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: http://ClinicalTrials.gov ID number NCT01685840.

June 2019 at a glance: from genetics to haemodynamics, biomarkers and imaging for the prediction of outcomes

“…Risk scores still have a major role in the clinical assessment and prognostic stratification of HF patients . O'Connor et al . analysed 35 baseline clinical variables from the 894 high‐risk HF patients enrolled in the GUIDE‐IT (Guiding Evidence‐Based Therapy Using Biomarker Intensified Treatment) trial with the aim to develop a prognostic score.…”

“…Hispanic ethnicity, low sodium and high heart rate were selected 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 …”

The GUIDE‐IT heart failure risk prediction model: another fish in the sea?

“…In this issue of the Journal, O'Connor and colleagues sought to address some of the limitations of the currently available predictive models with the GUIDE‐IT (Guiding Evidence‐Based Therapy Using Biomarker Intensified Treatment) HF risk prediction model . In total, 894 patients with chronic HF with reduced ejection fraction (HFrEF) were considered.…”

“…The prognostic and diagnostic roles of NT‐proBNP levels are undisputed . However, in daily clinical practice the ascertainment of NT‐proBNP still remains limited, as shown by NT‐proBNP being missing in a great proportion of patients enrolled in large HF registries . Therefore, might NT‐proBNP represent a major limitation to GUIDE‐IT HF risk prediction use in daily clinical practice?…”

Long-term mortality risk in hospitalized patients with heart failure after myocardial infarction

“…Тем не менее ХСН характеризуется неблагоприятным прогнозом [1]. Имеются многочисленные исследования по оценке риска неблагоприятных событий у пациентов с ХСН, предложены различные прогностические шкалы [2][3][4]. Однако предлагаемые шкалы не всегда удобны в практическом отношении.…”