Shun Fu Lee

Urinary Sodium and Potassium Excretion, Mortality, and Cardiovascular Events

BACKGROUND The optimal range of sodium intake for cardiovascular health is controversial. METHODS We obtained morning fasting urine samples from 101,945 persons in 17 countries and estimated 24-hour sodium and potassium excretion (used as a surrogate for intake). We examined the association between estimated urinary sodium and potassium excretion and the composite outcome of death and major cardiovascular events. RESULTS The mean estimated sodium and potassium excretion was 4.93 g per day and 2.12 g per day, respectively. With a mean follow-up of 3.7 years, the composite outcome occurred in 3317 participants (3.3%). As compared with an estimated sodium excretion of 4.00 to 5.99 g per day (reference range), a higher estimated sodium excretion (≥ 7.00 g per day) was associated with an increased risk of the composite outcome (odds ratio, 1.15; 95% confidence interval [CI], 1.02 to 1.30), as well as increased risks of death and major cardiovascular events considered separately. The association between a high estimated sodium excretion and the composite outcome was strongest among participants with hypertension (P=0.02 for interaction), with an increased risk at an estimated sodium excretion of 6.00 g or more per day. As compared with the reference range, an estimated sodium excretion that was below 3.00 g per day was also associated with an increased risk of the composite outcome (odds ratio, 1.27; 95% CI, 1.12 to 1.44). As compared with an estimated potassium excretion that was less than 1.50 g per day, higher potassium excretion was associated with a reduced risk of the composite outcome. CONCLUSIONS In this study in which sodium intake was estimated on the basis of measured urinary excretion, an estimated sodium intake between 3 g per day and 6 g per day was associated with a lower risk of death and cardiovascular events than was either a higher or lower estimated level of intake. As compared with an estimated potassium excretion that was less than 1.50 g per day, higher potassium excretion was associated with a lower risk of death and cardiovascular events. (Funded by the Population Health Research Institute and others.).

Identifying Novel Biomarkers for Cardiovascular Events or Death in People With Dysglycemia

Background— Serum biomarkers may identify people at risk for cardiovascular (CV) outcomes. Biobanked serum samples from 8494 participants with dysglycemia in the completed Outcome Reduction With Initial Glargine Intervention trial were assayed for 284 biomarkers to identify those that could identify people at risk for a CV outcome or death when added to clinical measurements. Methods and Results— A multiplex analysis measured a panel of cardiometabolic biomarkers in 1 mL of stored frozen serum from every participant who provided biobanked blood. After eliminating undetectable or unanalyzable biomarkers, 8401 participants who each had a set of 237 biomarkers were analyzed. Forward-selection Cox regression models were used to identify biomarkers that were each independent determinants of 3 different incident outcomes: (1) the composite of myocardial infarction, stroke, or CV death; (2) these plus heart failure hospitalization or revascularization; and (3) all-cause death. When added to clinical variables, 10 biomarkers were independent determinants of the 1405 CV composite outcomes observed during follow-up; 9 biomarkers (including 8 of these 10) were independent determinants of the 2435 expanded composite outcomes; and 15 (including the 10 CV composite biomarkers) were independent determinants of the 1340 deaths. Adjusted C statistics increased from 0.64 for the clinical variables to 0.71 and 0.68 for the 2 CV composite outcomes, respectively, with the greatest increase to 0.75 for death (P<0.001 for the change). Conclusions— A systematic hypothesis-free approach identified combinations of up to 15 cardiometabolic biomarkers as independent determinants of CV outcomes or death in people with dysglycemia. Clinical Trial Registration— URL: http://www.clinicaltrials.gov. Unique identifier: NCT00069784.

