Risk Assessment after ST-Segment Elevation Myocardial Infarction: Can Biomarkers Improve the Performance of Clinical Variables?

Introduction: Myocardial infarction with ST-segment elevation (STEMI) is the coronary artery disease associated with the highest risk of morbimortality; however, this risk is heterogeneous, usually being evaluated by clinical scores. Risk assessment is a key factor in personalized clinical management of patients with this disease. Aim: The aim of this study was to assess whether some new cardiac biomarkers considered alone, combined in a multibiomarker model or in association with clinical variables, improve the short- and long-term risk stratification of STEMI patients. Materials and Methods: This was a retrospective observational study of 253 patients with STEMI. Blood samples were obtained before or during the angiography. The assessed biomarkers were C-terminal fragment of insulin-like growth factor binding protein-4 (CT-IGFBP4), high sensitive cardiac troponin T (hs-cTnT), N-terminal fragment of probrain natriuretic peptide (NT-proBNP), and growth differentiation factor 15 (GDF-15); they reflect different cardiovascular (CV) physiopathological pathways and underlying pathologies. We registered in-hospital and follow-up mortalities and their causes (cardiovascular and all-cause) and major adverse cardiac events (MACE) during a two year follow-up. Discrimination, survival analysis, model calibration, and reclassification of the biomarkers were comprehensively evaluated. Results and Discussion: In total, 55 patients (21.7%) died, 33 in-hospital and 22 during the follow-up, most of them (69.1%) from CV causes; 37 MACE occurred during follow-up. Biomarkers showed good prognostic ability to predict mortality, alone and combined with the multibiomarker model. A predictive clinical model based on age, Killip–Kimball class, estimated glomerular filtration rate (eGFR), and heart rate was derived by multivariate analysis. GDF-15 and NT-proBNP significantly improved risk assessment of the clinical model, as shown by discrimination, calibration, and reclassification of all the end-points except for all-cause mortality. The combination of NT-proBNP and hs-cTnT improved CV mortality prediction. Conclusions: GDF-15 and NT-proBNP added value to the usual risk assessment of STEMI patients

Correction:Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department

[This corrects the article DOI: 10.1371/journal.pone.0187662.]

Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department

BACKGROUND: The 1-hour (h) algorithm triages patients presenting with suspected acute myocardial infarction (AMI) to the emergency department (ED) towards "rule-out," "rule-in," or "observation," depending on baseline and 1-h levels of high-sensitivity cardiac troponin (hs-cTn). The economic consequences of applying the accelerated 1-h algorithm are unknown. METHODS AND FINDINGS: We performed a post-hoc economic analysis in a large, diagnostic, multicenter study of hs-cTnT using central adjudication of the final diagnosis by two independent cardiologists. Length of stay (LoS), resource utilization (RU), and predicted diagnostic accuracy of the 1-h algorithm compared to standard of care (SoC) in the ED were estimated. The ED LoS, RU, and accuracy of the 1-h algorithm was compared to that achieved by the SoC at ED discharge. Expert opinion was sought to characterize clinical implementation of the 1-h algorithm, which required blood draws at ED presentation and 1h, after which "rule-in" patients were transferred for coronary angiography, "rule-out" patients underwent outpatient stress testing, and "observation" patients received SoC. Unit costs were for the United Kingdom, Switzerland, and Germany. The sensitivity and specificity for the 1-h algorithm were 87% and 96%, respectively, compared to 69% and 98% for SoC. The mean ED LoS for the 1-h algorithm was 4.3h-it was 6.5h for SoC, which is a reduction of 33%. The 1-h algorithm was associated with reductions in RU, driven largely by the shorter LoS in the ED for patients with a diagnosis other than AMI. The estimated total costs per patient were £2,480 for the 1-h algorithm compared to £4,561 for SoC, a reduction of up to 46%. CONCLUSIONS: The analysis shows that the use of 1-h algorithm is associated with reduction in overall AMI diagnostic costs, provided it is carefully implemented in clinical practice. These results need to be prospectively validated in the future.Correction in: PLoS ONE, vol. 13, issue 1, e0191348.DOI: 10.1371/journal.pone.0191348</p

Mean ED LoS for 1-h algorithm.

<p>Mean ED LoS for 1-h algorithm.</p

Clinical implementation of one-hour rule-out and rule-in algorithm.

<p>Abbreviations: AMI, acute myocardial infarction; ECG, electrocardiogram; ED, emergency department; hs-cTn, high-sensitivity cardiac troponin assays.</p

Multivariate regression model for LoS of patients with AMI and non-AMI diagnosis: SoC.

<p>Multivariate regression model for LoS of patients with AMI and non-AMI diagnosis: SoC.</p

Sensitivity, specificity, negative predictive value and positive predictive value for 1-h algorithm and SoC.

<p>Sensitivity, specificity, negative predictive value and positive predictive value for 1-h algorithm and SoC.</p

One-hour rule-out and rule-in Algorithm [16, 17].

<p>One-hour rule-out and rule-in Algorithm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187662#pone.0187662.ref016" target="_blank">16</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187662#pone.0187662.ref017" target="_blank">17</a>].</p

Impact of Frailty and Disability on 30-Day Mortality in Older Patients With Acute Heart Failure.

The objectives were to determine the impact of frailty and disability on 30-day mortality and whether the addition of these variables to HFRSS EFFECT risk score (FBI-EFFECT model) improves the short-term mortality predictive capacity of both HFRSS EFFECT and BI-EFFECT models in older patients with acute decompensated heart failure (ADHF) atended in the emergency department. We performed a retrospective analysis of OAK Registry including all consecutive patients ≥65 years old with ADHF attended in 3 Spanish emergency departments over 4 months. FBI-EFFECT model was developed by adjusting probabilities of HFRSS EFFECT risk categories according to the 6 groups (G1: non frail, no or mildly dependent; G2: frail, no or mildly dependent; G3: non frail, moderately dependent; G4: frail, moderately dependent; G5: severely dependent; G6: very severely dependent).We included 596 patients (mean age: 83 [SD7]; 61.2% females). The 30-day mortality was 11.6% with statistically significant differences in the 6 groups (p < 0.001). After adjusting for HFRSS EFFECT risk categories, we observed a progressive increase in hazard ratios from groups G2 to G6 compared with G1 (reference). FBI-EFFECT had a better prognostic accuracy than did HFRSS EFFECT (log-rank p < 0.001; Net Reclassification Improvement [NRI] = 0.355; p < 0.001; Integrated Discrimination Improvement [IDI] = 0.052; p ;< 0.001) and BI-EFFECT (log-rank p = 0.067; NRI = 0.210; p = 0.033; IDI = 0.017; p = 0.026). In conclusion, severe disability and frailty in patients with moderate disability are associated with 30-day mortality in ADHF, providing additional value to HFRSS EFFECT model in predicting short-term prognosis and establishing a care plan.This study was partially supported by grants from the Instituto de Salud Carlos III supported with funds from the Spanish Ministry of Health and FEDER (PI15/00773, PI15/01019, and PI11/01021) and Fundacio la Marato de TV3 (2015).S