Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD.
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ABSTRACT: Background
The 2017 guidelines of the American College of Cardiology and the American Heart Association propose substantial changes to hypertension management. The guidelines lower the blood pressure threshold defining hypertension and promote more aggressive treatments. Thus, more individuals are now classified as hypertensive and as a result, medication usage may become more extensive. An inevitable byproduct of greater medication use is higher incidence of adverse effects. Here, we examined these issues by developing models that predict both cardiovascular events and other adverse events based on the treatment chosen and other patient's data.Methods and results
We used data from the SPRINT trial to produce patient-specific predictions of the risks for adverse cardiovascular or kidney outcomes. Unlike prior models, we used both the baseline characteristics collected upon recruitment and the longitudinal data during the follow-up. Importantly, our cardiovascular predictor outperformed extant models on SPRINT participants, achieving AUC = 0.765, and was validated with good performance in an independent cohort of the ACCORD trial.Conclusions
Our study illustrates the importance of including longitudinal data for assessing personalized risk and provides means for recommending personalized treatment decisions.
SUBMITTER: Dinstag G
PROVIDER: S-EPMC6687091 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Orthostatic hypotension (OH) is frequently observed with hypertension treatment, but its contribution to adverse outcomes is unknown. The SPRINT (Systolic Blood Pressure Intervention Trial) was a randomized trial of adults, age ≥50 years at high risk for cardiovascular disease with a seated systolic blood pressure (BP) of 130 to 180 mm Hg and a standing systolic BP ≥110 mm Hg. Participants were randomized to a systolic BP treatment goal of either <120 or <140 mm Hg. OH was defined as a drop in systolic BP ≥20 or diastolic BP ≥10 mm Hg 1 minute after standing from a seated position. We used Cox models to examine the association of OH with cardiovascular disease or adverse study events by randomized BP goal. During the follow-up period (median 3years), there were 1170 (5.7%) instances of OH among those assigned a standard BP goal and 1057 (5.0%) among those assigned the intensive BP goal. OH was not associated with higher risk of cardiovascular disease events (primary outcome: hazard ratio 1.06 [95% CI, 0.78-1.44]). Moreover, OH was not associated with syncope, electrolyte abnormalities, injurious falls, or acute renal failure. OH was associated with hypotension-related hospitalizations or emergency department visits (hazard ratio, 1.77 [95% CI, 1.11-2.82]) and bradycardia (hazard ratio, 1.94 [95% CI, 1.19-3.15]), but these associations did not differ by BP treatment goal. OH was not associated with a higher risk of cardiovascular disease events, and BP treatment goal had no effect on OH's association with hypotension and bradycardia. Symptomless OH during hypertension treatment should not be viewed as a reason to down-titrate therapy even in the setting of a lower BP goal. Clinical Trial Registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT01206062.
| S-EPMC7261502 | biostudies-literature
Project description:SPRINT (Systolic Blood Pressure Intervention Trial) highlighted the benefits of intensive targeted antihypertensive therapy but resulted in higher rates of treatment-related adverse events. Blood pressure (BP) variability has emerged as a significant predictor of outcomes over and above levels of BP. Using the SPRINT data set, we aimed to determine the relationship of BP variability with cardiovascular outcomes and side effects of antihypertensive therapy. The analyses included all participants randomized in SPRINT who reached the target systolic BP (SBP) for their respective groups (intensive < 120 mm Hg; standard < 140 mm Hg). Coefficients of variation (CV) for SBP, diastolic BP (DBP), and PP for each patient characterized variability. Student t test was used to compare treatment arms for each CV metric. Cox proportional hazards regression was used to identify independent predictors of the SPRINT primary outcome and adverse events. P < .15 on univariate analysis was required to enter the model and P < .05 to remain in it. A total of 8884 patients (4561 standard group; 4323 intensive group) met inclusion criteria. DBP CV differed between the groups (9.12 ± 3.20 standard group; 9.47 ± 3.49 intensive group [P < .0001]). DBP CV predicted a greater hazard for the primary outcome (hazard ratio [HR], 1.14) in the overall model as well as separate analyses by treatment arms (standard group HR, 1.15; intensive group HR, 1.19), each P < .0001. DBP CV also independently predicted a greater hazard for acute kidney injury (HR, 1.12) and hypotensive events (HR, 1.12). Visit-to-visit DBP variability independently predicted worse cardiovascular outcomes and hypoperfusion-related adverse events in SPRINT.
