Thirty-day mortality risk in patients following radiofrequency and cryoballoon ablation for atrial fibrillation across the entire nation of Poland: An 8-year analysis from the National Health Fund of Poland.

INTRODUCTION: Atrial fibrillation (AF) is the most common sustained arrhythmia, and catheter ablation (CA) is a primary therapeutic option. However, the 30-day all-cause mortality risk associated with CA for AF may be underestimated due to selection bias. Our study aimed to assess 30-day mortality in an unselected cohort of patients. METHODS: Data from the National Health Fund-Poland covered over 99% of nationwide radiofrequency ablation (RF) or cryoballoon ablation for AF, with 100% recorded deaths. The study included consecutive CA procedures in adult patients between 2012 and 2019. Thirty-day mortality rates were calculated for each age group. RESULTS: A total of 31 214 CAs for AF were performed on 26 767 patients (34.8% female). Forty-four percent of patients had hypertension, 31.2% had coronary artery disease, 14.4% had heart failure, 11% had diabetes mellitus, 5.6% had malignant neoplasms, 2.7% had a previous myocardial infarction, 2.5% had a previous stroke, and 2.2% had kidney disease. Thirty-two deaths (0.1%) occurred within 30 days, with the highest mortality in the oldest age group (>80 years). Statistical analysis revealed higher incidences of kidney disease (p < 0.001) and heart failure (p = 0.001) in patients who died within 30 days. Mortality risk did not significantly differ between cryoballoon and RF ablation, as well as first and subsequent ablation for AF. The risk of death within 7 days postablation for AF was 1 in 2750 procedures, while the risk of death on the same day postablation was 1 in 6250 procedures. CONCLUSIONS: The 30-day mortality rate in a large, unselected AF ablation cohort, covering 99% of procedures in the country, is low except for the oldest patients. This factor should be taken into consideration when offering CA for AF.

Prevalence of frequent premature ventricular contractions and left-ventricular systolic dysfunction in patients receiving Holter monitoring.

BACKGROUND: Premature ventricular contractions (PVCs) are frequently observed with left ventricular (LV) systolic dysfunction, although the prevalence of these associated conditions in the general population remains unknown. OBJECTIVE: We sought to understand the prevalence of frequent PVCs (defined PVCs > 5%) and high burden PVCs (defined PVCs > 10%) and LV systolic dysfunction in patients receiving ambulatory Holter monitors (HM). METHODS: A prospective multicenter (eight US medical centers) cross-sectional study collected demographic and PVC burden data from consecutive patients undergoing 24-h, 48-h, and 14-day HM (July 2018-June 2020). Left ventricle ejection fraction (LVEF) data was collected if obtained within 6 months of HM. Four PVC burden groups were analyzed (<1%, 1%-5%, 5.1%-10%, and >10% burden) and stratified by normal LVEF (≥50%) or presence LVEF < 50%. RESULTS: The prevalence of PVC burden of 5.1%-10% and >10% was 4% and 5%, respectively in the population undergoing HM (n = 6529). Age was significantly different between PVC groups (p < .001). In those with LVEF assessment (n = 3713), the prevalence of LVEF < 50% and both LVEF < 50% and PVC > 5% was 16.4% and 4.2%, respectively. The prevalence of PVC > 5% and PVC > 10% in patients with LVEF < 50% was 26% and 16%, respectively. PVC > 5% were more prevalent in older, male, and Caucasians (p < .001). Females had a lower prevalence of PVC > 5% than males (6% vs. 11%; p < .001), but not among those with LVEF < 50% (24% vs. 26%, p = .10). CONCLUSION: PVC > 5% and PVC > 10% and LVEF < 50% are prevalent in patients undergoing HM. PVC > 5% are associated with older age. Females have a lower prevalence of PVC > 5% than males but similar combined PVC > 5% and LVEF < 50%. CLINICALTRIAL: gov identifier: NCT03228823.

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.

Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

Economics and laboratory efficiency of atrial fibrillation ablation.

