Comparative Analysis of Four Scores to Stratify Patients With Heart Failure and Reduced Ejection Fraction

What about chronotropic incompetence in heart failure with mildly reduced ejection fraction? Clinical and prognostic implications from the Metabolic Exercise combined with Cardiac and Kidney Indexes score dataset

AIMS

Chronotropic incompetence (CI) is a strong predictor of outcome in heart failure with reduced ejection fraction, however no data on its clinical and prognostic impacts in heart failure with mildly reduced ejection fraction (HFmrEF) are available. Therefore, the study aims to investigate, in a large multicentre HFmrEF cohort, the prevalence of CI as well as its relationship with exercise capacity and its prognostic role over the cardiopulmonary exercise testing (CPET) parameters.

METHODS AND RESULTS

Within the Metabolic Exercise combined with Cardiac and Kidney Indexes (MECKI) database, we analysed data of 864 HFmrEF out of 1164 stable outpatients who performed a maximal CPET at the cycle ergometer and who had no significant rhythm disorders or comorbidities. The primary study endpoint was cardiovascular (CV) death. All-cause death was also explored. Chronotropic incompetence prevalence differed depending on the method (peak heart rate, pHR% vs. pHR reserve, pHRR%) and the cut-off adopted (pHR% from ≤75% to ≤60% and pHRR% ≤ 65% to ≤50%), ranging from 11% to 62%. A total of 84 (9.7%) CV deaths were collected, with 39 (4.5%) occurring within 5 years. At multivariate analysis, both pHR% [hazard ratio 0.97 (0.95-0.99), P < 0.05] and pHRR% [hazard ratio 0.977 (0.961-0.993), P < 0.01] were associated with the primary endpoint. A pHR% ≤ 75% and a pHRR% ≤ 50% represented the most accurate cut-off values in predicting the outcome.

CONCLUSION

The study suggests an association between blunted exercise-HR response, functional capacity, and CV death risk among patients with HFmrEF. Whether the CI presence might be adopted in daily HFmrEF management needs to be addressed in larger prospective studies.

The cardiopulmonary exercise test in the prognostic evaluation of patients with heart failure and cardiomyopathies: the long history of making a one-size-fits-all suit

Cardiopulmonary exercise test (CPET) has become pivotal in the functional evaluation of patients with chronic heart failure (HF), supplying a holistic evaluation both in terms of exercise impairment degree and possible underlying mechanisms. Conversely, there is growing interest in investigating possible multiparametric approaches in order to improve the overall HF risk stratification. In such a context, in 2013, a group of 13 Italian centres skilled in HF management and CPET analysis built the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score, based on the dynamic assessment of HF patients and on some other instrumental and laboratory parameters. Subsequently, the MECKI score, initially developed on a cohort of 2716 HF patients, has been extensively validated as well as challenged with the other multiparametric scores, achieving optimal results. Meanwhile, the MECKI score research group has grown over time, involving up to now a total of 27 centres with an available database accounting for nearly 8000 HF patients. This exciting joint effort from multiple HF Italian centres allowed to investigate different HF research field in order to deepen the mechanisms underlying HF, to improve the ability to identify patients at the highest risk as well as to analyse particular HF categories. Most recently, some of the participants of the MECKI score group started to join the forces in investigating a possible additive role of CPET assessment in the cardiomyopathy setting too. The present study tells the ten-year history of the MECKI score presenting the most important results achieved as well as those projects in the pipeline, this exciting journey being far to be concluded.

International validation of the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score in heart failure

AIMS

Current European heart failure (HF) guidelines suggest the use of risk score: among them, the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score has demonstrated to be one of the most accurate. However, the risk scores are still poorly implemented in clinical practice, also due to the lack of strong evidence regarding their external validation in different populations. Thus, the current study was designed as an external validation test of the MECKI score in an international multicentre setting.

METHODS AND RESULTS

The study cohort consisted of patients diagnosed with HF with reduced ejection fraction (HFrEF) across international centres (not Italian), retrospectively recruited. Collected data included demographics, HF aetiology, laboratory testing, electrocardiogram (ECG), echocardiographic findings, and cardiopulmonary exercise testing (CPET) results as described in the original MECKI score publication. A total of 1042 patients across 8 international centres (7 European and 1 Asian) were included and followed up from 1998 till 2019. Patients were divided according to the calculated MECKI scores into three subgroups: (i) MECKI score <10%, (ii) 10-20%, and (iii) ≥ 20%. Survival analysis comparison among the three MECKI score subgroups showed a worse prognosis in patients with higher MECKI score value: median event-free survival times were 4396 days for MECKI score <10%, 3457 days for 10-20%, and 1022 days for ≥20% (P < 0.0001). Receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) were like those reported in the original internal validation studies.

