The 3A3B score: The simple risk score for heart failure with preserved ejection fraction - A report from the CHART-2 Study

Self-Reported Sodium Intake and Sodium Vulnerability in Heart Failure With Preserved Ejection Fraction

OBJECTIVE

To determine the pathophysiologic and prognostic meaning of patient self-reported sodium intake in heart failure (HF) with preserved ejection fraction (HFpEF).

METHODS

This cohort analysis used data from the Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist (TOPCAT) trial of patients enrolled in the Americas. Tertiles of baseline self-reported sodium intake were used to assess the relationship between self-reported sodium intake and clinical presentation/outcome and interactions with treatment effect of spironolactone.

RESULTS

Self-reported sodium intake of 1748 patients with HFpEF included in TOPCAT were divided according to tertiles of sodium intake (47% low, 35% moderate, and 18% high sodium intake). After covariate adjustment, lower self-reported sodium intake was associated with higher risk of HF hospital admission (P=.009). Patients with lower sodium intake had higher E-wave velocity, left ventricular end diastolic volume, and estimated plasma volume (P<.001). Lower sodium intake was associated with a larger treatment effect of spironolactone on HF hospitalizations (hazard ratio, 0.69; 95% CI, 0.53 to 0.91) vs the highest tertile (hazard ratio, 1.37; 95% CI, 0.79 to 2.38; interaction P=.030). In addition, linear mixed models indicated larger reductions in blood pressure, dyspnea, and edema (all interaction P<.001) in patients with lower sodium intake receiving spironolactone.

CONCLUSION

Low self-reported sodium level in HFpEF is associated with higher risk of HF hospital admissions and may indicate a sodium-vulnerable state; patients should not be falsely reassured that they are in a lower risk category despite greater adherence to medical recommendations.

Prognostic models for patients suffering a heart failure with a preserved ejection fraction: a systematic review

The purpose of this study was to systematically review the development, performance, and applicability of prognostic models developed for predicting poor events in patients with heart failure with preserved ejection fraction (HFpEF). Databases including Embase, PubMed, Web of Science Core Collection, the Cochrane Library, China National Knowledge Infrastructure, Wan Fang, Wei Pu, and China Biological Medicine were queried from their respective dates of inception to 1 June 2023, to examine multivariate models for prognostic prediction in HFpEF. Both forward and backward citations of all studies were included in our analysis. Two researchers individually used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to extract data and assess the quality of the models using the Predictive Mode Bias Risk Assessment Tool (PROBAST). Among the 6897 studies screened, 16 studies derived and/or validated a total of 39 prognostic models. The sample size ranges for model development, internal validation, and external validation are 119 to 5988, 152 to 1000, and 30 to 5957, respectively. The most frequently employed modelling technique was Cox proportional hazards regression. Six studies (37.50%) conducted internal validation of models; bootstrap and k-fold cross-validation were the commonly used methods for internal validation of models. Ten of these models (25.64%) were validated externally, with reported the c-statistic in the external validation set ranging from 0.70 to 0.96, while the remaining models await external validation. The MEDIA echo score and I-PRESERVE-sudden cardiac death prediction mode have been externally validated using multiple cohorts, and the results consistently show good predictive performance. The most frequently used predictors identified among the models were age, n-terminal pro-brain natriuretic peptide, ejection fraction, albumin, and hospital stay in the last 5 months owing to heart failure. All study predictor domains and outcome domains were at low risk of bias, high or unclear risk of bias of all prognostic models due to underreporting in the area of analysis. All studies did not evaluate the clinical utility of the prognostic models. Predictive models for predicting prognostic outcomes in patients with HFpEF showed good discriminatory ability but their utility and generalization remain uncertain due to the risk of bias, differences in predictors between models, and the lack of clinical application studies. Future studies should improve the methodological quality of model development and conduct external validation of models.

Prognostic Value of a Multi-mRNA Signature for 1-Year All-Cause Death in Hospitalized Patients With Heart Failure With a Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction is a major global public health problem, while effective risk stratification tools are still lacking. We sought to construct a multi-mRNA signature to predict 1-year all-cause death.