PURPOSE OF REVIEW: Atrial fibrillation is a growing public health problem and is associated with an increased risk of comorbidities with enormous socioeconomic implications. This review article focuses on fiscal burden of atrial fibrillation on the healthcare system and economic value of atrial fibrillation ablations brought to the patient and the payers by improvement in outcomes and reduction in treatment costs. RECENT FINDINGS: This article summarizes the recently published studies evaluating the economic impact of atrial fibrillation treatment. Catheter ablation have shown to be the most successful strategy for treatment of defibrillation. However, repeat ablation is associated with higher costs, over and above any subsequent procedural costs, compared with a single ablation procedure for atrial fibrillation. Cryoballoon ablation has been shown to have fewer repeat ablations than radiofrequency ablations, which resulted in overall cost reduction. Improvement in laboratory efficiency, better utilization of laboratory resources and same-day discharge strategy can lead to further healthcare savings and increase in value to all stakeholders. SUMMARY: The value of healthcare delivery for patient with atrial fibrillation can be improved by advancement in technology that demonstrates cost reduction to the health system and outcomes improvements. Implementation of tactics that decrease cost and improve outcomes can alleviate some of the financial strain on healthcare systems, which is of extreme importance in the current climate.

Enhanced susceptibility of SARS-CoV-2 spike RBD protein assay targeted by cellular receptors ACE2 and CD147: Multivariate data analysis of multisine impedimetric response.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters the cells through the binding of spike protein to the host cell surface-expressing angiotensin-converting enzyme 2 (ACE2) or by endocytosis mediated by extracellular matrix metalloproteinase inducer (CD147). We present extended statistical studies of the multisine dynamic electrochemical impedance spectroscopy (DEIS) revealing interactions between Spike RBD and cellular receptors ACE2 and CD147, and a reference anti-RBD antibody (IgG2B) based on a functionalised boron-doped diamond (BDD) electrode. The DEIS was supported by a multivariate data analysis of a SARS-CoV-2 Spike RBD assay and cross-correlated with the atomic-level information revealed by molecular dynamics simulations. This approach allowed us to study and detect subtle changes in the electrical properties responsible for the susceptibility of cellular receptors to SARS-CoV-2, revealing their interactions. Changes in electrical homogeneity in the function of the RBD concentration led to the conclusion that the ACE2 receptor delivers the most homogeneous surface, delivered by the high electrostatic potential of the relevant docking regions. For higher RBD concentrations, the differences in electrical homogeneity between electrodes with different receptors vanish. Collectively, this study reveals interdependent virus entry pathways involving separately ACE2, CD147, and spike protein, as assessed using a biosensing platform for the rapid screening of cellular interactions (i.e. testing various mutations of SARS-CoV-2 or screening of therapeutic drugs).

Thermal-Visible Face Recognition Based on CNN Features and Triple Triplet Configuration for On-the-Move Identity Verification.

Face recognition operating in visible domains exists in many aspects of our lives, while the remaining parts of the spectrum including near and thermal infrared are not sufficiently explored. Thermal-visible face recognition is a promising biometric modality that combines affordable technology and high imaging qualities in the visible domain with low-light capabilities of thermal infrared. In this work, we present the results of our study in the field of thermal-visible face verification using four different algorithm architectures tested using several publicly available databases. The study covers Siamese, Triplet, and Verification Through Identification methods in various configurations. As a result, we propose a triple triplet face verification method that combines three CNNs being used in each of the triplet branches. The triple triplet method outperforms other reference methods and achieves TAR @FAR 1% values up to 90.61%.

Compound motor action potentials in transient and persistent phrenic nerve injury - metanalysis.

BACKGROUND: The right phrenic nerve is vulnerable to injury (PNI) during cryoballoon ablation (CBA) isolation of the right pulmonary veins. The complication can be transient or persistent. The reported incidence of PNI fluctuates from 4.73% to 24.7% depending on changes over time, CBA generation, and selected protective methods. M e t h o d s: Through September 2019, a database search was performed on MEDLINE, EMBASE, and Cochrane Database. In the selected articles, the references were also extensively searched. The study provides a comprehensive meta-analysis of the overall prevalence of PNI, assesses the transient to persistent PNI ratio, the outcome of using compound motor action potentials (CMAP), and estimated average time to nerve recovery. R e s u l t s: From 2008 to 2019, 10,341 records from 48 trials were included. Out of 783 PNI retrieved from the studies, 589 (5.7%) and 194 (1.9%) were persistent. CMAP caused a significant reduction in the risk of persistent PNI from 2.3% to 1.1% (p = 0.05; odds ratio [OR] 2.13) in all CBA groups. The mean time to PNI recovery extended beyond the hospital discharge was significantly shorter in CMAP group at three months on average versus non CMAP at six months (p = 0.012). CMAP (in contrast to non-CMAP procedures) detects PNI earlier from 4 to 16 sec (p <0.05; I2 = 74.53%) and 3 to 9o (p <0.05; I2 = 97.24%) earlier. C o n c l u s i o n s: Right PNI extending beyond hospitalization is a relatively rare complication. CMAP use causes a significant decrease in the risk of prolonged injury and shortens the time to recovery.

