Geomechanical Modelling of Induced Seismicity

In recent years, there has been a dramatic increase in seismicity (earthquakes) due to anthropogenic activities related to the unconventional oil and gas exploration in the Western Canada Sedimentary Basin (WCSB) and the central U.S. There are compelling evidences that hydraulic fracturing and wastewater injection operations in those areas play a key role in induced seismicity. To better understand the physical mechanisms involved, this thesis aims to explore the mechanisms responsible for occurrence of induced earthquakes: mainshock-aftershock triggering mechanism and poroelastic response. In the first approach, the obtained results indicate the relationship between the Coulomb stress changes after 4 moderate earthquakes near Fox Creek, Alberta and Timpson, Texas and their subsequent events. In the second approach, two hydraulic fracturing operations near Fox Creek are modelled to study the poroelastic response of the medium due to the fluid injection under specific site conditions. Pore pressure and stress changes for these two related earthquake clusters are computed and the sensitivities of the model parameters are analyzed

Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

[EN] Purpose: To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging (MRI). Methods: This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17-segment model in both cine and late gadolinium enhancement (LGE) MRI. Infarcted myocardium regions were identified on LGE in short-axis views. Nonviable segments were identified as those showing LGE 50%, and viable segments those showing 0 < LGE < 50% transmural extension. Features derived from five texture analysis methods were extracted from the segments on cine images. A support vector machine (SVM) classifier was trained with different combination of texture features to obtain a model that provided optimal classification performance. Results: The best classification on testing set was achieved with local binary patterns features using a 2D + t approach, in which the features are computed by including information of the time dimension available in cine sequences. The best overall area under the receiver operating characteristic curve (AUC) were: 0.849, sensitivity of 92% to detect nonviable segments, 72% to detect viable segments, and 85% to detect remote segments. Conclusion: Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium-free approach.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grant BFU2015-64380-C2-2-R, by Instituto de Salud Carlos III and FEDER funds under grants FIS PI14/00271 and PIE15/00013 and by the Generalitat Valenciana under grant PROMETEO/2013/007. The first author, Andres Larroza, was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD).Larroza, A.; López-Lereu, M.; Monmeneu, J.; Gavara-Doñate, J.; Chorro, F.; Bodi, V.; Moratal, D. (2018). Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Medical Physics. 45(4):1471-1480. https://doi.org/10.1002/mp.12783S14711480454Castellano, G., Bonilha, L., Li, L. M., & Cendes, F. (2004). Texture analysis of medical images. Clinical Radiology, 59(12), 1061-1069. doi:10.1016/j.crad.2004.07.008Hodgdon, T., McInnes, M. D. F., Schieda, N., Flood, T. A., Lamb, L., & Thornhill, R. E. (2015). Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology, 276(3), 787-796. doi:10.1148/radiol.2015142215Larroza, A., Moratal, D., Paredes-Sánchez, A., Soria-Olivas, E., Chust, M. L., Arribas, L. A., & Arana, E. (2015). Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging, 42(5), 1362-1368. doi:10.1002/jmri.24913Thevenot, J., Hirvasniemi, J., Pulkkinen, P., Määttä, M., Korpelainen, R., Saarakkala, S., & Jämsä, T. (2014). Assessment of Risk of Femoral Neck Fracture with Radiographic Texture Parameters: A Retrospective Study. Radiology, 272(1), 184-191. doi:10.1148/radiol.14131390Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061Pfeiffer, M. P., & Biederman, R. W. W. (2015). Cardiac MRI. Medical Clinics of North America, 99(4), 849-861. doi:10.1016/j.mcna.2015.02.011Flett, A. S., Hasleton, J., Cook, C., Hausenloy, D., Quarta, G., Ariti, C., … Moon, J. C. (2011). Evaluation of Techniques for the Quantification of Myocardial Scar of Differing Etiology Using Cardiac Magnetic Resonance. JACC: Cardiovascular Imaging, 4(2), 150-156. doi:10.1016/j.jcmg.2010.11.015Engan K Eftestøl T Ørn S Kvaloy JT Woie L Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images 2010Kotu LP Engan K Eftestøl T Ørn S Woie L Segmentation of scarred and non-scarred myocardium in LG enhanced CMR images using intensity-based textural analysis 2011Kotu, L., Engan, K., Skretting, K., Måløy, F., Ørn, S., Woie, L., & Eftestøl, T. (2013). Probability mapping of scarred myocardium using texture and intensity features in CMR images. BioMedical Engineering OnLine, 12(1), 91. doi:10.1186/1475-925x-12-91Schofield, R., Ganeshan, B., Kozor, R., Nasis, A., Endozo, R., Groves, A., … Moon, J. C. (2016). CMR myocardial texture analysis tracks different etiologies of left ventricular hypertrophy. Journal of Cardiovascular Magnetic Resonance, 18(S1). doi:10.1186/1532-429x-18-s1-o82Larroza, A., Materka, A., López-Lereu, M. P., Monmeneu, J. V., Bodí, V., & Moratal, D. (2017). Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging. European Journal of Radiology, 92, 78-83. doi:10.1016/j.ejrad.2017.04.024Baessler, B., Mannil, M., Oebel, S., Maintz, D., Alkadhi, H., & Manka, R. (2018). Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images. Radiology, 286(1), 103-112. doi:10.1148/radiol.2017170213Hervas, A., Ruiz-Sauri, A., de Dios, E., Forteza, M. J., Minana, G., Nunez, J., … Bodi, V. (2015). Inhomogeneity of collagen organization within the fibrotic scar after myocardial infarction: results in a swine model and in human samples. Journal of Anatomy, 228(1), 47-58. doi:10.1111/joa.12395Heiberg, E., Sjögren, J., Ugander, M., Carlsson, M., Engblom, H., & Arheden, H. (2010). Design and validation of Segment - freely available software for cardiovascular image analysis. BMC Medical Imaging, 10(1). doi:10.1186/1471-2342-10-1Bodí, V., Sanchis, J., López-Lereu, M. P., Losada, A., Núñez, J., Pellicer, M., … Llácer, À. (2005). Usefulness of a Comprehensive Cardiovascular Magnetic Resonance Imaging Assessment for Predicting Recovery of Left Ventricular Wall Motion in the Setting of Myocardial Stunning. Journal of the American College of Cardiology, 46(9), 1747-1752. doi:10.1016/j.jacc.2005.07.039Rangayyan, R. M., Nguyen, T. M., Ayres, F. J., & Nandi, A. K. (2009). Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms. Journal of Digital Imaging, 23(5), 547-553. doi:10.1007/s10278-009-9238-0Materka A Strzelecki M On the importance of MRI nonuniformity correction for texture analysis 2013Collewet, G., Strzelecki, M., & Mariette, F. (2004). Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magnetic Resonance Imaging, 22(1), 81-91. doi:10.1016/j.mri.2003.09.001Vallières M MATLAB programming tools for radiomics analysis https://github.com/mvallieres/radiomicsZhao G Pietikainen M Center for machine vision and signal analysis http://www.cse.oulu.fi/CMV/Downloads/LBPMatlabZwanenburg A Leger S Vallières M Löck S Image biomarker standardisation initiative 2017 http://arxiv.org/abs/1612.07003Vallières, M., Freeman, C. R., Skamene, S. R., & El Naqa, I. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology, 60(14), 5471-5496. doi:10.1088/0031-9155/60/14/5471Zhao, G., & Pietikainen, M. (2007). Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915-928. doi:10.1109/tpami.2007.1110Ojala T Pietikäinen M Mäenpää T A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classificationDuan, K.-B., Rajapakse, J. C., Wang, H., & Azuaje, F. (2005). Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data. IEEE Transactions on Nanobioscience, 4(3), 228-234. doi:10.1109/tnb.2005.853657Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Machine Learning, 46(1/3), 389-422. doi:10.1023/a:1012487302797Wang, S., & Summers, R. M. (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933-951. doi:10.1016/j.media.2012.02.005Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. Journal of Statistical Software, 28(5). doi:10.18637/jss.v028.i05Colby J (multiple) Support Vector Machine Recursive Feature Elimination - mSVM-RFE http://www.colbyimaging.com/wiki/statistics/msvm-rfeSalzberg, S. L. (1997). Data Mining and Knowledge Discovery, 1(3), 317-328. doi:10.1023/a:1009752403260Bodí, V., Husser, O., Sanchis, J., Núñez, J., López-Lereu, M. P., Monmeneu, J. V., … Llácer, A. (2010). Contractile Reserve and Extent of Transmural Necrosis in the Setting of Myocardial Stunning: Comparison at Cardiac MR Imaging. Radiology, 255(3), 755-763. doi:10.1148/radiol.10091191Bodi, V., Monmeneu, J. V., Ortiz-Perez, J. T., Lopez-Lereu, M. P., Bonanad, C., Husser, O., … Chorro, F. J. (2016). Prediction of Reverse Remodeling at Cardiac MR Imaging Soon after First ST-Segment–Elevation Myocardial Infarction: Results of a Large Prospective Registry. Radiology, 278(1), 54-63. doi:10.1148/radiol.2015142674Shriki, J. E., Surti, K. S., Farvid, A. F., Lee, C. C., Samadi, S., Hirschbeinv, J., & Colletti, P. M. (2011). Chemical Shift Artifact on Steady-State Free Precession Cardiac Magnetic Resonance Sequences as a Result of Lipomatous Metaplasia: A Novel Finding in Chronic Myocardial Infarctions. Canadian Journal of Cardiology, 27(5), 664.e17-664.e23. doi:10.1016/j.cjca.2010.12.074Goldfarb, J. W., McLaughlin, J., Gray, C. A., & Han, J. (2011). Cyclic CINE-balanced steady-state free precession image intensity variations: Implications for the detection of myocardial edema. Journal of Magnetic Resonance Imaging, 33(3), 573-581. doi:10.1002/jmri.22368Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278(2), 563-577. doi:10.1148/radiol.201515116