Risk Prediction for Early CKD in Type 2 Diabetes

BACKGROUND AND OBJECTIVES Quantitative data for prediction of incidence and progression of early CKD are scarce in individuals with type 2 diabetes. Therefore, two risk prediction models were developed for incidence and progression of CKD after 5.5 years and the relative effect of predictors were ascertained. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Baseline and prospective follow-up data of two randomized clinical trials, ONgoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial (ONTARGET) and Outcome Reduction with Initial Glargine Intervention (ORIGIN), were used as development and independent validation cohorts, respectively. Individuals aged ≥55 years with type 2 diabetes and normo- or microalbuminuria at baseline were included. Incidence or progression of CKD after 5.5 years was defined as new micro- or macroalbuminuria, doubling of creatinine, or ESRD. The competing risk of death was considered as an additional outcome state in the multinomial logistic models. RESULTS Of the 6766 ONTARGET participants with diabetes, 1079 (15.9%) experienced incidence or progression of CKD, and 1032 (15.3%) died. The well calibrated, parsimonious laboratory prediction model incorporating only baseline albuminuria, eGFR, sex, and age exhibited an externally validated c-statistic of 0.68 and an R(2) value of 10.6%. Albuminuria, modeled to depict the difference between baseline urinary albumin/creatinine ratio and the threshold for micro- or macroalbuminuria, was mostly responsible for the predictive performance. Inclusion of clinical predictors, such as glucose control, diabetes duration, number of prescribed antihypertensive drugs, previous vascular events, or vascular comorbidities, increased the externally validated c-statistic and R(2) value only to 0.69 and 12.1%, respectively. Explained variation was largely driven by renal and not clinical predictors. CONCLUSIONS Albuminuria and eGFR were the most important factors to predict onset and progression of early CKD in individuals with type 2 diabetes. However, their predictive ability is modest. Inclusion of demographic, clinical, and other laboratory predictors barely improved predictive performance.

Prognostic validation of a non-laboratory and a laboratory based cardiovascular disease risk score in multiple regions of the world.

Objective To evaluate the performance of the non-laboratory INTERHEART risk score (NL-IHRS) to predict incident cardiovascular disease (CVD) across seven major geographic regions of the world. The secondary objective was to evaluate the performance of the fasting cholesterol-based IHRS (FC-IHRS). Methods Using measures of discrimination and calibration, we tested the performance of the NL-IHRS (n=100 475) and FC-IHRS (n=107 863) for predicting incident CVD in a community-based, prospective study across seven geographic regions: South Asia, China, Southeast Asia, Middle East, Europe/North America, South America and Africa. CVD was defined as the composite of cardiovascular death, myocardial infarction, stroke, heart failure or coronary revascularisation. Results Mean age of the study population was 50.53 (SD 9.79) years and mean follow-up was 4.89 (SD 2.24) years. The NL-IHRS had moderate to good discrimination for incident CVD across geographic regions (concordance statistic (C-statistic) ranging from 0.64 to 0.74), although recalibration was necessary in all regions, which improved its performance in the overall cohort (increase in C-statistic from 0.69 to 0.72, p<0.001). Regional recalibration was also necessary for the FC-IHRS, which also improved its overall discrimination (increase in C-statistic from 0.71 to 0.74, p<0.001). In 85 078 participants with complete data for both scores, discrimination was only modestly better with the FC-IHRS compared with the NL-IHRS (0.74 vs 0.73, p<0.001). Conclusions External validations of the NL-IHRS and FC-IHRS suggest that regionally recalibrated versions of both can be useful for estimating CVD risk across a diverse range of community-based populations. CVD prediction using a non-laboratory score can provide similar accuracy to laboratory-based methods.

Utility of the LACE index at the bedside in predicting 30-day readmission or death in patients hospitalized with heart failure

