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2.
Article in English | MEDLINE | ID: mdl-36521445

ABSTRACT

INTRODUCTION: Detection of atrial fibrillation (AF) is challenging in patients after ischemic stroke due to its paroxysmal nature. We aim to determine the utility of a combined clinical, electrocardiographic and genetic variables model to predict AF in a post-stroke population. MATERIALS AND METHODS: We performed a cohort study at a single comprehensive stroke centre from 09/11/2009 to 31/10/2017. All patients recruited were diagnosed with acute ischemic stroke or transient ischemic attacks. Electrocardiographic variables including p-wave terminal force (PWTF), corrected QT interval (QTc) and genetic variables including single nucleotide polymorphisms (SNP) at the 4q25 (rs2200733) were evaluated. Clinical, electrocardiographic and genetic variables of patients without AF and those who developed AF were compared. Multiple logistic regression analysis and receiver operating characteristics were performed to identify parameters and determine their ability to predict the occurrence of AF. RESULTS: Out of 709 patients (median age of 59 years, IQR 52-67) recruited, sixty (8.5%) were found to develop AF on follow-up. Age (odds ratio (OR): 3.49, 95% confidence interval (CI): 2.03-5.98, p<0.0001), hypertension (OR: 2.76, 95% CI: 1.36-5.63, p=0.0052) and valvular heart disease (OR: 8.49, 95% CI: 2.62-27.6, p<0.004 were the strongest predictors of AF, with area under receiver operating value of 0.76 (95% CI: 0.70-0.82), and 0.82 (95% CI: 0.77-0.87) when electrocardiographic variables (PWTF and QTc) were added. SNP did not improve prediction modelling. CONCLUSION: We demonstrated that a model combining clinical and electrocardiographic variables provided robust prediction of AF in our post-stroke population. Role of SNP in prediction of AF was limited.

3.
J Rheumatol ; 40(1): 23-9, 2013 Jan.
Article in English | MEDLINE | ID: mdl-23118111

ABSTRACT

OBJECTIVE: To compare the metric properties of a computer-assisted erosion segmentation volume measurement with scoring using the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) in a longitudinal cohort of patients with rheumatoid arthritis (RA). METHODS: Thirty-two sets of baseline and 2-year followup magnetic resonance imaging (MRI) of metacarpal phalangeal 2-5 joints of patients with RA were scored using RAMRIS and segmented using OSIRIS software. The smallest detectable difference (SDD), standardized response mean (SRM), and paired t-test were used to evaluate the sensitivity to change. Eleven of the 32 patients' MRI were segmented by both readers to evaluate interreader agreement. The 28-joint Disease Activity Score (DAS28) and Sharp erosion scores further evaluated construct and longitudinal validity. RESULTS: Reliability of erosion progression by computer-assisted volume measurement was superior to RAMRIS [intrareader interclass correlation coefficient (ICC) 0.97 (0.94-0.99) vs 0.52 (0.22-0.73)] and interreader ICC of volume measurement was 0.85 (0.53-0.96). Computer-assisted volume measurements identified 10 of 32 patients who progressed more than the SDD progression, whereas RAMRIS identified only 4 of 32 patients (p = 0.0013). By a paired t-test, however, all MRI measures progressed significantly over 2 years (irrespective of treatment arm) and there was little difference by SRM. Construct correlational validity of the MRI methods was 0.47-0.90 for status scores and 0.33-0.81 for progression. There was no relationship between the average DAS28 and erosion progression by any imaging method. CONCLUSION: Computer-assisted measurement of erosion volume has good performance metrics. It had excellent intrareader and interreader reliability and was more sensitive to change than RAMRIS in this group of patients. www.ClinicalTrials.gov, NCT00451971.


Subject(s)
Arthritis, Rheumatoid/pathology , Image Interpretation, Computer-Assisted , Metacarpophalangeal Joint/pathology , Adult , Aged , Disease Progression , Female , Humans , Magnetic Resonance Imaging , Male , Middle Aged , Reproducibility of Results , Sensitivity and Specificity , Severity of Illness Index
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