METHODS

We selected 30 patients with heart failure with preserved ejection fraction who died during 1-year follow-up and 30 who survived in the discovery set. One hundred seventy-one and 120 patients with heart failure with preserved ejection fraction were randomly selected as a test set and a validation set, respectively. We performed mRNA microarrays in all patients.

RESULTS

We constructed a 5-mRNA signature for predicting 1-year all-cause death. The scores of the 5-mRNA signature were significantly associated with the 1-year risk of all-cause death in both the test set (hazard ratio, 2.72 [95% CI, 1.98-3.74]; P<0.001) and the validation set (hazard ratio, 3.95 [95% CI, 2.40-6.48]; P<0.001). Compared with a reference model, which included sex, ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) score, history of HF and NT-proBNP (N-terminal pro-B-type natriuretic peptide), the 5-mRNA signature had a better discrimination capability, with an increased area under the curve from 0.696 to 0.813 in the test set and from 0.712 to 0.848 in the validation set. A composite model integrating the 5-mRNA risk score and variables in the reference model demonstrated an excellent discrimination capability, with an area under the curve of 0.861 (95% CI, 0.784-0.939) in the test set and an area under the curve of 0.859 (95% CI, 0.755-0.963) in the validation set. The net reclassification improvement and integrated discrimination improvement indicated that the composite model significantly improved patient classification compared with the reference model in both sets (P<0.001).

CONCLUSIONS

The 5-mRNA signature is a promising predictive tool for 1-year all-cause death and shows improved prognostic power over the established risk scores and NT-proBNP in patients with heart failure with preserved ejection fraction.

Prognostic Models for Mortality and Morbidity in Heart Failure With Preserved Ejection Fraction

Importance

Accurate risk prediction of morbidity and mortality in patients with heart failure with preserved ejection fraction (HFpEF) may help clinicians risk stratify and inform care decisions.

Objective

To develop and validate a novel prediction model for clinical outcomes in patients with HFpEF using routinely collected variables and to compare it with a biomarker-driven approach.

Design, Setting, and Participants

Data were used from the Dapagliflozin Evaluation to Improve the Lives of Patients With Preserved Ejection Fraction Heart Failure (DELIVER) trial to derive the prediction model, and data from the Angiotensin Receptor Neprilysin Inhibition in Heart Failure With Preserved Ejection Fraction (PARAGON-HF) and the Irbesartan in Heart Failure With Preserved Ejection Fraction Study (I-PRESERVE) trials were used to validate it. The outcomes were the composite of HF hospitalization (HFH) or cardiovascular death, cardiovascular death, and all-cause death. A total of 30 baseline candidate variables were selected in a stepwise fashion using multivariable analyses to create the models. Data were analyzed from January 2023 to June 2023.

Exposures

Models to estimate the 1-year and 2-year risk of cardiovascular death or hospitalization for heart failure, cardiovascular death, and all-cause death.

Results

Data from 6263 individuals in the DELIVER trial were used to derive the prediction model and data from 4796 individuals in the PARAGON-HF trial and 4128 individuals in the I-PRESERVE trial were used to validate it. The final prediction model for the composite outcome included 11 variables: N-terminal pro-brain natriuretic peptide (NT-proBNP) level, HFH within the past 6 months, creatinine level, diabetes, geographic region, HF duration, treatment with a sodium-glucose cotransporter 2 inhibitor, chronic obstructive pulmonary disease, transient ischemic attack/stroke, any previous HFH, and heart rate. This model showed good discrimination (C statistic at 1 year, 0.73; 95% CI, 0.71-0.75) in both validation cohorts (C statistic at 1 year, 0.71; 95% CI, 0.69-0.74 in PARAGON-HF and 0.75; 95% CI, 0.73-0.78 in I-PRESERVE) and calibration. The model showed similar discrimination to a biomarker-driven model including high-sensitivity cardiac troponin T and significantly better discrimination than the Meta-Analysis Global Group in Chronic (MAGGIC) risk score (C statistic at 1 year, 0.60; 95% CI, 0.58-0.63; delta C statistic, 0.13; 95% CI, 0.10-0.15; P < .001) and NT-proBNP level alone (C statistic at 1 year, 0.66; 95% CI, 0.64-0.68; delta C statistic, 0.07; 95% CI, 0.05-0.08; P < .001). Models derived for the prediction of all-cause and cardiovascular death also performed well. An online calculator was created to allow calculation of an individual's risk.