Predictors of joint damage progression and stringent remission in patients with established rheumatoid arthritis in clinical remission.

OBJECTIVES: To study if clinical, radiographic and MRI markers can predict MRI and radiographic damage progression and achievement of stringent remission in patients with established RA in clinical remission followed by a targeted treatment strategy. METHODS: RA patients (DAS28-CRP <3.2, no swollen joints) receiving conventional synthetic DMARDs were randomized to conventional or MRI-targeted treat-to-target strategies with predefined algorithmic treatment escalations. Potentially predictive baseline variables were tested in multivariate logistic regression analyses. RESULTS: In the 171 patients included, baseline MRI osteitis independently predicted progression in MRI erosion [odds ratio (OR) 1.13 (95% CI 1.06, 1.22)], joint space narrowing [OR 1.15 (95% CI 1.07, 1.24)] and combined damage [OR 1.23 (95% CI 1.13, 1.37)], while tenosynovitis independently predicted MRI erosion progression [OR 1.13 (95% CI 1.03, 1.25)]. A predictor of radiographic erosion progression was age, while gender predicted progression in joint space narrowing. Following an MRI treat-to-target strategy predicted stringent remission across all remission definitions: Clinical Disease Activity Index remission OR 2.94 (95% CI 1.25, 7.52), Simplified Disease Activity Index remission OR 2.50 (95% CI 1.01, 6.66), ACR/EULAR Boolean remission OR 5.47 (95% CI 2.33, 14.13). Similarly, low tender joint count and low patient visual analogue scale pain and global independently predicted achievement of more stringent remission. CONCLUSION: Baseline MRI osteitis and tenosynovitis were independent predictors of 2 year MRI damage progression in RA patients in clinical remission, while independent predictors of radiographic damage progression were age and gender. Following an MRI treat-to-target strategy, low scores of patient-reported outcomes and low tender joint count predicted achievement of stringent remission. TRIAL REGISTRATION: ClinicalTrials.gov (https://clinicaltrials.gov), NCT01656278.

Same-day discharge after cryoballoon ablation of atrial fibrillation: A multicenter experience.

BACKGROUND: It is common practice to observe patients during an overnight stay (ONS) following a catheter ablation procedure for the treatment of atrial fibrillation (AF). OBJECTIVES: To investigate the safety and economic impact of a same-day discharge (SDD) protocol after cryoballoon ablation for treatment of AF in high-volume, geographically diverse US hospitals. METHODS: We retrospectively reviewed 2374 consecutive patients (1119 SDD and 1180 ONS) who underwent cryoballoon ablation for AF at three US centers. Baseline characteristics, acute procedure-related complications, and longer-term evaluations of safety were recorded during routine clinical follow-up. The mean cost of an ONS was used in a one-way sensitivity analysis to evaluate yearly cost savings as a function of the percentage of SDD cases per year. RESULTS: The SDD and ONS cohorts were predominately male (69% vs. 67%; p = .3), but SDD patients were younger (64 ± 11 vs. 66 ± 10; p < .0001) with lower body mass index (30 ± 6 vs. 31 ± 61; p < .0001) and CHA2 DS2 -VASc scores (1.4 ± 1.0 vs. 2.2 ± 1.4; p < .0002). There was no difference between SDD and ONS in the 30-day total complication rate (n = 15 [1.26%] versus n = 24 [2.03%]; p = .136, respectively). The most common complication was hematoma in both the SDD (n = 8; 0.67%) and ONS (n = 11; 0.93%) cohorts. Sensitivity analysis demonstrated that when 50% of every 100 patients treated were discharged the same day, hospital cost savings ranged from $45 825 to $83 813 per year across US hospitals. CONCLUSIONS: SDD following cryoballoon ablation for AF appears to be safe and is associated with cost savings across different US hospitals.

