An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

Advances in congestive heart failure biomarkers

Congestive heart failure (CHF) is the leading cause of morbidity and mortality in the elderly worldwide. Although many biomarkers associated with in heart failure, these are generally prognostic and identify patients with moderate and severe disease. Unfortunately, the role of biomarkers in decision making for early and advanced heart failure remains largely unexplored. Previous studies suggest the natriuretic peptides have the potential to improve the diagnosis of heart failure, but they still have significant limitations related to cut-off values. Although some promising cardiac biomarkers have emerged, comprehensive data from large cohort studies is lacking. The utility of multiple biomarkers that reflect various pathophysiologic pathways are increasingly being explored in heart failure risk stratification and to diagnose disease conditions promptly and accurately. MicroRNAs serve as mediators and/or regulators of renin-angiotensin-induced cardiac remodeling by directly targeting enzymes, receptors and signaling molecules. The role of miRNA in HF diagnosis is a promising area of research and further exploration may offer both diagnostic and prognostic applications and phenotype-specific targets. In this review, we provide insight into the classification of different biochemical and molecular markers associated with CHF, examine clinical usefulness in CHF and highlight the most clinically relevant.

Relationship Between Preoperative NT-proBNP and Postoperative Adverse Events in Patients with Left Ventricular Assist Device

Background:

The aim of this study is to investigate the relationship of preoperative NT-proBNP values with postoperative adverse events in patient left ventricular assist device (LVAD) implantation.

Method:

Forty-six patients (35 males; mean age 49.4 ± 12.9 years) who underwent LVAD implantation between 2016 and 2018 were evaluated in this study. The analysis was made on the relationship between preoperative NT-proBNP and mortality, postoperative right ventricular failure (RVF), postoperative drainage, duration of intubation, and intensive care unit stay, was examined. The optimal NT-proBNP cut-off values for predicting mortality were determined using Receiver Operator Characteristic (ROC) curve analysis and the patients were divided into two groups according to the specified cut-off point.

Result:

Median NT-proBNP was higher in patients who died, had postoperative extracorporeal membrane oxygenation, and early RVF. The median NT-proBNP was 11,103 pg/ml in patients with IABP, and 2943 pg/ml in patients without IABP, and the difference was statistically significant ( p = 0.002). The cut-off point for NT-proBNP was found to be 1725.5 pg/ml (Sensitivity:0.929, Specificity:0.688). Accordingly, when the patients were divided into two groups and analyzed, no statistically significant difference was found between preoperative NT-proBNP below or above 1725.5 and postoperative adverse events. There was no statistically significant correlation between preoperative NT-proBNP and postoperative drainage, duration of intubation time, and duration of ICU stay ( p > 0.05).

Conclusion:

Routine monitoring of preoperative NT-proBNP and comparison with postoperative values are important in terms of patient selection, the timing of surgery, follow-up of postoperative adverse events, and improving outcomes in VAD patients.

Clinical Utility and Validation of the Krakow DCM Risk Score—A Prognostic Model Dedicated to Dilated Cardiomyopathy

Background: One of the most common causes of heart failure is dilated cardiomyopathy (DCM). In DCM, the mortality risk is high and reaches approximately 20% in 5 years. A patient’s prognosis should be established for appropriate HF management. However, so far, no validated tools have been available for the DCM population. Methods: The study population consisted of 735 DCM patients: 406 from the derivation cohort (previously described) and 329 from the validation cohort (from 2009 to 2020, with outcome data after a mean of 42 months). For each DCM patient, the individual mortality risk was calculated based on the Krakow DCM Risk Score. Results: During follow-up, 49 (15%) patients of the validation cohort died. They had shown significantly higher calculated 1-to-5-year mortality risks. The Krakow DCM Risk Score yielded good discrimination in terms of overall mortality risk, with an AUC of 0.704–0.765. Based on a 2-year mortality risk, patients were divided into non-high (≤6%) and high (>6%) mortality risk groups. The observed mortality rates were 8.3% (n = 44) vs. 42.6% (n = 75), respectively (HR 3.37; 95%CI 1.88–6.05; p < 0.0001). Conclusions: The Krakow DCM Risk Score was found to have good predictive accuracy. The 2-year mortality risk > 6% has good discrimination for the identification of high-risk patients and can be applied in everyday practice.

UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure

BACKGROUND

Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present.

METHODS

We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed.

DISCUSSION

This project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews.Registration: PROSPERO registration number CRD42019086990.

Interleukin-6 and adhesion molecules VCAM-1 and ICAM-1 as biomarkers of post-acute myocardial infarction heart failure

