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Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids

de Gonzalo-Calvo, David (author)
Hannover Med Sch, Inst Mol & Translat Therapeut Strategies IMTTS, Carl Neuberg Str 1, D-30625 Hannover, Germany.;IRBLleida, Univ Hosp Arnau de Vilanova & Santa Maria, Translat Res Resp Med, Lleida, Spain.;Inst Hlth Carlos III, CIBER Resp Dis CIBERES, Madrid, Spain.
Martinez-Camblor, Pablo (author)
Dartmouth Coll, Geisel Sch Med, Hanover, NH 03755 USA.
Baer, Christian (author)
Hannover Med Sch, Inst Mol & Translat Therapeut Strategies IMTTS, Carl Neuberg Str 1, D-30625 Hannover, Germany.;Hannover Med Sch, REBIRTH Ctr Translat Regenerat Med, Hannover, Germany.
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Duarte, Kevin (author)
Univ Lorraine, INSERM, Ctr Invest Clin Plurithemat 1433, Inserm U1116, Nancy, France.;CHRU Nancy, Nancy, France.;F CRIN INI CRCT Network, Nancy, France.
Girerd, Nicolas (author)
Univ Lorraine, INSERM, Ctr Invest Clin Plurithemat 1433, Inserm U1116, Nancy, France.;CHRU Nancy, Nancy, France.;F CRIN INI CRCT Network, Nancy, France.
Fellström, Bengt, 1947- (author)
Uppsala universitet,Njurmedicin
Schmieder, Roland E. (author)
Friedrich Alexander Univ Erlangen Nurnberg FAU, Univ Hosp, Dept Nephrol & Hypertens, Erlangen, Germany.
Jardine, Alan G. (author)
Univ Glasgow, Inst Cardiovasc & Med Sci, Glasgow, Lanark, Scotland.
Massy, Ziad A. (author)
Ambroise Pare Univ, AP HP, Med Ctr, Div Nephrol, F-92100 Paris, France.;Paris Sud Univ, Paris Saclay Univ, CESP Ctr Rech Epidemiol & Sante Populat, INSERM U1018,Team 5, F-94800 Villejuif, France.;Paris Ouest Versailles St Quentin en Yvelines Uni, F-94800 Villejuif, France.
Holdaas, Hallvard (author)
Oslo Univ Hosp, Rikshosp, Dept Transplantat Med, Oslo, Norway.
Rossignol, Patrick (author)
Univ Lorraine, INSERM, Ctr Invest Clin Plurithemat 1433, Inserm U1116, Nancy, France.;CHRU Nancy, Nancy, France.;F CRIN INI CRCT Network, Nancy, France.
Zannad, Faiez (author)
Univ Lorraine, INSERM, Ctr Invest Clin Plurithemat 1433, Inserm U1116, Nancy, France.;CHRU Nancy, Nancy, France.;F CRIN INI CRCT Network, Nancy, France.
Thum, Thomas (author)
Hannover Med Sch, Inst Mol & Translat Therapeut Strategies IMTTS, Carl Neuberg Str 1, D-30625 Hannover, Germany.;Hannover Med Sch, REBIRTH Ctr Translat Regenerat Med, Hannover, Germany.
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Hannover Med Sch, Inst Mol & Translat Therapeut Strategies IMTTS, Carl Neuberg Str 1, D-30625 Hannover, Germany;IRBLleida, Univ Hosp Arnau de Vilanova & Santa Maria, Translat Res Resp Med, Lleida, Spain.;Inst Hlth Carlos III, CIBER Resp Dis CIBERES, Madrid, Spain. Dartmouth Coll, Geisel Sch Med, Hanover, NH 03755 USA. (creator_code:org_t)
IVYSPRING INT PUBL, 2020
2020
English.
In: Theranostics. - : IVYSPRING INT PUBL. - 1838-7640. ; 10:19, s. 8665-8676
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Rationale: To test whether novel biomarkers, such as microribonucleic acids (miRNAs), and nonstandard predictive models, such as decision tree learning, provide useful information for medical decision-making in patients on hemodialysis (HD). Methods: Samples from patients with end-stage renal disease receiving HD included in the AURORA trial were investigated (n=810). The study included two independent phases: phase I (matched cases and controls, n=410) and phase II (unmatched cases and controls, n=400). The composite endpoint was cardiovascular death, nonfatal myocardial infarction or nonfatal stroke. miRNA quantification was performed using miRNA sequencing and RT-qPCR. The CART algorithm was used to construct regression tree models. A bagging-based procedure was used for validation. Results: In phase I, miRNA sequencing in a subset of samples (n=20) revealed miR-632 as a candidate (fold change=2.9). miR-632 was associated with the endpoint, even after adjusting for confounding factors (HR from 1.43 to 1.53). These findings were not reproduced in phase II. Regression tree models identified eight patient subgroups with specific risk patterns. miR-186-5p and miR-632 entered the tree by redefining two risk groups: patients older than 64 years and with hsCRP<0.827 mg/L and diabetic patients younger than 64 years. miRNAs improved the discrimination accuracy at the beginning of the follow-up (24 months) compared to the models without miRNAs (integrated AUC [iAUC]=0.71). Conclusions: The circulating miRNA profile complements conventional risk factors to identify specific cardiovascular risk patterns among patients receiving maintenance HD.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Urologi och njurmedicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Urology and Nephrology (hsv//eng)

Keyword

Biomarker
Cardiovascular risk
Hemodialysis
Kidney disease
Machine learning
microRNA

Publication and Content Type

ref (subject category)
art (subject category)

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