UNLABELLED The Length of stay, Acuity, Comorbidities, Emergency department visits in prior 6 months (LACE) index threshold of 10 predicts readmission or death in general medical patients in administrative databases. We assessed whether the unadjusted LACE index, computed at the bedside, can predict 30-day outcomes in patients hospitalized for heart failure. METHODS We used logistic regression with LACE as the continuous predictor and 30-day readmissions and 30-day readmission or death as outcomes. We determined a suitable LACE threshold using logistic regression and the closest-to-(0,1) criterion for dichotomized LACE scores. We assessed model discrimination with C statistics and 95% CI. RESULTS Of 378 patients, a majority (91%) had LACE scores ≥10. Incremental LACE scores increased the odds of 30-day readmissions (odds ratio [OR] 1.13, 95% CI 1.02-1.24) and 30-day readmissions or death (OR 1.11, 95% CI 1.01-1.22). C statistics for 30-day readmissions (0.59, 95% CI 0.52-0.65) and 30-day readmission or death (0.57, 95% CI 0.51-0.64) were nonsignificantly lower than the Centers for Medicare/Medicaid Services-endorsed readmission risk score (0.61, 95% CI 0.55-0.67 and 0.62, 95% CI 0.55-0.68, respectively). LACE ≥13 predicted 30-day readmissions (OR 1.91, 95% CI 1.17-3.09) and 30-day readmission or death (OR 1.59, 95% CI 1.00-2.54), and met the closest-to-(0,1) criterion for optimal threshold. CONCLUSIONS LACE calculated at the bedside predicts 30-day clinical outcomes in hospitalized heart failure patients. While there is a continuum of risk, a threshold of ≥13 is more suitable than ≥10 to identify high-risk patients. Given its modest discrimination, however, we do not recommend its preferential use over validated risk prediction tools such as readmission risk score.

Validation of the ORIGIN Cardiovascular Biomarker Panel and the Value of Adding Troponin I in Dysglycemic People

Background Analyses of stored blood from the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial identified biomarkers that supplemented clinical risk factors for cardiovascular (CV) events or death. Their performance in participants with diabetes in the Heart Outcomes Prevention Evaluation (HOPE) study and the incremental value of adding high-sensitivity assays of serum troponin I (hsTnI) in the ORIGIN study were assessed. Methods Levels of the 10 ORIGIN biomarkers for the composite CV outcome of myocardial infarction, stroke, or CV death were measured in 350 HOPE study participants with diabetes and stored serum that included all 77 who experienced this outcome. The effect of adding hsTnI levels to this panel, and the previously identified ORIGIN biomarkers for this composite outcome, this outcome, or revascularization or heart failure, and for mortality was also analyzed. Results Within the HOPE cohort, the ORIGIN biomarker panel increased the C statistic from 0.63 for clinical risk factors alone to 0.67 with the addition of the 10 biomarkers, and the net reclassification improvement was 0.14 (95% confidence interval, 0.01, 0.28). Within the ORIGIN cohort, hsTnI levels predicted all three outcomes during follow-up both alone, and independently of the other biomarkers, which all remained independent predictors of outcomes after inclusion of the hsTnI levels. The hsTnI level interacted with follow-up time such that it was a stronger predictor of earlier vs later events. Conclusion The ORIGIN biomarkers predicted CV outcomes in the independent HOPE cohort. Adding hsTnI levels to the previously identified models in ORIGIN modestly improved their performance.

The association of basal insulin treatment versus standard care with outcomes in anti-GAD positive and negative subjects: A post-hoc analysis of the ORIGIN trial

We compared cardiovascular and other outcomes in patients with dysglycaemia with or without anti‐glutamic acid dehydrogenase (GAD) antibodies participating in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial. Of the 12 537 participants, 8162 had anti‐GAD measured at baseline and 267 were anti‐GAD positive. The effects of insulin glargine versus standard care and of n‐3 fatty acids supplements versus placebo were compared by testing the interaction of the treatment effects and anti‐GAD status. The effect of glargine on development of new diabetes was assessed in participants without previous diabetes at baseline. The overall incidence of outcomes did not differ between anti‐GAD positive and anti‐GAD negative subjects. The incidence of the composite of cardiovascular death, non‐fatal myocardial infarction, or non‐fatal stroke did not differ between anti‐GAD positive participants randomized to insulin glargine or to standard care, with a hazard ratio (HR) (95% confidence interval [CI]) of 0.80 (0.44‐1.44) or in anti‐GAD negative participants with a HR of 1.07 (0.96‐1.20) (P for interaction = 0.20).

