Multivariate risk scores and patient outcomes in advanced heart failure

Heart failure risk scores in advanced heart failure patients: insights from the LEVO-D registry

AIMS

The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web-based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta-Analysis Global Group in Chronic HF (MAGGIC-HF) risk score and the model of the Barcelona Bio-HF Risk Calculator (BCN-Bio-HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy.

METHODS AND RESULTS

Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all-cause mortality was compared by area under the curve (AUC) and Harrell's C-statistic at 1 year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64-78] years, with reduced left ventricular ejection fraction (27.5 ± 9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1 year was 26.8%, while the predicted 1-year mortality by BCN-Bio-HF and MAGGIC-HF was 17.0% and 22.1%, respectively. BCN-Bio-HF AUC was 0.66 (Harrell's C-statistic 0.64), and MAGGIC-HF AUC was 0.62 (Harrell's C-statistic 0.61).

CONCLUSIONS

The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF.

Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score: Validation of a Simple Tool for the Prediction of Morbidity and Mortality in Heart Failure With Preserved Ejection Fraction

Background The Meta-Analysis Global Group in Chronic Heart Failure ( MAGGIC ) mortality risk score, derived from a large sample of patients with heart failure ( HF ) across the spectrum of ejection fraction ( EF ), has not yet been externally validated in a well-characterized HF with preserved EF cohort with adjudicated morbidity outcomes. Methods and Results We evaluated the MAGGIC risk score (composed of 13 clinical variables) in 407 patients with HF with preserved EF enrolled in a prospective registry and used Cox regression to evaluate its association with morbidity/mortality. We used receiver-operating characteristic analysis to compare the predictive ability of the MAGGIC risk score with the more complex Seattle Heart Failure Model, and we determined the value of adding B-type natriuretic peptide to the MAGGIC risk score for risk prediction. During a mean follow-up time of 3.6±1.8 years, 28% died, 32% were hospitalized for HF , and 55% had a cardiovascular hospitalization and/or death. The MAGGIC score, a mean± SD of 18±7, was significantly associated with mortality ( P<0.0001), HF hospitalizations ( P<0.0001), and the combined end point of cardiovascular-related hospitalizations or death (hazard ratio, 1.8 [95% confidence interval, 1.6-2.1], per 1- SD increase in the MAGGIC score; P<0.0001). Receiver-operating characteristic analyses showed that MAGGIC and Seattle Heart Failure Model performed similarly in predicting HF with preserved EF outcomes, but the MAGGIC score demonstrated better calibration for hospitalization outcomes. Further analyses showed that B-type natriuretic peptide was additive to the MAGGIC risk score for predicting outcomes ( P<0.01 by likelihood ratio test). Conclusions The MAGGIC risk score is a simple, yet powerful method of risk stratification for both morbidity and mortality in HF with preserved EF . Clinical Trial Registration URL: http://www.clinicaltrials.gov . Unique identifier: NCT01030991.

Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure

Background

In clinical practice, a risk prediction model is an effective solitary program to predict prognosis in particular patient groups. B-type natriuretic peptide (BNP)and N-terminal pro-b-type natriuretic peptide (NT-proBNP) are widely recognized outcome-predicting factors for patients with heart failure (HF).This study derived external validation of a risk score to predict 1-year mortality after discharge in hospitalized patients with HF using the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)program data. We also assessed the effect of adding BNP or NT-proBNP to this risk score model in a Korean HF registry population.

