Abstract 16309: Age Specific Baseline Predictors of All-cause Mortality in Systolic Blood Pressure Intervention Trial (SPRINT) Identified by Machine Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Nuha Gani ◽  
Anwar Husain ◽  
Gauri Dandi ◽  
Ian Atkinson ◽  
Zyannah Mallick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Age Groups ◽  
Cox Proportional Hazards Model ◽  
Data Repository ◽  
Diabetic Patients ◽  
Age Group ◽  
Intervention Trial ◽  
All Cause Mortality

Introduction: The NHLBI supported Systolic Blood Pressure (SBP) Intervention Trial (SPRINT) (NCT01206062) aimed to identify an SBP target to reduce incidence of cardiovascular (CV) morbidity and mortality in hypertensive, non-diabetic patients of age ≥ 50 at increased CV risk. It found that intensive treatment (SBP target <120 mmHg) led to fewer major CV events and death but higher rates of adverse events. We reused publicly available patient-level SPRINT data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF), to identify age specific baseline (bl) predictors for all-cause mortality (ACM). Methods: RSF was performed on 30 bl variables from 9361 patients in age group specific cohorts (50-59, 60-69, 70-79, 80-90). The identified top 10 predictors from each cohort were included in a multivariate analysis using a Cox proportional hazards model. Results: The top 10 predictors of ACM for age specific subgroups are shown in Figure 1. As expected, cardiovascular disease (CVD) predictors were selected, yet RSF distinctively identified renal biomarkers as important predictors, consistent with our previous analyses. Smoking status and history of CVD ranked as top predictors among age groups 50-59, 60-69, and 70-79. RSF also identified social factors, including race among age groups 60-69 and 80-90 and female gender among age groups 50-59 and 80-90 as important predictors for ACM. Lipid markers and medications used also showed up as top predictors. Specifically, polypharmacy emerged as a top predictor in age groups 60-69, 70-79, and 80-90, notably ranking higher in the 80-90 age group. Conclusions: Using ML, we uncovered in an unbiased fashion, unanticipated age specific top predictors for ACM in SPRINT trial. This highlights the value of ML for analyzing disease and therapeutic intervention outcomes and age specific prognostic factors to advance precision medicine.

Abstract 15572: Gender, Race, and Age Specific Baseline Predictors of All-cause Mortality in STICHES Trial Identified by Machine Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Ian C Atkinson ◽  
Anwar Husain ◽  
Gauri Dandi ◽  
Nuha Gani ◽  
Zyannah Mallick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Race And Ethnicity ◽  
Smoking Status ◽  
Left Ventricular ◽  
Cox Proportional Hazards Model ◽  
Data Repository ◽  
Plasma Sodium ◽  
Age Group ◽  
All Cause Mortality

Introduction: NHLBI supported STICHES trial (The Surgical Treatment for Ischemic Heart Failure Extended Study) (NCT00023595) was conducted to test whether blood flow restoration by coronary revascularization recovers chronic left ventricular dysfunction and improves survival, as compared to medical therapy alone in patients with congestive heart failure and coronary artery disease amenable to surgical revascularization. We reused publicly available individual patient-level STICHES trial data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF) to identify gender, race and ethnicity, and age specific predictors for all-cause mortality (ACM). Methods: The population was sub-grouped by gender (male vs. female), race (white vs. Hispanic/Latinos/non-white), and age (< 55, 55-60, 61-69, and ≥70). RSF was performed on 48 baseline variables from 1212 patients to identify predictors of ACM. Top 10 RSF predictors for each subgroup were included in a multivariate analysis using a Cox proportional hazards model. Results: Top 10 predictors of ACM are shown in Table 1. While known cardiometabolic and vascular predictors were among the top predictors, RSF uniquely identified renal function related biomarkers and plasma sodium among important top predictors across the subgroups. Age was an important predictor for male and female, Hispanics/Latinos/non-whites, and patient groups ≥70 years old. Also, top predictors of ACM were current smoking status among age groups of <55 and 55-60, clinical recruitment site in age group 61-69, and female gender in age group 55-60. Conclusions: Using ML, we uncovered in an unbiased fashion, gender, age, race and ethnicity specific, unanticipated top predictors of ACM in STICHES trial. This highlights the value of ML for analyzing disease and therapeutic intervention outcomes to help implement precision medicine.

