Prognostic Value of Serial ST2 Measurements in Patients With Acute Heart Failure

Vark, Laura C. van; Lesman‐Leegte, Ivonne; Baart, Sara J.; Postmus, Douwe; Pinto, Yigal M.; Orsel, Joke G.; Westenbrink, B. Daan; Rocca, Hans‐Peter Brunner‐La; Miltenburg, Addy J.M. van; Boersma, Eric; Hillege, Hans L.; Akkerhuis, K. Martijn; Investigators, Triumph

doi:10.1016/j.jacc.2017.09.026

Cited by 126 publications

(110 citation statements)

References 20 publications

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“…Soluble ST2 (sST2) is one of the isoforms of ST2, binding IL-33 which results in inhibition of cardioprotective antiremodeling effects of IL-33 [1,2]. Elevated sST2 level has been reported to be a marker of worse prognosis in patients with heart failure with reduced ejection fraction (HFrEF) [3][4][5][6]. Furthermore, several studies have shown that sST2 has a predictive value for mortality in patients with heart failure with preserved ejection fraction (HFpEF) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Soluble ST2 and brain natriuretic peptide predict different mode of death in patients with heart failure and preserved ejection fraction

Sugano

Seo

Ishizu

et al. 2019

Journal of Cardiology

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Keywords:Soluble ST2 Brain natriuretic peptide Heart failure with preserved ejection fraction Systemic inflammation A B S T R A C T Background: Soluble ST2 (sST2) is a marker of inflammation and fibrosis, which is a significant predictor of prognosis of heart failure (HF), independent of brain natriuretic peptide (BNP). This study aimed to clarify how sST2 associates with clinical outcome through investigations of clinical correlates and mode of death in patients with heart failure with preserved ejection fraction (HFpEF). Methods: A total 191 patients with acute decompensated HF and EF !50% were prospectively enrolled. Echocardiographic and laboratory data including sST2 were obtained in pre-discharge stable condition. Results: Serum sST2 level showed significant positive correlations with C-reactive protein and pentraxin3 levels, and negative correlations with body mass index, albumin, and hemoglobin. Serum sST2 level was significantly higher in patients with all-cause death and non-cardiovascular (CV) death compared to those without events, whereas there was no significant difference in sST2 level between patients with and without CV death. On the other hand, BNP level was significantly higher in patients with all-cause death and CV death compared to those without events. Cox regression analyses adjusted for age and sex revealed that sST2 was a significant predictor of non-CV death, whereas BNP was a significant predictor of CV death. Conclusions: Serum sST2 level was associated with non-CV death showing significant correlations with systemic factors including malnutrition and inflammation, while BNP was associated with CV death.

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Section: Introductionmentioning

confidence: 99%

Soluble ST2 and brain natriuretic peptide predict different mode of death in patients with heart failure and preserved ejection fraction

Sugano

Seo

Ishizu

et al. 2019

Journal of Cardiology

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show abstract

“…Baseline covariates used in the survival submodel are denoted by w i . The hazard for the survival outcome (T i , i ) is modeled with a proportional hazards model h i (t) defined in (1). Here, we assume m i (t) is the true and unobserved value of longitudinal outcome for patient i at time t, modeled by the longitudinal submodel.…”

Section: Model Specificationmentioning

confidence: 99%

“…By repeatedly collecting and analyzing measurements on patients, their progress is monitored more closely and temporal trends in the disease progress can be estimated, leading to improved prediction of outcomes. 1 In these kinds of studies two types of outcomes are collected: the longitudinal outcome (often a biomarker) and the time-to-event outcome, eg, death. When interest lies in using temporal patterns of the longitudinal response to estimate the event of interest, both outcomes can be modeled together by using the joint modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Joint models for longitudinal and time‐to‐event data in a case‐cohort design

Baart

Boersma

Rizopoulos

2019

Statistics in Medicine

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Studies with longitudinal measurements are common in clinical research. Particular interest lies in studies where the repeated measurements are used to predict a time‐to‐event outcome, such as mortality, in a dynamic manner. If event rates in a study are low, however, and most information is to be expected from the patients experiencing the study endpoint, it may be more cost efficient to only use a subset of the data. One way of achieving this is by applying a case‐cohort design, which selects all cases and only a random samples of the noncases. In the standard way of analyzing data in a case‐cohort design, the noncases who were not selected are completely excluded from analysis; however, the overrepresentation of the cases will lead to bias. We propose to include survival information of all patients from the cohort in the analysis. We approach the fact that we do not have longitudinal information for a subset of the patients as a missing data problem and argue that the missingness mechanism is missing at random. Hence, results obtained from an appropriate model, such as a joint model, should remain valid. Simulations indicate that our method performs similar to fitting the model on a full cohort, both in terms of parameters estimates and predictions of survival probabilities. Estimating the model on the classical version of the case‐cohort design shows clear bias and worse performance of the predictions. The procedure is further illustrated in data from a biomarker study on acute coronary syndrome patients, BIOMArCS.

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“…It has been established that sST2 is a predictor of both all-cause mortality and cardiovascular death in stable chronic heart failure outpatients5 as well as all-cause mortality and heart failure rehospitalisations in patients with acute heart failure independent of brain natriuretic peptides 7. Interestingly, sST2 levels appeared to elevate several weeks before the time of the primary endpoint in the latter study 7. Its role in patients with ACHD is unknown.…”

mentioning

confidence: 98%

“…This cardioprotective action occurs only through the ST2L receptor, while sST2 binds to IL-33 competing with ST2L, hence blocking the IL-33/ST2L system and eliminating the cardioprotective effects 6. It has been established that sST2 is a predictor of both all-cause mortality and cardiovascular death in stable chronic heart failure outpatients5 as well as all-cause mortality and heart failure rehospitalisations in patients with acute heart failure independent of brain natriuretic peptides 7. Interestingly, sST2 levels appeared to elevate several weeks before the time of the primary endpoint in the latter study 7.…”

mentioning

confidence: 99%

sST2: a new kid on the block for patients with ACHD

Tutarel

2019

Heart

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Prognostic Value of Serial ST2 Measurements in Patients With Acute Heart Failure

Cited by 126 publications

References 20 publications

Soluble ST2 and brain natriuretic peptide predict different mode of death in patients with heart failure and preserved ejection fraction

Soluble ST2 and brain natriuretic peptide predict different mode of death in patients with heart failure and preserved ejection fraction

Joint models for longitudinal and time‐to‐event data in a case‐cohort design

sST2: a new kid on the block for patients with ACHD

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