Method and results

We included 5625 patients from the Korean acute heart failure registry (KorAHF) and excluded those who died in hospital. The MAGGIC constructed a risk score to predict mortality in patients with HF by using 13 routinely available patient characteristics (age, gender, diabetes, chronic obstructive pulmonary disorder (COPD), HF diagnosed within the last 18 months, current smoker, NYHA class, use of beta blocker, ACEI or ARB, body mass index, systolic blood pressure, creatinine, and EF). We added BNP or NT-proBNP, which are the most important biomarkers, to the MAGGIC risk scoring system in patients with HF. The outcome measure was 1-year mortality. In multivariable analysis, BNP or NT-proBNP independently predicted death. The risk score was significantly varied between alive and dead groups (30.61 ± 6.32 vs. 24.80 ± 6.81, p < 0.001). After the conjoint use of BNP or NT-proBNP and MAGGIC risk score in patients with HF, a significant difference in risk score was noted (31.23 ± 6.46 vs. 25.25 ± 6.96, p < 0.001).The discrimination abilities of the risk score model with and without biomarker showed minimal improvement (C index of 0.734 for MAGGIC risk score and 0.736 for MAGGIC risk score plus BNP or NT-proBNP, p = 0.0502) and the calibration was found good. However, we achieved a significant improvement in net reclassification and integrated discrimination for mortality (NRI of 33.4%,p < 0.0001 and IDI of 0.002, p < 0.0001).

Conclusion

In the KorAHF, the MAGGIC project HF risk score performed well in a large nationwide contemporary external validation cohort. Furthermore, the addition of BNP or NT-proBNPto the MAGGIC risk score was beneficial in predicting more death in hospitalized patients with HF.

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

Emergency department triage of acute heart failure triggered by pneumonia; when an intensive care unit is needed?

Community acquired pneumonia (CAP) is a frequent triggering factor for decompensation of a chronic cardiac dysfunction, leading to acute heart failure (AHF). Patients with AHF exacerbated by CAP, are often admitted through the emergency department for ICU hospitalization, even though more than half the cases do not warrant any intensive care treatment. Emergency department physicians are forced to make disposition decisions based on subjective criteria, due to lack of evidence-based risk scores for AHF combined with CAP. Currently, the available risk models refer distinctly to either AHF or CAP patients. Extrapolation of data by arbitrarily combining these models, is not validated and can be treacherous. Examples of attempts to apply acuity scales provenient from different disciplines and the resulting discrepancies, are given in this review. There is a need for severity classification tools especially elaborated for use in the emergency department, applicable to patients with mixed AHF and CAP, in order to rationalize the ICU dispositions. This is bound to facilitate the efforts to save both lives and resources.

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation

OBJECTIVE

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients.

METHODS

Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients.

RESULTS

The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility.

CONCLUSIONS

Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care.

Clinical factors associated with early readmission among acutely decompensated heart failure patients

INTRODUCTION

Congestive heart failure (CHF) is a common cause of hospital readmission.

MATERIAL AND METHODS

A retrospective study was conducted at Harlem Hospital in New York City. Data were collected for 685 consecutive adult patients admitted for decompensated CHF from March, 2009 to December, 2012. Variables including patient demographics, comorbidities, laboratory studies, and medical therapy were compared between CHF patient admissions resulting in early CHF readmission and not resulting in early CHF readmission.

RESULTS

Clinical factors found to be independently significant for early CHF readmission included chronic obstructive pulmonary disease (odds ratio (OR) = 6.4), HIV infection (OR = 3.4), African-American ethnicity (OR = 2.2), systolic heart failure (OR = 1.9), atrial fibrillation (OR = 2.3), renal disease with glomerular filtration rate < 30 ml/min (OR = 2.7), evidence of substance abuse (OR = 1.7), and absence of angiotensin-converting enzyme inhibitors or angiotensin receptor blocker therapy after discharge (OR = 1.8). The ORs were used to develop a scoring system regarding the risk for early readmission.

CONCLUSIONS

Identifying patients with clinical factors associated with early CHF readmission after an index hospitalization for CHF using the proposed scoring system would allow for an early CHF readmission risk stratification protocol to target particularly high-risk patients.

The utility of biomarker risk prediction score in patients with chronic heart failure

Background

Chronic heart failure (CHF) has been remained a leading cause of cardiovascular morbidity and mortaluty. The risk stratification of CHF individuals based on clinical criteria and biomarkers' models may improve medical care and probably increase efficacy of treatment strategy. However, various predictive models approved for CHF patients appear to be distinguished in their prognostications. The study aim was to evaluate whether biomarker risk prediction score is powerful tool for risk assessment of three-year fatal and non-fatal cardiovascular events in CHF patients.

