bluuebunny/crossref_metadata_2025_split huggingface dataset:
P809Effects of air temperature and pollution on Takotsubo cardiomyopathy
Authors: Awada A , Argacha J F , Collart P , Unger P 
Published: October 2019

Abstract

Background
Air temperature and pollution are the main environmental factors influencing cardiovascular mortality and risk of myocardial infarction. Takotsubo cardiomyopathy (TCM) is a transient and reversible myocardial dysfunction whose cause and pathogenesis remain incompletely understood, but which, unlike acute coronary syndrome, does not involve obstructive coronary atherosclerosis or plaque rupture. The potential role of the environment on the risk to develop TCM remains poorly defined.

Methods
We aimed to study the effects of air temperature, particulate matter (PM) and gaseous pollutants (NO2 and ozone) on hospitalization rate forTCM.
All hospitalizations in Belgian Hospitals for TCM (ICD 9:429.83) from 2009 to 2014 were recorded. National air pollution parameters were extracted from the Belgian Environment Agency database. A time-stratified and temperature-matched (except for air temperature effect) case-crossover analysis of the risk of TCM was performed. The main analysis focused on 0-day lag time (lag 0) between exposure and TCM; a lag structure analysis up to lag 4 was also performed.

Results
1840 patients were included in the study (88% women). More TCM occurred during the warm compared to the cold period (fig.1; chi2p value <0.05). At lag 0, each decrease of 1°C in ambient air temperature increased the odds ratio (OR) of TCM of 1.020 (IC 95%: 1.003–1.035). This effect was more pronounced during the cold period and at lag 4 (RR 1.060; IC 95%: 1.031–1.091). Conversely, during the warm period and between lag 1 and 4, an increase of 1°C in ambient air temperature increased the RR of TCM (OR 1.053 at lag 3; IC 95%: 1.021–1.086). No significant association was found between TCM and PM10, PM2.5, NO2at any lag. During the warm period, each increase in 10μg/m3 in ozone from lag 1 to 3 increased the risk of TCM (OR 1.089 at lag 3, IC 95%: 1.017–1.168).

Conclusions
Air temperature strongly influences the onset of TCM. Both cold spells and heat waves seem to be associated with the development of TCM. Ozone exposure also increases the risk of TCM during the warm period, whereas particulate and NO2 pollution do not seem to play a significant role. These patterns seem to differ from those previously reported with STEMI.
P837Neural networks algorithms improve GRACE Score performance
Authors: Higa C C , Ciambrone M G , Gambarte M J , Novo F , Nogues I , Santillan J , Ginesi A , Giorgini J C , Amrein E , Frederik G , Abud M , Rizzo N , Piccininni R , Marin J , Borracci R A 
Published: October 2019

Abstract

Background
Global Registry of Acute Coronary Events (GRACE) score is a well-known model used to predict the probability of events in acute coronary syndrome (ACS). GRACE model was developed using a logistic regression approach that can only model linear functions, a limitation that could be prevented using artificial neural networks (NN) a recognized tool for nonlinear statistical modeling. The aim of this study was to develop, train and test different NN algorithm-based models to improve the GRACE score performance.

Methods
We analyzed a prospective database including 1,255 patients admitted with diagnosis of ACS in a community hospital, between June 2008 and June 2017. The database included 40 demographic and laboratory admission variables. In the guided approach, only the individual predictors included in the GRACE score were used to train and test three NN algorithm-based models, one- and two-hidden layer multilayer perceptron (MLP), and a radial basis function network. In addition, three extra unguided models were built using the 40 admission variables. Finally, expected mortality according to the GRACE score was calculated using the logistic regression equation.
The database was split into 2 datasets: 70% for model training and 30% for validation. In order to choose the best model, the training process was repeated 50 times. Every time the models were tested on the validation cohort, accuracy, receiver operating characteristic (ROC) area, negative predictive value (NPV), and positive predictive value (PPV) were recorded. Only models showing the best discrimination power were selected for comparison with logistic regression outcomes. The end point was in-hospital all-cause mortality.

Results
In terms of accuracy, ROC area and NPV, almost all NN algorithms outperformed the logistic regression approach (accuracy 97.1, 96.7, 96.2, 97.3 and 94.1%, p<0.001; ROC area 0.89, 0.86, 0.84, 0.84 and 0.75, Hanley-McNeil p≤0.05; for guided and unguided one- and two-hidden layers MLP and GRACE score, respectively). Only radial basis function models obtained a better accuracy level based on NPV improvement (100 vs. 98.8%, p=0.0001), at the expense of PPV reduction (0.0% vs. 13.2%, p<0.0001) (ROC are 0.84 vs. 0.75, p=0.043). Compared with the logistic regression approach, one- and two-hidden layers in guided and unguided MLP models improved PPV from 13.2 to 18.2% (38% increase), 15.4% (17% increase), 27.3% (107% increase), and 25.0% (89% increase), respectively, although these differences were not statistically significant.