Conclusions and Relevance

In this prognostic study, a robust prediction model for clinical outcomes in HFpEF was developed and validated using routinely collected variables. The model performed better than NT-proBNP level alone. The model may help clinicians to identify high-risk patients and guide treatment decisions in HFpEF.

A novel polygenic risk score improves prognostic prediction of heart failure with preserved ejection fraction in the Chinese Han population

AIMS

Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF.

METHODS AND RESULTS

We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827-0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658-0.734, P = 4 × 10-11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605-0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145-0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4-11.9; P = 5.6 × 10-5) and 30-fold (HR = 29.8, 95% CI 14.0-63.5; P = 1.4 × 10-18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure.

CONCLUSION

The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.

Validation of the MEDIA echo score for the prognosis of heart failure with preserved ejection fraction

There is currently no widely used prognostic score in heart failure (HF) with preserved ejection fraction (HFpEF). The MEDIA echo score, including four variables (pulmonary arterial systolic pressure > 40 mmHg, inferior vena cava collapsibility index < 50%, average E/e' > 9, and lateral mitral annular s' < 7 cm/s), has been proposed as a useful risk stratification tool. This study aimed at further validating the MEDIA echo score in both hospitalised and ambulatory HFpEF patients. The MEDIA echo score ranges from 0 to 4 (each criterion scores 1 point). The associations between MEDIA echo score and cardiovascular outcomes were assessed in two independent HFpEF cohorts, namely patients hospitalised for worsening HFpEF (N = 242, mean age 78 ± 11), and stable ambulatory HFpEF patients (N = 76, mean age 65 ± 8). Using multivariable Cox models, in the worsening HFpEF cohort, patients with a MEDIA echo score of 3-4 displayed a significant increased risk of death (HR 2.10, 95%CI 1.02-4.33, P = 0.043, score 0-1 as reference). In the ambulatory HFpEF cohort, patients with a MEDIA echo score of 2 had a significantly higher risk of death or HF hospitalisation (HR 3.44, 95%CI 1.27-9.30, P = 0.015, score 0 as reference), driven by HF hospitalisation; in that cohort, adding the MEDIA echo score to the clinical model significantly improved reclassification for the combined endpoint (integrated discrimination improvement 6.2%, P = 0.006). The MEDIA echo score significantly predicted the outcome of HFpEF patients in both hospital and ambulatory settings; its use may help refine routine risk stratification on top of well-established prognosticators in stable HFpEF patients.

Risk Prediction Models and Novel Prognostic Factors for Heart Failure with Preserved Ejection Fraction: A Systematic and Comprehensive Review

BACKGROUND

Patients with heart failure with preserved ejection fraction (HFpEF) have large individual differences, unclear risk stratification, and imperfect treatment plans. Risk prediction models are helpful for the dynamic assessment of patients' prognostic risk and early intensive therapy of high-risk patients. The purpose of this study is to systematically summarize the existing risk prediction models and novel prognostic factors for HFpEF, to provide a reference for the construction of convenient and efficient HFpEF risk prediction models.

METHODS

Studies on risk prediction models and prognostic factors for HFpEF were systematically searched in relevant databases including PubMed and Embase. The retrieval time was from inception to February 1, 2023. The Quality in Prognosis Studies (QUIPS) tool was used to assess the risk of bias in included studies. The predictive value of risk prediction models for end outcomes was evaluated by sensitivity, specificity, the area under the curve, C-statistic, C-index, etc. In the literature screening process, potential novel prognostic factors with high value were explored.

