A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE

An explainable model for predicting Worsening Heart Failure based on genetic programming

Heart Failure (HF) poses a challenge for our health systems, and early detection of Worsening HF (WHF), defined as a deterioration in symptoms and clinical and instrumental signs of HF, is vital to improving prognosis. Predicting WHF in a phase that is currently undiagnosable by physicians would enable prompt treatment of such events in patients at a higher risk of WHF. Although the role of Artificial Intelligence in cardiovascular diseases is becoming part of clinical practice, especially for diagnostic and prognostic purposes, its usage is often considered not completely reliable due to the incapacity of these models to provide a valid explanation about their output results. Physicians are often reluctant to make decisions based on unjustified results and see these models as black boxes. This study aims to develop a novel diagnostic model capable of predicting WHF while also providing an easy interpretation of the outcomes. We propose a threshold-based binary classifier built on a mathematical model derived from the Genetic Programming approach. This model clearly indicates that WHF is closely linked to creatinine, sPAP, and CAD, even though the relationship of these variables and WHF is almost complex. However, the proposed mathematical model allows for providing a 3D graphical representation, which medical staff can use to better understand the clinical situation of patients. Experiments conducted using retrospectively collected data from 519 patients treated at the HF Clinic of the University Hospital of Salerno have demonstrated the effectiveness of our model, surpassing the most commonly used machine learning algorithms. Indeed, the proposed GP-based classifier achieved a 96% average score for all considered evaluation metrics and fully supported the controls of medical staff. Our solution has the potential to impact clinical practice for HF by identifying patients at high risk of WHF and facilitating more rapid diagnosis, targeted treatment, and a reduction in hospitalizations.

Predictive value of a comprehensive atrial assessment with cardiac magnetic resonance in non-ischemic cardiomyopathy: keep it simple

Cardiac magnetic resonance (CMR) can provide a multi-parametric evaluation of left atrial (LA) size and function. A complete CMR-based LA assessment might improve the risk stratification of patients with non-ischemic dilated cardiomyopathy (DCM). We performed a comprehensive CMR-based evaluation of LA size and function, in order to assess the prognostic impact of specific LA parameters in DCM. Secondary analysis of a prospective registry (UHSM-CMR study, NCT02326324) including 648 consecutive patients with DCM and CMR evaluation of LA area and LA length. Of these, 456 had complete LA assessment covering reservoir, conduit and booster pump function and including LA reservoir strain evaluated with feature tracking. The heart failure (HF) endpoint included HF hospitalizations, HF death and heart transplant. The arrhythmic endpoint included ventricular arrhythmias (VA) (sustained or treated by implantable defibrillator) and sudden death (SD). At median follow-up of 23 months, 34 patients reached the HF endpoint; in a multivariable model including NYHA class and LVEF, LA length had incremental predictive value. LA length ≥ 69 mm was the best cut-off to predict HF events (adjusted HR 2.3, p = 0.03). Among the 456 patients with comprehensive LA assessment, only LA length was independently associated with the HF endpoint after adjusting for LVEF and NYHA class. By contrast, no LA parameter independently predicted the arrhythmic risk. In DCM patients, LA length is an independent predictor of HF events, showing stronger association than other more complex parameters of LA function. No atrial parameter predicts the risk of VA and SD.

The different risk of new-onset, chronic, worsening, and advanced heart failure: A systematic review and meta-regression analysis

AIMS

Heart failure (HF) is a chronic and progressive syndrome associated with a poor prognosis. While it may seem intuitive that the risk of adverse outcomes varies across the different stages of HF, an overview of these risks is lacking. This study aims to determine the risk of all-cause mortality and HF hospitalizations associated with new-onset HF, chronic HF (CHF), worsening HF (WHF), and advanced HF.

METHODS AND RESULTS

We performed a systematic review of observational studies from 2012 to 2022 using five different databases. The primary outcomes were 30-day and 1-year all-cause mortality, as well as 1-year HF hospitalization. Studies were pooled using random effects meta-analysis, and mixed-effects meta-regression was used to compare the different HF groups. Among the 15 759 studies screened, 66 were included representing 862 046 HF patients. Pooled 30-day mortality rates did not reveal a significant distinction between hospital-admitted patients, with rates of 10.13% for new-onset HF and 8.11% for WHF (p = 0.10). However, the 1-year mortality risk differed and increased stepwise from CHF to advanced HF, with a rate of 8.47% (95% confidence interval [CI] 7.24-9.89) for CHF, 21.15% (95% CI 17.78-24.95) for new-onset HF, 26.84% (95% CI 23.74-30.19) for WHF, and 29.74% (95% CI 24.15-36.10) for advanced HF. Readmission rates for HF at 1 year followed a similar trend.

CONCLUSIONS

Our meta-analysis of observational studies confirms the different risk for adverse outcomes across the distinct HF stages. Moreover, it emphasizes the negative prognostic value of WHF as the first progressive stage from CHF towards advanced HF.