Abstract 15971: Gender, Race, and Age Specific Baseline Predictors of All-Cause Mortality in BARI2D Trial Identified by Machine Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Zyannah Mallick ◽  
Nayab Mahmood ◽  
Gauri Dandi ◽  
Nowreen Haq ◽  
Avantika Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Medical Therapy ◽  
Proportional Hazards Model ◽  
Age Groups ◽  
Cox Proportional Hazards Model ◽  
Data Repository ◽  
All Cause Mortality ◽  
Hispanic White

Introduction: NHLBI supported Bypass Angioplasty Revascularization Investigation in Type 2 Diabetes trial (BARI2D) (NCT00006305) evaluated patients with type 2 diabetes and coronary artery disease. Primary trial analysis found no significant differences in rates of all-cause mortality (ACM) among patients who underwent 1) prompt revascularization with medical therapy versus aggressive medical therapy alone and 2) insulin-sensitization medical strategies versus insulin-provision. We reused publicly available individual patient-level data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analysis by machine learning (ML), using random survival forest (RSF) to identify gender, race, and age specific baseline predictors for ACM. Methods: The total 2368 trial participants was separated into several subgroups based on gender (female and male), age (40-49, 50-59, 60-69, 70-80), and race (Non-Hispanic White, Hispanic White, Non-Hispanic Non-White, and Hispanic Non-White). RSF was performed on 84 baseline variables to identify predictors of the primary outcome, ACM. The top 10 predictors for each subgroup were tested in a Cox proportional hazards model Results: Top 10 predictors of ACM are shown in Table 1. Although anticipated cardiovascular (CV) and diabetic predictors appeared among the top predictors, at the same time, renal function biomarkers like serum creatinine, urine albumin/creatinine ratio, and serum potassium uniquely showed among the top 5 predictors across the gender, age, and race specific subgroups. Conclusions: Using ML, we uncovered in an unbiased fashion, gender, race and age groups specific unanticipated top baseline predictors of ACM in BARI2D trial. This highlights the value of gender, race and age groups specific predictors of outcomes for determining the efficacy of therapeutic interventions and help advance precision medicine.

scholarly journals Use of advanced statistical techniques to predict all-cause mortality in the Systolic Blood Pressure Intervention Trial

2020 ◽  
Vol 7 ◽  
pp. 100053
Author(s):  
William J. Kostis ◽  
Javier Cabrera ◽  
Chun Pang Lin ◽  
John B. Kostis ◽  
Jennifer Wellings ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Statistical Techniques ◽  
Intervention Trial ◽  
All Cause Mortality

scholarly journals Age-specific associations of blood pressure variability with cardiovascular disease and mortality: 10-year Diabetes cohort study

2020 ◽  
Author(s):  
Eric Yuk Fai WAN ◽  
Esther Yee Tak Yu ◽  
Weng Yee Chin ◽  
Jessica K. Barrett ◽  
Ian Chi Kei Wong ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Cohort Study ◽  
Systolic Blood Pressure ◽  
Mortality Risk ◽  
Blood Pressure Variability ◽  
Hazard Ratio ◽  
Diabetic Patients ◽  
Age Group ◽  
Systolic Blood Pressure Variability

Abstract Background: This study evaluated the age-specific association of systolic blood pressure variability with cardiovascular disease and mortality in Type-2 diabetic patients. The detrimental effects of increased systolic blood pressure variability on cardiovascular disease and mortality risk in diabetic patients remains unclear. Methods: A retrospective cohort study investigated 155,982 diabetic patients aged from 45 to 84 years old without prior history of cardiovascular disease at baseline from 2008 to 2010). systolic blood pressure variability was estimated using systolic blood pressure standard deviation from mixed effects model to reduce regression dilution bias. Age-specific associations (45-54; 55-64; 65-74; 75-84 years) between systolic blood pressure variability, cardiovascular disease and mortality risk were assessed by Cox regression adjusted for patient characteristics with subgroups stratified by subject baseline characteristics. Results: After a median follow-up of 9.7 years (16.4 million person-years), 49,816 events (including 34,039 events with and 29,211 mortalities) were identified. Elevated and independent systolic blood pressure variability was positively and log-linearly associated with higher risk on cardiovascular disease and mortality among all age groups, without evidence of a specific threshold. The cardiovascular disease and mortality risk per 5mmHg increase in systolic blood pressure variability within 45-54 years age group is over three times higher than the 70-79 years age group [Hazard Ratio: 1.66 (1.49, 1.85) vs. Hazard Ratio: 1.19 (1.15, 1.23)]. The significant associations remained consistent among all subgroups. Patients with younger age, lower systolic blood pressure and comorbidity with more types of anti-hypertensive prescription drug users had higher hazard ratios. Conclusions: The findings suggest that systolic blood pressure variability was strongly associated with cardiovascular disease and mortality risk without evidence of a specific threshold in diabetic population. In addition to optimize blood pressure control, the systolic blood pressure variability particularly for younger patients should be monitored and evaluated in routine practice.

