Thrombolysis with tissue plasminogen activator in acute myocardial infarction: no additional benefit from immediate percutaneous coronary angioplasty

A randomised trial of 367 patients with acute myocardial infarction was performed to determine whether an invasive strategy combining thrombolysis with recombinant tissue-type plasminogen activator (rTPA), heparin, and acetylsalicylic acid, and immediate percutaneous transluminal coronary angioplasty (PTCA) would be superior to a noninvasive strategy with the same medical treatment but without immediate angiography and PTCA. Intravenous infusion of 100 mg rTPA was started within 5 h after onset of symptoms (median 156 min). Angiography was performed 6-165 min later in 180 out of 183 patients allocated to the invasive strategy; 184 patients were allocated to the non-invasive strategy. Immediate PTCA reduced the percentage stenosis of the infarc

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Current Medical Imaging Reviews. 15(10):933-947. https://doi.org/10.2174/1573405615666190109100503S9339471510Louis D.N.; Perry A.; Reifenberger G.; The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016,131(6),803-820Ostrom Q.T.; Gittleman H.; Fulop J.; CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-oncol 2015,17(Suppl. 4),iv1-iv62Yachida S.; Jones S.; Bozic I.; Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010,467(7319),1114-1117Gerlinger M.; Rowan A.J.; Horswell S.; Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012,366(10),883-892Sottoriva A.; Spiteri I.; Piccirillo S.G.M.; Intratumor heterogeneityin human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013,110(10),4009-4014Whiting P.F.; Rutjes A.W.; Westwood M.E.; QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011,155(8),529-536Stupp R.; Mason W.P.; van den Bent M.J.; Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005,352(10),987-996Ponte K.F.; Berro D.H.; Collet S.; In vivo relationship between hypoxia and angiogenesis in human glioblastoma: a multimodal imaging study. J Nucl Med 2017,58(10),1574-1579Pope W.B.; Kim H.J.; Huo J.; Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009,252(1),182-189Mörén L.; Bergenheim A.T.; Ghasimi S.; Brännström T.; Johansson M.; Antti H.; Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites 2015,5(3),502-520Prager A.J.; Martinez N.; Beal K.; Omuro A.; Zhang Z.; Young R.J.; Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015,36(5),877-885Kickingereder P.; Burth S.; Wick A.; Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016,280(3),880-889Yoo R-E.; Choi S.H.; Cho H.R.; Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 2013,38(4),852-860Liberman G.; Louzoun Y.; Aizenstein O.; Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol 2013,82(2),e87-e94Ramadan S.; Andronesi O.C.; Stanwell P.; Lin A.P.; Sorensen A.G.; Mountford C.E.; Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma. Radiology 2011,259(2),540-549Xintao H.; Wong K.K.; Young G.S.; Guo L.; Wong S.T.; Support vector machine multi-parametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011,33(2),296Ingrisch M.; Schneider M.J.; Nörenberg D.; Radiomic Analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 2017,52(6),360-366Ulyte A.; Katsaros V.K.; Liouta E.; Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016,58(12),1197-1208O’Neill A.F.; Qin L.; Wen P.Y.; de Groot J.F.; Van den Abbeele A.D.; Yap J.T.; Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma. J Neurooncol 2016,130(3),495-503Kickingereder P.; Bonekamp D.; Nowosielski M.; Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features. Radiology 2016,281(3),907-918Roberto S-R.; Antonio R-V.; Luis M-B.; Angel A-B.; Gracián G-M.; Quantitative mr perfusion parameters related to survival time in high-grade gliomas. European Radiology 2013,23(12),3456-3465Jain R.; Poisson L.; Narang J.; Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013,267(1),212-220Fathi K.A.; Mohseni M.; Rezaei S.; Bakhshandehpour G.; Saligheh R.H.; Multi-parametric (ADC/PWI/T2-W) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA 2015,28(1),13-22Caulo M.; Panara V.; Tortora D.; Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014,272(2),494-503Alexiou G.A.; Zikou A.; Tsiouris S.; Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro-oncol 2013,15(5),607-617Shankar J.J.S.; Woulfe J.; Silva V.D.; Nguyen T.B.; Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study. AJR