Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients
Abstract 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...
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2021
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oai:doaj.org-article:ecf4578a224648ed822651d87b9aa1b42021-12-02T17:39:31ZApplication of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients10.1038/s41598-021-88655-02045-2322https://doaj.org/article/ecf4578a224648ed822651d87b9aa1b42021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88655-0https://doaj.org/toc/2045-2322Abstract 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.Stanislas WerfelGeorg LorenzBernhard HallerRoman GünthnerJulia MatschkalMatthias C. BraunischCarolin SchallerPeter GundelStephan KemmnerSalim S. HayekChristian NusshagJochen ReiserPhilipp MoogUwe HeemannChristoph SchmadererNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) |
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Medicine R Science Q 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 Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| description |
Abstract 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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Stanislas Werfel |
| title |
Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| title_short |
Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| title_full |
Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| title_fullStr |
Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| title_full_unstemmed |
Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| title_sort |
application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/ecf4578a224648ed822651d87b9aa1b4 |
| work_keys_str_mv |
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| _version_ |
1718379856212787200 |