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Project description:Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.
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Project description:Background and objectivesMajor adverse kidney events, a composite of death, new kidney replacement therapy, or persistent kidney dysfunction, is a potential patient-centered outcome for clinical trials in sepsis-associated kidney injury. We sought to determine the incidence of major adverse kidney events within 30 days and validate this end point in pediatric sepsis.Design, setting, participants, & measurementsWe conducted a retrospective observational study using the Pediatric Health Information Systems Plus database of patients >6 months to <18 years old with a diagnosis of severe sepsis/septic shock; orders for bacterial blood culture, antibiotics, and at least one fluid bolus on hospital day 0/1; and known hospital disposition between January 2007 and December 2011. The primary outcome was incidence of major adverse kidney events within 30 days. Major adverse kidney events within 30 days were validated against all-cause mortality at hospital discharge, hospital length of stay, total hospital costs, hospital readmission within 30 days and 1 year, and lowest eGFR between 3 months and 1 year after discharge. We reported incidence of major adverse kidney events within 30 days with 95% confidence intervals using robust SEM and used multivariable logistic regression to test the association of major adverse kidney events within 30 days with hospital costs and mortality.ResultsOf 1685 admissions, incidence of major adverse kidney events within 30 days was 9.6% (95% confidence interval, 8.1% to 11.0%), including 4.5% (95% confidence interval, 3.5% to 5.4%) death, 1.7% (95% confidence interval, 1.1% to 2.3%) kidney replacement therapy, and 5.8% (95% confidence interval, 4.7% to 6.9%) persistent kidney dysfunction. Patients with versus without major adverse kidney events within 30 days had higher all-cause mortality at hospital discharge (28% versus 1%; P<0.001), higher total hospital costs ($61,188; interquartile range, $21,272-140,356 versus $28,107; interquartile range, $13,056-72,697; P<0.001), and higher proportion with eGFR<60 ml/min per 1.73 m2 between 3 months and 1 year after discharge (19% versus 4%; P=0.001). Major adverse kidney events within 30 days was not associated with length of stay or readmissions.ConclusionsIn children with sepsis, major adverse kidney events within 30 days are common, feasible to measure, and a promising end point for future clinical trials.
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Project description:BackgroundChronic kidney disease is common and is associated with cardiovascular disease, cerebrovascular disease, and cognitive function, although the nature of this relationship remains uncertain.Study designCross-sectional cohort using baseline data from the Systolic Blood Pressure Intervention Trial (SPRINT).Setting & participantsParticipants in SPRINT, a randomized clinical trial of blood pressure targets in older community-dwelling adults with cardiovascular disease, chronic kidney disease, or high cardiovascular disease risk and without diabetes or known stroke, who underwent detailed neurocognitive testing in the cognition substudy, SPRINT-Memory and Cognition in Decreased Hypertension (SPRINT-MIND).PredictorsUrine albumin-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR).OutcomesCognitive function, a priori defined as 5 cognitive domains based on 11 cognitive tests using z scores, and abnormal white matter volume quantified by brain magnetic resonance imaging.ResultsOf 9,361 SPRINT participants, 2,800 participated in SPRINT-MIND and 2,707 had complete data; 637 had brain imaging. Mean age was 68 years, 37% were women, 30% were black, and 20% had known cardiovascular disease. Mean eGFR was 70.8±20.9mL/min/1.73m2 and median urine ACR was 9.7 (IQR, 5.7-22.5) mg/g. In adjusted analyses, higher ACR was associated with worse global cognitive function, executive function, memory, and attention, such that each doubling of urine ACR had the same association with cognitive performance as being 7, 10, 6, and 14 months older, respectively. Lower eGFR was independently associated with worse global cognitive function and memory. In adjusted models, higher ACR, but not eGFR, was associated with larger abnormal white matter volume.LimitationsCross-sectional only, no patients with diabetes were included.ConclusionsIn older adults, higher urine ACR and lower eGFR have independent associations with global cognitive performance with different affected domains. Albuminuria concurrently identifies a higher burden of abnormal brain white matter disease, suggesting that vascular disease may mediate these relationships.
