International Validation of the Thrombolysis in Myocardial Infarction (TIMI) Risk Score for Secondary Prevention in Post‐MI Patients: A Collaborative Analysis of the Chronic Kidney Disease Prognosis Consortium and the Risk Validation Scientific Committee

Abstract

BackgroundThe Thrombolysis in Myocardial Infarction (TIMI) Risk Score for Secondary Prevention (TRS2°P), a 0‐to‐9‐point system based on the presence/absence of 9 clinical factors, was developed to classify the risk of major adverse cardiovascular events (MACE) (a composite of cardiovascular death, recurrent myocardial infarction, or ischemic stroke) among patients with a recent myocardial infarction. Its performance has not been examined internationally outside of a clinical trial setting.Methods and ResultsWe evaluated the performance of TRS2°P for predicting MACE in 53 599 patients with recent myocardial infarction in 5 international cohorts from New Zealand, South Korea, Sweden, and the United States participating in the Chronic Kidney Disease Prognosis Consortium. Overall, there were 19 444 cases of MACE across 5 cohorts over a mean follow‐up of 5 years, and the overall MACE rate ranged from 5.0 to 18.4 (per 100 person‐years). The TRS2°P showed modest calibration (Brier score ranged from 0.144 to 0.173) and discrimination (C‐statistics >0.61 in all studies except 1 from Korea with 0.55) across cohorts relative to its original Brier score of 0.098 and C‐statistic of 0.67 in the derived data set. Although there was some heterogeneity across cohorts, the 9 predictors in the TRS2°P were generally associated with higher MACE risk, with strongest associations observed (meta‐analyzed adjusted hazard ratio 1.6–1.7) for history of heart failure, age ≥75 years, and prior stroke, followed by peripheral artery disease, kidney dysfunction, diabetes mellitus, and hypertension (hazard ratio 1.3–1.4). Prior coronary bypass graft surgery and smoking did not reach statistical significance (hazard ratio ≈1.1).ConclusionsTRS2°P, a simple scoring system with 9 routine clinical factors, was modestly predictive of secondary events when applied in patients with recent myocardial infarction from diverse clinical and geographic settings.

Efficacy and Safety of Ombitasvir/Paritaprevir/Ritonavir in Patients With Hepatitis C Virus Genotype 1 or 4 Infection and Advanced Kidney Disease

“…Third, only 18 patients were enrolled into the RBV-free RUBY-II study; only 5 of these patients had HCV GT4 and the 4 patients who completed treatment achieved SVR12. Fourth, patients with CKD are at increased risk of major adverse cardiovascular events, 34 and RBV has the potential to cause hemolytic anemia and provoke myocardial ischemia in patients who experience substantial decreases in hemoglobin during treatment 35 . Overall, patients in these studies had a median hemoglobin level of 12.0 g/dl.…”

Value of TIMI Risk Score Combined With Global Longitudinal Strain for Predicting Major Adverse Cardiac Events After PCI in Patients With ST-Segment Elevation Myocardial Infarction

“…The TIMI risk score is a simple risk strati cation tool. Many studies have con rmed that the TIMI score can be used to assess the short-and medium-term prognosis of STEMI patients [15][16][17][18]. STEMI patients with high TIMI scores at admission have a much higher probability of developing MACEs after discharge than those with low scores; thus, the TIMI score is an independent predictor of the occurrence of MACEs.…”

Untargeted high-resolution plasma metabolomic profiling predicts outcomes in patients with coronary artery disease

“…[2] This risk score consists of nine easily measurable clinical variables (age, current smoking, hypertension, diabetes, heart failure, stroke, prior coronary artery bypass grafting, peripheral artery disease, and chronic kidney disease) and has been shown to perform well in external validation studies. [78,79] However, this approach does not identify individual plasma metabolites or the dysregulated metabolic pathways associated with mortality in this high-risk patient population. In addition to identifying the six significant metabolic pathways described above, we identified a unique subset of seven metabolites was identified using stringent statistical criteria and these were used to create a novel metabolomic risk score.…”