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

Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case-control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality along with rehospitalization. To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the hold-out cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF.

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system

BACKGROUND

Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

OBJECTIVE

To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

DESIGN

Retrospective, cohort study.

PARTICIPANTS

Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.

MAIN MEASURES

One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.

KEY RESULTS

Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum.

CONCLUSIONS

These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Clinical efficacy and safety of adjunctive treatment of chronic ischemic heart failure with Qishen Yiqi dropping pills: a systematic review and meta-analysis

Objectives

Our study was to evaluate the effect of Qishen Yiqi Dropping Pills(QSYQ) on the prognosis of chronic ischemic heart failure(CIHF) and its safety.

Methods

Databases including CNKI, Wanfang, VIP, CBM, PubMed, Web of Science, The Cochrane Library and EMbase were searched from their inception to April 2023 to screen relevant randomized controlled trials (RCTs). Primary indicators included readmission rates, rates of major adverse cardiovascular events (MACE), and all-cause mortality rates. The quality of the literature was assessed according to the Cochrane Reviewers' Handbook 5.0 and the Modified Jadad Scale (with a score of 4-7 rated as high quality). Meta-analysis was performed using the meta-package created by R software version 4.2.3, continuous data were compared using SMDs, and dichotomous and ordered data were compared using ORs; and the I2 test was used to assess the heterogeneity.

Results

Fifty-nine studies out of 1,745 publications were finally included, totalling 6,248 patients. Most studies were poorly designed and had some publication bias, with only 26 high-quality papers (Jadad score ≥4). Meta-analysis showed that the combined application of QSYQ was able to reduce the readmission rate [OR = 0.42, 95% CI (0.33, 0.53), P < 0.001], all-cause mortality rate [OR = 0.43, 95% CI (0.27, 0.68), P < 0.001], and the incidence of MACE [OR = 0.42, 95% CI (0.31, 0.56), P < 0.001]. Also, the treatment method can improve clinical effectiveness [OR = 2.25, 95% CI (1.97, 2.58), P < 0.001], increase 6-min walking distance (6MWD) [SMD = 1.87, 95% CI (1.33, 2.41), P < 0.0001] and left ventricular ejection fraction (LVEF) [SMD = 1.08, 95% CI (0.83, 1.33), P < 0.0001], and decrease the Minnesota Living with Heart Failure Questionnaire (MLHFQ) scores [SMD = -2.03, 95% CI (-3.0, -1.07), P < 0.0001], BNP levels [SMD = -2.07, 95% CI (-2.81, -1.33), P < 0.0001] and NT-ProBNP levels [SMD = -2.77, 95% CI (-4.90, -0.63), P < 0.05]. A total of 21 studies (n = 2,742) evaluated their adverse effects, of which 13 studies reported no adverse effects and 8 studies reported minor adverse effects.

Conclusion

Our results suggest that the combined application of QSYQ can further improve patients' cardiac function and exercise tolerance, improve their quality of life, and ultimately improve patients' prognosis with a favorable safety profile. Nonetheless, limited by the quality and high heterogeneity of the literature, we must be conservative and cautious about the present results.

Systematic Review Registration

PROSPERO (CRD42023449251).

Low Energy Intake Diagnosed Using the Harris-Benedict Equation Is Associated with Poor Prognosis in Elderly Heart Failure Patients

INTRODUCTION

Insufficient nutrient intake is a strong independent predictor of mortality in elderly patients with heart failure. However, it is unclear to what extent energy intake affects their prognosis. This study investigated the association between patient outcomes and actual measured energy intake in elderly patients (≥65 years) with heart failure.

METHODS

This study enrolled 139 elderly patients who were hospitalized with worsening heart failure at Shingu Municipal Medical Center, Shingu, Japan, between May 2017 and April 2018. Energy intake was evaluated for three days (from three days prior to the day of discharge until the day of discharge). Based on basal energy expenditure calculated using the Harris-Benedict equation, the patients were classified into a low-energy group (n = 38) and a high-energy group (n = 101). We assessed the prognosis in terms of both all-cause mortality and readmission due to worsening heart failure as a primary outcome.

RESULTS

Compared to the patients in the high-energy group, the patients in the low-energy group were predominantly female, less frequently had smoking habits and ischemic heart diseases, and had a higher left ventricular ejection fraction. The low-energy group had higher mortality than the high-energy group (p = 0.028), although the two groups showed equivalent event rates of the primary outcome (p = 0.569).

CONCLUSION

Calculations based on the Harris-Benedict equation revealed no significant difference in the primary outcome between the two groups, with a secondary outcome that showed worse mortality in the low-energy group. Given this result, energy requirement-based assessments using the Harris-Benedict equation might help in the management of elderly heart failure patients in terms of improved life outcomes.

