USE OF MACHINE LEARNING METHODOLOGY TO FIND PREDICTORS OF ALL-CAUSE MORTALITY IN THE SYSTOLIC BLOOD PRESSURE INTERVENTION TRIAL (SPRINT)
2020; Elsevier BV; Volume: 75; Issue: 11 Linguagem: Inglês
10.1016/s0735-1097(20)32688-7
ISSN1558-3597
AutoresNuha Gani, Gauri Dandi, Zyannah Mallick, Adrita Ashraf, Victoria Xin, Ian C. Atkinson, Anwar Husain, Noah Hasan, Xin Tian, Colin O. Wu, Tejas Patel, Anna Kettermann, George Sopko, Carlos Cure, György Csákó, Danielle Jateng, Jerome L. Fleg, Victor Crentsil, Iffat Chowdhury, Keith Burkhart, Amit Dey, Eileen Navarro, Frank Pucino, Yves Rosenberg, Ahmed Hasan,
Tópico(s)Hemodynamic Monitoring and Therapy
ResumoSPRINT was conducted to identify whether a lower systolic blood pressure target (SBP <120 vs. <140 mm Hg) would reduce incidence of prospective cardiovascular events as well as death. We conducted secondary analyses to find baseline factors predictive of mortality, using random survival forests (RSF
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