Factors associated with disease progression in patients with atrial fibrillation and heart failure anticoagulated with rivaroxaban

BACKGROUND

Patients with atrial fibrillation (AF) and heart failure (HF) have a high risk of thromboembolism and other outcomes and anticoagulation is recommended.

HYPOTHESIS

This study was aimed to explore the risk factors associated with HF worsening in patients with AF and HF taking rivaroxaban in Spain.

METHODS

Multicenter, prospective, observational study that included adults with AF and chronic HF, receiving rivaroxaban ≥4 months before entering. HF worsening was defined as first hospitalization or emergency visit because of HF exacerbation.

RESULTS

A total of 672 patients from 71 Spanish centers were recruited, of whom 658 (97.9%) were included in the safety analysis and 552 (82.1%) in the per protocol analysis. At baseline, mean age was 73.7 ± 10.9 years, 64.9% were male, CHA2 DS2 -VASc was 4.1 ± 1.5, HAS-BLED was 1.6 ± 0.9% and 51.3% had HF with preserved ejection fraction. After 24 months of follow-up, 24.9% of patients developed HF worsening, 11.6% died, 2.9% had a thromboembolic event, 3.1% a major bleeding, 0.5% an intracranial bleeding and no patient had a fatal hemorrhage. Older age, the history of chronic obstructive pulmonary disease, the previous use of vitamin K antagonists, and restrictive or infiltrative cardiomyopathies, were independently associated with HF worsening. Only 6.9% of patients permanently discontinued rivaroxaban treatment.

CONCLUSIONS

Approximately one out of four patients with HF and AF treated with rivaroxaban developed a HF worsening episode after 2 years of follow-up. The identification of those factors that increase the risk of HF worsening could be helpful in the comprehensive management of this population.

Analysis and prediction of readmissions for heart failure in the first year after discharge with INCA score

To determine the readmissions trends and the comorbidities of patients with heart failure that most influence hospital readmission rates. Heart failure (HF) is one of the most prevalent health problems as it causes loss of quality of life and increased health-care costs. Its prevalence increases with age and is a major cause of re-hospitalisation within 30 days after discharge. INCA study had observational and ambispective design, including 4,959 patients from 2000 to 2019, with main diagnosis of HF in Extremadura (Spain). The variables examined were collected from discharge reports. To develop the readmission index, capable of discriminating the population with higher probability of re-hospitalisation, a Competing-risk model was generated. Readmission rate have increased over the period under investigation. The main predictors of readmission were: age, diabetes mellitus, presence of neoplasia, HF without previous hospitalisation, atrial fibrillation, anaemia, previous myocardial infarction, obstructive pulmonary disease (COPD) and chronic kidney disease (CKD). These variables were assigned values with balanced weights, our INCA index showed that the population with values greater than 2 for men and women were more likely to be re-admitted. Previous HF without hospital admission, CKD, and COPD appear to have the greatest effect on readmission. Our index allowed us to identify patients with different risks of readmission.

Time to benefit of heart rate reduction with ivabradine in patients with heart failure and reduced ejection fraction

AIMS

In the SHIFT (Systolic Heart failure treatment with the If inhibitor ivabradine Trial, ISRCTN70429960) study, ivabradine reduced cardiovascular death or heart failure (HF) hospitalizations in patients with HF and reduced ejection fraction (HFrEF) in sinus rhythm and with a heart rate (HR) ≥70 bpm. In this study, we sought to determine the clinical significance of the time durations of HR reduction and the significant treatment effect on outcomes among patients with HFrEF.

METHODS AND RESULTS

The time to statistically significant reduction of the primary outcome (HF hospitalization and cardiovascular death) and its components, all-cause death, and HF death, were assessed in a post-hoc analysis of the SHIFT trial in the overall population (HR ≥70 bpm) and at HR ≥75 bpm, representing the approved label in many countries. Compared to placebo, the primary outcome and HF hospitalizations were significantly reduced at 102 days, while there was no effect on cardiovascular death, all-cause death, and HF death at HR ≥70 bpm. In the population with a baseline HR ≥75 bpm, a reduction of the primary outcome occurred after 67 days, HF hospitalization after 78 days, cardiovascular death after 169 days, death from HF after 157 days and all-cause death after 169 days.

CONCLUSION

Treatment with ivabradine should not be deferred in patients in sinus rhythm with a HR of ≥70 bpm to reduce the primary outcome and HF hospitalizations, in particular in patients with HR ≥75 bpm. At HR ≥75 bpm, the time to risk reduction was shorter for reduction of hospitalization and mortality outcomes in patients with HFrEF after initiation of guideline-directed medication, including beta-blockers at maximally tolerated doses.

Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients

Although many models are available to predict prognosis of heart failure patients, most tools combining survival analysis are based on proportional hazard model. Non-linear machine learning algorithms would overcome the limitation of the time-independent hazard ratio assumption and provide more information in readmission or mortality prediction among heart failure patients. The present study collected the clinical information of 1796 hospitalized heart failure patients surviving during hospitalization in a Chinese clinical center from December 2016 to June 2019. A traditional multivariate Cox regression model and three machine learning survival models were developed in derivation cohort. Uno's concordance index and integrated Brier score in validation cohort were calculated to evaluate the discrimination and calibration of different models. Time-dependent AUC and Brier score curves were plotted to assess the performance of models at different time phases.

