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: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: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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