Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients

Stanislas Werfel, Georg Lorenz, Bernhard Haller, Roman Günthner, Julia Matschkal, Matthias C. Braunisch, Carolin Schaller, Peter Gundel, Stephan Kemmner, Salim S. Hayek, Christian Nusshag, Jochen Reiser, Philipp Moog, Uwe Heemann, Christoph Schmaderer

Research output: Contribution to journalArticlepeer-review


Cohort studies often provide a large array of data on study participants. The techniques of statistical learning can allow an efficient way to analyze large datasets in order to uncover previously unknown, clinically relevant predictors of morbidity or mortality. We applied a combination of elastic net penalized Cox regression and stability selection with the aim of identifying novel predictors of mortality in a cohort of prevalent hemodialysis patients. In our analysis we included 475 patients from the “rISk strAtification in end-stage Renal disease” (ISAR) study, who we split into derivation and confirmation cohorts. A wide array of examinations was available for study participants, resulting in over a hundred potential predictors. In the selection approach many of the well established predictors were retrieved in the derivation cohort. Additionally, the serum levels of IL-12p70 and AST were selected as mortality predictors and confirmed in the withheld subgroup. High IL-12p70 levels were specifically prognostic of infection-related mortality. In summary, we demonstrate an approach how statistical learning can be applied to a cohort study to derive novel hypotheses in a data-driven way. Our results suggest a novel role of IL-12p70 in infection-related mortality, while AST is a promising additional biomarker in patients undergoing hemodialysis.

Original languageEnglish (US)
Article number9287
JournalScientific reports
Issue number1
StatePublished - Dec 2021
Externally publishedYes

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients'. Together they form a unique fingerprint.

Cite this