Acute coronary syndromes are associated with a high prevalence of complications including heart failure (HF). The aim of this study was to investigate the association of novel biomarkers with the occurrence of post-acute myocardial infarction (AMI) HF. A prospective study was conducted with patients admitted to the emergency department with ST-segment elevation myocardial infarction (STEMI). Blood and urine samples were collected for analysis of traditional and novel biomarkers, including interleukin-6, vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1). We compared the levels of these biomarkers between patients with and without post-STEMI HF. A total of 48 patients were assessed, with a prevalence of males. Fifteen patients (31.2%) had post-STEMI HF. Patients with HF had higher mean values of IL-6, VCAM-1, and ICAM-1 compared to those who did not develop HF (57.06 vs 14.03 pg/mL, P=0.001; 1719.58 vs 1304.34 ng/mL, P=0.001; and 1594.20 vs 1158.74 ng/mL, P<0.001, respectively). The three biomarkers were shown to be good predictors of post-STEMI HF (IL-6: AUC 0.786, P=0.002; VCAM-1: AUC 0.797, P=0.001; and ICAM-1: AUC 0.825, P<0.0001), with the respective cutoff points being calculated based on the best sensitivity and specificity indexes (IL-6: 8.67 pg/mL; VCAM-1: 1501.42 ng/mL; and ICAM-1: 1262.38 ng/mL). Of the three biomarkers, only VCAM-1 and ICAM-1 had a direct linear association between them (r=0.470, P<0.0001). IL-6, VCAM-1, and ICAM-1 were associated with the development of new post-AMI HF symptoms, but only VCAM-1 and ICAM-1 correlated with each other, possibly because they have the same pathophysiological mechanism of action.

Cardiac Biomarkers in Advanced Heart Failure: How Can They Impact Our Pre-transplant or Pre-LVAD Decision-making

PURPOSE OF REVIEW

Decision-making in advanced heart failure (HF) is a complex process that involves careful consideration of competing tradeoffs of risks and benefits in regard to heart transplantation (HT) or left ventricular assist device (LVAD) placement. The purpose of this review is to discuss how biomarkers may affect decision-making for HT or LVAD implantation.

RECENT FINDINGS

N-Terminal probrain natriuretic peptide, soluble suppression of tumorigenicity-2, galectin-3, copeptin, and troponin T levels are associated with HF survival and can help identify the appropriate timing for advanced HF therapies. Patients at risk of right ventricular failure after LVAD implantation can be identified with preimplant biomarkers of extracellular matrix turnover, neurohormonal activation, and inflammation. There is limited data on the adoption of biomarker measurement for decision-making in the allocation of advanced HF therapies. Nonetheless, biomarkers can improve risk stratification and prognostication thereby optimizing patient selection for HT and LVAD implantation.

Hyponatremia in Heart Failure: Pathogenesis and Management

Hyponatremia is a very common electrolyte abnormality, associated with poor short- and long-term outcomes in patients with heart failure (HF). Two opposite processes can result in hyponatremia in this setting: Volume overload with dilutional hypervolemic hyponatremia from congestion, and hypovolemic hyponatremia from excessive use of natriuretics. These two conditions require different therapeutic approaches. While sodium in the form of normal saline can be lifesaving in the second case, the same treatment would exacerbate hyponatremia in the first case. Hypervolemic hyponatremia in HF patients is multifactorial and occurs mainly due to the persistent release of arginine vasopressin (AVP) in the setting of ineffective renal perfusion secondary to low cardiac output. Fluid restriction and loop diuretics remain mainstay treatments for hypervolemic/dilutional hyponatremia in patients with HF. In recent years, a few strategies, such as AVP antagonists (Tolvaptan, Conivaptan, and Lixivaptan), and hypertonic saline in addition to loop diuretics, have been proposed as potentially promising treatment options for this condition. This review aimed to summarize the current literature on pathogenesis and management of hyponatremia in patients with HF.

Improving risk prediction in heart failure using machine learning

BACKGROUND

Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions.

METHODS AND RESULTS

We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations.

CONCLUSIONS

Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging.

Clinical factors related to morbidity and mortality in high-risk heart failure patients: the GUIDE-IT predictive model and risk score

BACKGROUND

Most heart failure (HF) risk scores have been derived from cohorts of stable HF patients and may not incorporate up to date treatment regimens or deep phenotype characterization that change baseline risk over the short- and long-term follow-up period. We undertook the current analysis of participants in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment) trial to address these limitations.

METHODS AND RESULTS

The GUIDE-IT study randomized 894 high-risk patients with HF and reduced ejection fraction (≤ 40%) to biomarker-guided treatment strategy vs. usual care. We performed risk modelling using Cox proportional hazards models and analysed the relationship between 35 baseline clinical factors and the primary composite endpoint of cardiovascular (CV) death or HF hospitalization, the secondary endpoint of all-cause mortality, and the exploratory endpoint of 90-day HF hospitalization or death. Prognostic relationships for continuous variables were examined and key predictors were identified using a backward variable selection process. Predictive models and risk scores were developed. Over a median follow-up of 15 months, the cumulative number of HF hospitalizations and CV deaths was 328 out of 894 patients (Kaplan-Meier event rate 34.5% at 12 months). Frequency of all-cause deaths was 143 out of 894 patients (Kaplan-Meier event rate 12.2% at 12 months). Outcomes for the primary and secondary endpoints between strategy arms of the study were similar. The most important predictor that was present in all three models was the baseline natriuretic peptide level. Hispanic ethnicity, low sodium and high heart rate were present in two of the three models. Other important predictors included the presence or absence of a device, New York Heart Association class, HF duration, black race, co-morbidities (sleep apnoea, elevated creatinine, ischaemic heart disease), low blood pressure, and a high congestion score.

CONCLUSION

Risk models using readily available clinical information are able to accurately predict short- and long-term CV events and may be useful in optimizing care and enriching patients for clinical trials.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID number NCT01685840.