Machine Learning for Mortality Prediction in Patients With Heart Failure With Mildly Reduced Ejection Fraction

Background Machine-learning-based prediction models (MLBPMs) have shown satisfactory performance in predicting clinical outcomes in patients with heart failure with reduced and preserved ejection fraction. However, their usefulness has yet to be fully elucidated in patients with heart failure with mildly reduced ejection fraction. This pilot study aims to evaluate the prediction performance of MLBPMs in a heart failure with mildly reduced ejection fraction cohort with long-term follow-up data. Methods and Results A total of 424 patients with heart failure with mildly reduced ejection fraction were enrolled in our study. The primary outcome was all-cause mortality. Two feature selection strategies were introduced for MLBPM development. The "All-in" (67 features) strategy was based on feature correlation, multicollinearity, and clinical significance. The other strategy was the CoxBoost algorithm with 10-fold cross-validation (17 features), which was based on the selection result of the "All-in" strategy. Six MLBPMs with 5-fold cross-validation based on the "All-in" and the CoxBoost algorithm with 10-fold cross-validation strategy were developed by the eXtreme Gradient Boosting, random forest, and support vector machine algorithms. The logistic regression model with 14 benchmark predictors was used as a reference model. During a median follow-up of 1008 (750, 1937) days, 121 patients met the primary outcome. Overall, MLBPMs outperformed the logistic model. The "All-in" eXtreme Gradient Boosting model had the best performance, with an accuracy of 85.4% and a precision of 70.3%. The area under the receiver-operating characteristic curve was 0.916 (95% CI, 0.887-0.945). The Brier score was 0.12. Conclusions The MLBPMs could significantly improve outcome prediction in patients with heart failure with mildly reduced ejection fraction, which would further optimize the management of these patients.

Validation of the Meta-Analysis Global Group in Chronic Heart Failure risk score for the prediction of 1-year mortality in a Chinese cohort

BACKGROUND

The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was developed in 2013 to predict survival in heart failure (HF) patients. However, it has yet to be validated in a Chinese population. Our study aimed to investigate the ability of the score to predict 1-year mortality in a Chinese population.

METHODS

Consecutive patients with HF were retrospectively selected from the inpatient electronic medical records of the cardiology department in a regional hospital in China. A total integer score was calculated for each enrolled patient based on the value of each risk factor in the MAGGIC scoring system. Each enrolled patient was followed for at least 1 year. The observational endpoint of this study was all-cause mortality. The predictive ability of the MAGGIC score was assessed by comparing observed and predicted mortality within 1 year.

RESULTS

Between January 2018 and December 2020, a total of 635 patients were included in the study: 57 (9.0%) of whom died within 1 year after discharge. The average age of all patients was 74.6 ± 11.2 years, 264 of them (41.6%) were male, and the average left ventricular ejection fraction was 50.7% ± 13.2%. The area under the receiver operating characteristic curve was 0.840 (95% confidence interval: 0.779, 0.901), which indicated a fair discriminatory ability of the score. The Hosmer-Lemeshow test result ( χ2  = 12.902, degree of freedom = 8, P   =  0.115) indicated that the MAGGIC score had good calibration. The decision curve analysis showed that the MAGGIC score yielded a good clinical net benefit and net reduction in interventions.

CONCLUSIONS

This validation of the MAGGIC score showed that it has a good ability to predict 1-year mortality in Chinese patients with HF after discharge. Due to regional and inter-hospital differences, external validation studies need to be further confirmed in other centers.

The MAGGIC risk score in the prediction of death or hospitalization in patients with heart failure: Comparison with natriuretic peptides

BACKGROUND

The MAGGIC risk score has been validated to predict mortality in patients with heart failure (HF).

OBJECTIVES

To assess the score ability to predict hospitalization and death and to compare with natriuretic peptides.

METHODS

Ninety-three consecutive patients (mean age 62±10 years) with chronic HF and left ventricular ejection fraction (EF) <50% were studied. The MAGGIC score was applied at baseline and the patients were followed for 219±86 days. MAGGIC score was compared with NT-proBNP in the prediction of events. The primary end point was the time to the first event, which was defined as cardiovascular death or hospitalization for HF.

RESULTS

There were 23 (24.7%) events (3 deaths and 20 hospitalizations). The median score in patients with and without events was, respectively, 20 [interquartile range 14.2-22] vs. 15.5 [11/21], p=0.16. A ROC curve was performed and a cutoff point of 12 points showed a sensitivity of 87% and specificity of 37% with an area under the curve of 0.59 (95% CI 0.48-0.69) which was lower than that of NT-proBNP (AUC 0.67; 95% CI 0.56-0.76). The mean event-free survival time for patients above and below this cutpoint was 248.8±13 vs. 290±13.7 days (log rank test with p=0.044). Using the COX proportional hazard model, age (p=0.004), NT-proBNP >1000 pg/mL (p=0.014) and the MAGGIC score (p=0.025) were independently associated with the primary outcome.

CONCLUSION

The MAGGIC risk score was an independent predictor of events, including heart failure hospitalization. The addition of biomarkers improved the accuracy of the score.

Incremental prognostic value of lung ultrasound on contemporary heart failure risk scores

Introduction: Over the last decades, several scores have been developed to aid clinicians in assessing prognosis in patients with heart failure (HF) based on clinical data, medications and, ultimately, biomarkers. Lung ultrasound (LUS) has emerged as a promising prognostic tool for patients when assessed at discharge after a HF hospitalization. We hypothesized that contemporary HF risk scores can be improved upon by the inclusion of the number of B-lines detected by LUS at discharge to predict death, urgent visit, or HF readmission at 6- month follow-up.

Methods: We evaluated the discrimination improvement of adding the number of B-lines to 4 contemporary HF risk scores (Get with the Guidelines -GWTG-, MAGGIC, Redin-SCORE, and BCN Bio-HF) by comparing the change in the area under the receiver operating curve (AUC), the net reclassification index (NRI), and the integrated discrimination improvement (IDI). The population of the study was constituted by the 123 patients enrolled in the LUS-HF trial, adjusting the analyses by the intervention.

