Simple Laboratory Test-Based Risk Scores in Coronary Catheterization: Development, Validation, and Comparison to Conventional Risk Factors

2020 ◽  
Vol 5 (4) ◽  
pp. 616-630
Author(s):  
Michael E Gerling ◽  
Yuan Dong ◽  
Beelal Abdalla ◽  
Matthew T James ◽  
Stephen B Wilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Laboratory Tests ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Mean Corpuscular Hemoglobin ◽  
Blood Cell Count ◽  
Corpuscular Hemoglobin ◽  
Coronary Catheterization ◽  
Clinical Risk

Abstract Background We developed and validated laboratory test–based risk scores (i.e., lab risk scores) to reclassify mortality risk among patients undergoing their first coronary catheterization. Methods Patients were catheterized between 2009 and 2015 in Calgary, Alberta, Canada (n = 14 135, derivation cohort), and in Edmonton, Alberta, Canada (n = 12 143, validation cohort). Logistic regression with group LASSO (least absolute shrinkage and selection operator) penalty was used to select quintiles of the last laboratory tests (red blood cell count, mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, mean corpuscular volume, red cell distribution width, platelet count, total white blood cell count, plasma sodium, potassium, chloride, CO2, international normalized ratio, estimated glomerular filtration rate) performed <30 days before catheterization and by age and sex that were significantly associated with death ≤60  and >60 days after catheterization. Follow-up was until 2016. Risk scores were developed from significant tests, internally validated in Calgary among bootstrap samples and externally validated in Edmonton after recalibration using coefficients developed in Calgary. Interaction tests were performed, and net reclassification improvement vs conventional demographic and clinical risk factors was determined. Results Lab risk scores were strongly associated with mortality (29–40× for top vs bottom quintile, P for trends <0.01), had good discrimination and were well calibrated in Calgary (C = 0.80–0.85, slope = 0.99–1.01) and Edmonton (C = 0.80–0.82; slope = 1.02–1.05)—similar to demographic and clinical risk factors alone. Associations were attenuated by several comorbidities; however, scores appropriately reclassified 11%–20% of deaths (both follow-up periods) and 6%–9% of survivors (>60 days) after catheterization vs demographic and clinical risk factors. Conclusions In 2 populations of patients undergoing their first coronary catheterization, risk scores based on simple laboratory tests were as powerful as a combination of demographic and clinical risk factors in predicting mortality. Lab risk scores should be used for patients undergoing coronary catheterization.

Long-Term Follow-Up of Patients With Isolated Side Branch Coronary Artery Disease

Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Vessel Diameter ◽  
Side Branch ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Artery Disease

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.

scholarly journals Prognostic Value of D-dimer in patients with acute coronary syndrome treated by percutaneous coronary intervention: a retrospective cohort study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Runzhen Chen ◽  
Chen Liu ◽  
Peng Zhou ◽  
Yu Tan ◽  
Zhaoxue Sheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Percutaneous Coronary Intervention ◽  
Prognostic Value ◽  
Coronary Intervention ◽  
Risk Scores ◽  
Risk Of Death ◽  
Clinical Risk ◽  
Percutaneous Coronary ◽  
D Dimer