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity

Sepsis at ICU admission does not decrease 30-day survival in very old patients: a post-hoc analysis of the VIP1 multinational cohort study.

BACKGROUND: The number of intensive care patients aged ≥ 80 years (Very old Intensive Care Patients; VIPs) is growing. VIPs have high mortality and morbidity and the benefits of ICU admission are frequently questioned. Sepsis incidence has risen in recent years and identification of outcomes is of considerable public importance. We aimed to determine whether VIPs admitted for sepsis had different outcomes than those admitted for other acute reasons and identify potential prognostic factors for 30-day survival. RESULTS: This prospective study included VIPs with Sequential Organ Failure Assessment (SOFA) scores ≥ 2 acutely admitted to 307 ICUs in 21 European countries. Of 3869 acutely admitted VIPs, 493 (12.7%) [53.8% male, median age 83 (81-86) years] were admitted for sepsis. Sepsis was defined according to clinical criteria; suspected or demonstrated focus of infection and SOFA score ≥ 2 points. Compared to VIPs admitted for other acute reasons, VIPs admitted for sepsis were younger, had a higher SOFA score (9 vs. 7, p < 0.0001), required more vasoactive drugs [82.2% vs. 55.1%, p < 0.0001] and renal replacement therapies [17.4% vs. 9.9%; p < 0.0001], and had more life-sustaining treatment limitations [37.3% vs. 32.1%; p = 0.02]. Frailty was similar in both groups. Unadjusted 30-day survival was not significantly different between the two groups. After adjustment for age, gender, frailty, and SOFA score, sepsis had no impact on 30-day survival [HR 0.99 (95% CI 0.86-1.15), p = 0.917]. Inverse-probability weight (IPW)-adjusted survival curves for the first 30 days after ICU admission were similar for acute septic and non-septic patients [HR: 1.00 (95% CI 0.87-1.17), p = 0.95]. A matched-pair analysis in which patients with sepsis were matched with two control patients of the same gender with the same age, SOFA score, and level of frailty was also performed. A Cox proportional hazard regression model stratified on the matched pairs showed that 30-day survival was similar in both groups [57.2% (95% CI 52.7-60.7) vs. 57.1% (95% CI 53.7-60.1), p = 0.85]. CONCLUSIONS: After adjusting for organ dysfunction, sepsis at admission was not independently associated with decreased 30-day survival in this multinational study of 3869 VIPs. Age, frailty, and SOFA score were independently associated with survival

Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU: a prospective cohort study.

