Prognostic Biomarker Discovery in Heart Failure

From risk to prognosis

Earlier Hurdle work modelled incident cardiovascular outcomes across a broad population and found cardiovascular death to be a particularly informative endpoint. The prognostic pipeline keeps that same CV-death endpoint, so results stay comparable, but shifts the question from "who will develop CV disease?" to "among people who already have heart failure, who will die of a cardiovascular cause?"

Cohort and approach

• Inclusion: participants with an I50.* heart failure diagnosis (primary run requires HF at any time, with no diagnosis-based exclusions to preserve statistical power).
• Outcome: cases are HF patients whose cause of death is cardiovascular (ICD I00–I99); controls are HF patients who survived, were censored, or died of a non-CV cause.
• Model: penalised Cox time-to-event model across stacked data modalities, from routine clinical data through to proteomics and polygenic risk scores.
• Engineering: refactored per-period inclusion/exclusion with staged filtering and a can_use_in_cohort audit flag; stricter variants are one config edit away for sensitivity analysis.

Result 1: discrimination improves with each data layer

Adding clinical labs and proteomics lifts the C-index from ~0.60 (baseline demographics) to 0.76 for the best multimodal model. Genetics alone adds little; the clinical + proteomic stack carries most of the gain, consistent with Hurdle's clinical-enrichment experiments on CV death. The cohort comprised 2,870 cardiovascular deaths and 13,506 sampled controls (~1:4.7); 73.2% of cases were male.

Result 2: multi-protein signatures beat a single marker

NT-proBNP is the canonical prognostic marker for heart failure, but on its own it gives only modest separation (HR 1.77 per SD). A multi-protein Cox signature roughly doubles the hazard ratio to 3.50 and widens 15-year survival separation between high- and low-risk tertiles from ~22 to ~46 percentage points. Prognosis is multi-dimensional; Hurdle's panel-level discovery captures signal that a single assay misses.

Discovered biomarkers tell a coherent clinical story

These are cardiovascular- and HF-specific signals (natriuretic peptides, RAAS, fibrosis and remodelling markers), not arbitrary noise. That coherence, together with C-index 0.76, makes the discovery both statistically useful and clinically interpretable.

Why it matters and where it goes next

The prognostic pipeline and per-period inclusion/exclusion filtering work together on a real UK Biobank cohort, turning the same Aurora engine that powers risk discovery into a prognostic-biomarker discovery platform. The framework generalises to any disease: a new cohort is defined by editing cohort JSON and enabling prognostic mode in the pipeline config, supporting trial enrichment, patient stratification, and companion-diagnostic development across therapeutic areas.