One-year rehospitalisation and mortality after acute heart failure hospitalisation: a competing risk analysis

OBJECTIVE

To identify factors that independently predict the risk of rehospitalisation and death after acute heart failure (AHF) hospital discharge in a real-world setting, considering death without rehospitalisation as a competing event.

METHODS

Single-centre, retrospective, observational study enrolling 394 patients discharged from an index AHF hospitalisation. Overall survival was evaluated using Kaplan-Meier and Cox regression models. For the risk of rehospitalisation, survival analysis considering competing risks was performed: rehospitalisation was the event of interest, and death without rehospitalisation was the competing event.

RESULTS

During the first year after discharge, 131 (33.3%) patients were rehospitalised for AHF and 67 (17.0%) died without being readmitted; the remaining 196 patients (49.7%) lived without further hospitalisations. The 1-year overall survival estimate was 0.71 (SE=0.02). After adjusting for gender, age and left ventricle ejection fraction, the results showed that the risk of death was higher in patients with dementia, higher levels of plasma creatinine (PCr), lower levels of platelet distribution width (PDW) and at Q4 of red cell distribution width (RDW). Multivariable models showed that the risk of rehospitalisation was increased in patients with atrial fibrillation, higher PCr or taking beta-blockers at discharge. Furthermore, the risk of death without AHF rehospitalisation was higher in males, those aged ≥80 years, patients with dementia or RDW at Q4 on admission (compared with Q1). Taking beta-blockers at discharge and having a higher PDW on admission reduced the risk of death without rehospitalisation.

CONCLUSION

When assessing rehospitalisation as a study endpoint, death without rehospitalisation should be considered a competing event in the analyses. Data from this study reveal that patients with atrial fibrillation, renal dysfunction or taking beta-blockers are more likely to be rehospitalised for AHF, while older men with dementia or high RDW are more prone to die without hospital readmission.

Treatment outcomes and associated factors among chronic ambulatory heart failure patients at Jimma Medical Center, South West Ethiopia: prospective observational study

BACKGROUND

Heart failure has been one of the major causes of hospitalization across the world. Focusing on the treatment outcomes of ambulatory heart failure patients will reduce the burden of heart failure such as hospitalization and improve patient quality of life. Even if research is conducted on acute heart failure patients, there is limited data about treatment outcomes of chronic ambulatory heart failure patients. Therefore, this study aimed to assess treatment outcomes and associated factors of chronic ambulatory heart failure patients at Jimma Medical Center, South West Ethiopia.

METHODS

A hospital-based prospective observational study was conducted on 242 chronic ambulatory heart failure patients at Jimma Medical Center from November 2020 to June 2021. The data were collected with pretested data collection format, and analyzed with Statistical Package for Social Sciences version 23. Both univariate and multivariate logistic regression model were used to identify factors associated with treatment outcomes of outpatient heart failure, and with a reported p value < 0.05, 95% confidence interval (CI) was considered statistical significance.

RESULT

From 242 patients, 126 (52.1%) were males and 121 (50.0%) patients were aged between 45 and 65 years. Regarding treatment outcomes, 51 (21.1%) of patients were hospitalized, and 58 (24.0%) and 28 (11.6%) of patients had worsened and improved clinical states respectively. Clinical inertia [AOR = 2.820; 95% CI (1.301, 6.110), p = 0.009], out-of-pocket payment [AOR = 2.790; 95% CI (1.261, 6.172), p = 0.011] and New York Heart Association class II [AOR = 2.534; 95% CI (1.170, 5.488), p = 0.018] were independent predictors of hospitalization.

CONCLUSION

Hospitalization of ambulatory heart failure patients was relatively high. More than half of the patients had clinical inertia. And also, this study showed most ambulatory HF patients had inadequate self-care. Clinical inertia, out-of-pocket payment, and New York Heart Association class II were independent predictors of hospitalization in ambulatory heart failure patients. Therefore, it is better to give more attention to ambulatory heart failure patients to prevent hospitalization and the burden of heart failure.

Positive airway pressure therapy in heart failure patients comorbid with obstructive sleep apnea: Cardiovascular outcomes and nighttime-duration effect

BACKGROUND

Comorbidity of obstructive sleep apnea (OSA) and heart failure (HF) is becoming increasingly common. This is a global analysis of positive airway pressure (PAP) efficacy for the cardiovascular outcomes in those HF Patients with comorbid OSA.

METHODS

Related randomized controlled trials were included. Analysed indicators covered primary outcomes (cardiac function, motor ability and life quality) and secondary outcomes (blood pressure and OSA-accompanying symptoms). Weighted mean difference was used to analyse the PAP-control difference in different experimental phases and the baseline-post difference in different groups.

