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

Abstract

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.

Keywords: Natriuretic peptides; Predictive risk model; Risk stratification; Systolic heart failure.

© 2019 The Authors. European Journal of Heart Failure © 2019 European Society of Cardiology.

Figures

Figures 1a and 1b.

Calibration plots according to deciles of predicted risk for a) HF hospitalization or CV death and b) all cause death. Error bars depict the upper and lower 95% confidence bounds for the observed event rates.

Figures 1a and 1b.

Calibration plots according to deciles of predicted risk for a) HF hospitalization or CV death and b) all cause death. Error bars depict the upper and lower 95% confidence bounds for the observed event rates.

Figures 2a and 2b.

Screenshot of web based tool for risk prediction. A) Representation of patient with NT-pro-BNP of 1000 pg/mL, creatinine of 50 mg/dL, sodium of 135 mmol/L, heart rate of 79, and reference categories for the other variables. B) Representation of patient with NT-pro-BNP of 1000 pg/mL, heart failure duration of 7 months, ischemic heart disease, sodium of 127 mmol/L, heart rate of 97, and diastolic blood pressure of 74.

Figures 2a and 2b.

Screenshot of web based tool for risk prediction. A) Representation of patient with NT-pro-BNP of 1000 pg/mL, creatinine of 50 mg/dL, sodium of 135 mmol/L, heart rate of 79, and reference categories for the other variables. B) Representation of patient with NT-pro-BNP of 1000 pg/mL, heart failure duration of 7 months, ischemic heart disease, sodium of 127 mmol/L, heart rate of 97, and diastolic blood pressure of 74.