Results: The AUC of the GWTG score increased from 0.682 to 0.789 (p = 0.02), resulting in a NRI of 0.608 and an IDI of 0.136 (p < 0.05). Similar results were observed when adding the number of B-lines to the MAGGIC score, with an AUC that increased from 0.705 to 0.787 (p < 0.05). This increase translated into a NRI of 0.608 and an IDI of 0.038 (p < 0.05). Regarding Redin-SCORE at 1-month and 1-year, the AUC increased from 0.714 to 0.773 and from 0.681 to 0.757, although it did not reach statistical significance (p = 0.08 and p = 0.06 respectively). Both IDI and NRI were significantly improved (0.093 and 0.509 in the 1-month score, p < 0.05; 0.056 and 0.111 in the 1-year score, p < 0.05). Lastly, the AUC for the BCN Bio-HF score increased from 0.733 to 0.772, which was statistically non-significant, with a NRI value of 0.363 (p = 0.06) and an IDI of 0.092 (p < 0.05).

Conclusion: Adding the results of LUS evaluated at discharge improved the predictive value of most of the contemporary HF risk scores. As it is a simple, fast, and non-invasive test it may be recommended to assess prognosis at discharge in HF patients.

The Utility of Pentraxin and Modified Prognostic Scales in Predicting Outcomes of Patients with End-Stage Heart Failure

Risk stratification is an important element of management in patients with heart failure (HF). We aimed to determine factors associated with predicting outcomes in end-stage HF patients listed for heart transplantation (HT), with particular emphasis placed on pentraxin-3 (PXT-3). In addition, we investigated whether the combination of PTX-3 with the Heart Failure Survival Score (HFSS), the Seattle Heart Failure Model (SHFM), or the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) improved the prognostic strength of these scales in the study population. We conducted a prospective analysis of 343 outpatients with end-stage HF who accepted the HT waiting list between 2015 and 2018. HFSS, SHFM, and MAGGIC scores were calculated for all patients. PTX3 was measured by sandwich enzyme-linked immunosorbent assay with a commercially available kit. The endpoints were death, left ventricular assist device implantation, and HT during the one-year follow-up. The median age was 56 (50−60) years, and 86.6% were male. During the follow-up period, 173 patients reached the endpoint. Independent risk factors associated with outcomes were ischemic etiology of HF [HR 1.731 (1.227−2.441), p = 0.0018], mean arterial pressure (MAP) [1.026 (1.010−1.042), p = 0.0011], body mass index (BMI) [1.055 (1.014−1.098), p = 0.0083], sodium [1.056 [(1.007−1.109), p = 0.0244] PTX-3 [1.187 (1.126−1.251, p < 0.0001) and N-terminal pro-brain natriuretic peptide (NT-proBNP) [HR 1.004 (1.000−1.008), p = 0.0259]. The HFSS-PTX-3, SHFM-PTX-3 and MAGGIC-PTX-3 scores had significantly higher predictive power [AUC = 0.951, AUC = 0.973; AUC = 0.956, respectively] than original scores [AUC for HFSS = 0.8481, AUC for SHFM = 0.7976, AUC for MAGGIC = 0.7491]. Higher PTX-3 and NT-proBNP concentrations, lower sodium concentrations, lower MAP and BMI levels, and ischemic etiology of HF are associated with worse outcomes in patients with end-stage HF. The modified SHFM-PTX-3, HFSS-PTX-3, and MAGGIC-PTX-3 scores provide effective methods of assessing the outcomes in the analyzed group.

miR-182-5p combined with brain-derived neurotrophic factor assists the diagnosis of chronic heart failure and predicts a poor prognosis

Objective

Chronic heart failure (CHF) is a general progressive disorder with high morbidity and poor prognosis. This study analyzed the serum expression and clinical value of miR-182-5p and brain-derived neurotrophic factor (BDNF) in CHF patients.

Methods

A total of 82 CHF patients were selected as the study subjects (15 cases in NYHA stage I, 29 cases in stage II, 27 cases in stage III, and 11 cases in stage IV), with another 78 healthy people as the controls. The expression of serum miR-182-5p was detected by RT-qPCR. BDNF expression was measured by ELISA. Furthermore, the Pearson coefficient was used to analyze the correlation of miR-182-5p/BDNF with BNP and LVEF. ROC curve was employed to assess the potential of miR-182-5p or/and BDNF for the diagnosis of CHF. Kaplan–Meier survival curve was implemented to evaluate the prognostic value of miR-182-5p and BDNF.

Results

Serum miR-182-5p level was elevated and BDNF expression was lowered in CHF patients. Serum miR-182-5p in CHF patients was positively-related with BNP and inversely-correlated with LVEF, while serum BDNF was negatively-linked with BNP and positively-correlated with LVEF. ROC curve indicated the diagnostic value of serum miR-182-5p and BDNF for CHF and the diagnostic accuracy of miR-182-5p combined with BDNF was improved. Kaplan–Meier analysis unveiled that miR-182-5p low expression and BDNF high expression could predict the overall survival in CHF patients.

Conclusion

miR-182-5p expression is increased and BDNF level is decreased in CHF patients. miR-182-5p combined with BDNF can assist the diagnosis of CHF and predict a poor prognosis.

The EPICTER score: a bedside and easy tool to predict mortality at 6 months in acute heart failure

Aims

Estimating the prognosis in heart failure (HF) is important to decide when to refer to palliative care (PC). Our objective was to develop a tool to identify the probability of death within 6 months in patients admitted with acute HF.