Methods

It was studied prospectively the incidence of fatal and non-fatal cardiovascular events in a cohort of 388 patients with ischemic-induced CHF within 3 years. Circulating biomarkers were collected at baseline of the study.

Results

Independent predictors of clinical outcomes in patients with CHF were NT-pro-BNP, galectin-3, hs-CRP, osteoprotegerin, CD31⁺/annexin V⁺ endothelail-derived microparticles (EMPs) and CD31⁺/annexin V⁺ EMPs to CD14⁺CD309⁺ monuclear progenitor cells (MPCs) ratio. Index of cardiovascular risk was calculated by mathematical summation of all ranks of independent predictors, which occurred in the patients included in the study. Kaplan-Meier analysis showed that patients with CHF and the magnitude of the risk of less than 4 units have an advantage in survival when compared with patients for whom obtained higher values of cardiovascular risk score ranks.

Conclusion

Biomarker risk score for cumulative cardiovascular events, constructed by measurement of circulating NT-pro-BNP, galectin-3, hs-CRP, osteoprotegerin, CD31+/annexin V+ EMPs and CD31⁺/annexin V⁺ EMPs to CD14⁺CD309⁺ MPCs ratio, allowing reliably predict the probability survival of patients with CHF.

Predicting mortality in patients with acute heart failure: Role of risk scores

Acute heart failure is a leading cause of hospitalization and death, and it is an increasing burden on health care systems. The correct risk stratification of patients could improve clinical outcome and resources allocation, avoiding the overtreatment of low-risk subjects or the early, inappropriate discharge of high-risk patients. Many clinical scores have been derived and validated for in-hospital and post-discharge survival; predictive models include demographic, clinical, hemodynamic and laboratory variables. Data sets are derived from public registries, clinical trials, and retrospective data. Most models show a good capacity to discriminate patients who reach major clinical end-points, with C-indices generally higher than 0.70, but their applicability in real-world populations has been seldom evaluated. No study has evaluated if the use of risk score-based stratification might improve patient outcome. Some variables (age, blood pressure, sodium concentration, renal function) recur in most scores and should always be considered when evaluating the risk of an individual patient hospitalized for acute heart failure. Future studies will evaluate the emerging role of plasma biomarkers.

Predicting short-term mortality in advanced decompensated heart failure - role of the updated acute decompensated heart failure/N-terminal pro-B-type natriuretic Peptide risk score

BACKGROUND

The first few months after admission are the most vulnerable period in patients with acute decompensated heart failure (ADHF).

METHODS AND RESULTS

We assessed the association of the updated ADHF/N-terminal pro-B-type natriuretic peptide (NT-proBNP) risk score with 90-day and in-hospital mortality in 701 patients admitted with advanced ADHF, defined as severe symptoms of worsening HF, severely depressed left ventricular ejection fraction, and the need for i.v. diuretic and/or inotropic drugs. A total of 15.7% of the patients died within 90 days of admission and 5.2% underwent ventricular assist device (VAD) implantation or urgent heart transplantation (UHT). The C-statistic of the ADHF/NT-proBNP risk score for 90-day mortality was 0.810 (95% CI: 0.769-0.852). Predicted and observed mortality rates were in close agreement. When the composite outcome of death/VAD/UHT at 90 days was considered, the C-statistic decreased to 0.741. During hospitalization, 7.6% of the patients died. The C-statistic for in-hospital mortality was 0.815 (95% CI: 0.761-0.868) and Hosmer-Lemeshow χ(2)=3.71 (P=0.716). The updated ADHF/NT-proBNP risk score outperformed the Acute Decompensated Heart Failure National Registry, the Organized Program to Initiate Lifesaving Treatment in Patients Hospitalized for Heart Failure, and the American Heart Association Get with the Guidelines Program predictive models.