CONCLUSION

In patients diagnosed with HFrEF, the power of the MECKI score was confirmed in terms of prognosis and risk stratification, supporting its implementation as advised by the HF guidelines.

Exercise Oscillatory Ventilation Improves Heart Failure Prognostic Scores

BACKGROUND

Several heart failure (HF) prognostic risk scores are available to guide the ideal time for listing candidates for a heart transplant (HTx). The detection of exercise oscillatory ventilation (EOV) during cardiopulmonary exercise testing (CPET) is associated with advanced HF and a worse prognosis, and yet it is not accounted for in these risk scores. Therefore, this study aimed to assess whether EOV further adds prognostic value to HF scores.

METHODS

A single-centre retrospective cohort study was undertaken of consecutive HF patients with reduced ejection fraction (HFrEF) who underwent CPET from 1996 to 2018. The Heart Failure Survival Score (HFSS), Seattle Heart Failure Model (SHFM), Meta-analysis Global Group In Chronic Heart Failure (MAGGIC), and Metabolic Exercise Cardiac Kidney Index (MECKI) were calculated. The added value of EOV on top of those scores was assessed using a Cox proportional hazard model. The added discriminative power was also assessed by receiver operating characteristic curve comparison.

RESULTS

A total of 390 HF patients with a median age of 58 (IQR 50-65) years were investigated, of whom 78% were male and 54% had ischaemic heart disease. The median peak oxygen consumption was 15.7 mL/kg/min (IQR 12.8-20.1). Exercise oscillatory ventilation was detected in 153 (39.2%) patients. Over a median follow-up of 2 years, 61 patients died (49 due to a cardiovascular reason) and 54 had a HTx. Exercise oscillatory ventilation independently predicted the composite outcome of all-cause death and HTx. Furthermore, the presence of this ventilatory pattern significantly improved the prognostic performance of both HFSS and MAGGIC scores.

CONCLUSION

Exercise oscillatory ventilation was often found in a cohort of HF patients with reduced LVEF who underwent CPET. It was found that EOV added further prognostic value to contemporary HF scores, suggesting that this easily obtained parameter should be included in future modified HF scores.

Cardiopulmonary exercise testing and heart failure: a tale born from oxygen uptake

Since 50 years, cardiopulmonary exercise testing (CPET) plays a central role in heart failure (HF) assessment. Oxygen uptake (VO2) is one of the main HF prognostic indicators, then paralleled by ventilation to carbon dioxide (VE/VCO2) relationship slope. Also anaerobic threshold retains a strong prognostic power in severe HF, especially if expressed as a percent of maximal VO2 predicted value. Moving beyond its absolute value, a modern approach is to consider the percentage of predicted value for peak VO2 and VE/VCO2 slope, thus allowing a better comparison between genders, ages, and races. Several VO2 equations have been adopted to predict peak VO2, built considering different populations. A step forward was made possible by the introduction of reliable non-invasive methods able to calculate cardiac output during exercise: the inert gas rebreathing method and the thoracic electrical bioimpedance. These techniques made possible to calculate the artero-venous oxygen content differences (ΔC(a-v)O2), a value related to haemoglobin concentration, pO2, muscle perfusion, and oxygen extraction. The role of haemoglobin, frequently neglected, is however essential being anaemia a frequent HF comorbidity. Finally, peak VO2 is traditionally obtained in a laboratory setting while performing a standardized physical effort. Recently, different wearable ergo-spirometers have been developed to allow an accurate metabolic data collection during different activities that better reproduce HF patients' everyday life. The evaluation of exercise performance is now part of the holistic approach to the HF syndrome, with the inclusion of CPET data into multiparametric prognostic scores, such as the MECKI score.

The importance of re-evaluating the risk score in heart failure patients: An analysis from the Metabolic Exercise Cardiac Kidney Indexes (MECKI) score database

BACKGROUND

The role of risk scores in heart failure (HF) management has been highlighted by international guidelines. In contrast with HF, which is intrinsically a dynamic and unstable syndrome, all its prognostic studies have been based on a single evaluation. We investigated whether time-related changes of a well-recognized risk score, the MECKI score, added prognostic value. MECKI score is based on peak VO₂, VE/VCO₂ slope, Na⁺, LVEF, MDRD and Hb.