Methods and results

A total of 2848 patients admitted with HF in 74 Spanish hospitals were prospectively included and followed for 6 months. Each factor independently associated with death in the derivation cohort (60% of the sample) was assigned a prognostic weight, and a risk score was calculated. The accuracy of the score was verified in the validation cohort. The characteristics of the population were as follows: advanced age (mean 78 years), equal representation of men and women, significant comorbidity, and predominance of HF with preserved ejection fraction. During follow‐up, 753 patients (26%) died. Seven independent predictors of mortality were identified: age, chronic obstructive pulmonary disease, cognitive impairment, New York Heart Association class III–IV, chronic kidney disease, estimated survival of the patient less than 6 months, and acceptance of a palliative approach by the family or the patient. The area under the ROC curve for 6 month death was 0.74 for the derivation and 0.68 for the validation cohort. The model showed good calibration (Hosmer and Lemeshow test, P value 0.11). The 6 month death rates in the score groups ranged from 6% (low risk) to 54% (very high risk).

Conclusions

The EPICTER score, developed from a prospective and unselected cohort, is a bedside and easy‐to‐use tool that could help to identify high‐risk patients requiring PC.

The Value of Admission Serological Indicators for Predicting 28-Day Mortality in Intensive Care Patients With Acute Heart Failure: Construction and Validation of a Nomogram

Background: Acute heart failure (AHF) is a severe clinical syndrome characterized as rapid onset or worsening of symptoms of chronic heart failure (CHF). Risk stratification for patients with AHF in the intensive care unit (ICU) may help clinicians to predict the 28-day mortality risk in this subpopulation and further raise the quality of care.

Methods: We retrospectively reviewed and analyzed the demographic characteristics and serological indicators of patients with AHF in the Medical Information Mart for Intensive Care III (MIMIC III) (version 1.4) between June 2001 and October 2012 and our medical center between January 2019 and April 2021. The chi-squared test and the Fisher's exact test were used for comparison of qualitative variables among the AHF death group and non-death group. The clinical variables were selected by using the least absolute shrinkage and selection operator (LASSO) regression. A clinical nomogram for predicting the 28-day mortality was constructed based on the multivariate Cox proportional hazard regression analysis and further validated by the internal and external cohorts.

Results: Age > 65 years [hazard ratio (HR) = 2.47], the high Sequential Organ Failure Assessment (SOFA) score (≥3 and ≤8, HR = 2.21; ≥9 and ≤20, HR = 3.29), lactic acid (Lac) (>2 mmol/l, HR = 1.40), bicarbonate (HCO3-) (>28 mmol/l, HR = 1.59), blood urea nitrogen (BUN) (>21 mg/dl, HR = 1.75), albumin (<3.5 g/dl, HR = 2.02), troponin T (TnT) (>0.04 ng/ml, HR = 4.02), and creatine kinase-MB (CK-MB) (>5 ng/ml, HR = 1.64) were the independent risk factors for predicting 28-day mortality of intensive care patients with AHF (p < 0.05). The novel nomogram was developed and validated with a promising C-index of 0.814 (95% CI: 0.754–0.882), 0.820 (95% CI: 0.721–0.897), and 0.828 (95% CI: 0.743–0.917), respectively.

Conclusion: This study provides a new insight in early predicting the risk of 28-day mortality in intensive care patients with AHF. The age, the SOFA score, and serum TnT level are the leading three predictors in evaluating the short-term outcome of intensive care patients with AHF. Based on the nomogram, clinicians could better stratify patients with AHF at high risk and make adequate treatment plans.

A Multivariate Model to Predict Chronic Heart Failure after Acute ST-Segment Elevation Myocardial Infarction: Preliminary Study

A multivariate model for predicting the risk of decompensated chronic heart failure (CHF) within 48 weeks after ST-segment elevation myocardial infarction (STEMI) has been developed and tested. Methods. The study included 173 patients with acute STEMI aged 51.4 (95% confidence interval (CI): 42–61) years. Two-dimensional (2D) speckle-tracking echocardiography (STE) has been performed on the 7th–9th days, and at the 12th, 24th, and 48th weeks after the index event with the analysis of volumetric parameters and values for global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS). A 24-h ECG monitoring (24 h ECG) of the electrocardiogram (ECG) to assess heart rate turbulence (HRT) has been performed on the 7th–9th days of STEMI. The study involved two stages of implementation. At the first stage, a multivariate model to assess the risk of CHF progression within 48 weeks after STEMI has been built on the basis of examination and follow-up data for 113 patients (group M). At the second stage, the performance of the model has been assessed based on a 48-week follow-up of 60 patients (group T). Results. A multivariate regression model for CHF progression in STEMI patients has been created based on the results of the first stage. It included the following parameters: HRT, left ventricular (LV) end-systolic dimension (ESD), and GLS. The contribution of each factor for the relative risk (RR) of decompensated CHF has been found: 3.92 (95% CI: 1.66–9.25) (p = 0.0018) for HRT; 1.04 (95% CI: 1.015–1.07) (p = 0.0027) for ESD; 0.9 (95% CI: 0.815–0.98) (p = 0.028) for GLS. The diagnostic efficiency of the proposed model has been evaluated at the second stage. It appeared to have a high specificity of 83.3%, a sensitivity of 95.8%, and a diagnostic accuracy of 93.3%. Conclusion. The developed model for predicting CHF progression within 48 weeks after STEMI has a high diagnostic efficiency and can be used in early stages of myocardial infarction to stratify the risk of patients.

