. 2018; 391: 572-580https://doi.org/10.1016/S0140-6736(17)32520-5
- Interventional heart failure therapy: a new concept fighting against heart failure.J Cardiol. 2022; 80: 101-109https://doi.org/10.1016/J.JJCC.2021.11.018
- The annual global economic burden of heart failure.Int J Cardiol. 2014; 171: 368-376https://doi.org/10.1016/j.ijcard.2013.12.028
- 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the american College of Cardiology/American Heart Association joint committee on clinical practice guidelines.Circulation. 2022; 145: e895-e1032https://doi.org/10.1161/CIR.0000000000001063
- A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program.Circ Cardiovasc Qual Outcomes. 2010; 3: 25-32https://doi.org/10.1161/CIRCOUTCOMES.109.854877
- ADHERE scientific advisory committee, study group, and investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis.JAMA. 2005; 293: 572-580https://doi.org/10.1001/jama.293.5.572
- Predictors of in-hospital mortality in patients hospitalized for heart failure. Insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF).J Am Coll Cardiol. 2008; 52: 347-356https://doi.org/10.1016/j.jacc.2008.04.028
- Analysis of machine learning techniques for heart failure readmissions.Circ Cardiovasc Qual Outcomes. 2016; 9: 629-640https://doi.org/10.1161/CIRCOUTCOMES.116.003039
- Predicting the future - big data, machine learning, and clinical medicine.N Engl J Med. 2016; 375: 1216-1219https://doi.org/10.1056/NEJMp1606181
- Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Eur Heart J. 2017; 38: 1805-1814https://doi.org/10.1093/eurheartj/ehw302
- How to develop a more accurate risk prediction model when there are few events.BMJ. 2015; 351: 7-11https://doi.org/10.1136/bmj.h3868
- Time-to-furosemide treatment and mortality in patients hospitalized with acute heart failure.J Am Coll Cardiol. 2017; 69: 3042-3051https://doi.org/10.1016/j.jacc.2017.04.042
- Survival after the onset of congestive heart failure in Framingham heart study subjects.Circulation. 1993; 88: 107-115https://doi.org/10.1161/01.cir.88.1.107
- High mean corpuscular volume is a new indicator of prognosis in acute decompensated heart failure.Circ J. 2013; 77: 2766-2771https://doi.org/10.1253/circj.cj-13-0718
- Prognostic value of urinary neutrophil gelatinase-associated lipocalin on the first day of admission for adverse events in patients with acute decompensated heart failure.J Am Heart Assoc. 2017; 6e004582https://doi.org/10.1161/JAHA.116.004582
- Simple risk score to predict survival in acute decompensated heart failure: A2B score.Circ J. 2019; 83: 1019-1024https://doi.org/10.1253/circj.CJ-18-1116
- Effect of variable selection strategy on the performance of prognostic models when using multiple imputation.Circ Cardiovasc Qual Outcomes. 2019; 12: 1-14https://doi.org/10.1161/CIRCOUTCOMES.119.005927
- Usefulness of incorporating hypochloremia into the get with the guidelines-heart failure risk model in patients with acute heart failure.Am J Cardiol. 2022; 162: 122-128https://doi.org/10.1016/j.amjcard.2021.09.020
- Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.Stat Med. 2011; 30: 11-21https://doi.org/10.1002/sim.4085
- The JRS guidelines for the management of community-acquired pneumonia in adults: an update and new recommendations.Intern Med. 2006; 45: 419-428https://doi.org/10.2169/internalmedicine.45.1691
- Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack.Lancet. 2007; 369: 283-292https://doi.org/10.1016/S0140-6736(07)60150-0
- Regression modeling strategies.2nd ed. Springer, New York2015
- Model comparison approach.2nd. ed. Routledge, New York City2015
- An evaluation of penalised survival methods for developing prognostic models with rare events.Stat Med. 2012; 31: 1150-1161https://doi.org/10.1002/sim.4371
- Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.JAMA. 2003; 290: 2581-2587https://doi.org/10.1001/jama.290.19.2581
- Elevated blood urea nitrogen level as a predictor of mortality in patients admitted for decompensated heart failure.Am J Med. 2004; 116: 466-473https://doi.org/10.1016/j.amjmed.2003.11.014
- Admission or changes in renal function during hospitalization for worsening heart failure predict postdischarge survival: results from the outcomes of a prospective trial of intravenous milrinone for exacerbations of chronic heart failure (OPTIME-CHF).Circ Heart Fail. 2008; 1: 25-33https://doi.org/10.1161/CIRCHEARTFAILURE.107.746933
- Mammalian urea transporters.Annu Rev Physiol. 2003; 65: 543-566https://doi.org/10.1146/annurev.physiol.65.092101.142638
- Management of hypertension associated with cardiovascular failure.J Cardiol. 2022; 79: 698-702https://doi.org/10.1016/j.jjcc.2021.11.012
- Systolic blood pressure at admission, clinical characteristics, and outcomes in patients hospitalized with acute heart failure.JAMA. 2006; 296: 2217-2226https://doi.org/10.1001/jama.296.18.2217
- C-reactive protein decrease associates with mortality reduction only in heart failure with preserved ejection fraction.J Cardiovasc Med (Hagerstown). 2019; 20: 23-29https://doi.org/10.2459/JCM.0000000000000726
- Prognostic value of C-reactive protein in adults with congenital heart disease.Heart. 2020; 107: 474-481https://doi.org/10.1136/heartjnl-2020-316813
- The C-reactive protein to prealbumin ratio on admission and its relationship with outcome in patients hospitalized for acute heart failure.J Cardiol. 2021; 78: 308-313https://doi.org/10.1016/j.jjcc.2021.05.009
- Relation of serum cytokine concentrations to cardiovascular risk factors and coronary heart disease.Heart. 1997; 78: 273-277https://doi.org/10.1136/hrt.78.3.273
- Intestinal congestion and right ventricular dysfunction: a link with appetite loss, inflammation, and cachexia in chronic heart failure.Eur Heart J. 2016; 37: 1684-1691https://doi.org/10.1093/eurheartj/ehw008
- Lower serum sodium is associated with increased short-term mortality in hospitalized patients with worsening heart failure: results from the outcomes of a prospective trial of intravenous milrinone for exacerbations of chronic heart failure (OPTIME-CHF) study.Circulation. 2005; 111: 2454-2460https://doi.org/10.1161/01.CIR.0000165065.82609.3D
- Prognostic role of serum chloride levels in acute decompensated heart failure.J Am Coll Cardiol. 2015; 66: 659-666https://doi.org/10.1016/j.jacc.2015.06.007
- Implications of serum chloride homeostasis in acute heart failure (from ROSE-AHF).Am J Cardiol. 2017; 119: 78-83https://doi.org/10.1016/j.amjcard.2016.09.014
- Hypochloremia and diuretic resistance in heart failure: mechanistic insights.Circ Heart Fail. 2016; : 9https://doi.org/10.1161/CIRCHEARTFAILURE.116.003180
- Effect of chloride on renin and blood pressure responses to sodium chloride.Ann Intern Med. 1983; 98: 817-822https://doi.org/10.7326/0003-4819-98-5-817
Article info
Publication history
Published online: March 15, 2023
Accepted:
February 7,
2023
Received in revised form:
January 18,
2023
Received:
December 1,
2022
Publication stage
In Press Corrected ProofFootnotes
☆IRB information: The present study was approved by the Ethical Committee at the Nara Medical University (Reference number: 2456).
☆Clinical trial registration: http://www.umin.ac.jp/ctr/ (unique identifier: UMIN000014105).
Identification
Copyright
© 2023 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.