METHODS

A multi-centre retrospective study was conducted involving 660 patients who performed MECKI re-evaluation at least 6 months apart. Based on the difference between II and I evaluation of MECKI values (MECKI II - MECKI I = ∆ MECKI) the study population was divided in 2 groups: those presenting a score reduction (∆ MECKI <0, i.e. clinical improvement), vs. patients presenting an increase (∆ MECKI >0, clinical deterioration).

RESULTS

The prognostic value of MECKI score is confirmed also when re-assessed during follow-up. The group with improved MECKI (366 patients) showed a better prognosis compared to patients with worsened MECKI (294 patients) (p < 0.0001). At 1st evaluation, the two groups differentiated by LVEF, V_E/VCO₂ slope and blood Na⁺ concentration, while at 2nd evaluation they differentiated in all 6 parameters considered in the score. The patients who improved MECKI score, improved in all components of the score but hemoglobin, while patients who worsened the score, worsened all parameters.

CONCLUSIONS

This study shows that re-assessment of MECKI score identifies HF subjects at higher risk and that score improvement or deterioration regards several MECKI score generating parameters confirming the holistic background of HF.

Performance of current risk stratification models for predicting mortality in patients with heart failure: a systematic review and meta-analysis

AIMS

There are several risk scores designed to predict mortality in patients with heart failure (HF). This study aimed to assess performance of risk scores validated for mortality prediction in patients with acute HF (AHF) and chronic HF.

METHODS AND RESULTS

MEDLINE and Scopus were searched from January 2015 to January 2021 for studies which internally or externally validated risk models for predicting all-cause mortality in patients with AHF and chronic HF. Discrimination data were analysed using C-statistics, and pooled using generic inverse-variance random-effects model. Nineteen studies (n = 494 156 patients; AHF: 24 762; chronic HF mid-term mortality: 62 000; chronic HF long-term mortality: 452 097) and 11 risk scores were included. Overall, discrimination of risk scores was good across the three subgroups: AHF mortality [C-statistic: 0.76 (0.68-0.83)], chronic HF mid-term mortality [1 year; C-statistic: 0.74 (0.68-0.79)], and chronic HF long-term mortality [≥2 years; C-statistic: 0.71 (0.69-0.73)]. MEESSI-AHF [C-statistic: 0.81 (0.80-0.83)] and MARKER-HF [C-statistic: 0.85 (0.80-0.89)] had an excellent discrimination for AHF and chronic HF mid-term mortality, respectively, whereas MECKI had good discrimination [C-statistic: 0.78 (0.73-0.83)] for chronic HF long-term mortality relative to other models. Overall, risk scores predicting short-term mortality in patients with AHF did not have evidence of poor calibration (Hosmer-Lemeshow P > 0.05). However, risk models predicting mid-term and long-term mortality in patients with chronic HF varied in calibration performance.

CONCLUSIONS

The majority of recently validated risk scores showed good discrimination for mortality in patients with HF. MEESSI-AHF demonstrated excellent discrimination in patients with AHF, and MARKER-HF and MECKI displayed an excellent discrimination in patients with chronic HF. However, modest reporting of calibration and lack of head-to-head comparisons in same populations warrant future studies.

Predicting major events in ambulatory patients with advanced heart failure awaiting heart transplantation: a pilot study

AIMS

In heart failure (HF), prognostic risk scores focus on all-cause mortality prediction. However, in advanced HF (AdHF) ambulatory patients awaiting heart transplantation (HTx), hospitalizations for acutely decompensated/worsening HF are relevant to clinical decision-making, but unpredicted by common risk functions.

METHODS

Among consecutive ambulatory patients added to the waitlist for HTx, event discriminators within 2 years from recruitment were assessed prospectively by area under the curve from receiver-operating characteristic curves, and by Cox proportional hazards models. Primary composite end points included the first between all-cause mortality and acutely decompensated/worsening HF requiring hospitalization and specific treatments.