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Project description:We aimed to determine whether urinary neutrophil gelatinase-associated lipocalin (uNGAL) can accurately predict persistent AKI, major adverse kidney events at 30 days (MAKE30) and 365 days (MAKE365) in hospitalized AKI patients. This is a retrospective study of adult patients who were admitted at King Chulalongkorn Memorial Hospital. We performed multivariable logistic regression for persistent AKI, MAKE30, and MAKE365. We developed equations for predicting MAKE30 and MAKE365 and divided the dataset into derivation and validation cohorts. uNGAL performance and predictive models were assessed using the area under the receiver operating characteristic curve (AROC). Among 1,322 patients with AKI, 76.9%, 45.1%, and 61.7% had persistent AKI, MAKE30, and MAKE365. The AROC were 0.75 (95% confidence interval[CI] 0.70-0.80), 0.66 (95%CI 0.61-0.71), and 0.64 (95%CI 0.59-0.70) for prediction of persistent AKI, MAKE30, and MAKE365 by uNGAL. The AROC in the validation dataset combining uNGAL with clinical covariates were 0.74 (95%CI 0.69-0.79) and 0.72 (95%CI 0.67-0.77) for MAKE30 and MAKE365. We demonstrated an association between uNGAL and persistent AKI, MAKE30, and MAKE365. Prediction models combining uNGAL can modestly predict MAKE30 and MAKE365. Therefore, uNGAL is a useful tool for improving AKI risk stratification.
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Project description:Background Assessing the risk of serious adverse events (SAEs) during hypertension treatment is important for understanding the benefit-harm trade-offs of lower blood pressure goals. It is unknown whether high-sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) provide information about SAEs. Methods and Results In SPRINT (Systolic Blood Pressure Intervention Trial), hs-cTnT and NT-proBNP were measured at baseline in 8828 (94.3%) and 8836 (94.4%) participants, respectively. Multivariable Cox proportional hazards models were used to evaluate hs-cTnT and NT-proBNP associations with a composite of SPRINT's SAEs of interest: hypotension, syncope, bradycardia, acute kidney injury, electrolyte abnormalities, and injurious falls. Elevations in hs-cTnT and NT-proBNP were associated with increased composite SAE risk (hazard ratio [HR] per 2-fold higher hs-cTnT: 1.15; 95% CI, 1.06‒1.25; HR per 2-fold higher NT-proBNP: 1.09; 95% CI, 1.05‒1.14). Compared with both hs-cTnT and NT-proBNP in the lower tertiles, both biomarkers in the highest tertile was associated with increased composite SAE risk (HR, 1.56; 95% CI, 1.32‒1.84). Composite SAE risk was higher in the intensive-treatment group than in the standard-treatment group for participants with both biomarkers in the lower tertiles, but similar between treatment groups for participants with both biomarkers in the highest tertile (P for interaction=0.008). Conclusions Elevations in hs-cTnT and NT-proBNP individually and in combination are associated with higher composite SAE risk in SPRINT. The differential impact of blood pressure treatment on SAE risk across combined biomarker categories may have implications for identifying individuals with more favorable benefit-harm profiles for intensive blood pressure lowering.
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Project description:BackgroundSeveral biomarkers of AKI have been examined for their ability to predict AKI before serum creatinine. Few studies have focused on using kidney biomarkers to better predict major adverse kidney events (MAKE), an increasingly used composite outcome in critical care nephrology research.MethodsSingle-center prospective study collecting blood and urine samples from critically ill patients with AKI Kidney Disease Improving Global Outcomes stage 2 or above, and matched controls from a single, tertiary care intensive care unit (ICU). Samples were collected at 24-48 hours after AKI diagnosis (patients) or ICU admission (controls), 5-7 days later, and 4-6 weeks after discharge for patients with AKI. The primary outcome of interest was MAKE at hospital discharge (MAKE-DC), consisting of the composite end point of death, RRT dependence, or a decrease in estimated glomerular filtration to <75% of baseline.ResultsSerum/urinary neutrophil gelatinase-associated lipocalin (NGAL), serum/urinary cystatin C, and urinary kidney injury molecule-1 early in the AKI or ICU course were all significantly higher in patients with MAKE-DC compared with those not experiencing MAKE-DC. Additionally, serum/urinary NGAL and serum cystatin C measurements at the first time point remained significantly associated with MAKE events at 3, 6, and 12 months. Serum cystatin C, and to a lesser extent serum NGAL, significantly improved upon a logistic regression clinical prediction model of MAKE-DC (AUROC 0.94 and 0.87 versus 0.83; P=0.001 and P=0.02, respectively). Patients without MAKE-DC experienced a greater decline in serum NGAL from first to second measurement than those patients experiencing MAKE-DC.ConclusionsEarly measures of kidney biomarkers in patients who are critically ill are associated with MAKE-DC. This relationship appears to be greatest with serum NGAL and cystatin C, which display additive utility to a clinical prediction model. Trending serum NGAL may also have utility in predicting MAKE-DC.