CONCLUSIONS

Updated ADHF/NT-proBNP risk score is a valuable tool for predicting short-term mortality in severe ADHF, outperforming existing inpatient predictive models.

The ADHF/NT-proBNP risk score to predict 1-year mortality in hospitalized patients with advanced decompensated heart failure

BACKGROUND

The acute decompensated heart failure/N-terminal pro-B-type natriuretic peptide (ADHF/NT-proBNP) score is a validated risk scoring system that predicts mortality in hospitalized heart failure patients with a wide range of left ventricular ejection fractions (LVEFs). We sought to assess discrimination and calibration of the score when applied to patients with advanced decompensated heart failure (AHF).

METHODS

We studied 445 patients hospitalized for AHF, defined by the presence of severe symptoms of worsening HF at admission, severely depressed LVEF, and the need for intravenous diuretic and/or inotropic drugs. The primary outcome was cumulative (in-hospital and post-discharge) mortality and post-discharge 1-year mortality. Separate analyses were performed for patients aged ≤ 70 years. A Seattle Heart Failure Score (SHFS) was calculated for each patient discharged alive.

RESULTS

During follow-up, 144 patients (32.4%) died, and 69 (15.5%) underwent heart transplantation (HT) or ventricular assist device (VAD) implantation. After accounting for the competing events (VAD/HT), the ADHF/NT-proBNP score's C-statistic for cumulative mortality was 0.738 in the overall cohort and 0.771 in patients aged ≤ 70 years. The C-statistic for post-discharge mortality was 0.741 and 0.751, respectively. Adding prior (≤6 months) hospitalizations for HF to the score increased the C-statistic for post-discharge mortality to 0.759 in the overall cohort and to 0.774 in patients aged ≤ 70 years. Predicted and observed mortality rates by quartiles of score were highly correlated. The SHFS demonstrated adequate discrimination but underestimated the risk. The ADHF/NT-proBNP risk calculator is available at http://www.fsm.it/fsm/file/NTproBNPscore.zip.

CONCLUSIONS

Our data suggest that the ADHF/NT-proBNP score may efficiently predict mortality in patients hospitalized with AHF.

Association of low body temperature and poor outcomes in patients admitted with worsening heart failure: a substudy of the Efficacy of Vasopressin Antagonism in Heart Failure Outcome Study with Tolvaptan (EVEREST) trial

AIMS

Risk stratification in patients admitted with worsening heart failure (HF) is essential for tailoring therapy and counselling. Risk models are available but rarely used, in part because many require laboratory and imaging results that are not routinely available. Body temperature is associated with prognosis in other illnesses, and we hypothesized that low body temperature would be associated with worse outcomes in patients admitted with worsening HF.

METHODS AND RESULTS

The Efficacy of Vasopressin Antagonism in Heart Failure Outcome Study with Tolvaptan (EVEREST) trial was an event-driven, randomized, double-blind, placebo-controlled study of tolvaptan in 4133 patients hospitalized for worsening HF with an EF <40%. Co-primary endpoints were all-cause mortality and cardiovascular (CV) death or HF rehospitalization. Body temperature was measured orally at randomization and entered in analyses both as a continuous variable and categorized into three groups (<36 °C, 36-36.5 °C, and >36.5 °C) using Cox regression models. The composite of CV death or HF rehospitalization occurred in 1544 patients within 1 year. For every 1 °C decrease in body temperature, the risk of adverse outcomes increased by 16% [hazard raio (HR) 1.16, 95% confidence interval (CI) 1.04-1.28], after adjustment for age, gender, race, systolic blood pressure, EF, blood urea nitrogen, and serum sodium. In fully adjusted analysis, the risk of adverse outcomes in the lowest body temperature group (<36 °C) was 51% higher than that of the index group (>36.5 °C) (HR 1.35, 95% CI 1.15-1.58).