RESULTS

In 89 patients, 36 primary composite events were recorded in a 2-year follow-up (40% of the study sample), and associated with nonischemic etiology and nonsinus rhythm, with lower systolic blood pressure (BP), lower plasma sodium and hemoglobin concentrations, and with higher N-terminal pro-brain natriuretic peptide (NT-proBNP), larger left ventricular (LV) dimensions and lower LV ejection fraction, greater proportion of significant mitral regurgitation, lower tricuspid annulus peak systolic excursion (TAPSE), lower percentage of predicted distance at 6-minute walking test (%p6MWT) and lower global symptoms burden by the Kansas City Cardiomyopathy Questionnaire, lower peak oxygen uptake by cardiopulmonary exercise, and higher wedge pressure by right heart catheterization, as compared with those with no events (P < 0.05). Only Metabolic Exercise Cardiac Kidney Index (MECKI) at recruitment was higher with patients reporting events, which predicted composite end points in addition to and independently of NT-proBNP, and lower systolic BP (all P < 0.05). In an alternative risk model, severe mitral regurgitation and lower TAPSE replaced MECKI and BP but not NT-proBNP (all P < 0.01).

CONCLUSION

Higher NT-pro-BNP, lower systolic BP and higher MECKI may contribute to predicting all-cause death and acutely decompensated/worsening HF among ambulatory patients awaiting HTx, with lower TAPSE and severe mitral regurgitation representing further alternative independent prognosticators.

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.

Role of impaired iron transport on exercise performance in heart failure patients

AIMS

Impaired iron transport (IIT) occurs frequently in heart failure (HF) patients, even in the absence of anaemia and it is associated with a poor quality of life and prognosis. The impact of IIT on exercise capacity, as assessed by the cardiopulmonary exercise test (CPET), in HF is at present unknown. The aim of this article is to evaluate in HF patients the impact on exercise performance of IIT, defined as transferrin saturation (TSAT) <20%.

METHODS AND RESULTS

We collected data of 676 patients hospitalized for HF. All underwent laboratory analysis, cardiac ultrasound, and CPET. Patients were grouped by the presence/absence of IIT and anaemia (haemoglobin <13 and <12 g/dL in male and female, respectively): Group 1 (G1) no anaemia, no IIT; Group 2 (G2) anaemia, no IIT; Group 3 (G3) no anaemia, IIT; Group 4 (G4) anaemia and IIT. Peak oxygen uptake (peakVO2) reduced from G1 to G3 and from G2 to G4 (G1: 1266 ± 497 mL/min, G2: 1011 ± 385 mL/min, G3: 1041 ± 395 mL/min, G4: 833 ± 241 mL/min), whereas the ventilation to carbon dioxide relationship slope (VE/VCO2 slope) increased (G1: 31.8 ± 7.5, G2: 34.5 ± 7.4, G3: 36.1 ± 10.2, G4: 37.5 ± 8.4). At multivariate regression analysis, peakVO2 independent predictors were anaemia, brain natriuretic peptide (BNP), and left ventricular ejection fraction, whereas VE/VCO2 slope independent predictors were IIT and BNP.

CONCLUSION

In HF IIT is associated with exercise performance impairment independently from anaemia, and it is a predictor of elevated VE/VCO2 slope, a pivotal index of HF prognosis.

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.

Listing Criteria for Heart Transplant: Role of Cardiopulmonary Exercise Test and of Prognostic Scores

Patients with advanced heart failure (AdHF) have a reduced quality of life and poor prognosis. A heart transplant (HT) is an effective treatment for such patients. Still, because of a shortage of donor organs, the final decision to place a patient without contraindications on the HT waiting list is based on detailed risk-benefit analysis. Cardiopulmonary exercise tests (CPETs) play a pivotal role in guiding selection in patients with AdHF considered for an HT. Furthermore, several validated multivariable predicting scores obtained through various techniques, including the CPETs, are available and part of the decision-making process for HT listing.

Machine Learning Applications in Heart Failure Disease Management: Hype or Hope?

Purpose of the review

Machine learning (ML) approaches have emerged as powerful tools in medicine. This review focuses on the use ML to assess risk of events in patients with heart failure (HF). It provides an overview of the ML process, challenges in developing risk scores, and strategies to mitigate problems.

Recent findings

Risk scores developed using standard statistical methods have limited accuracy, particularly when they are applied to populations other than the one in which they were developed. Computerized ML algorithms which identify correlations between descriptive variables in complex, non-linear, multi-dimensional systems provide an alternative approach to predicting risk of events. The MARKER-HF mortality risk score was developed using data from the electronic health record of HF patients followed at a large academic medical center. The risk score, which uses eight commonly available variables, proved to be highly accurate in predicting mortality across the spectrum of risk. It retained accuracy in independent populations and was superior to other risk scores.

Summary

Machine learning approaches can be used to develop risk scores that are superior to ones based on standard statistical methods. Careful attention to detail in curating data, selecting covariates, and trouble-shooting the process is required to optimize results.

