Use of risk reclassification with multiple biomarkers improves mortality prediction in acute lung injury

Abstract

Objective: Multiple single biomarkers have been associated with poor outcomes in acute lung injury; however, no single biomarker has sufficient discriminating power to clearly indicate prognosis. Using both derivation and replication cohorts, we tested novel risk reclassification methods to determine whether measurement of multiple plasma biomarkers at the time of acute lung injury diagnosis would improve mortality prediction in acute lung injury.

Design: Analysis of plasma biomarker levels and prospectively collected clinical data from patients enrolled in two randomized controlled trials of ventilator therapy for acute lung injury.

Setting: Intensive care units of university hospitals participating in the National Institutes of Health Acute Respiratory Distress Syndrome Network.

Patients: Subjects enrolled in a trial of lower tidal volume ventilation (derivation cohort) and subjects enrolled in a trial of higher vs. lower positive end-expiratory pressure (replication cohort).

Interventions: None.

Measurements and main results: The plasma biomarkers were intercellular adhesion molecule-1, von Willebrand factor, interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D. In the derivation cohort (n = 547), adding data on these biomarkers to clinical predictors (Acute Physiology and Chronic Health Evaluation III score) at the time of study enrollment improved the accuracy of risk prediction, as reflected by a net reclassification improvement of 22% (95% confidence interval 13% to 32%; p < .001). In the replication cohort (n = 500), the net reclassification improvement was 17% (95% confidence interval 7% to 26%; p < .001). A reduced set of three biomarkers (interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D) had nearly equivalent prognostic value in both cohorts.

Conclusions: When combined with clinical data, plasma biomarkers measured at the onset of acute lung injury can improve the accuracy of risk prediction. Combining three or more biomarkers may be useful for selecting a high-risk acute lung injury population for enrollment in clinical trials of novel therapies.

Figure 1

Risk reclassification of all patients enrolled in derivation (a) and replication (b) cohorts. The figure represent mortality prediction in the clinical model alone (x-axis) vs. the clinical model plus biomarkers (y-axis); patients who died and patients who did not die are represented separately, since the direction of improved classification differs between these two groups. The line of equivalence is marked in blue; green dots represent subjects with more accurate risk prediction in the biomarker model, and red dots represent subjects with less accurate risk prediction in the biomarker model. Patients above the line of equivalence had higher risk predicted from the biomarker model than the clinical prediction model (APACHE III plus lower tidal volume ventilation); patients below the line of equivalence had higher risk predicted from the clinical prediction model than the biomarker model.