RESULTS

A total of 21 eligible HFpEF risk prediction models and 22 relevant studies were included. Except for 2 studies with a high risk of bias and 2 studies with a moderate risk of bias, other studies that proposed risk prediction models had a low risk of bias overall. Potential novel prognostic factors for HFpEF were classified and described in terms of demographic characteristics (age, sex, and race), lifestyle (physical activity, body mass index, weight change, and smoking history), laboratory tests (biomarkers), physical inspection (blood pressure, electrocardiogram, imaging examination), and comorbidities.

CONCLUSION

It is of great significance to explore the potential novel prognostic factors of HFpEF and build a more convenient and efficient risk prediction model for improving the overall prognosis of patients. This review can provide a substantial reference for further research.

Targeting efficacy of spironolactone in patients with heart failure with preserved ejection fraction: the TOPCAT study

AIMS

We aimed to explore the heterogeneous treatment effects (HTEs) for spironolactone treatment in patients with Heart failure with preserved ejection fraction (HFpEF) and examine the efficacy and safety of spironolactone medication, ensuring a better individualized therapy.

METHODS AND RESULTS

We used the causal forest algorithm to discover the heterogeneous treatment effects (HTEs) from patients in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial. Cox regressions were performed to assess the hazard ratios (HRs) of spironolactone medication for cardiovascular death and drug discontinuation in each group. The causal forest model revealed three representative covariates and participants were partitioned into four subgroups which were Group 1 (baseline BMI ≤ 31.71 kg/m² and baseline ALP ≤ 80 U/L, n = 759); Group 2 (BMI ≤ 31.71 kg/m² and ALP > 80 U/L, n = 1088); Group 3 (BMI > 31.71 kg/m² , and WBC ≤ 6.6 cells/μL, n = 633); Group 4 (BMI > 31.71 kg/m² and WBC > 6.6 cells/μL, n = 832), respectively. In the four subgroups, spironolactone therapy reduced the risk of cardiovascular death in high-risk group (Group 4) with both high BMI and WBC count (HR: 0.76; 95% CI 0.58 to 0.99; P = 0.045) but increased the risk in low-risk group (Group 1) with both low BMI and ALP (HR: 1.45; 95% CI 1.02 to 2.07; P = 0.041; P for interaction = 0.020) but showed similar risk of drug discontinuation (P for interaction = 0.498).

CONCLUSION

Our study manifested the HTEs of spironolactone in patients with HFpEF. Spironolactone treatment in HFpEF patients is feasible and effective in patients with high BMI and WBC while harmful in patients with low BMI and ALP. Machine learning model could be meaningful for improved categorization of patients with HFpEF, ensuring a better individualized therapy in the clinical setting.

Biomarker-driven prognostic models in chronic heart failure with preserved ejection fraction: the EMPEROR-Preserved trial

AIMS

Biomarker-driven prognostic models incorporating N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) in heart failure (HF) with preserved ejection fraction (HFpEF) are lacking. We aimed to generate a biomarker-driven prognostic tool for patients with chronic HFpEF enrolled in EMPEROR-Preserved.

METHODS AND RESULTS

Multivariable Cox regression models were created for (i) the primary composite outcome of HF hospitalization or cardiovascular death, (ii) all-cause death, (iii) cardiovascular death, and (iv) HF hospitalization. PARAGON-HF was used as a validation cohort. NT-proBNP and hs-cTnT were the dominant predictors of the primary outcome, and in addition, a shorter time since last hospitalization, New York Heart Association (NYHA) class III or IV, history of chronic obstructive pulmonary disease (COPD), insulin-treated diabetes, low haemoglobin, and a longer time since HF diagnosis were key predictors (eight variables, all p < 0.001). The consequent primary outcome risk score discriminated well (c-statistic = 0.75) with patients in the top 10th of risk having an event rate >22× higher than those in the bottom 10th. A model for HF hospitalization alone had even better discrimination (c = 0.79). Empagliflozin reduced the risk of cardiovascular death or hospitalization for HF in patients across all risk levels. NT-proBNP and hs-cTnT were also the dominant predictors of all-cause and cardiovascular mortality followed by history of COPD, low albumin, older age, left ventricular ejection fraction ≥50%, NYHA class III or IV and insulin-treated diabetes (eight variables, all p < 0.001). The mortality risk model had similar discrimination for all-cause and cardiovascular mortality (c-statistic = 0.72 for both). External validation provided c-statistics of 0.71, 0.71, 0.72, and 0.72 for the primary outcome, HF hospitalization alone, all-cause death, and cardiovascular death, respectively.