Comparison among different multiparametric scores for risk stratification in heart failure patients with reduced ejection fraction

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. The guidelines of the European Society of Cardiology for the diagnosis and treatment of acute and chronic heart failure have identified individual markers in patients with heart failure, including demographic data, aetiology, comorbidities, clinical, radiological, haemodynamic, echocardiographic and biochemical parameters. Several scoring systems have been proposed to identify adverse events, such as destabilizations, re-hospitalizations and mortality. This article reviews scoring systems for heart failure prognostication, with particular mention of those models with exercise tolerance objective definition. Although most of the models include readily available clinical information, quite a few of them comprise circulating levels of natriuretic peptides and a more objective evaluation of exercise tolerance. A literature review was also conducted to (a) identify heart failure risk-prediction models, (b) assess statistical approach, and (c) identify common variables.

The MECKI score initiative: Development and state of the art

The high morbidity and poor survival rates associated with chronic heart failure still represent a big challenge, despite improvements in treatments and the development of new therapeutic opportunities. The prediction of outcome in heart failure is gradually moving towards a multiparametric approach in order to obtain more accurate models and to tailor the prognostic evaluation to the individual characteristics of a single subject. The Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score was developed 10 years ago from 2715 patients and subsequently validated in a different population. The score allows an accurate evaluation of the risk of heart failure patients using only six variables that include the evaluation of the exercise capacity (peak oxygen uptake and ventilation/CO₂ production slope), blood samples (haemoglobin, Na⁺, Modification of Diet in Renal Disease) and echocardiography (left ventricular ejection fraction). Over the following years, the MECKI score was tested taking into account therapies and specific markers of heart failure, and it proved to be a simple, useful tool for risk stratification and for therapeutic strategies in heart failure patients. The close connection between the centres involved and the continuous updating of the data allow the participating sites to propose substudies on specific subpopulations based on a common dataset and to put together and develop new ideas and perspectives.

Cardiovascular Death Risk in Recovered Mid-Range Ejection Fraction Heart Failure: Insights From Cardiopulmonary Exercise Test

BACKGROUND

Heart failure with midrange ejection fraction (HFmrEF) represents a heterogeneous category where phenotype, as well as prognostic assessment, remains debated. The present study explores a specific HFmrEF subset, namely those who recovered from a reduced EF (rec-HFmrEF) and, particularly, it focuses on the possible additive prognostic role of cardiopulmonary exercise testing.

METHODS AND RESULTS

We analyzed data from 4535 patients with HFrEF and 1176 patients with rec-HFmrEF from the Metabolic Exercise combined with Cardiac and Kidney Indexes database. The end point was cardiovascular death at 5 years. The median follow-up was 1343 days (25th-75th range 627-2403 days). Cardiovascular death occurred in 552 HFrEF and 61 rec-HFmrEF patients. The multivariate analysis confirmed an independent role of the MECKI score's variables in HFrEF (C-index = 0.744) whereas, in the rec-HFmrEF group, only age and peak oxygen uptake (pVO₂) remained associated to the end point (C-index = 0.745). A peak oxygen uptake of ≤55% of predicted and a ventilatory efficiency of ≥31 resulted as the most accurate cut-off values in the outcome prediction.

CONCLUSIONS

Present data support the cardiopulmonary exercise test and, particularly, the peak oxygen uptake, as a useful tool in the rec-HFmrEF prognostic assessment. A peak VO₂ of ≤55% predicted and ventilatory efficiency of ≥31 might help to identify a high-risk rec-HFmrEF subgroup.

Heart failure mortality prediction using PRISM score and development of a classification and regression tree model to refer patients for palliative care consultation

INTRODUCTION

We sought to assess one-year mortality in heart failure (HF) patients by using (Placement Resource Indicator for Systems Management) PRISM, a disease nonspecific risk stratification score, and use it along with modified Seattle Heart Failure Model (SHFM) to guide patient selection for palliative care consultation.

METHODS

A retrospective study design was used to examine 1-year mortality in 689 HF patients admitted from 2012 to 2014. One-year mortality was calculated using Pmort30/PRISM and modified SHFM scores, and the predicted scores were validated using the area under the ROC curve. CART was used to develop an algorithm to classify patients based on their mortality risk.

RESULTS

The discriminatory ability of PRISM categorical score (AUC = 0.701) was not significantly different than the discriminatory ability of modified SHFM (AUC = 0.686) (DeLong's test p = 0.56) but improved significantly with the combination of PRISM (categorical) score + modified SHFM (AUC = 0.740) (p = 0.002). The predictive capability of the CART tree model after cross-validation was 72.2% (AUC 0.631).