RESULTS

Compared with control group, PAP was associated with a 3.48% increase in left ventricle ejection fraction (LVEF) (p < .00001) and little decrease in heart rate (HR) (p = .67). Over 6 h of mean nighttime use was associated with a greater increase in LVEF of 5.21% (p = .0002) and a significant reduction in HR of 3.81 bpm (p = .03). There was no significant difference between PAP and control group in their association with change in motor ability and life quality. Besides, PAP was associated with a 13.08 mm Hg decrease in systolic blood pressure and great improvements in OSA-accompanying symptoms: Apnea-Hypopnea Index, -23.73 e/h; mean oxygen saturation, 1.86%; minimum oxygen saturation, 8.78%; Epworth Sleepiness Scale, -1.39 point; arousal index, -16.41 e/h. There was also no significant difference in diastolic blood pressure.

CONCLUSIONS

Positive airway pressure treatment improves cardiac function in HF patients with comorbid OSA, but the improved magnitude is associated with the duration of nighttime use rather than the duration of treatment.

Evaluation of the HEFESTOS scale to predict outcomes in emergency department acute heart failure patients

The HEFESTOS scale was developed in 14 Spanish primary care centres and validated in 9 primary care centres of other European countries. It showed good performance to predict death/hospitalisation during the first 30 days after an episode of acute heart failure (AHF), with c-statistics of 0.807/0.730 in the derivation/validation cohorts. We evaluated this scale in the emergency department (ED) setting, comparing it to the EHMRG and MEESSI scales in the ED and the EFFECT and GWTG scales in hospitalised patients, to predict 30-day outcomes, including death and hospitalisation. Consecutive AHF patients were enrolled in 34 Spanish EDs in January-February 2016, 2018, and 2019 with variables needed to calculate outcome scores. Thirty-day hospitalisation/death (together and separately) and post-discharge combined adverse event (ED revisit or hospitalisation for AHF or all-cause death) were determined for patients discharged home after ED care. Predictive capacity was assessed by c-statistic with 95% confidence intervals. Of 10,869 patients, 4,044 were included (median age: 83 years, 54% women). The performance of HEFESTOS was modest for 30-day hospitalisation/death, c-statistic=0.656 (0.637-0.675), hospitalisation, 0.650 (0.631-0.669), and death, 0.610 (0.576-0.644). Of 1,034 patients with scores for the 5 scales, HEFESTOS had the numerically highest c-statistic for hospitalisation/death at 30 days, 0.666 (0.627-0.704), vs. MEESSI= 0.650 (0.612-0.687, p=0.51), EFFECT=0.633 (0.595-0.672, p=0.21), GWTG=0.618 (0.578-0.657, p=0.06) and EHMRG=0.617 (0.577-0.704, p=0.07). Similar modest performances were observed for predicting hospitalisation [ranging from HEFESTOS=0.656 (0.618-0.695) to GWTG=0.603 (0.564-0.643)]. Conversely, prediction of 30-day death was good with the MEESSI=0.787 (0.728-845), EFFECT=0.754 (0.691-0.818) and GWTG=0.749 (0.689-0.809) scales, and modest with EHMRG=0.649 (0.581-0.717) and HEFESTOS=0.610 (0.538-0.683). Although the HEFESTOS scale was numerically better for predicting 30-day hospitalisation/death in ED AHF patients, its modest performance precludes routine use. Only 30-day mortality was adequately predicted by some scales, with the MEESSI achieving the best results.

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.

Remotely Monitored Cardiac Implantable Electronic Device Data Predict All-Cause and Cardiovascular Unplanned Hospitalization

Background Unplanned hospitalizations are common in patients with cardiovascular disease. The "Triage Heart Failure Risk Status" (Triage-HFRS) algorithm in patients with cardiac implantable electronic devices uses data from up to 9 device-derived physiological parameters to stratify patients as low/medium/high risk of 30-day heart failure (HF) hospitalization, but its use to predict all-cause hospitalization has not been explored. We examined the association between Triage-HFRS and risk of all-cause, cardiovascular, or HF hospitalization. Methods and Results A prospective observational study of 435 adults (including patients with and without HF) with a Medtronic Triage-HFRS-enabled cardiac implantable electronic device (cardiac resynchronization therapy device, implantable cardioverter-defibrillator, or pacemaker). Cox proportional hazards models explored association between Triage-HFRS and time to hospitalization; a frailty term at the patient level accounted for repeated measures. A total of 274 of 435 patients (63.0%) transmitted ≥1 high HFRS transmission before or during the study period. The remaining 161 patients never transmitted a high HFRS. A total of 153 (32.9%) patients had ≥1 unplanned hospitalization during the study period, totaling 356 nonelective hospitalizations. A high HFRS conferred a 37.3% sensitivity and an 86.2% specificity for 30-day all-cause hospitalization; and for HF hospitalizations, these numbers were 62.5% and 85.6%, respectively. Compared with a low Triage-HFRS, a high HFRS conferred a 4.2 relative risk of 30-day all-cause hospitalization (8.5% versus 2.0%), a 5.0 relative risk of 30-day cardiovascular hospitalization (3.6% versus 0.7%), and a 7.7 relative risk of 30-day HF hospitalization (2.0% versus 0.3%). Conclusions In patients with cardiac implantable electronic devices, remotely monitored Triage-HFRS data discriminated between patients at high and low risk of all-cause hospitalization (cardiovascular or noncardiovascular) in real time.