Conclusions
NN algorithms improve GRACE score performance in terms of discriminatory power for the prediction of in-hospital mortality. Its application should become a useful tool for the decision making in ACS patients
P838Is multi-biomarker combination approach comparable to the GRACE risk score for short-term mortality prediction in acute myocardial infarction cases?
Authors: Kawamura Y , Yoshimachi F , Nakamura N , Minakawa S , Yamamoto Y , Morita N , Kobayashi Y 
Published: October 2019

Abstract

Introduction
The Global Registry of Acute Coronary Events (GRACE) score is said to be a superior predictor of mortality in acute myocardial infarction (AMI) patients, and same applies to biochemical parameters as N-terminal pro-B-type natriuretic peptide (N-BNP), Troponin-T (TnT) and high-sensitivity C- reactive protein (hs-CRP) levels.

Purpose
We validated that whether each or combination of biochemical parameters are comparable to GRACE score or not for mortality prediction in AMI patients.

Method
We investigated about clinical background including The GRACE score, above parameters and in-hospital mortality in 754 AMI patients (mean age 66±13y/o, 609 ST-elevated AMI cases) received emergency percutaneous coronary intervention (PCI) successfully during 8 years in a single center retrospectively. Combination of biochemical parameters are derived from N-BNP, TnT and hs-CRP by logistic regression analyses. We compared The GRACE score with each or combination of biochemical parameters between survival (SG) and non-survival group (nSG) on receiver operating characteristic (ROC) analysis.

Result
In-hospital mortality was 6.8%. The GRACE score (106±33 versus 161±32; p<0.005) and N-BNP level (2458±7058 versus 8880±11331pg/ml; p<0.005) were significantly lower in SG than nSG. Area under the ROC curve about in-hospital mortality of The GRACE score were higher (0.868) than N-BNP (0.787; p=0.007), TnT (0.613; p<0.005), hs-CRP levels (0.614; p<0.005) and multi-biomarker combination (0.742; p=0.016) as Figure 1.
Area under the curve of the composite with the GRACE score and multi-biomarker is not increased compared with the GRACE score alone (0.868 versus 0.865; p=n.s.).
Figure 1

Conclusion
The GRACE score is a superior predictor about in-hospital mortality than each or combination of biochemical parameters in AMI patients. Multi-biomarker combination dose not refine the accuracy of the GRACE score.
P841Derivation and validation of novel score system for predicting all-cause death and myocardial infarction in coronary artery ectasia
Authors: Wang Y T , Song W H , Wu Y J , Zhang P 
Published: October 2019

Abstract

Background
Coronary artery ectasia (CAE) bears high risk of death and myocardial infarction. Risk stratification in CAE patients is crucial for their management, but there were no risk score systems intended for risk evaluation of CAE patients so far.

Methods
In a retrospective cohort of 595 patients with CAE, we collected the baseline characteristics (clinical history, biomarkers and quantitative coronary angiography variables). Follow-up were conducted and the end-point event was the composite of all-cause death and non-fatal myocardial infarction. The candidate predictors of end-point event were analyzed using Cox proportional hazards regression models to derive a risk score in the form of nomogram. The predictive performance and discriminative ability of the novel nomogram were determined by concordance index (C-index) and calibration curve, that were validated internally. Risk stratification by nomogram-predicted risk score was further evaluated.

Results
During a median follow-up time of 62.3 months, 26 all-cause deaths and 37 non-fatal myocardial infarctions were identified. The final risk-prediction model named ABCD-CAE score included four items: age (A), Brain natriuretic peptide (B), high sensitivity C-reactive protein (C) and maximum Dilated area of ectatic lesions (D). The nomogram yielded a C-index for end-point event of 0.72 (95% confidence interval, 0.64 - 0.79). The calibration curve demonstrated that there is good agreement between prediction by nomogram and actual observation of end-point events. Compared with the low-risk group (score ≤100), the risk of composite events was significantly increased in the intermediate-risk group (score: 100–130) and high-risk group (score >130) [hazard ratio (95% confidence interval): 2.23 (1.23–4.06), P=0.008 and 7.02 (3.81–12.97), P<0.001 respectively].
ABCD-CAE nomogram for risk prediction