CONCLUSIONS

Low body temperature is an independent marker of poor cardiovascular outcomes in patients admitted with worsening HF and reduced EF.

Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score

BACKGROUND

NT-proBNP has been associated with prognosis in acute decompensated heart failure (ADHF). Whether NT-proBNP provides additional prognostic information beyond that obtained from standard clinical variables is uncertain. We sought to assess whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) determination improves risk reclassification of patients with ADHF and to develop and validate a point-based NT-proBNP risk score.

METHODS

This study included 824 patients with ADHF (453 in the derivation cohort, 371 in the validation cohort). We compared two multivariable models predicting 1-year all-cause mortality, including clinical variables and clinical variables plus NT-proBNP. We calculated the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). Then, we developed and externally validated the NT-proBNP risk score.

RESULTS

One-year mortalities for the derivation and validation cohorts were 28.3% and 23.4%, respectively. Multivariable predictors of mortality included chronic obstructive pulmonary disease, estimated glomerular filtration rate, sodium, hemoglobin, left ventricular ejection fraction, and moderate to severe tricuspid regurgitation. Adding NT-proBNP to the clinical variables only model significantly improved the NRI (0.129; p=0.0027) and the IDI (0.037; p=0.0005). In the derivation cohort, the NT-proBNP risk score had a C index of 0.839 (95% CI: 0.798-0.880) and the Hosmer-Lemeshow statistic was 1.23 (p=0.542), indicating good calibration. In the validation cohort, the risk score had a C index of 0.768 (95% CI: 0.711-0.817); the Hosmer-Lemeshow statistic was 2.76 (p=0.251), after recalibration.

CONCLUSIONS

The NT-proBNP risk score provides clinicians with a contemporary, accurate, easy-to-use, and validated predictive tool. Further validation in other datasets is advisable.

The etiology-filling pattern-pulmonary artery pressure score: a simple tool for risk stratification of patients with systolic heart failure

Heart failure (HF) is a leading cause of morbidity and mortality. The detection of patients at high risk for death is a major challenge in HF management. The authors compared the prognostic value of 23 clinical Doppler echocardiography and cardiopulmonary exercise indexes in a stable, moderately symptomatic, systolic HF outpatient population receiving optimal medical therapy. The end point was the incidence of overall mortality. Between January 2002 and December 2008, a total of 146 patients with left ventricular (LV) ejection fraction 0.31±0.8 and New York Heart Association functional class II or III were enrolled. The prognostic power of single variables was assessed using chi-square test for categoric variables and t test for continuous variables. Variables associated with the prespecified end point were included as predictors in a binary logistic regression multivariate model. At multivariate analysis, "restrictive" LV filling pattern (P=.004), ischemic etiology (P=.022), pulmonary artery systolic pressure (PASP) ≥50 mm Hg (P=.027), and peak oxygen uptake (VO(2) ) <15.9 mL/kg/min (P=.046) resulted independent predictors of the outcome. A simple risk score was then obtained using these significant independent variables, excluding peak VO(2) because of only borderline significance. Patients with ischemic etiology, restrictive LV filling pattern, and PASP ≥50 mm Hg have a very high risk of death (odds ratio, 33.77; 95% confidence interval, 5.74-198.8; P<.001, compared with patients with no risk factors). In this high-risk group, evaluation of peak VO(2) could be superfluous. A very simple clinical echocardiographic model based on etiology-LV filling and pulmonary pressure is a powerful tool for risk stratification of systolic HF in ambulatory patients.

Therapeutic potential of thymosin β4 in myocardial infarct and heart failure

Thymosin β4 (Tβ4) is a peptide known for its abilities to protect and facilitate regeneration in a number of tissues following injury. Its cardioprotective effects have been evaluated in different animal models and, currently, a clinical trial is being planned in patients suffering from acute myocardial infarction. This paper focuses on the effects of Tβ4 on cardiac function in animal studies utilizing different imaging modalities for outcome measurements.