Group Publications:
Pooled patient level data are better suited than study level data to investigate the link between dipeptidyl peptidase-4 inhibitors and the risk of heart failure in type 2 diabetes. BMJ. 2016 353:i2920. DOI: 10.1136/bmj.i2920.
Does insulin therapy affect all-cause mortality? Machine learning complements propensity score analysis in a pharmacoepidemiologic study of adult diabetic females in Barranquilla, Colombia. J Diabetes Metab Disord Control. 2023;10(2):144-155. DOI: 10.15406/jdmdc.2023.10.00267
Summer Students’ Abstracts:
Effect of HDL-C Raising and/or Triglyceride Lowering on Cardiovascular Outcomes and All-Cause Mortality in the AIM-HIGH Trial. (Circulation. 2016;134(Suppl 1):A19139)
Gender Differences in Hospitalization or Death Due to Heart Failure as Related to Glycemic Control in the ACCORD Trial. (Circulation. 2016;134(Suppl 1):A18861)
Risk of Hospitalization or Death due to Heart Failure with Intensive Glucose-Lowering Therapy in Diabetic Women: Subgroup Analyses by History of Cardiovascular Disease in the ACCORD Trial. (J Am Coll Cardiol. 2017 Mar 21;69(11):783. )
Identifying Predictors for All-cause Mortality in Diabetic Patients in the ACCORD Trial Using Random Survival Forests. (Circulation. 2017;136:A18043)
Risk of hospitalization or death due to heart failure with intensive glucose-lowering therapy in diabetic women: subgroup analyses by history of cardiovascular disease in the accord trial. (Journal of the American College of Cardiology, 2017. 69:1163-275).
Predictors of Hospitalization or Death due to Heart Failure in Diabetic Patients by Gender in the ACCORD Trial Using Random Survival Forests. Poster presentation at 2017 AHA Scientific Sessions, November at Anaheim (Circulation. 2017;136:A18061)
Sex Differences in Cardiac Outcomes in Response to Fenofibrate Therapy in the ACCORD Trial. (J Am Coll Cardiol. 2018;71(11):A166).
Identifying predictors for all-cause mortality in diabetic patients in the ACCORD Trial using random survival forests. (Circulation. 2018. 136, no. suppl_1 (2017): A18043-A18043.)
Predictors of all-cause mortality and their gender differences in a Hispanic population from Barranquilla-Colombia using machine learning with random survival forests.” (Circulation. 2018 138; A16252.)
Re-use of clinical trial data from the NHLBI data repository (BioLINCC) for patient-level meta-analyses of cardiovascular outcomes: challenges and opportunities. (Circulation 2018; 138:A17059)
Predictors of major adverse cardiovascular events in the ACCORD trial identified by random survival forest-based machine-learning. (Circulation. 2018; 136:A17121)
Predictors of all-cause mortality and their gender differences in a Hispanic population from Barranquilla-Colombia using machine-learning with random survival forests. (Circulation 138:A16252)
Predictors of Hard Outcomes in the ALLHAT Trial Identified With Machine Learning. (Atherosclerosis, Thrombosis and Vascular Biology. 2019.39:626.)
Predictors of all-cause mortality in the SPRINT trial identified by machine learning. (Journal of the American College of Cardiology, 2019; 73: 1332-442).
Predictors of all-cause mortality in the AIM-HIGH trial identified by machine learning. (Journal of the American College of Cardiology, 2019; 73, 1331-424)
Identifying predictors of hospitalization due to heart failure in the TOPCAT trial using machine learning techniques. (Journal of the American College of Cardiology. 2019. 73:1342-528).
Identification of Gender- and Age-specific Top Predictors of Hospitalization Due to Heart Failure Using Machine Learning in TOPCAT. (Circulation. 2020. 142:15924)
Age Specific Baseline Predictors of All-cause Mortality in Systolic Blood Pressure Intervention Trial (SPRINT) Identified by Machine Learning (Circulation 2020. 142:16309)
Gender, Race, and Age Specific Baseline Predictors of All-cause Mortality in STICHES Trial Identified by Machine Learning. (Circulation. 2020.142:15572)
Gender, Race, and Age Specific Baseline Predictors of All-Cause Mortality in BARI2D Trial Identified by Machine Learning (Circulation 2020. 142:15971)
Gender and Age Specific Baseline Predictors of MACE in PEACE Trial Identified by Machine Learning (Circulation 2020. 142:16998)
Use of machine learning methodology to find predictors of all-cause mortality in the systolic blood pressure intervention trial (SPRINT). (Journal of the American College of Cardiology. 2020; 75:1464-090)
Use of machine learning to find predictors of all-cause mortality in the treatment of preserved cardiac function heart failure with an aldosterone antagonist trial (TOPCAT). (Journal of the American College of Cardiology. 2020; 75:1028)
Use of machine learning methodology to find predictors of all-cause mortality in prevention of events with angiotensin-converting enzyme inhibition (PEACE) TRIAL (Journal of the American College of Cardiology. 2020; 75:1355-100)
Deep phenotyping using unsupervised machine learning of HFPEF patients with diabetes mellitus in the TOPCAT Americas cohort. (Journal of the American College of Cardiology. 2020; 75:1355-100)
Deep phenotyping by machine learning of participants in the systolic blood pressure intervention trial (SPRINT) (Journal of the American College of Cardiology. 2022; 79:39)
Deep phenotyping by unsupervised machine learning of participants in the BARI 2D trial (Journal of the American College of Cardiology. 2021; 77:37)
Deep Learning Based Personalized Treatment Recommender System for HFpEF Patients in the TOPCAT Americas Population. (Circulation 2022.146 (S1):15835)
Cluster Analysis of baseline top predictors for all-cause mortality in Barranquilla, Colombia with machine learning (Journal of the American College of Cardiology 2025; 85(12):1125-51)