Performance of Prognostic Risk Scores in Elderly Chinese Patients with Heart Failure

Purpose

Elderly heart failure (HF) patients have different clinical characteristics and poorer prognosis compared with younger patients. Prognostic risk scores for HF have not been validated well in elderly patients. We aimed to validate the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score in an elderly Chinese HF cohort.

Patients and Methods

This retrospective study enrolled 675 elderly HF patients (age≥70 years) discharged from our hospital between 2012 and 2017. The performance of the two risk scores was evaluated in terms of discrimination, using receiver-operating characteristic analysis, and calibration using a calibration plot and Hosmer–Lemeshow (H-L) test. Absolute risk reclassification was used to compare the two scores.

Results

During the mean follow-up time of 32.6 months, 193 patients (28.6%) died, and 1-year mortality was 10.5%. The predicted median 1-year mortality was 8% for the SHFM and 18% for the MAGGIC score. A Kaplan–Meier survival curve demonstrated that event rates of all-cause mortality significantly increased with increasing SHFM and MAGGIC scores. The discriminatory capacity of the SHFM was greater than that of the MAGGIC score (c-statistics were 0.72 and 0.67, respectively; P = 0.05). The calibration plot for the SHFM was better than that for MAGGIC score for 1-year mortality (SHFM: H-L χ² =8.2, P = 0.41; MAGGIC: H-L χ² =18.8, P =0.02). Compared with the MAGGIC score, the net reclassification index (NRI) of the SHFM was 2.96% (Z=5.88, P< 0.0001).

Conclusion

The SHFM performs better than MAGGIC score, having good discrimination, calibration and risk classification for the prediction of 1-year mortality in elderly Chinese HF patients.

Head-to-head comparison of contemporary heart failure risk scores

AIMS

Several heart failure (HF) web-based risk scores are currently used in clinical practice. Currently, we lack head-to-head comparison of the accuracy of risk scores. This study aimed to assess correlation and mortality prediction performance of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, which includes clinical variables + medications; Seattle Heart Failure Model (SHFM), which includes clinical variables + treatments + analytes; PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, which also include biomarkers, like N-terminal pro B-type natriuretic peptide (NT-proBNP).

METHODS AND RESULTS

A total of 1166 consecutive patients with HF from different aetiologies that had NT-proBNP measurement at first visit were included. Discrimination for all-cause mortality was compared by Harrell's C-statistic from 1 to 5 years, when possible. Calibration was assessed by calibration plots and Hosmer-Lemeshow test and global performance by Nagelkerke's R² . Correlation between scores was assessed by Spearman rank test. Correlation between the scores was relatively poor (rho value from 0.66 to 0.79). Discrimination analyses showed better results for 1-year mortality than for longer follow-up (SHFM 0.817, MAGGIC-HF 0.801, PREDICT-HF 0.799, BCN-Bio-HF 0.830). MAGGIC-HF showed the best calibration, BCN-Bio-HF overestimated risk while SHFM and PREDICT-HF underestimated it. BCN-Bio-HF provided the best discrimination and overall performance at every time-point.

CONCLUSIONS

None of the contemporary risk scores examined showed a clear superiority over the rest. BCN-Bio-HF calculator provided the best discrimination and overall performance with overestimation of risk. MAGGIC-HF showed the best calibration, and SHFM and PREDICT-HF tended to underestimate risk. Regular updating and recalibration of online web calculators seems necessary to improve their accuracy as HF management evolves at unprecedented pace.

Machine learning-based model for predicting 1 year mortality of hospitalized patients with heart failure

Aims

Individual risk stratification is a fundamental strategy in managing patients with heart failure (HF). Artificial intelligence, particularly machine learning (ML), can develop superior models for predicting the prognosis of HF patients, and administrative claim data (ACD) are suitable for ML analysis because ACD is a structured database. The objective of this study was to analyse ACD using an ML algorithm, predict the 1 year mortality of patients with HF, and finally develop an easy‐to‐use prediction model with high accuracy using the top predictors identified by the ML algorithm.

Methods and results

Machine learning‐based prognostic prediction models were developed from the ACD on 10 175 HF patients from the Japanese Registry of Acute Decompensated Heart Failure with 17% mortality during 1 year follow‐up. The top predictors for prognosis in HF were identified by the permutation feature importance technique, and an easy‐to‐use prediction model was developed based on these predictors. The c‐statistics and Brier scores of the developed ML‐based models were compared with those of conventional risk models: Seattle Heart Failure Model (SHFM) and Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC). A voting classifier algorithm (ACD‐VC) achieved the highest c‐statistics among the six ML algorithms. The permutation feature importance technique enabled identification of the top predictors such as Barthel index, age, body mass index, duration of hospitalization, last hospitalization, renal disease, and non‐loop diuretics use (feature importance values were 0.054, 0.025, 0.010, 0.005, 0.005, 0.004, and 0.004, respectively). Upon combination of some of the predictors that can be assessed from a brief interview, the Simple Model by ARTificial intelligence for HF risk stratification (SMART‐HF) was established as an easy‐to‐use prediction model. Compared with the conventional models, SMART‐HF achieved a higher c‐statistic {ACD‐VC: 0.777 [95% confidence interval (CI) 0.751–0.803], SMART‐HF: 0.765 [95% CI 0.739–0.791], SHFM: 0.713 [95% CI 0.684–0.742], MAGGIC: 0.726 [95% CI 0.698–0.753]} and better Brier scores (ACD‐VC: 0.121, SMART‐HF: 0.124, SHFM: 0.139, MAGGIC: 0.130).

