PMID- 31112000
OWN - NLM
STAT- MEDLINE
DCOM- 20200819
LR  - 20231014
IS  - 1752-8062 (Electronic)
IS  - 1752-8054 (Print)
IS  - 1752-8054 (Linking)
VI  - 12
IP  - 5
DP  - 2019 Sep
TI  - Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial 
      Applying Machine Learning Techniques.
PG  - 519-528
LID - 10.1111/cts.12647 [doi]
AB  - Applying data mining and machine learning (ML) techniques to clinical data might 
      identify predictive biomarkers for diabetic nephropathy (DN), a common 
      complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the 
      Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to 
      identify such factors using ML. The longitudinal data were stratified by time 
      after patient enrollment to differentiate early and late predictors. Our results 
      showed that Random Forest and Simple Logistic Regression methods exhibited the 
      best performance among the evaluated algorithms. Baseline values for glomerular 
      filtration rate (GFR), urinary creatinine, urinary albumin, potassium, 
      cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio 
      were identified as DN predictors. Early predictors were the baseline values of 
      GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and 
      potassium at month 4. Changes per year in GFR, FPG, and triglycerides were 
      recognized as predictors of late development. In conclusion, ML-based methods 
      successfully identified predictive factors for DN among patients with T2DM.
CI  - (c) 2019 The Authors. Clinical and Translational Science published by Wiley 
      Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and 
      Therapeutics.
FAU - Rodriguez-Romero, Violeta
AU  - Rodriguez-Romero V
AD  - Division of Clinical Pharmacology, Department of Medicine, Indiana University 
      School of Medicine, Indianapolis, Indiana, USA.
AD  - Indiana Clinical and Translational Sciences Institute (CTSI), Indianapolis, 
      Indiana, USA.
FAU - Bergstrom, Richard F
AU  - Bergstrom RF
AD  - Division of Clinical Pharmacology, Department of Medicine, Indiana University 
      School of Medicine, Indianapolis, Indiana, USA.
AD  - Indiana Clinical and Translational Sciences Institute (CTSI), Indianapolis, 
      Indiana, USA.
FAU - Decker, Brian S
AU  - Decker BS
AD  - Division of Clinical Pharmacology, Department of Medicine, Indiana University 
      School of Medicine, Indianapolis, Indiana, USA.
FAU - Lahu, Gezim
AU  - Lahu G
AD  - Translational Research and Early Clinical, Takeda Pharmaceutical International 
      Co., Cambridge, Massachusetts, USA.
FAU - Vakilynejad, Majid
AU  - Vakilynejad M
AD  - Translational Research and Early Clinical, Takeda Pharmaceutical International 
      Co., Cambridge, Massachusetts, USA.
FAU - Bies, Robert R
AU  - Bies RR
AD  - Indiana Clinical and Translational Sciences Institute (CTSI), Indianapolis, 
      Indiana, USA.
AD  - Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical 
      Sciences, State University of New York at Buffalo, Buffalo, New York, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190531
PL  - United States
TA  - Clin Transl Sci
JT  - Clinical and translational science
JID - 101474067
RN  - 0 (Biomarkers)
SB  - IM
MH  - Biomarkers/metabolism
MH  - Data Mining
MH  - Diabetes Mellitus, Type 2/*complications
MH  - Diabetic Nephropathies/*complications/*diagnosis/epidemiology
MH  - Female
MH  - Humans
MH  - Incidence
MH  - *Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Models, Theoretical
MH  - ROC Curve
MH  - Risk Factors
MH  - Sensitivity and Specificity
PMC - PMC6742939
COIS- All authors declared no competing interests for this work.
EDAT- 2019/05/22 06:00
MHDA- 2020/08/20 06:00
PMCR- 2019/09/01
CRDT- 2019/05/22 06:00
PHST- 2018/12/07 00:00 [received]
PHST- 2019/04/21 00:00 [accepted]
PHST- 2019/05/22 06:00 [pubmed]
PHST- 2020/08/20 06:00 [medline]
PHST- 2019/05/22 06:00 [entrez]
PHST- 2019/09/01 00:00 [pmc-release]
AID - CTS12647 [pii]
AID - 10.1111/cts.12647 [doi]
PST - ppublish
SO  - Clin Transl Sci. 2019 Sep;12(5):519-528. doi: 10.1111/cts.12647. Epub 2019 May 
      31.