Am J Roentgenol 2013,200(5)Zinn P.O.; Mahajan B.; Sathyan P.; Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011,6(10)Matsusue E.; Fink J.R.; Rockhill J.K.; Ogawa T.; Maravilla K.R.; Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010,52(4),297-306Juan-Albarracín J.; Fuster-Garcia E.; Manjón J.V.; Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 2015,10(5)Itakura H.; Achrol A.S.; Mitchell L.A.; Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015,7(303)Ion-Margineanu A.; Van Cauter S.; Sima D.M.; Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Res Int 2015,2015Durst C.R.; Raghavan P.; Shaffrey M.E.; Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 2014,56(2),107-115Yoon J.H.; Kim J.H.; Kang W.J.; Grading of cerebral glioma with multi-parametric MR Imaging and 18F-FDG-PET: concordance and accuracy. European Radiol 2014,24(2),380-389Demerath T.; Simon-Gabriel C.P.; Kellner E.; Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017,30(1),36-47Qin L.; Li X.; Stroiney A.; Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017,59(2),135-145Boult J.K.R.; Borri M.; Jury A.; Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd-DTPA and USPIO-enhanced imaging. NMR Biomed 2016,29(11),1608-1617Server A.; Kulle B.; Gadmar Ø.B.; Josefsen R.; Kumar T.; Nakstad P.H.; Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 2011,80(2),462-470Chang P.D.; Chow D.S.; Yang P.H.; Filippi C.G.; Lignelli A.; Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images. AJR Am J Roentgenol 2017,208(1),57-65Yi C.; Shangjie R.; Volume of high-risk intratumoralsubregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017,27,3583-3592Khalifa J.; Tensaouti F.; Chaltiel L.; Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation. Eur Radiol 2016,26(11),4194-4203Prateek P.; Jay P.; Partovi S.; Madabhushi A.; Tiwari P.; Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastomamultiforme: preliminary findings. Eur Radiol 2017,27(10),4188-4197Lemasson B.; Chenevert T.L.; Lawrence T.S.; Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol 2013,6(6),766-774Inano R.; Oishi N.; Kunieda T.; Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images. Sci Rep 2016,6,30344Delgado-Goñi T.; Ortega-Martorell S.; Ciezka M.; MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis. NMR Biomed 2016,29(6),732-743Cui Y.; Tha K.K.; Terasaka S.; Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016,278(2),546-553Price S.J.; Young A.M.H.; Scotton W.J.; Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016,43(2),487-494Sauwen N.; Acou M.; Van Cauter S.; Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016,12,753-764Jena A.; Taneja S.; Gambhir A.; Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI. Clin Nucl Med 2016,41(5),e228-e236Kim H.S.; Goh M.J.; Kim N.; Choi C.G.; Kim S.J.; Kim J.H.; Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014,273(3),831-843Christoforidis G.A.; Yang M.; Abduljalil A.; “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology 2012,264(1),210-217Wang S.; Kim S.; Chawla S.; Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011,32(3),507-514Hanahan D.; Weinberg R.A.; Hallmarks of cancer: the next generation. Cell 2011,144(5),646-674Macdonald D.R.; Cascino T.L.; Schold S.C.; Cairncross J.G.; Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990,8(7),1277-1280Wen P.Y.; Macdonald D.R.; Reardon D.A.; Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010,28(11),1963-1972Sorensen A.G.; Batchelor T.T.; Wen P.Y.; Zhang W-T.; Jain R.K.; Response criteria for glioma. Nat Clin Pract Oncol 2008,5(11),634-644Rosenkrantz A.B.; Friedman K.; Chandarana H.; Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol 2016,206(1),162-172Castiglioni I.; Gallivanone F.; Canevari C.; Hybrid PET/MRI for In vivo imaging of cancer: current clinical experiences and recent advances. Curr Med Imaging 2016,12,106Mainta I.C.; Perani D.; Delattre B.M.A.; FDG PET/MR imaging in major neurocognitive disorders. Curr Alzheimer Res 2017,14,186-197Marner L.; Henriksen O.M.; Lundemann M.; Larsen V.A.; Law I.; Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 2017,5(2),135-149R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. Available from: https://www.R-project.org

Heterozygous aggrecan variants are associated with short stature and brachydactyly: Description of 16 probands and a review of the literature.