BACKGROUND: The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. METHODS: We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for both ICU and 30-day mortality included the CFS score as either a categorical, continuous or dichotomous variable and were adjusted for patient's age, sex, reason for admission to the ICU, and admission Sequential Organ Failure Assessment score. RESULTS: The median age in the sample of 7487 consecutive patients was 84 years (IQR 81-87). The highest fraction of new prognostic information from frailty in the context of 30-day mortality was observed when the CFS score was treated as either a categorical variable using all original levels of frailty or a nonlinear continuous variable and was equal to 9% using these modelling approaches (p < 0.001). The relationship between the CFS score and mortality was nonlinear (p < 0.01). CONCLUSION: Knowledge about a patient's frailty status adds a substantial amount of new prognostic information at the moment of admission to the ICU. Arbitrary simplification of the CFS score into fewer groups than originally intended leads to a loss of information and should be avoided. Trial registration NCT03134807 (VIP1), NCT03370692 (VIP2)

Effects of alirocumab on types of myocardial infarction: insights from the ODYSSEY OUTCOMES trial

Aims  The third Universal Definition of Myocardial Infarction (MI) Task Force classified MIs into five types: Type 1, spontaneous; Type 2, related to oxygen supply/demand imbalance; Type 3, fatal without ascertainment of cardiac biomarkers; Type 4, related to percutaneous coronary intervention; and Type 5, related to coronary artery bypass surgery. Low-density lipoprotein cholesterol (LDL-C) reduction with statins and proprotein convertase subtilisin–kexin Type 9 (PCSK9) inhibitors reduces risk of MI, but less is known about effects on types of MI. ODYSSEY OUTCOMES compared the PCSK9 inhibitor alirocumab with placebo in 18 924 patients with recent acute coronary syndrome (ACS) and elevated LDL-C (≥1.8 mmol/L) despite intensive statin therapy. In a pre-specified analysis, we assessed the effects of alirocumab on types of MI. Methods and results  Median follow-up was 2.8 years. Myocardial infarction types were prospectively adjudicated and classified. Of 1860 total MIs, 1223 (65.8%) were adjudicated as Type 1, 386 (20.8%) as Type 2, and 244 (13.1%) as Type 4. Few events were Type 3 (n = 2) or Type 5 (n = 5). Alirocumab reduced first MIs [hazard ratio (HR) 0.85, 95% confidence interval (CI) 0.77–0.95; P = 0.003], with reductions in both Type 1 (HR 0.87, 95% CI 0.77–0.99; P = 0.032) and Type 2 (0.77, 0.61–0.97; P = 0.025), but not Type 4 MI. Conclusion  After ACS, alirocumab added to intensive statin therapy favourably impacted on Type 1 and 2 MIs. The data indicate for the first time that a lipid-lowering therapy can attenuate the risk of Type 2 MI. Low-density lipoprotein cholesterol reduction below levels achievable with statins is an effective preventive strategy for both MI types.For complete list of authors see http://dx.doi.org/10.1093/eurheartj/ehz299</p

K2 Observations of SN 2018oh Reveal a Two-Component Rising Light Curve for a Type Ia Supernova

We present an exquisite, 30-min cadence Kepler (K2) light curve of the Type Ia supernova (SN Ia) 2018oh (ASASSN-18bt), starting weeks before explosion, covering the moment of explosion and the subsequent rise, and continuing past peak brightness. These data are supplemented by multi-color Pan-STARRS1 and CTIO 4-m DECam observations obtained within hours of explosion. The K2 light curve has an unusual two-component shape, where the flux rises with a steep linear gradient for the first few days, followed by a quadratic rise as seen for typical SNe Ia. This "flux excess" relative to canonical SN Ia behavior is confirmed in our $i$-band light curve, and furthermore, SN 2018oh is especially blue during the early epochs. The flux excess peaks 2.14$\pm0.04$ days after explosion, has a FWHM of 3.12$\pm0.04$ days, a blackbody temperature of $T=17,500^{+11,500}_{-9,000}$ K, a peak luminosity of $4.3\pm0.2\times10^{37}\,{\rm erg\,s^{-1}}$, and a total integrated energy of $1.27\pm0.01\times10^{43}\,{\rm erg}$. We compare SN 2018oh to several models that may provide additional heating at early times, including collision with a companion and a shallow concentration of radioactive nickel. While all of these models generally reproduce the early K2 light curve shape, we slightly favor a companion interaction, at a distance of $\sim$$2\times10^{12}\,{\rm cm}$ based on our early color measurements, although the exact distance depends on the uncertain viewing angle. Additional confirmation of a companion interaction in future modeling and observations of SN 2018oh would provide strong support for a single-degenerate progenitor system

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011