Conclusions

The ML model based on ACD predicted the 1 year mortality of HF patients with high accuracy, and SMART‐HF along with the ML model achieved superior performance to that of the conventional risk models. The SMART‐HF model has the clear merit of easy operability even by non‐healthcare providers with a user‐friendly online interface (https://hfriskcalculator.herokuapp.com/). Risk models developed using SMART‐HF may provide a novel modality for risk stratification of patients with HF.

Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

OBJECTIVE

Machine learning (ML) algorithm can improve risk prediction because ML can select features and segment continuous variables effectively unbiased. We generated a risk score model for mortality with ML algorithms in East-Asian patients with heart failure (HF).

METHODS

From the Korean Acute Heart Failure (KorAHF) registry, we used the data of 3683 patients with 27 continuous and 44 categorical variables. Grouped Lasso algorithm was used for the feature selection, and a novel continuous variable segmentation algorithm which is based on change-point analysis was developed for effectively segmenting the ranges of the continuous variables. Then, a risk score was assigned to each feature reflecting nonlinear relationship between features and survival times, and an integer score of maximum 100 was calculated for each patient.

RESULTS

During 3-year follow-up time, 32.8% patients died. Using grouped Lasso, we identified 15 highly significant independent clinical features. The calculated risk score of each patient ranged between 1 and 71 points with a median of 36 (interquartile range: 27-45). The 3-year survival differed according to the quintiles of the risk score, being 80% and 17% in the 1st and 5th quintile, respectively. In addition, ML risk score had higher AUCs than MAGGIC-HF score to predict 1-year mortality (0.751 vs. 0.711, P < 0.001).

CONCLUSIONS

In East-Asian patients with HF, a novel risk score model based on ML and the new continuous variable segmentation algorithm performs better for mortality prediction than conventional prediction models.

CLINICAL TRIAL REGISTRATION

Unique identifier: INCT01389843 https://clinicaltrials.gov/ct2/show/NCT01389843 .

Prognostic value of natriuretic peptides in heart failure: systematic review and meta-analysis

Risk models, informing optimal long-term medical management, seldom use natriuretic peptides (NP) in ascertaining the absolute risk of outcomes for HF patients. Individual studies evaluating the prognostic value of NPs in HF patients have reported varying effects, arriving at best estimates requires a systematic review. We systematically summarized the best evidence regarding the prognostic value of brain natriuretic peptide (BNP) and NT-proBNP in predicting mortality and hospitalizations in ambulatory heart failure (HF) patients. We searched bibliographic databases from 2005 to 2018 and included studies evaluating the association of BNP or NT-proBNP with mortality or hospitalization using multivariable Cox proportional hazard models. We pooled hazard ratios using random-effect models, explored heterogeneity using pre-specified subgroup analyses, and evaluated the certainty of evidence using the Grading of Recommendations and Development Evaluation framework. We identified 67 eligible studies reporting on 76,178 ambulatory HF patients with a median BNP of 407 pg/mL (261-574 pg/mL). Moderate to high-quality evidence showed that a 100-pg/mL increase in BNP was associated with a 14% increased hazard of mortality (HR 1.14, 95% CI 1.06-1.22); a 1-log-unit increase was associated with a 51% increased hazard of mortality (HR 1.51, 95% CI 1.41-1.61) and 48% increased hazard of mortality or hospitalization (HR 1.48, 95% CI 1.29-1.69). With moderate to high certainty, we observed a 14% independent relative increase in mortality, translating to a clinically meaningful increase in absolute risk even for low-risk patients. The observed associations may help in developing more accurate risk models that incorporate NPs and accurately prognosticate HF patients.

A 5-Year Survival Prediction Model for Chronic Heart Failure Patients Induced by Coronary Heart Disease with Traditional Chinese Medicine Intervention

Objective

This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model.

Methods

Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively.

Results

A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability.

Conclusion

This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF.

MAGGIC Risk Model Predicts Adverse Events and Left Ventricular Remodeling in Non-Ischemic Dilated Cardiomyopathy

Purpose

We aimed to study the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk model’s prognostic value and relationship with left ventricular remodeling in dilated cardiomyopathy.

Patients and Methods

Dilated cardiomyopathy patients were prospectively recruited and underwent clinical assessments. MAGGIC risk score was calculated. Patients were followed up for adverse events and echocardiography. Primary endpoints were all-cause mortality and first rehospitalization due to heart failure. Secondary endpoint was left ventricular remodeling defined as a decline in left ventricular ejection fraction >10% or an increase in left ventricular end-diastolic diameter >10%. Survival status was examined using Cox regression analysis. The model’s ability to discriminate adverse events and left ventricular remodeling was calculated using a receiver operating characteristics curve.

Results

In total, 114 patients were included (median follow-up time = 31 months). The risk score was independently related to adverse events (2-year all-cause mortality: hazard ratio [HR] = 1.122; 95% confidence interval [CI], 1.043–1.208; 1-year first rehospitalization due to heart failure: HR = 1.094; 95% CI, 1.032–1.158; 2-year first rehospitalization due to heart failure: HR = 1.088; 95% CI, 1.033–1.147, all P < 0.05). One-year change in left ventricular end-diastolic diameter was correlated with the risk score (r = 0.305, P = 0.002). The model demonstrated modest ability in discriminating adverse events and left ventricular remodeling (all areas under the curve were 0.6–0.7).