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.

A review of literature on risk prediction tools for hospital readmissions in older adults

PURPOSE

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

DESIGN/METHODOLOGY/APPROACH

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

FINDINGS

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5-0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

RESEARCH LIMITATIONS/IMPLICATIONS

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

ORIGINALITY/VALUE

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

Risk stratification in heart failure decompensation in the community: HEFESTOS score

Aims

Because evidence regarding risk stratification predicting prognosis of patients with heart failure (HF) decompensation attended in primary care is lacking, we developed and externally validated a model to forecast death/hospitalization during the first 30 days after an episode of decompensation. The predictive model is based on variables easily obtained in primary care settings.

Methods and results

HEFESTOS is a multinational study consisting of a derivation cohort of HF patients recruited in 14 primary healthcare centres in Barcelona and a validation cohort from primary healthcare in 9 other European countries. The derivation and validation cohorts included 561 and 250 patients, respectively. Percentages of women in the derivation and validation cohorts were 56.3% and 47.6% (P = 0.026), respectively. Mean age was 82.2 years (SD 8.03) in the derivation cohort, and 79.3 years (SD 10.3) in the validation one (P = 0.001). HF with preserved ejection fraction represented 72.1% in the derivation cohort and 58.8% in the validation one (P = 0.004). Mortality/hospitalization during the first 30 days after a decompensation episode was 30.5% and 26% (P = 0.225) for the derivation and validation cohorts, respectively. Multivariable logistic regression models were performed to develop a score of risk. The identified predictors were worsening of dyspnoea [odds ratio (OR): 2.5; P = 0.001], orthopnoea (OR: 2.16; P = 0.01), paroxysmal nocturnal dyspnoea (OR: 2.25; P = 0.01), crackles (OR: 2.35; P = 0.01), New York Heart Association functional class III/IV (OR: 2.11; P = 0.001), oxygen saturation ≤ 90% (OR: 4.98; P < 0.001), heart rate > 100 b.p.m. (OR: 2.72; P = 0.002), and previous hospitalization due to HF (OR: 2.45; P < 0.001). The model showed an area under the curve (AUC) of 0.807, 95% confidence interval (CI): [0.770; 0.845] in the derivation cohort and AUC 0.73, 95% CI: [0.660; 0.808] in the validation one. No significant differences between both cohorts were observed (P = 0.08). Regarding probability of hospitalization/death, three risk groups were defined: low <5%, medium 5–20%, and high >20%. Outcome incidence was 2.7% for the low‐risk group, 12.8% for medium risk, and 46.2% for high risk in the derivation cohort, and 9.1%, 12.9%, and 39.6% in the validation one.

Conclusions

The HEFESTOS score, based on variables easily accessible in a community setting and validated in an external European cohort, properly predicted the risk of death/hospitalization during the first 30 days after an HF decompensation episode.

A 3-Biomarker 2-Point-Based Risk Stratification Strategy in Acute Heart Failure

Introduction and Objectives: Most multi-biomarker strategies in acute heart failure (HF) have only measured biomarkers in a single-point time. This study aimed to evaluate the prognostic yielding of NT-proBNP, hsTnT, Cys-C, hs-CRP, GDF15, and GAL-3 in HF patients both at admission and discharge. Methods: We included 830 patients enrolled consecutively in a prospective multicenter registry. Primary outcome was 12-month mortality. The gain in the C-index, calibration, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) was calculated after adding each individual biomarker value or their combination on top of the best clinical model developed in this study (C-index 0.752, 0.715-0.789) and also on top of 4 currently used scores (MAGGIC, GWTG-HF, Redin-SCORE, BCN-bioHF). Results: After 12-month, death occurred in 154 (18.5%) cases. On top of the best clinical model, the addition of NT-proBNP, hs-CRP, and GDF-15 above the respective cutoff point at admission and discharge and their delta during compensation improved the C-index to 0.782 (0.747-0.817), IDI by 5% (p < 0.001), and NRI by 57% (p < 0.001) for 12-month mortality. A 4-risk grading categories for 12-month mortality (11.7, 19.2, 26.7, and 39.4%, respectively; p < 0.001) were obtained using combination of these biomarkers. Conclusion: A model including NT-proBNP, hs-CRP, and GDF-15 measured at admission and discharge afforded a mortality risk prediction greater than our clinical model and also better than the most currently used scores. In addition, this 3-biomarker panel defined 4-risk categories for 12-month mortality.

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 factors for hospital readmission of patients with heart failure: A cohort study

Aim:

The aim of this study was to develop a risk factor model for hospital readmission in patients with heart failure.

Background:

Identification of risk factors and predictors of readmission to hospital in patients with heart failure is very crucial for improved clinical outcomes.

Objective:

The objective of the current study was to investigate and delineate the risk factors that may be implicated in putting a patient at greater risk of readmission due to uncontrolled heart failure.