scholarly journals Systolic blood pressure and all-cause mortality in normoglycemia, prediabetes, and diabetes

2020 ◽  
Author(s):  
Chao-lei Chen ◽  
Lin Liu ◽  
Jia-yi Huang ◽  
Yu-ling Yu ◽  
Kenneth Lo ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Mortality Risk ◽  
Cox Regression ◽  
Reference Group ◽  
Diabetic Patients ◽  
Diabetes Status ◽  
All Cause Mortality ◽  
Glucose Status

Abstract Background The optimal blood pressure (BP) level for diabetic patients remains controversial, and population-based evidence on BP management for individuals with normoglycemia and prediabetes is insufficient. We aimed to investigate the associations between systolic blood pressure (SBP) and all-cause mortality among US adults with different glucose metabolism.Methods We used data from the 1999–2014 National Health and Nutrition Examination Survey (NHANES, n = 40,046) with comprehensive baseline examination and follow-up assessment. Restricted cubic spline was performed to examine dose-response relationship between continuous SBP and all-cause mortality. Cox regression models were used to estimate hazard ratios of all-cause mortality for SBP categories.Results Over 32,5450 person-years of follow-up (median 8.1 years), 4745 all-cause death (11.8%) were recorded, corresponding to an event rate of 14.58 per 1000 patient years. U-shaped associations between SBP and all-cause mortality were observed regardless of glucose status. The lowest mortality risk of optimal SBP (mmHg) by group was 115–120 (normoglycemia), 120–130 (prediabetes), and 125–135 (diabetes). Compared with the reference group, SBP < 100 mmHg was significantly associated with 49% (HR = 1.49, 95%CI: 1.13–1.96), 57% (1.57, 1.07–2.3), and 59% (1.59, 1.12–2.25) higher mortality risk in normoglycemia, prediabetes, and diabetes, respectively. The multivariable-adjusted HRs of all-cause mortality for SBP of 150–159 mmHg and ≥ 160 mmHg were 1.35 (1.08–1.70) and 1.61 (1.31–1.98), 1.44 (1.13–1.83) and 1.66 (1.33–2.08), and 1.29 (1.02–1.65) and 1.37 (1.09–1.72), respectively.Conclusions U-shaped relationships between SBP and all-cause mortality existed regardless of diabetes status. The optimal SBP range for the lowest mortality was gradually higher with worsening glucose status.

scholarly journals Age Differences in the Relationships between Risk Factors and Loss of Kidney Function: a General Population Cohort Study

2020 ◽  
Author(s):  
Tadashi Toyama ◽  
Kiyoki Kitagawa ◽  
Megumi Oshima ◽  
Shinji Kitajima ◽  
Akinori Hara ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cohort Study ◽  
Systolic Blood Pressure ◽  
Kidney Function ◽  
Age Groups ◽  
Older Age ◽  
Age Group ◽  
Personalized Care ◽  
Annual Decline

Abstract Background: Annual decline in kidney function is a widely applied surrogate outcome of renal failure. It is important to understand the relationships between known risk factors and the annual decline in estimated glomerular filtration rate (eGFR) according to baseline age; however, these remain unclear.Methods: A community-based retrospective cohort study of adults who underwent annual medical examinations between 1999 and 2013 was conducted. The participants were stratified into different age groups (40–49, 50–59, 60–69, 70–79, and ≥80 years) to assess the risk for loss of kidney function. A mixed-effects model was used to estimate the association between risk factors and annual changes in eGFR.Results: A total of 51,938 participants were included in the analysis. The age group of ≥80 years included 8,127 individuals. The mean annual change in eGFR was -0.39 (95% confidence interval -0.41 to -0.37) mL/min/1.73 m2 per year. Older age was related to faster loss of kidney function. In the older age group, higher systolic blood pressure, proteinuria, and current smoking were related to faster loss of kidney function (p trend <0.01, 0.03, and <0.01, respectively). Conversely, each age group showed similar annual loss of kidney function related to lower hemoglobin levels and diabetes mellitus (p trend 0.47 and 0.17, respectively).Conclusions: Higher systolic blood pressure, proteinuria, and smoking were related to faster loss of kidney function, and greater effect size was observed in the older participants. More risk assessments for older people are required for personalized care.