CONCLUSION

Our study suggests PRISM score performed as well as modified SHFM for one-year mortality prediction. Moreover, the addition of modified SHFM to PRISM score increases discriminatory ability in predicting 1-year mortality in heart failure patients compared to either of the two models alone. Together, when combined in a CART model, they can be used to identify the population subset with the highest mortality risk and hence guide goals of care discussion.

Exercise oscillatory ventilation and prognosis in heart failure patients with reduced and mid-range ejection fraction

AIMS

Exercise oscillatory ventilation (EOV) is a pivotal cardiopulmonary exercise test parameter for the prognostic evaluation of patients with chronic heart failure (HF). It has been described in patients with HF with reduced ejection fraction (<40%, HFrEF) and with HF with preserved ejection fraction (>50%, HFpEF), but no data are available for patients with HF with mid-range ejection fraction (40-49%, HFmrEF). The aim of the study was to evaluate the prognostic role of EOV in HFmrEF patients.

METHODS AND RESULTS

We analysed 1239 patients with HFmrEF and 4482 patients with HFrEF, enrolled in the MECKI score database, with a 2-year follow-up. The study endpoint was the composite of cardiovascular death, urgent heart transplant, and ventricular assist device implantation. We identified EOV in 968 cases (16% and 17% of cases in HFmrEF and HFrEF, respectively). HFrEF EOV+ patients were significantly older, and their parameters suggested a more severe HF than HFrEF EOV- patients. A similar behaviour was found in HFmrEF EOV+ vs. EOV- patients. Kaplan-Meier analysis, irrespective of ejection fraction, showed that EOV is associated with a worse survival, and that patients with HFrEF and HFmrEF EOV+ had a significantly worse outcome than the EOV- of the same ejection fraction groups. EOV-associated survival differences in HFmrEF patients started after 18 months of follow-up.

CONCLUSION

Exercise oscillatory ventilation has a similar prevalence and ominous prognostic value in both HFmrEF and HFrEF patients, indicating a group of patients in need of a more intensive follow-up and a more aggressive therapy. In HFmrEF, the survival curves between EOV+ and EOV- patients diverged only after 18 months.

Improving risk prediction in heart failure using machine learning

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.

Prediction of maximum oxygen uptake through incremental exercise testing using ventilometry: a cross-sectional study

BACKGROUND

Cardiopulmonary exercise testing is the main tool assessing cardiorespiratory fitness. However, cardiopulmonary exercise testing devices are expensive and often cannot be used.

OBJECTIVE

The present study sought to develop cardiopulmonary exercise testing equations for estimating maximum oxygen uptake from ergometric testing combined with ventilometry.

METHODS

181 volunteers of both sexes were submitted to cardiopulmonary exercise testing on treadmill using an incremental protocol. Volunteers were randomized into two groups: regression group: composed of 68 women with age 24.7±6.0 years and 54 men aged 21.5±3.9 years; and a cross-validation group composed of 29 women with mean age of 23.8±4.7 years and 30 men with a mean age of 23.1±4.4 years. The estimating equations were developed using multiple stepwise linear regressions; comparison of means was done using a t test and reliability assessed by Cronbach's alpha.

RESULTS

8 independent variables exhibited a significant result for estimating VO_2max: minute ventilation (E) at second ventilatory threshold (VT-II): (E _VT-II); heart rate at VT-II (HR_VT-II); body mass (BM); body mass index (BMI); fat percentage (F%); age; sex; velocity at VT-II (Vel_VT-II); test time of VT-II (T_VT-II) and final test velocity (Vel_final). Two equations presented more accurate results; for active subjects: Equation₂ = 33.08 + 2.41*(Vel_final) - 0.32*(F%) + 0.40*(VE_VT-II) - 0.26*(BM) - 0.09*(HR_VT-II); for sedentary subjects: Equation₃ for = 54.65 + 1.37*(T_VT-II) + 8.24*(sex) - 1.26*(BMI) + 0.37*(VE_VT-II) - 0.12*(HR_VT-II).

CONCLUSION

The present study demonstrated that the use of parameters collected during maximal ergometric test combined with ventilometry, improved the accuracy of equations for estimating maximum oxygen uptake.