Materials and Methods:

This is a prospective follow-up cohort study of 170 patients with heart failure at a tertiary hospital in Al Ain city in the United Arab Emirates. We have developed a risk factor model based on the recommendations of validated published data. We have used univariate and multivariate logistic regression analyses on structured steps based on the published data. The main outcome was the risk factors for readmission to hospital due to heart failure.

Results:

A final predictive model (10 variables) was produced for unplanned readmission of patients with heart failure. The risk factors identified in the final model with their odds ratios (ORs) and confidence intervals (CIs) were as follows: four or more prescribed medicines (OR = 4.13; CI = 3.5–4.1; P = 0.003), more than twice daily dosing regimen (OR = 2.34; CI = 1.0–5.0; P = 0.023), poor knowledge of prescribed medications (OR = 4.24; CI = 1.213–14.781; P = 0.006), diabetes mellitus (OR = 3.78; CI = 1.6–8.7; P = 0.006), edema (OR = 2.64; CI = 1.2–5.6; P = 0.011), being house bound (OR = 2.77; CI = 1.2–6.2; P = 0.014), and being prescribed diuretics (OR = 3.69; CI = 1.4–9.2; P = 0.042).

Conclusion:

The specificity of the developed risk prediction model was 82.2%, the sensitivity was 74.3%, and the overall accuracy was 72.9%. The model can be emulated in population with similar characteristics to prevent early readmission of patient with heart failure.

Changes in causes of death and influence of therapeutic improvement over time in patients with heart failure and reduced ejection fraction

INTRODUCTION AND OBJECTIVES

In patients with heart failure and reduced ejection fraction (HFrEF), several therapies have been proven to reduce mortality in clinical trials. However, there are few data on the effect of the use of evidence-based therapies on causes of death in clinical practice.

METHODS

This study included 2351 outpatients with HFrEF (< 40%) from 2 multicenter prospective registries: MUSIC (n=641, period: 2003-2004) and REDINSCOR I (n=1710, period: 2007-2011). Variables were recorded at inclusion and all patients were followed-up for 4 years. Causes of death were validated by an independent committee.

RESULTS

Patients in REDINSCOR I more frequently received beta-blockers (85% vs 71%; P <.001), mineralocorticoid antagonists (64% vs 44%; P <.001), implantable cardioverter-defibrillators (19% vs 2%; P <.001), and resynchronization therapy (7.2% vs 4.8%; P=.04). In these patients, sudden cardiac death was less frequent than in those in MUSIC (6.8% vs 11.4%; P <.001). After propensity score matching, we obtained 2 comparable populations differing only in treatments (575 vs 575 patients). In patients in REDINSCOR I, we found a lower risk of total mortality (HR, 0.70; 95%CI, 0.57-0.87; P=.001) and sudden cardiac death (sHR, 0.46; 95%CI, 0.30-0.70; P <.001), and a trend toward lower mortality due to end-stage HF (sHR, 0.73; 95%CI, 0.53-1.01; P=.059), without differences in other causes of death (sHR, 1.17; 95%CI, 0.78-1.75; P=.445), regardless of functional class.

CONCLUSIONS

In ambulatory patients with HFrEF, implementation of evidence-based therapies was associated with a lower risk of death, mainly due to a significant reduction in sudden cardiac death.

Short term prognosis of heart failure after first hospital admission

BACKGROUND

Death and unexpected readmission are frequent among heart failure patients. We aimed to assess 30-day readmission and mortality rate as well as to identify predictive factors for patients discharged from a first HF related hospital admission.

METHODS AND RESULTS

Retrospective, single-center, cohort study, using administrative data from a tertiary care hospital in Barcelona, Spain. Patients discharged alive from a first HF related admission from 2010 to 2014 were assessed for 30-day death, readmission and adverse outcome rate. A Linear Logistic Regression Model was fitted for each outcome. The set accounted for 3642 patients; 50.1% female and 49.9% male. Mean age was 76 years (SD=12). 30-Days rates were 9.2% for readmission, 5.6% for death and 13.8% for adverse outcome. Admission to an ED within 30 days was strongly linked to readmission (OR=6.97), death (OR=2.31) and adverse outcome (OR=8.55), as well as chronic kidney disease (OR=1.44/1.61/2.86 respectively). Discharge to a Long Stay Care (LSC) facility was linked to lower readmission and adverse event rates (OR=.57 and OR=.15).

CONCLUSION

Pre and post-index discharge use of health care resources is related to adverse outcome rates. Our findings point out the potential benefit for a more tailored approach in the management of HF patients.

Evaluating risk prediction models for adults with heart failure: A systematic literature review

Background

The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers’ demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB).

Methods

Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including all-cause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation.

Conclusions

The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined.

Registration number

The SLR was registered in Prospero (ID: CRD42018100709).