CONCLUSIONS

The combination of NT-proBNP and hs-cTnT along with a few readily available clinical variables provides effective risk discrimination both for morbidity and mortality in patients with HFpEF. A predictive tool-kit facilitates the ready implementation of these risk models in routine clinical practice.

Validation and derivation of short-term prognostic risk score in acute decompensated heart failure in China

Background

Few prognostic risk scores (PRSs) have been routinely used in acute decompensated heart failure (ADHF). We, therefore, externally validated three published PRSs (3A3B, AHEAD, and OPTIME-CHF) and derived a new PRS to predict the short-term prognosis in ADHF.

Methods

A total of 4550 patients from the Heb-ADHF registry in China were randomly divided into the derivation and validation cohorts (3:2). Discrimination of each PRS was assessed by the area under the receiver operating characteristic curve (AUROC). Logistic regression was exploited to select the predictors and create the new PRS. The Hosmer–Lemeshow goodness-of-fit test was used to assess the calibration of the new PRS.

Results

The AUROCs of the 3A3B, AHEAD, and OPTIME-CHF score in the derivation cohort were 0.55 (95% CI 0.53–0.57), 0.54 (95% CI 0.53–0.56), and 0.56 (95% CI 0.54–0.57), respectively. After logistic regression analysis, the new PRS computed as 1 × (diastolic blood pressure < 80 mmHg) + 2 × (lymphocyte > 1.11 × 10⁹/L) + 1 × (creatinine > 80 μmol/L) + 2 × (blood urea nitrogen > 21 mg/dL) + 1 × [BNP 500 to < 1500 pg/mL (NT-proBNP 2500 to < 7500 pg/mL)] or 3 × [BNP ≥ 1500 (NT-proBNP ≥ 7500) pg/mL] + 3 × (QRS fraction of electrocardiogram < 55%) + 4 × (ACEI/ARB not used) + 1 × (rhBNP used), with a better AUROC of 0.67 (95% CI 0.64–0.70) and a good calibration (Hosmer–Lemeshow χ² = 3.366, P = 0.186). The results in validation cohort verified these findings.

Conclusions

The short-term prognostic values of 3A3B, AHEAD, and OPTIME-CHF score in ADHF patients were all poor, while the new PRS exhibited potential predictive ability. We demonstrated the QRS fraction of electrocardiogram as a novel predictor for the short-term outcomes of ADHF for the first time. Our findings might help to recognize high-risk ADHF patients.

Prognostic risk scores for patients with heart failure

Heart failure has many causes. Although new drugs, devices and technologies are available, the survival rate and prognosis of patients with heart failure remain poor, placing a significant burden on individuals and society. Attempts to improve outcomes for patients with heart failure include developing prognostic risk scores. With medical advances, however, previous heart failure risk scores are not fully applicable to current practice, particularly because of the classification as heart failure with reduced ejection fraction, heart failure with mildly reduced ejection fraction, and heart failure with preserved ejection fraction. This article describes the use of risk prediction scores for heart failure patients with different clinical status and discusses their clinical applicability.