The New Heart Failure Association Definition of Advanced Heart Failure

The clinical course of heart failure is characterised by progressive worsening of cardiac function and symptoms. Patients progress to a condition where traditional treatment is no longer effective and advanced therapies, such as mechanical circulatory support, heart transplantation and/or palliative care, are needed. This condition is called advanced chronic heart failure. The Heart Failure Association first defined it in 2007 and this definition was updated in 2018. The updated version emphasises the role of comorbidities, including tachyarrhythmias, and the role of heart failure with preserved ejection fraction. Improvements in mechanical circulatory support technology and better disease management programmes are major advances and are radically changing the management of these patients.

Cardiac contractility modulation improves long-term survival and hospitalizations in heart failure with reduced ejection fraction

AIMS

Cardiac contractility modulation (CCM) improves symptoms and exercise tolerance and reduces heart failure (HF) hospitalizations over 6-month follow-up in patients with New York Heart Association (NYHA) class III or IV symptoms, QRS < 130 ms and 25% ≤ left ventricular ejection fraction (LVEF) ≤ 45% (FIX-HF-5C study). The current prospective registry study (CCM-REG) aimed to assess the longer-term impact of CCM on hospitalizations and mortality in real-world experience in this same population.

METHODS AND RESULTS

A total of 140 patients with 25% ≤ LVEF ≤ 45% receiving CCM therapy (CCM-REG_25-45 ) for clinical indications were included. Cardiovascular and HF hospitalizations, Minnesota Living with Heart Failure Questionnaire (MLHFQ) and NYHA class were assessed over 2 years. Mortality was tracked through 3 years and compared with predictions by the Seattle Heart Failure Model (SHFM). A separate analysis was performed on patients with 35% ≤ LVEF ≤ 45% (CCM-REG_35-45 ) and 25% ≤ LVEF < 35% (CCM-REG_25-34 ). Hospitalizations decreased by 75% (from 1.2/patient-year the year before, to 0.35/patient-year during the 2 years following CCM, P < 0.0001) in CCM-REG_25-45 and by a similar amount in CCM-REG_35-45 (P < 0.0001) and CCM-REG_25-34 . MLHFQ and NYHA class improved in all three cohorts_, with progressive improvements over time (P < 0.002). Three-year survival in CCM-REG_25-45 (82.8%) and CCM-REG_24-34 (79.4%) were similar to those predicted by SHFM (76.7%, P = 0.16; 78.0%, P = 0.81, respectively) and was better than predicted in CCM-REG_35-45 (88.0% vs. 74.7%, P = 0.046).

CONCLUSION

In real-world experience, CCM produces results similar to those of previous studies in subjects with 25% ≤ LVEF ≤ 45% and QRS < 130 ms; cardiovascular and HF hospitalizations are reduced and MLHFQ and NYHA class are improved. Overall mortality was comparable to that predicted by the SHFM but was lower than predicted in patients with 35% ≤ LVEF ≤ 45%.

Critical Appraisal of Multivariable Prognostic Scores in Heart Failure: Development, Validation and Clinical Utility

Optimal management of heart failure requires accurate risk assessment. Many prognostic risk models have been proposed for patient with chronic and acute heart failure. Methodological critical issues are the data source, the outcome of interest, the choice of variables entering the model, the validation of the model in external population. Up to now, the proposed risk models can be a useful tool to help physician in the clinical decision-making. The availability of big data and of new methods of analysis may lead to developing new models in the future.

Definitions of Stage D heart failure and outcomes among outpatients with heart failure and reduced ejection fraction

BACKGROUND

An operational consensus definition of Stage D heart failure (HF) is currently lacking.

METHODS

We evaluated 512 outpatients (median age, 63 years; 35.0% women; 45.5% white and 45.9% black; median ejection fraction was 25%; 67.4% had coronary artery disease) with HF and reduced (≤40%) ejection fraction. We applied 3 hypothetical definitions for Stage D: (1) designation as "Stage D" or "advanced" HF by treating physician; (2) INTERMACS profiles, defining Stage D as profiles 2-6; and (3) European Society of Cardiology Heart Failure Association (ESC-HFA) criteria.

RESULTS

Physicians, INTERMACS profiles, and ESC-HFA criteria identified 64 (12.5%), 93 (18.2%), and 67 (13.1%) patients, respectively, as Stage D, with modest concordance between definitions (κ = 0.37). After a median of 3.1 years, 97 patients died (3-year mortality 20.4%). Among patients identified as Stage D by physicians, 3-year mortality was 43.7% vs. 17.0% for non-Stage D patients (age-adjusted hazard ratio [HR] 3.17; 95%CI 1.94-5.18; P < 0.001). The corresponding mortalities for the INTERMACS-based definition were 41.0% vs. 16.2% (HR 3.28; 95%CI 2.11-5.11; P < 0.001) and for ESC-HFA criteria 33.5% vs. 18.6% (HR 2.02; 95%CI 1.22-3.33; P = 0.006); the INTERMACS-based definition provided the best prognostic separation. Results were similar with an alternative INTERMACS-based definition considering only profiles 2-5 as Stage D HF. The INTERMACS-based definition best separated all-cause and HF-specific hospitalization and composite endpoint risk between Stage D and non-Stage D patients also.