Passive imaging at 250†GHz for detection of face presentation attacks.

Face presentation attacks are becoming more efficient since new 3D facial masks are used. Passive terahertz imaging offers specific physical properties that may improve presentation attack detection capabilities. The non-zero transmission capability through a variety of non-metallic materials may provide necessary information for presentation attack detection. The aim of this paper is to present outcomes of a study on face presentation attack detection using passive imaging at 250 GHz. An analysis of presentation attacks for facial recognition systems using custom displayed and printed photographs, 3D-printed and full-face flexible 3D-latex masks, is provided together with spectral characterization of various presentation attack instruments. A set of experiments with various instruments and various sets of clothing is described and discussed. Finally, two presentation attack detection methods are proposed. The first method is based on a threshold corresponding to a difference between mean intensities of selected regions of interests while the second method uses eight different deep learning classifiers to detect presentation attacks. Results of two validation schemes are presented.

Is age and not antiretroviral therapy the strongest risk factor for chronic pain in HIV-infected population?

BACKGROUND: Chronic pain in HIV-infected patients on effective antiretroviral therapy (ART) limits patients' normal functioning both somatically and psychologically. The current state of knowledge on the topic is insufficient, with the underlying causes of this pain unexplained. Therefore we analyzed the frequency and factors associated with chronic pain in HIV-infected patients on ART. METHODS: We conducted a prospective, survey study, including consecutive HIV-infected patients under specialist care at the HIV Outpatient Clinic of the Hospital for Infectious Disease in Warsaw between February 2014 and December 2016. During their routine visit all patients who agreed to participate in the study were surveyed using a study questionnaire. For all patients reporting any pain the Brief Pain Inventory (BPI) form and Douleur Neuropathique 4 Questions form (DN4) were completed. Data on history and current ART and laboratory measurements were obtained from electronical database. Chi-squared and Kruskal-Wallis tests were used for group comparison. The potential factors associated with chronic pain were identified via logistic regression models. RESULTS: In total 196 HIV-infected patients were included in the study, 57 (29,1%) of them reported chronic pain. The reported pain was mostly (75%) limited to a single area of the body. In univariable logistic regression model the odds of chronic pain were significantly higher with increasing age (OR 1.36 [95%CI:1.17-1.58]), time under specialist care (OR 2.25 [95%CI:1.42-35.7]), time on ART (OR2.96 [95%CI:1.60-5.49]), previous ART with zidovudine (OR 2.00[95%CI:1.06-1.55]) and previous treatment with ddI, ddC or d4T (OR4.13 [95%CI:1.92-8.91]). Homosexual route of HIV infection as compared to injecting drug use was decreasing the odds of chronic pain (OR0.33 [95%CI: 014-0.75]). In multivariable analyses, adjusting for all above the only factor associated with chronic pain was age (OR1.28 [95%CI:1.06-1.55]). CONCLUSIONS: The prevalence of chronic pain in the studied population of HIV-infected Polish patients was high. The only risk factor for chronic pain identified was age. With ageing HIV population it is therefore imperative to develop cooperation protocols for specialist HIV treatment clinics, pain treatment clinics, and rehabilitation units.

Thermal Face Verification through Identification.

This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient's death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods.

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU's financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.

A Study on Presentation Attack Detection in Thermal Infrared.

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject's state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

Towards Fingerprint Spoofing Detection in the Terahertz Range.

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time-frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.

Real-time concealed object detection and recognition in passive imaging at 250 GHz.

The presented study concerns detection and recognition of hidden objects covered with various types of clothing by using passive imagers operating in a terahertz (THz) range at 1.2 mm (250 GHz). The aim of this study is to propose a detection and classification algorithm operating robustly at a high processing speed. The paper briefly describes properties of the THz spectrum, theoretical limitations, performance of the imager, and physical properties of fabrics in a wide range of frequencies. Two methods have been presented, trained, and tested using a dataset with various configurations in sessions each lasting 30 min. During experiments, different clothes and hidden objects have been combined. The paper presents a comparison of robust detection and recognition methods for concealed objects using a multiframe single-shot detector and region-based fully convolutional networks. The comparison of the original results of various experiments is presented.

Convergent ablation for persistent atrial fibrillation: Single center experience.