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Project description:Prediction of primary cardiovascular events has been thoroughly investigated since the landmark Framingham risk score was introduced. However, prediction of secondary events after initial events of coronary artery disease (CAD) poses a new challenge. In a cohort of coronary angiography patients (n?=?1760), we examined readily available hematological parameters from the UPOD (Utrecht Patient Oriented Database) and their addition to prediction of secondary cardiovascular events. Backward stepwise multivariable Cox regression analysis was used to test their ability to predict death and major adverse cardiovascular events (MACE). Continuous net reclassification improvement (cNRI) and integrated discrimination improvement (IDI) measures were calculated for the hematological parameters on top of traditional risk factors to assess prediction improvement. Panels of 3 to 8 hematological parameters significantly improved prediction of death and adverse events. The IDIs ranged from 0.02 to 0.07 (all P?<?0.001) among outcome measures and the cNRIs from 0.11 to 0.40 (P?<?0.001 in 5 of 6 outcome measures). In the hematological panels red cell distribution width (RDW) appeared most often. The multivariable adjusted hazard ratio of RDW per 1 standard deviation (SD) increase for MACE was 1.19 [1.08-1.32], P?<?0.001. Routinely measured hematological parameters significantly improved prediction of mortality and adverse events in coronary angiography patients. Accurately indicating high-risk patients is of paramount importance in clinical decision-making.
| S-EPMC4912281 | biostudies-literature
Project description:BACKGROUND:The ability of urinary biomarkers to complement established clinical risk prediction models for postoperative adverse kidney events is unclear. We assessed the effect of urinary biomarkers linked to suspected pathogenesis of cardiac surgery-induced acute kidney injury (AKI) on the performance of the Cleveland Score, a risk assessment model for postoperative adverse kidney events. METHODS:This pilot study included 100 patients who underwent open-heart surgery. We determined improvements to the Cleveland Score when adding urinary biomarkers measured using clinical laboratory platforms (neutrophil gelatinase-associated lipocalin [NGAL], interleukin-6) and those in the preclinical stage (hepcidin-25, midkine, alpha-1 microglobulin), all sampled immediately post-surgery. The primary endpoint was major adverse kidney events (MAKE), and the secondary endpoint was AKI. We performed ROC curve analysis, assessed baseline model performance (odds ratios [OR], 95% CI), and carried out statistical reclassification analyses to assess model improvement. RESULTS:NGAL (OR [95% CI] per 20 concentration-units wherever applicable): (1.07 [1.01-1.14]), Interleukin-6 (1.51 [1.01-2.26]), midkine (1.01 [1.00-1.02]), 1-hepcidin-25 (1.08 [1.00-1.17]), and NGAL/hepcidin-ratio (2.91 [1.30-6.49]) were independent predictors of MAKE and AKI (1.38 [1.03-1.85], 1.08 [1.01-1.15], 1.01 [1.00-1.02], 1.09 [1.01-1.18], and 3.45 [1.54-7.72]). Category-free net reclassification improvement identified interleukin-6 as a model-improving biomarker for MAKE and NGAL for AKI. However, only NGAL/hepcidin-25 improved model performance for event- and event-free patients for MAKE and AKI. CONCLUSIONS:NGAL and interleukin-6 measured immediately post cardiac surgery may complement the Cleveland Score. The combination of biomarkers with hepcidin-25 may further improve diagnostic discrimination.
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