Conclusion

The MAGGIC risk score was related to adverse events and left ventricular remodeling in dilated cardiomyopathy.

Physical Activity, Exercise Prescription for Health and Home-Based Rehabilitation

The aim of this overview was to recommend individual training plans using exercise prescriptions for adults and older adults during home-based rehabilitation. Over the last decade, many regular physical activity studies with large prospective cohorts have been conducted. Taken together, more than a million subjects have been included in these exercise studies. The risk of morbidity and mortality has been reduced by 30% to 40% as a result of exercise. These risk reductions hold true for many diseases, as well as for prevention and rehabilitation. Physical activity has also been in the treatment of many diseases, such as cardiopulmonary, metabolic or neurologic/psychiatric diseases, all with positive results. Based on these results, the prescription of exercise was developed and is now known as the exercise prescription for health in many European countries. Details have been published by the European Federation of Sports Medicine Associations (EFSMA). The exercise prescription is strongly recommended for inpatients, discharged patients and outpatients who have recovered from severe diseases. Rehabilitation improves general health, physical fitness, quality of life and may increase longevity of life.

MAGGIC, STS, and EuroSCORE II Risk Score Comparison After Aortic and Mitral Valve Surgery

OBJECTIVES

To compare the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score with the established Society of Thoracic Surgeons (STS) and EuroSCORE II risk prediction models regarding mortality discrimination after aortic and mitral valve surgery.

DESIGN

Retrospective cohort study.

SETTING

Single tertiary academic medical center.

PARTICIPANTS

A total of 259 patients who underwent open aortic valve replacement or open mitral valve repair/replacement from 2009-2014.

INTERVENTIONS

Retrospective chart review.

MEASUREMENTS AND MAIN RESULTS

MAGGIC, STS, and EuroSCORE II risk scores for each patient were studied using binary logistic regression and receiver operating characteristic analysis for the primary endpoint of one-year mortality and secondary endpoint of 30-day mortality. One-year mortality C-statistics were similar across risk scores (STS 0.709, 95% confidence interval [CI] 0.578-0.841; MAGGIC 0.673, 95% CI 0.547-0.799; EuroSCORE II 0.642, 95% CI 0.521-0.762; p = 0.56 between STS and MAGGIC; p = 0.20 between STS and EuroSCORE II; and p = 0.69 between MAGGIC and EuroSCORE II). Thirty-day mortality C-statistics also were similar between STS (0.797, 95% CI 0.655-0.939; p < 0.0001 v null hypothesis), MAGGIC (0.721, 95% CI 0.581-0.860; p = 0.33 v STS), and EuroSCORE II (0.688, 95% CI 0.557-0.818; p = 0.06 v STS; p = 0.68 v MAGGIC).

CONCLUSIONS

The MAGGIC risk score performs similarly to STS and EuroSCORE II risk models in mortality discrimination after aortic and mitral valve surgery, albeit in a small sample size. This finding has important implications in establishing MAGGIC as a viable prognostic model in this population subset, with fewer variables and ease of use representing key advantages over STS and EuroSCORE II.

Incremental Predictive Value of Plasma Renin Activity as a Prognostic Biomarker in Patients with Heart Failure

BACKGROUND

The association of N-terminal pro-B type natriuretic peptide (NT-proBNP) and plasma renin activity (PRA) for the prognosis of the patients with acute heart failure (HF) has not been fully investigated. This study aimed to determine the association between NT-proBNP and PRA and to investigate the incremental value of PRA to NT-proBNP for predicting long term prognosis in patients with acute HF.

METHODS

Three hundred and ninety-six patients (mean age, 64.7 ± 15.9 years; 46.5% female) presenting with acute HF were enrolled between December 2004 and July 2013. Patients with newly diagnosed HF as well as patients with acute exacerbated chronic HF were included. The prognosis was assessed with the composite event of all-cause mortality and readmission for HF during a 2-year follow-up period.

RESULTS

The etiology of HF was ischemic in 116 (29.3%) patients. In a Cox proportional hazards model, log-transformed PRA (hazard ratio [HR], 1.205; P = 0.007) was an independent predictor of the composite outcome of all-cause mortality and readmission for HF in addition to age (HR, 1.032; P = 0.001), white blood cell (WBC) count (HR, 1.103; P < 0.001), and left ventricular ejection fraction (LVEF) (HR, 0.978; P = 0.013). Adding PRA to age, sex, LVEF, and NT-proBNP significantly improved the prediction for the composite outcome of all-cause mortality and readmission for HF, as shown by the net reclassification improvement (0.47; P < 0.001) and integrated discrimination improvement (0.10; P < 0.001).

CONCLUSION

PRA could provide incremental predictive value to NT-proBNP for predicting long term prognosis in patients with acute HF.

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

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.

Comparison of predicted and observed mortality in patients with heart failure treated at a specialized unit

INTRODUCTION AND OBJECTIVES

To analyze survival in heart failure (HF) patients treated at a specialized unit.

METHODS

Prospective cohort-based study of HF patients treated at a specialized unit from 2011 to 2017. Observed 1- and 3-year mortality rates were compared with those predicted by the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score.