Conclusions
The ABCD-CAE score is a simple four-item risk score, that provides a clinically useful tool for the risk prediction of all-cause death and myocardial infarction in patients with CAE. This user-friendly tool might support clinical decision making for the management of CAE.
P849Comparison of long-term mortality risk assessed with recalculated (maximal) CADILLAC score vs. baseline (admission) CADILLAC score in STEMI patients undergoing primary PCI
Authors: Mehmedbegovic Z , Milasinovic D , Jelic D , Zobenica V , Radomirovic M , Veljic I , Pavlovic A , Dedovic V , Dudic J , Asanin M , Vukcevic V , Stankovic G 
Published: October 2019

Abstract

Background
Since patients with STEMI have high rate of adverse events not only during hospital stay, but also during short and long-term follow–up, appropriate risk stratification is a key part of the management of these patients following hospital discharge. CADILLAC score was derived and subsequently validated as accurate clinical tool for identifying patients with heightened risk following index event.

Purpose
We aimed to compare predictive value of recalculated, maximal, (M-) CADILLAC score vs. baseline (B-) CADILLAC score for long-term mortality in hospital survivors.

Methods
From a prospective electronic registry of a high-volume catheterization laboratory in a period from January 2009 to December 2017, a total of 5387 consecutive patients STEMI who underwent primary PCI were included in analysis. For each patient B-CADILLAC score was calculated, and for survivors, we recalculated M-CADILLAC score, incorporating changes in three variable score individual contributors (worsening of Killip class, anemia development and renal function deterioration). As in original score derivation, patients with cardiogenic shock were excluded from analysis. Discrimination of the two risk models was evaluated by the C-statistic, Net reclassification index (NRI) and Integrated Discrimination Improvement (IDI) index.

Results
For 111 (2.1%) patients that died in-hospital, B-CADILLAC very well predicted the event (AUC 0.87, 95% CI 0.86–0.88; p<0.0001) (Figure 1A). For hospital survivors, both evaluated scores showed good discriminative ability for long-term mortality (11.7%) but recalculated M-CADILLAC score was statistically better predictor of long-term mortality, as assessed by C-statistics (Table 1 & Figure 1B):
NRI showed that 38% of patients were reclassified with M-CADILLAC with IDI slope 0.8% higher than in first model.
Table 1 4723 pts (follow-up=90% pts, 41±27 months) AUC 95% CI p B-CADILLAC 0.756 0.744–0.768 p=0.018 M-CADILLAC 0.776 0.754–0.779
Figure 1

Conclusions
Baseline CADILLAC score has very good predictive ability for in-hospital mortality, but recalculated, maximal CADILLAC score offers discriminative advantage in hospital survivors for prediction of long-term mortality in STEMI patients undergoing primary PCI.
P851Incidence and prognostic impact of acute infection among patients with type 1 and 2 myocardial infarction
Authors: Sandoval Y , Smith S W , Schulz K , Sexter A , Apple F S 
Published: October 2019

Abstract

Background
The incidence and impact of acute infection among patients with type 1 (T1MI) and 2 myocardial infarction (T2MI) is not well known. Inflammation and increased procoagulant activity can lead to T1MI, whereas cytokine release can cause hemodynamic alterations affecting myocardial oxygen consumption and contribute to T2MI.

Purpose
Determine the incidence and prognostic impact of acute infection among patients with T1MI and T2MI.

Methods
Post-hoc analysis of UTROPIA (NCT02060760), a prospective, observational cohort study involving 1,640 consecutive emergency department patients with serial cardiac troponin (cTn) I measurements obtained on clinical indication. The incidence of acute infection, antibiotic use, and/or laboratory evidence of infection were examined among patients with T1MI and T2MI. 5-year cumulative survival curves were plotted using the Kaplan-Meier method, frequencies were compared via Chi-Square, and a multivariate cox-proportional hazards model was used to assess the impact of infection on mortality.

Results
Among 217 patients with acute MI (T1MI, n=77 and T2MI, n=140), acute infection occurred in 63 (29%) of patients. Acute infection was more common in patients with T2MI than T1MI (35% vs. 18%, p=0.009). The predominant source of infection was pneumonia (59%), followed by urinary tract infections (11%). Patients with T2MI were also more likely than T1MI to have sepsis (11% vs. 1%, p=0.012), bacteremia (30% vs. 18%, p=0.057), and antibiotic use (35% vs. 17%, p=0.005). At 5-years, patients with acute MI and acute infection had a higher mortality rate than patients without infection (49% vs. 25%, p=0.0006) (Figure). Among the 31 deaths in MI patients with acute infection, most deaths were non-cardiac (65%) and occurred in those with T2MI (77%). Following adjustment for age, sex, and comorbidities in a Cox proportional hazards model, acute infection was an independent predictor of death (hazard ratio: 2.2, 95% CI: 1.3–3.5, p=0.0016).

