Change in Multiple Filtration Markers and Subsequent Risk of Cardiovascular Disease and Mortality

Rebholz, Casey M.; Grams, Morgan E.; Matsushita, Kunihiro; Inker, Lesley A.; Foster, Meredith C.; Levey, Andrew S.; Selvin, Elizabeth; Coresh, Josef

doi:10.2215/cjn.10101014

Cited by 17 publications

(18 citation statements)

References 42 publications

(51 reference statements)

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“…AKI in survivors of critical illness predicts a worse frailty status after discharge [17]. The presence of AKI is also strongly associated with the risk of CVDs and death in critically ill patients [18]. Our study also revealed that patients with HS had a higher incidence of CKD during the follow-up period.…”

Section: Discussionmentioning

confidence: 52%

The association between heat stroke and subsequent cardiovascular diseases

et al. 2019

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Background Recent studies have indicated that several critical illnesses are associated with an increased risk of cardiovascular diseases (CVDs). Nonetheless, studies of the association between heat-related illnesses (HRIs) and subsequent CVDs are still limited. We sought to evaluate whether heat stroke (HS) was associated with an increased CVD incidence. Methods The data from the nationwide, population-based, retrospective, cohort study described herein were obtained from the National Health Insurance Research Database in Taiwan. The outcome evaluated in this study was the cumulative incidence of CVDs, which was compared between patients with HS, patients with other HRIs and a control group during a 14-year follow-up period. Results Our analyses included 150 HS cases, 150 patients with other HRIs and 150 patients without HRIs. The HS patients had a significantly higher incidence of developing CVDs than the other HRI and control patients (32.67% vs. 23.33% vs. 16.67%, p = 0.005). Patients with HS had an increased incidence of acute myocardial infarction (AMI) compared with that of the controls (6% vs. 2.67%, p = 0.042) and an increased incidence of acute ischemic stroke (AIS) compared with those of the other HRI and control patients (12% vs. 6% vs. 4.67%, p = 0.038). An increased risk of chronic kidney disease (CKD) was also found in the patients with HS and other HRIs compared to that in the controls (17.33% vs. 14.67% vs. 6.67%, p = 0.016). Conclusion Prior HS was associated with an increased incidence of CVDs, particularly AMI and AIS, and an increased incidence of CKD.

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Section: Discussionmentioning

confidence: 52%

The association between heat stroke and subsequent cardiovascular diseases

et al. 2019

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“…Machine learning methods, and RF particularly, have been used before for CVD risk prediction. 18–27 In this study, we were able to use deeply phenotyped data to predict outcomes in a population study that accounts for time to event. 28 The added advantage is that these methods can be extended and refined, regularly, with new data.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Event Prediction by Machine Learning

Ambale-Venkatesh¹,

Yang

et al. 2017

Circulation Research

468

255

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Rationale Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. Objective To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. Methods and Results We included participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across USA were included. 735 variables from imaging and non-invasive tests, questionnaires and biomarker panels were obtained. We used the RF technique to identify the top 20 predictors of each outcome. Imaging, electrocardiography and serum biomarkers featured heavily on the top-20 lists as opposed to traditional CV risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age and ankle brachial index were among the top predictors of atrial fibrillation. Tissue necrosis factor-α and interleukin-2 soluble receptors, and N-terminal pro-Brain Natriuretic Peptide levels were important across all outcomes. The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10–25%). Conclusions Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights regarding subclinical disease markers without apriori assumptions of causality. Clinical Trial Registration Multi-Ethnic Study of Atherosclerosis (MESA) http://mesa-nhlbi.org/. ClinicalTrials.gov Identifier NCT00005487

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“…Based on results from prior studies, 22,23 we assessed change in eGFR in five categories (substantial decline [>30% decline], moderate decline [10%-30% decline], stable [± < 10%], moderate increase [10%-30% increase], substantial increase [>30% increase], with ± < 10% as the reference category) and the primary outcome. 22,23 For this categorical exposure, we only examined the combined outcome of major macrovascular and microvascular events, because the number of events in each cell was too small. We additionally adjusted for body mass index (BMI), total cholesterol, high-density lipoprotein (HDL) cholesterol, and smoking status in the fully adjusted models.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…[16][17][18][19][20][21] Recently, several studies have reported that >30% change in alternative filtration markers alone and in combination with creatinine is associated with a strong risk of CKD progression in CKD populations, those at high risk of CKD, and the general population. 17,22,23 However, there have been limited data on alternative kidney filtration markers and health outcomes in individuals with type 2 diabetes. Studies have shown that a single measurement of cystatin C-based eGFR is associated with adverse outcomes in those with diabetes, but associations with B2M were reported only in studies of modest sample sizes or with a specific race/ethnic group.…”

Section: Introductionmentioning

confidence: 99%

Alternative kidney filtration markers and the risk of major macrovascular and microvascular events, and all‐cause mortality in individuals with type 2 diabetes in the ADVANCE trial

Kim

Wang

Chalmers

et al. 2020

Journal of Diabetes

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Background: Creatinine-based estimated glomerular filtration rate (eGFR) is biased in the setting of obesity and other conditions. Alternative kidney filtration markers may be useful in adults with diabetes, but few studies examined the associations with risk of clinical outcomes. Methods: In the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial, we evaluated whether baseline levels and change in eGFR based on creatinine (Cr), cystatin c (Cys), β 2-microglobulin (B2M), eGFR Cr-Cys , and the average of three estimates (eGFR Cr-Cys-B2M) assessed in 7217 participants at baseline and a Abbreviations: ACE inhibitors, angiotensin-converting enzyme inhibitors; ADVANCE, Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation; B2M, β 2-microglobulin; BMI, body mass index; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; eGFR B2M , B2M-based eGFR; eGFR Cr , creatinine-based eGFR; eGFR Cr-Cys , combined creatinine and cystatin C-based eGFR; eGFR Cr-Cys-B2M , the average of creatinine-, cystatin C-, and B2M-based eGFR; eGFR Cys , cystatin C-based eGFR; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; HR, hazard ratio. ClinicalTrials.gov identifier: NCT00145925.

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Change in Multiple Filtration Markers and Subsequent Risk of Cardiovascular Disease and Mortality

Cited by 17 publications

References 42 publications

The association between heat stroke and subsequent cardiovascular diseases

The association between heat stroke and subsequent cardiovascular diseases

Cardiovascular Event Prediction by Machine Learning

Alternative kidney filtration markers and the risk of major macrovascular and microvascular events, and all‐cause mortality in individuals with type 2 diabetes in the ADVANCE trial

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