BACKGROUND: Endocardial catheter ablation has been shown to be effective in patients with paroxysmal atrial fibrillation (AF), and significantly less effective in patients with persistent AF (PAF). Lately, there is a trend toward a hybrid approach in the treatment of PAF that may be a more durable treatment for patients with PAF. In this manuscript we report our experience with the convergent ablation procedure in a PAF cohort. METHODS: This is a single center retrospective analysis of 31 patients with PAF who underwent the convergent procedure. All patients underwent surgical epicardial ablation of the posterior left atrial through a subxiphoid approach, followed by radiofrequency endocardial ablations on the same day. Patients were followed at 6 months intervals with static electrocardiograms or implanted devices. RESULTS: Sinus rhythm was achieved intraoperatively in all patients. Recurrence was defined according to Hearlt Rhythm Society definitions. At a median follow up of 17.7 months (IQR 11-24), the recurrence of atrial tachyarrhythmia (AF and atrial flutter) by Kaplan-Meier event free survival analysis occurred in 9 (29%) patients at 1-year follow up and 15 (48%) patients at 2-year follow up with or without the use of antiarrhythmic drugs. Recurrence of AF alone occurred in 4 (13%) patients at 1-year follow up and 9 (29%) patients at 2-year follow up patients. Complication rate in perioperative period was 12.9%. CONCLUSION: Our experience showed the hybrid procedure is a relatively safe and effective option for patients with PAF. Further studies are needed to better determine its long-term outcomes.

Effect of Magnetic Resonance Imaging vs Conventional Treat-to-Target Strategies on Disease Activity Remission and Radiographic Progression in Rheumatoid Arthritis: The IMAGINE-RA Randomized Clinical Trial.

Importance: Whether using magnetic resonance imaging (MRI) to guide treatment in patients with rheumatoid arthritis (RA) improves disease activity and slows joint damage progression is unknown. Objective: To determine whether an MRI-guided treat-to-target strategy vs a conventional clinical treat-to-target strategy improves outcomes in patients with RA in clinical remission. Design, Setting, and Participants: Two-year, randomized, multicenter trial conducted at 9 hospitals in Denmark. Two hundred patients with RA in clinical remission (disease activity score in 28 joints-C-reactive protein [DAS28-CRP] <3.2 and no swollen joints) were enrolled between April 2012 and June 2015. The final follow-up visit was April 2017. Interventions: Patients were randomly allocated (1:1) to an MRI-guided vs a conventional treat-to-target strategy. In the MRI-guided group, the treatment goal was absence of MRI bone marrow edema combined with clinical remission, defined as DAS28-CRP of 3.2 or less and no swollen joints. In the conventional group, the treatment goal was clinical remission. Main Outcomes and Measures: Co-primary outcomes were proportions of patients achieving DAS28-CRP remission (DAS28-CRP <2.6) and with no radiographic progression (no increase in total van der Heijde-modified Sharp score) at 24 months. Significance testing for the primary outcome was based on 1-sided testing. Secondary outcomes were clinical and MRI measures of disease activity, physical function, and quality of life. Results: Of 200 patients randomized (133 women [67%]; mean [SD] age, 61.6 [10.5] years; median baseline DAS28-CRP, 1.9 [interquartile range, 1.7-2.2]; van der Heijde-modified Sharp score, 18.0 [interquartile range, 7.0-42.5]), 76 patients (76%) in the MRI-guided group and 95 (95%) in the conventional group completed the study. Of these, 64 (85%) vs 83 (88%), respectively, reached the primary clinical end point (risk difference, -4.8% [1-sided 95% CI, -13.6% to + ∞; 1-sided P = .19]) and 49 (66%) vs 58 (62%), respectively, reached the primary radiographic end point (risk difference, 4.7% [1-sided 95% CI, -7.0% to + ∞; 1-sided P = .25). Of 10 key secondary end points, 8 were null and 2 showed statistically significant benefit for the MRI treat-to-target group. Seventeen patients (17%) in the MRI-guided treat-to-target group and 6 patients (6%) in the conventional treat-to-target group experienced serious adverse events. Conclusions and Relevance: Among patients with RA in clinical remission, an MRI-guided treat-to-target strategy compared with a conventional treat-to-target strategy did not result in improved disease activity remission rates or reduce radiographic progression. These findings do not support the use of an MRI-guided strategy for treating patients with RA. Trial Registration: ClinicalTrials.gov Identifier: NCT01656278.