Conclusion
Acute infection occurs in almost a third of patients that are diagnosed with acute MI during the index hospitalization. Most infections are due to pneumonia and occur most often in patients classified as having T2MI. Acute infection is an independent risk factor for 5-year mortality, with nearly half of all patients with acute MI in whom concomitant acute infection is present during the index hospitalization dead at 5-years. Most deaths are non-cardiac and the vast majority occur in patients with T2MI.

Acknowledgement/Funding
Abbott Diagnostics and Hennepin Healthcare Research Institute (formerly MMRF)e
P852Frequency and prognosis of type 2 myocardial infarction vs non ischemic myocardial injury: large observational study from an emergency department
Authors: Putot A , Buet-Derrida S , Avondo A , Ray P , Maza M , Zeller M , Cottin Y 
Published: October 2019

Abstract

Background
Type 2 Myocardial Infarction (T2MI), due to myocardial oxygen supply-demand mismatch in the absence of atherothrombosis and non-ischemic myocardial injury (NIMI), corresponding to troponin elevation without overt ischemia, are emerging concepts which are suspected to be common in patients hospitalized. However, their respective frequencies, risk profiles and short term prognosis in current routine clinical practice of emergency unit remains to be investigated.

Methods
Among the medical records of all the patients admitted from January 2014 to December 2016 in a university hospital emergency unit (n=82 543), patients with elevated troponin Ic (≥0.10μg/L) (n=4568) were systematically adjudicated as T2MI in the presence of symptoms or signs of myocardial ischemia (typical chest pain and/or ECG changes), or as NIMI without such signs. Patients with missing biological data on admission (n=112) or T1MI diagnosis (n=2467) were excluded.

Results
Among the 1989 patients included, 539 (27%) were classified as T2MI and 1450 (73%) as NIMI. When compared with patients with NIMI, T2MI had higher troponin levels (0.27 (0.14–0.71) vs 0.22 (0.13–0.54) μg/L, p=0.008, respectively). NIMI and T2MI had similar risk factors (age (84 (74–90) vs 84 (75–91) y, p=0.3), male sex (43 vs 48%, p=0.07), hypertension (67 vs 71%, p=0.133), diabetes (25 vs 25%, p=0.9), prior CAD (24 vs 26%, p=0.342), systemic inflammatory response syndrome (SIRS, 47 vs 49%, p=0.3), and systolic blood pressure (SBP) (130 (111–153) vs 132 (112–153) mmHg, p=0.545). Biological data on admission were also similar (hyperglycemia (glucose ≥11 mmol/L), 14 vs 13%, p=0.37, creatinine (96 (72–148) vs 94 (72–141) μmole/L, p=0.598), anemia (Hemoglobin rate ≤10g/dL, 13 vs 14%, p=0.5), C-reactive protein elevation (CRP ≥3 mg/L, 88 vs 89%, p=0.7)). Moreover, in-hospital mortality was high and similar for both groups (15 vs 18%, p=0.2). In multivariate analysis, age, troponin rate, SIRS, anemia, SBP, hyperglycemia, creatinine and CRP elevation were independent factors associated with hospital mortality, but not T2MI (vs NIMI) (OR: 0.88 (0.66–1.17)). Older age and hyperglycemia were specific covariates associated with increased risk of mortality in T2MI, but not in NIMI.

Conclusions
This large real-life study of non-T1MI inpatients with elevated troponins from emergency department shows that myocardial injury without necrosis and T2MI share the same risk factors, characterized by a high rate of infections and anemia and a high risk of hospital mortality.

Acknowledgement/Funding
University Hospital Center Dijon Bourgogne, France
P855Evaluation of epicardial coronary resistance using computed tomography angiography
Authors: Mizukami T , Tanaka K , Sonck J , Vandeloo B , Roosens B , Lochy S , Argacha J F , Schoors D , Suzuki H , De Mey J , De Bruyne B , Cosyns B , Collet C 
Published: October 2019

Abstract

Background
A Fractional flow reserve (FFR) pullback allows assessing the distribution of pressure loss along the vessel. FFR derived from CT (FFRCT) provides a virtual pullback curve that may also aid in the assessment of epicardial coronary resistance in the non-invasive setting.

Purpose
The present study aims to determine the accuracy of the virtual FFRCT pullback curve using a motorized invasive FFR pullback as reference in patients with stable coronary artery disease.