Risk prediction model of in-hospital mortality in heart failure with preserved ejection fraction and mid-range ejection fraction: a retrospective cohort study

Aim: To develop and validate internally a multivariate risk model for predicting the in-hospital mortality of patients with heart failure with preserved ejection fraction (HFpEF) and heart failure with mid-range ejection fraction (HFmrEF). Methods & results: The clinical data of 8172 inpatients with HFpEF and HFmrEF was used to establish a retrospective database. These patients, among whom 307 in-hospital deaths (3.8%) occurred, were randomly assigned to derivation and verification cohort. Among the extracted data from the derivation cohort were nine variables significantly related to in-hospital mortality, which were scored 0-4, for a total score of 24, which allowed formation of a risk predictive model. The verification cohort was then used to validate the discrimination and calibration capacities of this predictive model: the area under curve equaled 0.8575 (0.8285, 0.8865) for the derivation cohort, and 0.8323 (0.7999, 0.8646) for the verification cohort. According to this risk score, we divided patients into four risk classes (low-, medium-, high- and extremely high-risk) and revealed that the risk of in-hospital mortality increased with increasing risk class with an obvious linear relationship between actual and predicted mortality (r = 0.998, p < 0.001). Conclusion: The model based on nine common clinical variables should provide an accurate prediction of in-hospital mortality and appears to be a reliable risk classification system for patients with HFpEF and HFmrEF.

In-hospital Heart Rate Reduction With Beta Blockers and Ivabradine Early After Recovery in Patients With Acute Decompensated Heart Failure Reduces Short-Term Mortality and Rehospitalization

Objective: In the past years, heart rate (HR) has emerged as a highly relevant modifiable risk factor for heart failure (HF) patients. However, most of the clinical trials so far evaluated the role of HR in stable chronic HF cohorts. The aim of this multi-center, prospective observational study was to assess the association between HR and therapy with HR modulators (beta blockers, ivabradine, or a combination of ivabradine and beta blockers) at hospital discharge with patients' cardiovascular mortality and re-hospitalization at 6 months in acutely decompensated HF patients.

Materials and Methods: We recruited 289 HF patients discharged alive after admission for HF decompensation from 10 centers in northern Italy over 9 months (from April 2017 to January 2018). The primary endpoint was the combination of cardiovascular mortality or re-hospitalizations for HF at 6 months.

Results: At 6 months after discharge, 64 patients were readmitted (32%), and 39 patients died (16%). Multivariate analysis showed that HR at discharge ≥ 90 bpm (OR = 8.47; p = 0.016) independently predicted cardiovascular mortality, while therapy with beta blockers at discharge was found to reduce the risk of the composite endpoint. In patients receiving HR modulators the event rates for the composite endpoint, all-cause mortality, and cardiovascular mortality were lower than in patients not receiving HR modulators.

Conclusions: Heart rate at discharge ≥90 bpm predicts cardiovascular mortality, while therapy with beta blockers is negatively associated with the composite endpoint of cardiovascular mortality and hospitalization at 6 months in acutely decompensated HF patients. Patients receiving a HR modulation therapy at hospital discharge showed the lowest rate of cardiovascular mortality and re-hospitalization.

Risk Scores and Prediction Models in Chronic Heart Failure: A Comprehensive Review

BACKGROUND

Heart failure affects a substantial proportion of the adult population, with an estimated prevalence of 1-2% in developed countries. Over the previous decades, many prediction models have been introduced for this specific population in an attempt to better stratify and manage heart failure patients.

OBJECTIVE

The aim of this study is the systematic review of recent, relevant literature regarding risk scores or prediction models in ambulatory patients with an established diagnosis of chronic heart failure.

METHODS

We conducted a systematic search of the literature in PubMed and CENTRAL from their inception up till December 2019 for studies assessing the performance of risk scores and prediction models and original research studies. Grey literature was searched as well. This review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement.

RESULTS

We included 16 eligible studies in this systematic review. Major heart failure risk scores derived from large heart failure populations were among the included studies. Due to significant heterogeneity regarding the main endpoints, a direct comparison of the included prediction scores was inevitable. The majority referred to patients with heart failure with reduced ejection fraction, while only two out of 16 prediction scores have been developed exclusively for heart failure patients with preserved ejection fraction. Ischemic heart disease was the most common aetiology of heart failure in the included studies. Finally, more than half of the prediction scores have not been externally validated.