Abstract Background Associations between D-dimer and outcomes of patients with acute coronary syndromes (ACS) remain controversial. This study aimed to investigate the prognostic value of D-dimer in ACS patients treated by percutaneous coronary intervention (PCI). Methods In this observational study, 3972 consecutive patients with ACS treated by PCI were retrospectively recruited. The X-tile program was used to determine the optimal D-dimer thresholds for risk stratifications. Cox regression with multiple adjustments was used for outcome analysis. Restricted cubic spline (RCS) analysis was performed to assess the dose-response association between D-dimer and outcomes. The C-index was calculated to evaluate the additional prognostic value of D-dimer when added to clinical risk factors and commonly used clinical risk scores, with internal validations using bootstrapping methods. The primary outcome was all-cause death. Results During a median follow-up of 720 days, 225 deaths occurred. Based on the thresholds generated by X-tile, ACS-PCI patients with median (420–1150 ng/mL, hazard ratio [HR]: 1.58, 95 % confidence interval [CI]: 1.14–2.20, P = 0.007) and high (≥ 1150 ng/mL, HR: 1.98, 95 % CI: 1.36–2.89, P < 0.001) levels of D-dimer showed substantially higher risk of death compared to those with low D-dimer (< 420 ng/mL). RCS analysis depicted a constant relation between D-dimer and various outcomes. The addition of D-dimer levels significantly improved risk predictions for all-cause death when combined with the fully adjusted models (C-index: 0.853 vs. 0.845, P difference = 0.021), the GRACE score (C-index: 0.826 vs. 0.814, P difference = 0.027), and the TIMI score (C-index: 0.804 vs. 0.776, P difference < 0.001). The predicted mortality at the median follow-up (two years) was 1.7 %, 5.2 %, and 10.9 % for patients with low, median, and high D-dimer, respectively, which was well matched with the observed mortality (low D-dimer group: 1.2 %, median D-dimer group: 5.2 %, and high D-dimer group: 12.6 %). Conclusions For ACS patients treated by PCI, D-dimer level was an independent predictor for adverse outcomes, and provided additional prognostic value when combined with clinical risk factors and risk scores. Risk stratifications based on D-dimer was plausible to differentiate ACS-PCI patients with higher risk of death.

scholarly journals Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Erick A. Perez-Alday ◽  
Aron Bender ◽  
David German ◽  
Srini V. Mukundan ◽  
Christopher Hamilton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Short Term ◽  
Clinical Risk ◽  
Atherosclerosis Risk

Abstract Background The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD). Conclusion Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

Abstract P251: The Impact of Clinical Risk Factors on Risk of Peripheral Arterial Disease in Men

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Michel M Joosten ◽  
Jennifer K Pai ◽  
Eric B Rimm ◽  
Donna Spiegelman ◽  
Murray A Mittleman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Peripheral Arterial Disease ◽  
Arterial Disease ◽  
Clinical Risk Factors ◽  
Individual Risk ◽  
Clinical Risk ◽  
Hazard Ratios ◽  
History Of ◽  
Peripheral Arterial

Background: Previous studies have examined individual risk factors in relation to peripheral arterial disease (PAD) but the combined effects of these factors are largely unknown. We investigated the degree to which clinical risk factors may explain the risk of PAD among men. Methods: We prospectively followed 45,596 men from the Health Professional Follow-up Study without a history of cardiovascular disease at baseline during a 22-year period (1986–2008). We defined four clinical risk factors - smoking, history of type 2 diabetes, hypertension, and hypercholesterolemia - that were updated biennially during follow-up. Cox proportional hazard models were used to compare PAD risk across individual and joint risk factors. Results: During 874,769 person-years of follow-up, 497 confirmed PAD cases occurred. All four clinical risk factors were significantly and independently associated with a higher risk of PAD after multivariate adjustment (Figure). Risk of PAD more than doubled (hazard ratio: 2.14; 95% confidence interval [95% CI]: 1.95–2.35) for each additional risk factor compared with the group free of risk factors. Men without any of the four risk factors had a relative risk of PAD of 0.19 compared with all other men (95% CI: 0.11–0.31). In 96.8% (95% CI: 95.2–98.3%) of the PAD cases, at least one of the four risk factors was present. Overall, 8 out of 10 cases of PAD appeared to be attributable to these four conventional risk factors. Conclusion: The great majority of PAD can be explained by four conventional risk factors. Figure legend: Hazard ratios for incident peripheral arterial disease (PAD) according to individual and joint risk factors. Hazard ratios are adjusted for age, height, aspirin use, family history of myocardial infarction before age 60 y, geographical region, body mass index, physical activity, alcohol consumption (and each of the other three binary clinical risk factors in the individual risk factor analyses).

scholarly journals Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals with Atrial Fibrillation

2021 ◽  
Author(s):  
Jack W. O'Sullivan ◽  
Anna Shcherbina ◽  
Johanne M. Justesen ◽  
Mintu Turakhia ◽  
Marco Perez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Predictive Ability ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Clinical Risk ◽  
Increased Risk ◽  
The Uk

Background - Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA 2 DS 2 -VASc) don't include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS). Methods - Using data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Results - Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Using this improved tool, >115,000 people with AF would have improved risk classification in the US. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.06 to 1.23)). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson's correlation coefficient: -0.018). Conclusions - In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

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