OBJECTIVE: Mutations in the aggrecan gene (ACAN) have been identified in two autosomal dominant skeletal dysplasias, spondyloepiphyseal dysplasia, Kimberley type (SEDK), and osteochondritis dissecans, as well as in a severe recessive dysplasia, spondyloepimetaphyseal dysplasia, aggrecan type. Next-generation sequencing (NGS) has aided the identification of heterozygous ACAN mutations in individuals with short stature, minor skeletal defects and mild facial dysmorphisms, some of whom have advanced bone age (BA), poor pubertal spurt and early growth cessation as well as precocious osteoarthritis. DESIGN AND METHODS: This study involves clinical and genetic characterization of 16 probands with heterozygous ACAN variants, 14 with short stature and mild skeletal defects (group 1) and two with SEDK (group 2). Subsequently, we reviewed the literature to determine the frequency of the different clinical characteristics in ACAN-positive individuals. RESULTS: A total of 16 ACAN variants were located throughout the gene, six pathogenic mutations and 10 variants of unknown significance (VUS). Interestingly, brachydactyly was observed in all probands. Probands from group 1 with a pathogenic mutation tended to be shorter, and 60% had an advanced BA compared to 0% in those with a VUS. A higher incidence of coxa valga was observed in individuals with a VUS (37% vs 0%). Nevertheless, other features were present at similar frequencies. CONCLUSIONS: ACAN should be considered as a candidate gene in patients with short stature and minor skeletal defects, particularly those with brachydactyly, and in patients with spondyloepiphyseal dysplasia. It is also important to note that advanced BA and osteoarticular complications are not obligatory conditions for aggrecanopathies/aggrecan-associated dysplasias

Toward Male Individualization with Rapidly Mutating Y-Chromosomal Short Tandem Repeats

Peer reviewe

Assessment of plasma chitotriosidase activity, CCL18/PARC concentration and NP-C suspicion index in the diagnosis of Niemann-Pick disease type C: A prospective observational study

Background: Niemann-Pick disease type C (NP-C) is a rare, autosomal recessive neurodegenerative disease caused by mutations in either the NPC1 or NPC2 genes. The diagnosis of NP-C remains challenging due to the non-specific, heterogeneous nature of signs/symptoms. This study assessed the utility of plasma chitotriosidase (ChT) and Chemokine (C-C motif) ligand 18 (CCL18)/pulmonary and activation-regulated chemokine (PARC) in conjunction with the NP-C suspicion index (NP-C SI) for guiding confirmatory laboratory testing in patients with suspected NP-C. Methods: In a prospective observational cohort study, incorporating a retrospective determination of NP-C SI scores, two different diagnostic approaches were applied in two separate groups of unrelated patients from 51 Spanish medical centers (n = 118 in both groups). From Jan 2010 to Apr 2012 (Period 1), patients with =2 clinical signs/symptoms of NP-C were considered ''suspected NP-C'' cases, and NPC1/NPC2 sequencing, plasma chitotriosidase (ChT), CCL18/PARC and sphingomyelinase levels were assessed. Based on findings in Period 1, plasma ChT and CCL18/PARC, and NP-C SI prediction scores were determined in a second group of patients between May 2012 and Apr 2014 (Period 2), and NPC1 and NPC2 were sequenced only in those with elevated ChT and/or elevated CCL18/PARC and/or NP-C SI =70. Filipin staining and 7-ketocholesterol (7-KC) measurements were performed in all patients with NP-C gene mutations, where possible. Results: In total across Periods 1 and 2, 10/236 (4%) patients had a confirmed diagnosis o NP-C based on gene sequencing (5/118 4.2%] in each Period): all of these patients had two causal NPC1 mutations. Single mutant NPC1 alleles were detected in 8/236 (3%) patients, overall. Positive filipin staining results comprised three classical and five variant biochemical phenotypes. No NPC2 mutations were detected. All patients with NPC1 mutations had high ChT activity, high CCL18/PARC concentrations and/or NP-C SI scores =70. Plasma 7-KC was higher than control cut-off values in all patients with two NPC1 mutations, and in the majority of patients with single mutations. Family studies identified three further NP-C patients. Conclusion: This approach may be very useful for laboratories that do not have mass spectrometry facilities and therefore, they cannot use other NP-C biomarkers for diagnosis