CONCLUSIONS

INTERMACS profiles provide a practical alternative for the identification of Stage D HF in ambulatory populations with systolic HF. The ESC-HFA criteria offer limited prognostic information.

INTERMACS Profiles and Outcomes Among Non-Inotrope-Dependent Outpatients With Heart Failure and Reduced Ejection Fraction

OBJECTIVES

This study sought to evaluate INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) profiles for prognostic use among ambulatory non-inotrope-dependent patients with heart failure with reduced ejection fraction (HFrEF).

BACKGROUND

Data for INTERMACS profiles and prognoses in ambulatory patients with HFrEF are limited.

METHODS

We evaluated 3-year outcomes in 969 non-inotrope-dependent outpatients with HFrEF (EF: ≤40%) not previously receiving advanced HF therapies. Patients meeting an INTERMACS profile at baseline were classified as profile 7 (n = 348 [34.7%]); 146 patients (14.5%) were classified profile 6; and 52 patients (5.2%) were classified profile 4 to 5. Remaining patients were classified "stable Stage C" (n = 423 [42.1%]).

RESULTS

Three-year mortality rate was 10.0% among stable Stage C patients compared with 21.8% among INTERMACS profile 7 (hazard ratio [HR] vs. Stage C: 2.45; 95% confidence interval [CI]: 1.64 to 3.66), 26.0% among profile 6 (HR: 3.93; 95% CI: 1.64 to 3.66), and 43.8% among profile 4 to 5 (HR: 6.35; 95% CI: 3.51 to 11.5) patients. Hospitalization rates for HF were 4-fold higher among INTERMACS profile 7 (38 per 100 patient-years; rate ratio [RR] vs. Stage C: 3.88; 95% CI: 2.70 to 5.35), 6-fold higher among profile 6 patients (54 per 100 patient-years; RR: 5.69; 95% CI: 3.72 to 8.71), and 10-fold higher among profile 4 to 5 patients (69 per 100 patient-years; RR: 9.96; 95% CI: 5.15 to 19.3) than stable Stage C patients (11 per 100 patient-years). All-cause hospitalization rates had similar trends. INTERMACS profiles offered better prognostic separation than NYHA functional classifications.

CONCLUSIONS

INTERMACS profiles strongly predict subsequent mortality and hospitalization burden in non-inotrope-dependent outpatients with HFrEF. These simple profiles could therefore facilitate and promote advanced HF awareness among clinicians and planning for advanced HF therapies.

Multiparametric prognostic scores in chronic heart failure with reduced ejection fraction: a long-term comparison

AIMS

Risk stratification in heart failure (HF) is crucial for clinical and therapeutic management. A multiparametric approach is the best method to stratify prognosis. In 2012, the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score was proposed to assess the risk of cardiovascular mortality and urgent heart transplantation. The aim of the present study was to compare the prognostic accuracy of MECKI score to that of HF Survival Score (HFSS) and Seattle HF Model (SHFM) in a large, multicentre cohort of HF patients with reduced ejection fraction.

METHODS AND RESULTS

We collected data on 6112 HF patients and compared the prognostic accuracy of MECKI score, HFSS, and SHFM at 2- and 4-year follow-up for the combined endpoint of cardiovascular death, urgent cardiac transplantation, or ventricular assist device implantation. Patients were followed up for a median of 3.67 years, and 931 cardiovascular deaths, 160 urgent heart transplantations, and 12 ventricular assist device implantations were recorded. At 2-year follow-up, the prognostic accuracy of MECKI score was significantly superior [area under the curve (AUC) 0.781] to that of SHFM (AUC 0.739) and HFSS (AUC 0.723), and this relationship was also confirmed at 4 years (AUC 0.764, 0.725, and 0.720, respectively).

CONCLUSION

In this cohort, the prognostic accuracy of the MECKI score was superior to that of HFSS and SHFM at 2- and 4-year follow-up in HF patients in stable clinical condition. The MECKI score may be useful to improve resource allocation and patient outcome, but prospective evaluation is needed.