CONCLUSION

Prediction models aiming at heart failure patients with a preserved or mid-range ejection fraction are lacking. Prediction scores incorporating recent advances in pharmacotherapy should be developed in the future.

Risk Stratification and Efficacy of Spironolactone in Patients with Heart Failure with Preserved Ejection Fraction: Secondary Analysis of the TOPCAT Randomized Clinical Trial

PURPOSE

We aimed to develop a simple risk score for patients with HFpEF and assessed the efficacy of spironolactone across baseline risk.

METHODS

We developed risk stratification scheme for cardiovascular death in placebo arm of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist trial (TOPCAT). We screened candidate risk indicators and determined strong risk predictors using COX regression. The absolute risk reduction (ARR) in cardiovascular death with spironolactone was evaluated across baseline risk groups. COX regressions were performed to assess the hazard ratios (HRs) of spironolactone therapy for cardiovascular death and drug discontinuation in each risk category.

RESULTS

A simple risk score scheme was constructed based on five risk indicators weighted by estimates from the model, including age, diastolic blood pressure, renal dysfunction, white blood cell, and left ventricular ejection fraction. The risk score scheme showed good discrimination in placebo cohort (C index=0.70). ARR with spironolactone therapy was observed only in patients at very high risk (7.9%). Spironolactone therapy significantly reduced the risk of cardiovascular death in the very high-risk group (HR: 0.57; 95%CI, 0.39-0.84; P =0.005 and P for interaction 0.03) but showed similar risk of drug discontinuation across risk categories (P for interaction=0.928).

CONCLUSION

This simple risk score stratifies patients with HFpEF by their baseline risk of cardiovascular death. Patients at very high risk derive great benefits from spironolactone therapy. This easy-to-use risk score provides a practical tool that can facilitate risk stratification and tailoring therapy for those who benefit most from spironolactone.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT00094302.

C2HEST score predicts clinical outcomes in heart failure with preserved ejection fraction: a secondary analysis of the TOPCAT trial

BACKGROUND

The C₂HEST score has been validated for predicting AF in the general population or post-stroke patients. We aimed to assess whether this risk score could predict incident AF and other clinical outcomes in heart failure with preserved ejection fraction (HFpEF) patients.

METHODS

A total of 2202 HFpEF patients without baseline AF in the TOPCAT trial were stratified by baseline C₂HEST score. Cox proportional hazard model and competing risk regression model was used to explore the relationship between C₂HEST score and outcomes, including incident AF, stroke, all-cause death, cardiovascular death, any hospitalization, and HF hospitalization. The discriminative ability of the C₂HEST score for various outcomes was assessed by calculating the area under the curve (AUC).

RESULTS

The incidence rates of incident AF, stroke, all-cause death, cardiovascular death, any hospitalization, and HF hospitalization were 1.79, 0.70, 3.81, 2.42, 15.50, and 3.32 per 100 person-years, respectively. When the C₂HEST score was analyzed as a continuous variable, increased C₂HEST score was associated with increased risk of incident AF (HR 1.50, 95% CI 1.29-1.75), as well as increased risks of all-cause death, cardiovascular death, any hospitalization, and HF hospitalization. The AUC for the C₂HEST score in predicting incident AF (0.694, 95% CI 0.640-0.748) was higher than all-cause death, cardiovascular death, any hospitalization, or HF hospitalization.

CONCLUSIONS

The C₂HEST score could predict the risk of incident AF as well as death and hospitalization with moderately good predictive abilities in patients with HFpEF. Its simplicity may allow the possibility of quick risk assessments in busy clinical settings.

A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts

BACKGROUND

Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF.

METHODS

Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration.

RESULTS

3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF < 40%), 588 as having HF with midrange EF (HFmrEF; EF 40-49%), and 621 as having HF with preserved EF (HFpEF; EF ≥ 50%). A total of 11 readily available variables built up the prognostic index. For four of these predictor variables, namely systolic blood pressure, serum creatinine, myocardial infarction, and diabetes, the effect differed across the three HF phenotypes. With a weighted IECV-adjusted AUC of 0.79 (0.74-0.83) for HFrEF, 0.74 (0.70-0.79) for HFmrEF, and 0.74 (0.71-0.77) for HFpEF, the model showed excellent discrimination. Moreover, there was a good agreement between the average observed and predicted 1-year mortality risks, especially after recalibration of the baseline mortality risks.

