Evaluation of a Rapid Immunoassay for Molecular Subphenotype Classification in Pediatric Acute Cardiorespiratory Failure

OBJECTIVES: Molecular subphenotypes, identified through latent class analysis (LCA) of biomarker profiles, have the potential to guide targeted therapeutics in critical care. We have previously published that intensive insulin management has subphenotype-specific beneficial effects among children with hyperglycemia accompanying cardiorespiratory failure. However, the real-time application of subphenotype-based strategies in clinical settings remains challenging due to the operational aspects of biomarker assays. Our study had three objectives: 1) to compare biomarker measurements from rapid immunoassay and conventional multiplex platforms; 2) to evaluate the cross-platform transportability of a conventional assay-based parsimonious classifier for LCA-derived subphenotypes and compare it with a classifier trained directly on rapid immunoassay data; and 3) to assess the prognostic and predictive significance of rapid immunoassay-based subphenotypes. DESIGN: Retrospective cohort study. SETTING: Multicenter PICUs. PATIENTS: Two hundred sixty-nine critically ill children with acute cardiorespiratory failure and hyperglycemia (2012-2016). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: LCA was previously used to derive hyperinflammatory and hypoinflammatory classes using a conventional multiplex assay of 13 plasma biomarkers. After feature selection, a parsimonious classifier using interleukin (IL)-6, IL-8, and soluble tumor necrosis factor receptor 1 (sTNFR-1) was developed to predict LCA-derived subphenotypes. We applied this classifier to rapid immunoassay biomarker measurements, yielding an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI, 0.85-0.95). However, calibration was poor due to systematic underestimation of sTNFR-1 concentrations by the rapid platform. We then derived and internally validated via bootstrapping a de novo classifier using rapid immunoassay data, achieving an AUROC of 0.90 (95% CI, 0.86-0.95) and excellent calibration. Using a probability threshold of ≥ 0.5, the de novo classifier matched LCA-derived classifications in 241 of 269 cases (accuracy 89.6%). Rapid immunoassay-based subphenotypes demonstrated differences in mortality (33.3% in hyperinflammatory vs. 11.8% in hypoinflammatory; p = 0.009) and differential response to intensive insulin management (interaction p = 0.024). CONCLUSIONS: A parsimonious, rapid immunoassay-based classifier can approximate LCA-derived molecular subphenotypes while preserving their prognostic and predictive significance, with the potential to inform future subphenotype-based precision trials.