Change in Cardiac Biomarkers and Risk of Incident Heart Failure and Atrial Fibrillation in CKD: The Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Circulating cardiac biomarkers may signal potential mechanistic pathways involved in heart failure (HF) and atrial fibrillation (AF). Single measures of circulating cardiac biomarkers are strongly associated with incident HF and AF in chronic kidney disease (CKD). We tested the associations of longitudinal changes in the N-terminal fragment of the prohormone brain natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hsTnT), galectin-3, growth differentiation factor 15 (GDF-15), and soluble ST-2 (sST-2) with incident HF and AF in patients with CKD. STUDY DESIGN: Observational, case-cohort study design. SETTING & PARTICIPANTS: Adults with CKD enrolled in the Chronic Renal Insufficiency Cohort study. EXPOSURES: Biomarkers were measured at baseline and 2 years later among those without kidney failure. We created 3 categories of absolute change in each biomarker: the lowest quartile, the middle 2 quartiles, and the top quartile. OUTCOMES: The primary outcomes were incident HF and AF. ANALYTICAL APPROACH: Cox proportional hazards regression models were used to test the associations of the change categories of each cardiac biomarker with each outcome (with the middle 2 quartiles of change as the referent group), adjusting for potential confounders and baseline concentrations of each biomarker. RESULTS: The incident HF analysis included 789 participants (which included 138 incident HF cases), and the incident AF analysis included 774 participants (123 incident AF cases). In multivariable models, the top quartile of NT-proBNP change (>232pg/mL over 2years) was associated with increased risk of incident HF (HR, 1.79 [95% CI, 1.06-3.04]) and AF (HR, 2.32 [95% CI, 1.37-3.93]) compared with the referent group. Participants in the top quartile of sST2 change (>3.37ng/mL over 2years) had significantly greater risk of incident HF (HR, 1.89 [95% CI, 1.13-3.16]), whereas those in the bottom quartile (≤-3.78ng/mL over 2years) had greater risk of incident AF (HR, 2.43 [95% CI, 1.39-4.22]) compared with the 2 middle quartiles. There was no association of changes in hsTnT, galectin-3, or GDF-15 with incident HF or AF. LIMITATIONS: Observational study. CONCLUSIONS: In CKD, increases in NT-proBNP were significantly associated with greater risk of incident HF and AF, and increases in sST2 were associated with HF. Further studies should investigate whether these markers of subclinical cardiovascular disease can be modified to reduce the risk of cardiovascular disease in CKD.

Combining biomarker trajectories to improve diagnostic accuracy in prospective cohort studies with verification bias.

In this paper, we develop methods to combine multiple biomarker trajectories into a composite diagnostic marker using functional data analysis (FDA) to achieve better diagnostic accuracy in monitoring disease recurrence in the setting of a prospective cohort study. In such studies, the disease status is usually verified only for patients with a positive test result in any biomarker and is missing in patients with negative test results in all biomarkers. Thus, the test result will affect disease verification, which leads to verification bias if the analysis is restricted only to the verified cases. We treat verification bias as a missing data problem. Under both missing at random (MAR) and missing not at random (MNAR) assumptions, we derive the optimal classification rules using the Neyman-Pearson lemma based on the composite diagnostic marker. We estimate thresholds adjusted for verification bias to dichotomize patients as test positive or test negative, and we evaluate the diagnostic accuracy using the verification bias corrected area under the ROC curves (AUCs). We evaluate the performance and robustness of the FDA combination approach and assess the consistency of the approach through simulation studies. In addition, we perform a sensitivity analysis of the dependency between the verification process and disease status for the approach under the MNAR assumption. We apply the proposed method on data from the Religious Orders Study and from a non-small cell lung cancer trial.

Prognostic performance of serial in-hospital measurements of copeptin and multiple novel biomarkers among patients with worsening heart failure: results from the MOLITOR study.

AIMS: In heart failure, various biomarkers are established for diagnosis and risk stratification; however, little is known about the relevance of serial measurements during an episode worsening heart failure (WHF). This study sought to investigate the trajectory of natriuretic peptides and multiple novel biomarkers during hospitalization for WHF and to determine the best time point to predict outcome. METHODS AND RESULTS: MOLITOR (Impact of Therapy Optimisation on the Level of Biomarkers in Patients with Acute and Decompensated Chronic Heart Failure) was an eight-centre prospective study of 164 patients hospitalized with a primary diagnosis of WHF. C-terminal fragment of pre-pro-vasopressin (copeptin), N-terminal pro-B-type natriuretic peptide (NT-proBNP), mid-regional pro-atrial natriuretic peptide (MR-proANP), mid-regional pro-adrenomedullin (MR-proADM), and C-terminal pro-endothelin-1 (CT-proET1) were measured on admission, after 24, 48, and 72 h, and every 72 h thereafter, at discharge and follow-up visits. Their performance to predict all-cause mortality and rehospitalization at 90 days was compared. All biomarkers decreased during recompensation (P < 0.05) except MR-proADM. Copeptin at admission was the best predictor of 90 day mortality or rehospitalization (χ2  = 16.63, C-index = 0.724, P < 0.001), followed by NT-proBNP (χ2  = 10.53, C-index = 0.646, P = 0.001), MR-proADM (χ2  = 9.29, C-index = 0.686, P = 0.002), MR-proANP (χ2  = 8.75, C-index = 0.631, P = 0.003), and CT-proET1 (χ2  = 6.60, C-index = 0.64, P = 0.010). Re-measurement of copeptin at 72 h and of NT-proBNP at 48 h increased prognostic value (χ2  = 23.48, C-index = 0.718, P = 0.00001; χ2  = 14.23, C-index = 0.650, P = 0.00081, respectively). CONCLUSIONS: This largest sample of serial measurements of multiple biomarkers in WHF found copeptin at admission with re-measurement at 72 h to be the best predictor of 90 day mortality and rehospitalization.

Dynamic Optimal Strategy for Monitoring Disease Recurrence.

Surveillance to detect cancer recurrence is an important part of care for cancer survivors. In this paper we discuss the design of optimal strategies for early detection of disease recurrence based on each patient's distinct biomarker trajectory and periodically updated risk estimated in the setting of a prospective cohort study. We adopt a latent class joint model which considers a longitudinal biomarker process and an event process jointly, to address heterogeneity of patients and disease, to discover distinct biomarker trajectory patterns, to classify patients into different risk groups, and to predict the risk of disease recurrence. The model is used to develop a monitoring strategy that dynamically modifies the monitoring intervals according to patients' current risk derived from periodically updated biomarker measurements and other indicators of disease spread. The optimal biomarker assessment time is derived using a utility function. We develop an algorithm to apply the proposed strategy to monitoring of new patients after initial treatment. We illustrate the models and the derivation of the optimal strategy using simulated data from monitoring prostate cancer recurrence over a 5-year period.