Methods
This is a single centre, prospective study of patients with stable coronary artery disease in whom FFRCT was performed as standard of care for non-invasive assessment. Patients referred to coronary angiography with clinically indicated invasive FFR measurement were included. FFRCT and invasive FFR values were extracted from coronary vessels every 1 mm to generate pullback curves. Invasive FFR pullbacks were acquired using a dedicated device at a speed of 1 mm/s. The area under the pullback curve (AUPC), defined as the sum of areas under the FFR pullback curve, was compared between FFRCT and invasive FFR pullbacks. Lesions were defined based on invasive angiography. FFR gradients in lesions and non-obstructive segments were defined as the difference between FFR values at the proximal and distal edge of the segments. FFR vessel gradient was defined as the difference between the most distal FFR value and the FFR at the ostium of the vessel. Mixed effect model was used to account for the correlation of FFR values within vessels. The agreement between FFRCT and FFR gradients was assessed using the Passing Bablok regression analysis and Bland-Altman methods at the vessel, lesion and non-obstructive level.

Results
A total of 3172 matched FFRCT and FFR values were obtained in 24 vessels. The correlation coefficient between FFRCT and FFR was 0.76 (95% CI 0.75 to 0.78; p<0.001). The mean difference between the FFRCT and invasive FFR pullback values was 0.07 (LOA −0.11 to 0.24). AUPC was similar between FFRCT and invasive FFR (79.0±16.1 vs. 85.3±16.4, p=0.097); the mean slope of FFRCT pullback curve was steeper compared to invasive FFR (p<0.001). The mean difference in lesion gradient was −0.07 (LOA −0.26 to 0.13) and −0.01 (LOA −0.06 to 0.05) in non-obstructive segments. There were no systematic or proportional differences between FFRCT and FFR gradients either in lesion or non-obstructive segments); however, vessel gradients were overestimated by FFRCT with a bias of −0.12 (LOA −0.35 to 0.12) driven by a higher mean difference in lesion gradients (−0.07; 95% CI −0.26 to 0.13).

Conclusions
The evaluation of epicardial coronary resistance using coronary CT angiography with FFRCT was feasible. FFRCT pullbacks were accurate in the assessment of lesion and non-obstructive gradients. FFRCT can identify the physiological pattern of coronary artery disease in the non-invasive setting.
P861A quantitative CCTA evaluation in non-obstructive coronary artery disease for the diagnosis of vessel-specific ischemia: results from the prospective, multicenter, international CREDENCE trial
Authors: Gianni U , Van Rosendael A R , Van Den Hoogen I J , Al Hussein Alawamlh O , Stuijfzand W , Al'aref S J , Pena J M , Lu Y , Chang H J , Berman D S , Shaw L J , Min J K , Lin F Y 
Published: October 2019

Abstract

Aim
To improve the diagnosis of coronary vessel-specific ischemia in non-obstructive coronary artery disease (CAD) using a quantitative whole-heart coronary computed tomography angiography (CCTA) evaluation. To date, predictors of ischemia in non-obstructive CAD remain underexplored.

Methods
Within the CREDENCE trial, 612 patients with suspected CAD at 13 sites (64±10 years, 70% men) underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography with 3-vessel fractional flow reserve (FFR) measurements. For this specific analysis, only vessels with non-obstructive plaque (1–49% maximal diameter stenosis) by CCTA were included. The primary endpoint was coronary vessel-specific ischemia which was defined as FFR ≤0.80 (or ≥90% stenosis). Multivariable logistic regression modeling was performed to evaluate the effect of quantitative CCTA features beyond coronary stenosis on the prevalence of vessel-specific ischemia.

Results
FFR ≤0.80 (or ≥90% stenosis) was prevalent in 22.8% of 1,102 vessels with non-obstructive plaque. Using a step-wise approach, in addition to diameter stenosis (χ2=72), non-calcified PAV (χ2=126, P<0.001), lumen volume (χ2=175, P<0.001) and number of lesions with >30% stenosis (χ2=187, P=0.001) were independent CCTA-predictors of coronary vessel-specific ischemia (Figure 1). In the final model, diameter stenosis was no longer significantly associated with ischemia (P=0.236).
Figure 1

Conclusion
In vessels with non-obstructive plaque on CCTA, ischemia was present in approximately 20%. Measures of overall non-calcified plaque burden and smaller lumen volume were more important determinants of vessel-specific ischemia than maximal diameter stenosis.

Acknowledgement/Funding
NIH R01-HLL118019; Dalio Foundation and Michael J. Wolk Foundation