Global longitudinal strain can predict heart failure exacerbation in stable outpatients with ischemic left ventricular systolic dysfunction

Background

Despite advancements in pharmacological and device-based treatment, heart failure (HF) continues to impose an enormous burden for health care system worldwide. Decompensation of HF is one of the main causes of hospitalization, therefore the identification of patients with the highest risk of such complication is still of great clinical importance. The prognostic significance and utility of global longitudinal strain (GLS) has been previously studied in patients with the broad spectrum of cardiovascular diseases in various endpoints, however its role in assessing the risk of hospitalization due to HF exacerbation of optimally treated outpatients has not been fully explored. Therefore, the aim of the study was to verify whether the GLS of the left ventricle (LV) derived by 2D speckle tracking echocardiography has, independently of other well-known clinical parameters, an additional impact on the risk of HF decompensation in stable patients with LV systolic dysfunction of ischemic origin.

Methods

In 193 clinically stable HF outpatients with LV ejection fraction (LVEF) ≤ 50%, GLS, additionally to other clinical parameters, was analyzed. During 34 (14–71) months of follow-up, 58 patients were hospitalized due to HF decompensation (EVENT).

Results

EVENT was significantly associated with age, QRS width, NYHA functional class, left atrium diameter, LV systolic and diastolic volume, LVEF, hemoglobin, brain natriuretic peptide, diuretic treatment, absence of beta-blockers, impaired renal function and history of diabetes in univariate Cox analyzes. GLS with pre-specified cut-off value of -9.4% was also significantly associated with the EVENT (HR 15.16; 95% CI 1.81–126.91). After adjusting for above-mentioned parameters GLS was still a significant predictor of hospitalization due to HF decompensation.

Conclusions

GLS measurement can provide incremental information on the risk of HF decompensation in stable outpatients with LV systolic dysfunction of ischemic origin.

A novel validated method for predicting the risk of re-hospitalization for worsening heart failure and the effectiveness of the diuretic upgrading therapy with tolvaptan

Increased re-hospitalization due to acute decompensated heart failure (ADHF) is a modern issue in cardiology. The aim of this study was to investigate risk factors for re-hospitalization due to worsening heart failure, and the effect of tolvaptan (TLV) on decreasing the number of re-hospitalizations. This was a multicenter, retrospective study. The re-hospitalization factors for 1191 patients with ADHF were investigated; patients receiving continuous administration of TLV when they were discharged from the hospital (n = 194) were analyzed separately. Patients were classified into 5 risk groups based on their calculated Preventing Re-hospitalization with TOLvaptan (Pretol) score. The total number of patients re-hospitalized due to worsening heart failure up to one year after discharge from the hospital was 285 (23.9%). Age ≥80 years, duration since discharge from the hospital after previous heart failure <6 months, diabetes mellitus, hemoglobin <10 g/dl, uric acid >7.2 mg/dl, left ventricular ejection fraction (LVEF) <40%, left atrial volume index (LAVI) >44.7 ml/m2, loop diuretic dose ≥20 mg/day, hematocrit <31.6%, and estimated glomerular filtration rate (eGFR) <50 ml/min/1.73m2 were independent risk factors for re-hospitalization for worsening heart failure. There was a significant reduction in the re-hospitalization rate among TLV treated patients in the Risk 3 group and above. In conclusions, age, duration since previous heart failure, diabetes mellitus, hemoglobin, uric acid, LVEF, LAVI, loop diuretic dose, hematocrit, and eGFR were all independent risk factors for re-hospitalization for worsening heart failure. Long-term administration of TLV significantly decreases the rate of re-hospitalization for worsening heart failure in patients with a Pretol score of 7.

Mid-range left ventricular ejection fraction: Clinical profile and cause of death in ambulatory patients with chronic heart failure

BACKGROUND

The intermediate group of patients with heart failure (HF) and mid-range left ventricular ejection fraction (HFmrEF) may constitute a specific phenotype, but a direct evidence is lacking. This study aimed to know whether this HF category is accompanied by a particular clinical phenotype and prognosis.

METHODS AND RESULTS

This study includes 3446 ambulatory patients with chronic HF from two national registries. According to EF at enrollment, patients were classified as reduced (HFrEF, <40%), mid-range (HFmrEF, 40-49%) or preserved (HFpEF, ≥50%). Patients were followed-up for a median of 41months and the specific cause of death was prospectively registered. Patients with HFmrEF represented 13% of population and they exhibited a phenotype closer to HFrEF, except for a higher rate of coronary revascularization and diabetes, and a less advanced HF syndrome. The observed all-cause mortality was higher among HFrEF (33.0%), and similar between HFmrEF (27.8%) and HFpEF (28.0%) (p=0.012); however, the contribution of each cause of death differed significantly between categories (p<0.001). After propensity score matching, the risk of cardiovascular death, HF death or sudden cardiac death did not differ between HFmrEF and HFrEF in paired samples; however, patients with HFmrEF were at higher risk of cardiovascular death (sHR 1.71, 95% CI 1.13-2.57, p=0.011) and sudden cardiac death (sHR 2.73, 95% CI 1.07-6.98, p=0.036) than patients with HFpEF.

CONCLUSIONS

Patients in the intermediate category of HFmrEF conform a phenotype closer to the clinical profile of HFrEF, and associated to higher risk of sudden cardiac death and cardiovascular death than patients with HFpEF.

