Time-dependent Cox Regression: Serial Measurement of the Cardiovascular Biomarker Proadrenomedullin Improves Survival Prediction in Patients with Lower Respiratory Tract Infection

Background: The aim of this study was to determine the prognostic utility of serial measurement of the cardiovascular biomarker midregion proadrenomedullin (MR-proADM) in patients admitted with lower respiratory tract infection. In a previous trial in dyspneic patients (BACH trial) we could show that serial measurement of MR-proADM proves useful for risk assessment and patient monitoring. Models designed to evaluate serial biomarker measurements usually fail to answer two fundamental questions necessary to judge their clinical relevance: whether serial measurements provide additional information on top of the first measurement, and, if yes, at which time point a re-evaluation may be clinically useful.

Method: We apply an adapted time-dependent Cox model to data from the ProHosp trial, a prospective trial, which was observational in regards to application of prognostic biomarkers, where blood draws for biomarker evaluation were collected at day of patient inclusion, days 3, 5 and 7. In this trial, the cardiovascular biomarker MR-proADM was evaluated for its ability to predict survival in comparison to clinical risk scores.

Results: With the adapted time-dependent Cox model, we could demonstrate a significant added value of the follow up measurements on top of that obtained on admission. Despite a high correlation between serial measurements, the gain can be observed as early as 3 days after inclusion. We illustrate the added prognostic value and clinical relevance of re-evaluation via Kaplan-Meier plots.

Conclusion: We could demonstrate that the prognostic biomarker MR-proADM can potentially serve as a outcome monitoring marker in patients admitted with lower respiratory tract infections.