CONCLUSIONS

Our HF phenotype stratified model showed excellent generalizability across four European cohorts and may provide a useful tool in HF phenotype-specific clinical decision-making.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

BACKGROUND

Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records.

METHODS

In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

RESULTS

Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival.

CONCLUSIONS

This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

Modified Glasgow Prognostic Score is a novel predictor of clinical outcome in heart failure with preserved ejection fraction

Objectives. Although the modified Glasgow Prognostic Score (mGPS) has been reported to have prognostic value in patients with various cancers, the association between mGPS and prognosis in patients with heart diseases have not been well studied. The aim of this study was to evaluate the predictive value of mGPS in outcomes of patients with heart failure and preserved ejection fraction (HFpEF). Design. We prospectively followed consecutive adult patients with HFpEF admitted to the cardiology outpatient unit. Echocardiographic and laboratory data were recorded at enrolment. mGPS was scored as 0, 1, or 2 based on C-reactive protein (CRP) and albumin levels. Patients with both elevated CRP (>1 mg/dL) and hypoalbuminemia (<3.5 g/dL) are given mGPS of 2, patients with serum CRP ≤ 1 g/dL with or without hypoalbuminemia received scores of 0. Patients with only elevated CRP levels received mGPS of 1. The primary composite endpoint of the study was all-cause mortality or heart failure hospitalization through one year. Results. A total of 315 HFpEF outpatients were included, and 42 (13.3%) reached the primary endpoint at one year of follow-up. Compared to patients without mortality or heart failure-related hospitalization, patients who reached the primary endpoint during follow-up were older, were more likely be symptomatic, had higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and mGPS levels at study entry. Multivariate analysis showed that both NT-proBNP and mGPS were independent predictors of primary composite endpoint. Combining NT-proBNP with mGPS improved its prognostic value with an increase of area under the receiver operating characteristic curve from 0.759 to 0.822 (p = .001). Conclusion. This is the first study which demonstrates that mGPS is a predictor of outcomes in patients with HFpEF.

Prognostic Significance of Serum Cholinesterase Level in Patients With Acute Decompensated Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT-HFpEF Registry

Background

Malnutrition is one of the most important comorbidities in patients with heart failure with preserved ejection fraction. We recently reported the prognostic significance of serum cholinesterase level and superior predictive power of cholinesterase level to other objective nutritional indices such as the controlling nutritional status score, prognostic nutritional index, and geriatric nutritional risk index in patients with acute decompensated heart failure. The aim of this study was to clarify the prognostic role of cholinesterase in patients with heart failure with preserved ejection fraction/acute decompensated heart failure and investigate incremental cholinesterase value.

Methods and Results

We prospectively studied 274 consecutive patients from the PURSUIT‐HFpEF (Prospective Multicenter Observational Study of Patients with Heart Failure With Preserved Ejection Fraction) study. During a follow‐up period of 1.2±0.6 years, 56 patients reached the composite end points (cardiovascular death and readmission for worsening heart failure). In the multivariable Cox analysis, cholinesterase level was significantly associated with the composite end points after adjustment for major confounders. A Kaplan–Meier analysis revealed that patients with low cholinesterase levels (stratified by tertile) had significantly greater risk of reaching the composite end points than those with middle or high cholinesterase levels (P=0.0025). Cholinesterase level showed the best C‐statistics (0.703) for prediction of the composite end points among the objective nutritional indices. C‐statistics of the Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score for prediction of the composite end points were improved when cholinesterase level was added (C‐statistics, from 0.601 to 0.705; P=0.0408).

Conclusions

Cholinesterase was a useful prognostic marker for prediction of adverse outcome in patients with heart failure with preserved ejection fraction/acute decompensated heart failure.