Measurement of the azimuthal anisotropy of Y(1S) and Y(2S) mesons in PbPb collisions at √S^{S}NN = 5.02 TeV

The second-order Fourier coefficients (υ$_{2}$) characterizing the azimuthal distributions of Υ(1S) and Υ(2S) mesons produced in PbPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are studied. The Υmesons are reconstructed in their dimuon decay channel, as measured by the CMS detector. The collected data set corresponds to an integrated luminosity of 1.7 nb$^{-1}$. The scalar product method is used to extract the υ$_{2}$ coefficients of the azimuthal distributions. Results are reported for the rapidity range |y| < 2.4, in the transverse momentum interval 0 < p$_{T}$ < 50 GeV/c, and in three centrality ranges of 10–30%, 30–50% and 50–90%. In contrast to the J/ψ mesons, the measured υ$_{2}$ values for the Υ mesons are found to be consistent with zero

Measurement of prompt D0^{0} and D‾\overline{D}0^{0} meson azimuthal anisotropy and search for strong electric fields in PbPb collisions at root SNN\sqrt{S_{NN}} = 5.02 TeV

The strong Coulomb field created in ultrarelativistic heavy ion collisions is expected to produce a rapiditydependent difference (Av2) in the second Fourier coefficient of the azimuthal distribution (elliptic flow, v2) between D0 (uc) and D0 (uc) mesons. Motivated by the search for evidence of this field, the CMS detector at the LHC is used to perform the first measurement of Av2. The rapidity-averaged value is found to be (Av2) = 0.001 ? 0.001 (stat)? 0.003 (syst) in PbPb collisions at ?sNN = 5.02 TeV. In addition, the influence of the collision geometry is explored by measuring the D0 and D0mesons v2 and triangular flow coefficient (v3) as functions of rapidity, transverse momentum (pT), and event centrality (a measure of the overlap of the two Pb nuclei). A clear centrality dependence of prompt D0 meson v2 values is observed, while the v3 is largely independent of centrality. These trends are consistent with expectations of flow driven by the initial-state geometry. ? 2021 The Author. Published by Elsevier B.V. This is an open access article under the CC BY licens

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Performance of reconstruction and identification of τ leptons decaying to hadrons and vτ in pp collisions at √s=13 TeV

The algorithm developed by the CMS Collaboration to reconstruct and identify τ leptons produced in proton-proton collisions at √s=7 and 8 TeV, via their decays to hadrons and a neutrino, has been significantly improved. The changes include a revised reconstruction of π⁰ candidates, and improvements in multivariate discriminants to separate τ leptons from jets and electrons. The algorithm is extended to reconstruct τ leptons in highly Lorentz-boosted pair production, and in the high-level trigger. The performance of the algorithm is studied using proton-proton collisions recorded during 2016 at √s=13 TeV, corresponding to an integrated luminosity of 35.9 fb¯¹. The performance is evaluated in terms of the efficiency for a genuine τ lepton to pass the identification criteria and of the probabilities for jets, electrons, and muons to be misidentified as τ leptons. The results are found to be very close to those expected from Monte Carlo simulation