The effect of basal insulin glargine on the fibrinolytic system and von Willebrand factor in people with dysglycaemia and high risk for cardiovascular events : Swedish substudy of the Outcome Reduction with an Initial Glargine Intervention trial

Introduction: Fibrinolytic factors, plasminogen activator inhibitor-1, tissue plasminogen activator, tissue plasminogen activator/plasminogen activator-complex and the haemostatic factor von Willebrand factor are known markers of cardiovascular disease. Their plasma levels are adversely affected in patients with dysglycaemia, and glucose normalization with insulin glargine might improve the levels of these factors. Methods: Prespecified Swedish substudy of the Outcome Reduction with an Initial Glargine Intervention trial (ClinicalTrials.gov number, NCT00069784). Tissue plasminogen activator activity, tissue plasminogen activator antigen, plasminogen activator inhibitor-1 antigen, tissue plasminogen activator/plasminogen activator inhibitor-1 complex and von Willebrand factor were analysed at study start, after 2 years and at the end of the study (median follow-up of 6.2 years). Results: Of 129 patients (mean age of 64 ± 7 years, females: 19%), 68 (53%) and 61 (47%) were randomized to the insulin glargine and standard care group, respectively. Allocation to insulin glargine did not significantly affect the studied fibrinolytic markers or von Willebrand factor compared to standard care. Likewise, there were no significant differences in plasminogen activator inhibitor-1, tissue plasminogen activator antigen and von Willebrand factor. During the whole study period, the within-group analysis revealed a curvilinear pattern and significant changes for tissue plasminogen activator/plasminogen activator inhibitor-1 complex, tissue plasminogen activator antigen and von Willebrand factor in the insulin glargine but not in the standard care group. Conclusion: In people with dysglycaemia and other cardiovascular risk factors, basal insulin does not improve the levels of markers of fibrinolysis or von Willebrand factor compared to standard glucose-lowering treatments.

Knowledge to action: Rationale and design of the patient-centered care transitions in heart failure (PACT-HF) stepped wedge cluster randomized trial

Introduction: Heart Failure (HF) is a common cause of hospitalization in older adults. The transition from hospital to home is high‐risk, and gaps in transitional care can increase the risk of re‐hospitalization and death. Combining health care services supported by meta‐analyses, we designed the PACT‐HF transitional care model. Methods: Adopting an integrated Knowledge Translation (iKT) approach in which decision‐makers and clinicians are partners in research, we implement and test the effectiveness of PACT‐HF among patients hospitalized for HF. We use a pragmatic stepped wedge cluster randomized trial design to introduce the complex health service intervention to 10 large hospitals in a randomized sequence until all hospitals initiate the intervention. The goal is for all patients hospitalized with HF to receive self‐care education, multidisciplinary care, and early follow‐up with their health care providers; and in addition, for high‐risk patients to receive post‐discharge nurse‐led home visits and outpatient care in Heart Function clinics. This requires integration of care across hospitals, home care agencies, and outpatient clinics in our publicly funded health care system. While hospitals are the unit of recruitment and analysis, patients (estimated sample size of 3200) are the unit of analysis. Primary outcomes are hierarchically ordered as time to composite all‐cause readmissions / emergency department (ED) visits / death at 3 months and time to composite all‐cause readmissions / ED visits at 30 days. In a nested study of 8 hospitals, we measure the patient‐centered outcomes of Discharge Preparedness, Care Transitions Quality, and Quality Adjusted Life Years (QALY); and the 6‐month health care resource use and costs. We obtain all clinical and cost outcomes via linkages to provincial administrative databases. Conclusions: This protocol describes the implementation and testing of a transitional care model comprising health care services informed by high‐level evidence. The study adopts an iKT and pragmatic approach, uses a robust study design, links clinical trial data with outcomes held in administrative databases, and includes patient‐reported outcomes. Findings will have implications on clinical practice, health care policy, and Knowledge Translation (KT) research methodology.