RESULTS

We studied 1280 patients, whose median MAGGIC risk score was 19 [interquartile range, 13-24]. Prescription rates of beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, mineralocorticoid receptor antagonists, and sacubitril-valsartan were 93%, 67%, 22%, 73%, and 16%, respectively. The MAGGIC risk score showed good discrimination for mortality at 1 year (c-statistic=0.71) and 3 years (c-statistic=0.76). Observed mortality was significantly lower than predicted mortality, both at 1 year (6.2% vs 10.9%; observed/predicted ratio=0.57; P<.001) and at 3 years (16.7% vs 27.7%; observed/predicted ratio=0.60; P<.001). This discrepancy was found in several subgroups, except in patients aged> 70 years (29.9% vs 34.7%; observed/predicted ratio=0.86; P=.126) and in patients with ejection fraction> 40% (19.6% vs 20.7%; observed/predicted ratio=0.95; P=.640).

CONCLUSIONS

Mortality in HF patients treated at a specialized clinic was significantly lower than that predicted by the MAGGIC risk score.

Catheter ablation of atrial fibrillation in heart failure: clinical, prognostic, and echocardiographic outcome

PURPOSE

Catheter ablation (CA) for atrial fibrillation (AF) in heart failure (HF) patients is associated with a lower rate of cardiac events compared with medical therapy. This study deals with the clinical, echocardiographic, and prognostic outcomes in these patients. Prognostic scores, as MAGGIC (Meta-analysis Global Group in Chronic Heart Failure) score, may help to predict the outcomes.

METHODS

From a single center, 47 patients with AF, HF, and left ventricular ejection fraction (LVEF) < 50% underwent CA. The primary endpoints were NYHA functional class, LVEF, and MAGGIC score.

RESULTS

The median age of patients was 59 years; 49% had paroxysmal AF. At 12 months, a significant improvement of NYHA class (median before II [interquartile range (IQR) II-III] vs median after I [IQR I-II]) and of LVEF (median before 44% [IQR 37-47] vs median after 55% [IQR49-57]) was observed (p value < 0.001). The MAGGIC 1-year and 3-year probability of death was estimated before (mean score 13 [IQR 11-17]) and at 12-month (mean score 11 [IQR 8-13]), showing a significant decrease in the probability of death (p value <0.001). At 12-month, a lower LVEF was associated with more HF hospitalizations (p value 0.035). Coronary artery disease (CAD) (HR 5, p value 0.035) and MAGGIC score (HR 1.2, p value 0.030) were predictors of HF hospitalization.

CONCLUSIONS

CA for AF in HF patients was associated with a significant improvement of NYHA functional class and LVEF and a higher life expectation. CAD history, LVEF < 40%, and MAGGIC score before ablation were predictors of HF hospitalization.

Paradigm Shifts of Heart Failure Therapy: Do We Need Another Paradigm?

Heart failure (HF) is a progressive condition with intermittent acute decompensation leading to poor prognosis despite established guideline-directed therapy. A paradigm of HF therapy has been shifted over last four decades. Until the early 1970s, HF was empirically managed, then was managed with the hemodynamic concept until the early 1980s. According to the results of large randomized clinical trials, HF therapy has been shifted to the neurohormonal paradigm since the late 1980s until recently. Korean Acute Heart Failure (KorAHF) registry is a multi-center registry that recruited a total of 5625 admitted patients with acute HF from 2011 to 2014 and followed until 2019. Through KorAHF registry, we could obtain invaluable information or messages in various fields such as epidemiology, clinical characteristics, and treatment of acute HF in Korea and also had opportunities to fill the gap between guideline-directed care and real-world practice. Considering significant unmet needs in HF therapy even at this moment, we do need another paradigm shift for HF therapy, such as molecular and regenerative paradigm using gene, stem cells, mechanical support as well as novel pharmacological agents.

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

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.

Prognostic value of NT-proBNP added to clinical parameters to predict two-year prognosis of chronic heart failure patients with mid-range and reduced ejection fraction - A report from FAR NHL prospective registry

Background

According to guidelines, the prognosis of patients with chronic heart failure can be predicted by determining the levels of natriuretic peptides, the NYHA classification and comorbidities. The aim our work was to develop a prognostic score in chronic heart failure patients that would take account of patients’ comorbidities, NYHA and NT-proBNP levels.

Methods and results

A total of 1,088 patients with chronic heart failure with reduced ejection fraction (HFrEF) (LVEF<40%) and mid-range EF (HFmrEF) (LVEF 40–49%) were enrolled consecutively. Two-year all-cause mortality, heart transplantation and/or LVAD implantation were defined as the primary endpoint (EP). The occurrence of EP was 14.9% and grew with higher NYHA, namely 4.9% (NYHA I), 11.4% (NYHA II) and 27.8% (NYHA III–IV) (p<0.001). The occurrence of EP was 3%, 10% and 15–37% in patients with NT-proBNP levels ≤125 ng/L, 126–1000 ng/L and >1000 ng/L respectively. Discrimination abilities of NYHA and NT-proBNP were AUC 0.670 (p<0.001) and AUC 0.722 (p<0.001) respectively. The predictive value of the developed clinical model, which took account of older age, advanced heart failure (NYHA III+IV), anaemia, hyponatraemia, hyperuricaemia and being on a higher dose of furosemide (>40 mg daily) (AUC 0.773; p<0.001) was increased by adding the NT-proBNP level (AUC 0.790).

Conclusion

The use of prediction models in patients with chronic heart failure, namely those taking account of natriuretic peptides, should become a standard in routine clinical practice. It might contribute to a better identification of a high-risk group of patients in which more intense treatment needs to be considered, such as heart transplantation or LVAD implantation.