Abstract 16998: Gender and Age Specific Baseline Predictors of MACE in PEACE Trial Identified by Machine Learning

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Victoria Xin ◽  
Scout Hayashi ◽  
Anwar Husain ◽  
Ahmed A Hasan ◽  
Amit Dey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Proportional Hazards Model ◽  
Coronary Revascularization ◽  
Stable Coronary Artery Disease ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Therapeutic Interventions ◽  
Data Repository ◽  
Age Group ◽  
Gender And Age

Introduction: The NHLBI supported Prevention of Events with Angiotensin-Converting Enzyme (ACE) Therapy trial (PEACE) (NCT00000558) found that the addition of ACE inhibitor trandolapril to conventional therapy in 8290 patients with stable coronary artery disease and preserved ejection fraction provided no benefit against MACE (cardiovascular death, nonfatal myocardial infarction, or the need for coronary revascularization), the composite primary endpoint. We reused publicly available individual patient-level PEACE data from NHLBI Data Repository (BioLINCC) to perform hypothesis-generating secondary analyses by machine learning (ML) using random survival forest (RSF) to identify gender and age group specific predictors for MACE. Methods: RSF was performed on 50 baseline variables for the MACE outcome in male and female and in age group (<60, 60-69, >69) cohorts. The top ten predictors identified in each cohort were included in a multivariate analysis using a Cox proportional hazards model with a multiple regression approach. Results: The top 10 predictors for the MACE selected by RSF are shown in Figure 1. Expected cardiovascular (CV) risk predictors like blood pressure, Canadian CV Society angina classification (CCS), age, and a history of various CV procedures consistently emerge amongst the top ten predictors of the primary MACE outcome across all gender and age specific subgroups. Interestingly, RSF also identified renal function biomarkers like serum potassium and glomerular filtration rate as common top ten predictors. Conclusion: Using ML, we uncovered in an unbiased fashion, gender and age groups specific unanticipated top predictors for MACE in PEACE trial. This underscores the value of gender and age specific predictors to examine the efficacy and outcomes of therapeutic interventions in advancing precision and personalized medicine.

scholarly journals Age differences in the relationships between risk factors and loss of kidney function: a general population cohort study

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Tadashi Toyama ◽  
Kiyoki Kitagawa ◽  
Megumi Oshima ◽  
Shinji Kitajima ◽  
Akinori Hara ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cohort Study ◽  
Systolic Blood Pressure ◽  
Kidney Function ◽  
Age Groups ◽  
Older Age ◽  
Age Group ◽  
Personalized Care ◽  
Annual Decline

Abstract Background Annual decline in kidney function is a widely applied surrogate outcome of renal failure. It is important to understand the relationships between known risk factors and the annual decline in estimated glomerular filtration rate (eGFR) according to baseline age; however, these remain unclear. Methods A community-based retrospective cohort study of adults who underwent annual medical examinations between 1999 and 2013 was conducted. The participants were stratified into different age groups (40–49, 50–59, 60–69, 70–79, and ≥ 80 years) to assess the risk for loss of kidney function. A mixed-effects model was used to estimate the association between risk factors and annual changes in eGFR. Results In total, 51,938 participants were included in the analysis. The age group of ≥80 years included 8127 individuals. The mean annual change in eGFR was − 0.39 (95% confidence interval: − 0.41 to − 0.37) mL/min/1.73 m2 per year. Older age was related to faster loss of kidney function. In the older age group, higher systolic blood pressure, proteinuria, and current smoking were related to faster loss of kidney function (p trend < 0.01, 0.03, and < 0.01, respectively). Conversely, each age group showed similar annual loss of kidney function related to lower hemoglobin levels and diabetes mellitus (p trend 0.47 and 0.17, respectively). Conclusions Higher systolic blood pressure, proteinuria, and smoking were related to faster loss of kidney function, and a greater effect size was observed in the older participants. More risk assessments for older people are required for personalized care.

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