Suppression tumorigenicity 2 (ST2) turbidimetric immunoassay compared to enzyme-linked immunosorbent assay in predicting survival in heart failure patients with reduced ejection fraction

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Clinica chimica acta; international journal of clinical chemistry


BACKGROUND: Suppressor of tumorigenicity 2 (ST2) is a powerful marker of prognosis and treatment response in heart failure (HF), however, it is an enzyme-linked immunosorbent assay (ELISA) which may be cumbersome and costly. A turbidimetric immunoassay (TIA) that can run on common chemistry analyzers could overcome this. We studied a novel TIA for ST2, comparing it to commercial ST2 (ELISA).

METHODS: Patients age ≥18 years meeting Framingham definition for HF were enrolled in a prospective registry (Oct 2007 - March 2015) at Henry Ford Hospital and donated blood samples. Participants with reduced ejection fraction (<50%) and available plasma samples were included and valid ST2 measurements were obtained on the same sample using both TIA and ELISA (N=721). The primary endpoint was all cause death. Correlation between the methods was quantified. The association with survival was tested using unadjusted and adjusted (for MAGGIC score and NTproBNP) Cox models and comparing the Area Under the Curve (AUC).

RESULTS: The inter-assay Spearman correlation coefficient was 0.77. Nonparametric regression showed no significant proportional difference (slope = 0.97) and a very small systematic difference (3.2 ng/mL). In univariate analyses, both TIA and ELISA ST2 were significant associates of survival with similar effect sizes (HR 4.46 and 3.50, respectively, both p < 0.001). In models adjusted for MAGGIC score, both ST2 remained significant in Cox models and incrementally improved AUC vs. MAGGIC alone (MAGGIC AUC = 0.757; TIA + MAGGIC AUC = 0.786, p = 0.025; ELISA + MAGGIC AUC = 0.793, p = 0.033). In models with both MAGGIC and NTproBNP included, both ST2 still remained significant but did not improve AUC.

CONCLUSIONS: A novel TIA method for ST2 quantification correlates highly with ELISA and offers similarly powerful risk-stratification.

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ePub ahead of print



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