Influence of influenza vaccination on recurrent hospitalization in patients with heart failure

BACKGROUND

The current study aimed to evaluate the influence of regular annual influenza vaccinations on cardiovascular (CV) death and heart failure-related hospitalizations (HFrH) in stable outpatients with heart failure with reduced ejection fraction.

METHODS

The Turkish research team-HF (TREAT-HF) is a network undertaking multicenter, observational cohort studies in HF. This study is a subgroup analysis of TREAT-HF outpatient cohorts who completed a questionnaire on influenza vaccination status and for whom follow-up data were available. A total of 656 patients with available follow-up data for CV death and HFrH including recurrent hospitalization were included in the study. Patients were classified into two groups: those who received regular influenza vaccination (40 %) and those who did not receive vaccination.

RESULTS

During a mean follow-up of 15 ±6 months, 113 (18 %) patients had CV death and 471 (72 %) patients had at least one HFrH. The CV death rate was similar in both groups of patients (16 vs. 19 %, p = 0.37), whereas, HFrH and recurrent HFrH were significantly less frequently encountered in patients who received regular influenza vaccination than in those who did not receive vaccination (43 vs. 92 % and 16 vs. 66 %, p < 0.001, respectively). In a multivariate Cox proportional hazards model - in addition to a few clinical factors - vaccination status (HR = 0.30, 95 % CI = 0.17-0.51, p < 0.001) and graduation from university (HR = 0.35, 95 % CI = 0.17-0.72, p = 0.004) remained independently associated with the risk of recurrent HFrH.

CONCLUSION

Regular influenza vaccination does not influence CV deaths; however, it decreases HFrH including recurrent episodes of HFrH in outpatients with heart failure with reduced ejection fraction.

Autonomic Predictors of Hospitalization Due to Heart Failure Decompensation in Patients with Left Ventricular Systolic Dysfunction

Introduction

Autonomic nervous system balance can be significantly deteriorated during heart failure exacerbation. However, it is still unknown whether these changes are only the consequence of heart failure decompensation or can also predict development thereof. Objectives were to verify if simple, non-invasive autonomic parameters, such as baroreflex sensitivity and short-term heart rate variability can provide independent of other well-known clinical parameters information on the risk of heart failure decompensation in patients with left ventricular systolic dysfunction.

Methods

In 142 stable patients with left ventricular ejection fraction ≤ 40%, baroreflex sensitivity and short-term heart rate variability, as well as other well-known clinical parameters, were analyzed. During 23 ± 9 months of follow-up 19 patients were hospitalized due to the heart failure decompensation (EVENT).

Results

Pre-specified cut-off values of baroreflex sensitivity (≤2.4 ms/mmHg) and low frequency power index of heart rate variability (≤19 ms²) were significantly associated with the EVENTs (hazard ratio 4.43, 95% confidence interval [CI] 1.35–14.54 and 5.41, 95% CI 1.87–15.65 respectively). EVENTs were also associated with other parameters, such as left ventricular ejection fraction, NYHA class, diuretic use, renal function, brain natriuretic peptide and hemoglobin level, left atrial size, left and right ventricular heart failure signs. After adjusting baroreflex sensitivity and low frequency power index for each of the abovementioned parameters, autonomic parameters were still significant predictors of hospitalization due to the heart failure decompensation.

Conclusion

Simple, noninvasive autonomic indices can be helpful in identifying individuals with increased risk of hospitalization due to the heart failure decompensation among clinically stable patients with left ventricular systolic dysfunction, even when adjusted for other well-known clinical parameters.

Persistent pulmonary congestion before discharge predicts rehospitalization in heart failure: a lung ultrasound study

Background

B-lines evaluated by lung ultrasound (LUS) are the sonographic sign of pulmonary congestion, a major predictor of morbidity and mortality in patients with heart failure (HF). Our aim was to assess the prognostic value of B-lines at discharge to predict rehospitalization at 6 months in patients with acute HF (AHF).

Methods

A prospective cohort of 100 patients admitted to a Cardiology Department for dyspnea and/or clinical suspicion of AHF were enrolled (mean age 70 ± 11 years). B-lines were evaluated at admission and before discharge. Subjects were followed-up for 6-months after discharge.

Results

Mean B-lines at admission was 48 ± 48 with a statistically significant reduction before discharge (20 ± 23, p < .0001). During follow-up, 14 patients were rehospitalized for decompensated HF. The 6-month event-free survival was highest in patients with less B-lines (≤ 15) and lowest in patients with more B-lines (> 15) (log rank χ² 20.5, p < .0001). On multivariable analysis, B-lines > 15 before discharge (hazard ratio [HR] 11.74; 95 % confidence interval [CI] 1.30–106.16) was an independent predictor of events at 6 months.

Conclusions

Persistent pulmonary congestion before discharge evaluated by ultrasound strongly predicts rehospitalization for HF at 6-months. Absence or a mild degree of B-lines identify a subgroup at extremely low risk to be readmitted for HF decompensation.