Flare Prediction Research Articles

Anti-myeloperoxidase and proteinase 3 antibodies for nephritis flare prediction in anti-neutrophil cytoplasmic antibody-associated vasculitis.

The value of myeloperoxidase (MPO) and proteinase 3 (PR3) antibody titres in the assessment of renal disease activity and flare prediction in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) is not well known. We performed a retrospective study including 113 AVV patients with renal biopsy-proven pauci-immune necrotizing glomerulonephritis from seven Spanish hospitals. The main inclusion criteria were assessment of MPO antibodies using multiplex flow immunoassay and PR3 antibody measurements using immunoassay chemiluminescence with an identical range of values for all participating centres. Serum MPO antibodies 3 ± 1.2 months before relapse were higher in patients who relapsed [19.2 ± 12.2 versus 3.2 ± 5.1 antibody index (AI); P < 0.001]. The discrimination value of MPO antibodies 3 months before renal relapse had an area under the receiver operating characteristics curve (AUC) of 0.82 [95% confidence interval (CI) 0.73-0.92; P < 0.001]. ΔMPO antibodies (change in antibodies titration 6 months before relapse) were higher in patients who relapsed (8.3 ± 12 versus 0.9 ± 3.1 AI; P = 0.001). The discrimination value of ΔMPO had an AUC of 0.76 (95% CI 0.63-0.88; P < 0.001). The positive predictive value of renal relapse in PR3 patients is 100% and the negative predictive value of renal relapse in patients with PR3-positive titres is 57.1%. Serum PR3 antibodies were higher in patients who relapsed 2.8 ± 1.4 months before relapse (58.6 ± 24.6 versus 2.0 ± 0.6 AI; P < 0.001). MPO level monitoring using multiplex flow immunoassay and PR3 measurements using immunoassay chemiluminescence are useful and sensitive tools for the prediction of renal relapse in the follow-up of AAV patients with renal disease and relevant surrogate markers of renal disease activity.

Testing and Validating Two Morphological Flare Predictors by Logistic Regression Machine Learning

Whilst the most dynamic solar active regions (ARs) are known to flare frequently, predicting the occurrence of individual flares and their magnitude, is very much a developing field with strong potentials for machine learning applications. The present work is based on a method which is developed to define numerical measures of the mixed states of ARs with opposite polarities. The method yields compelling evidence for the assumed connection between the level of mixed states of a given AR and the level of the solar eruptive probability of this AR by employing two morphological parameters: 1) the separation parameter Sl−f and 2) the sum of the horizontal magnetic gradient GS. In this work, we study the efficiency of Sl−f and GS as flare predictors on a representative sample of ARs, based on the SOHO/MDI-Debrecen Data (SDD) and the SDO/HMI - Debrecen Data (HMIDD) sunspot catalogues. In particular, we investigate about 1,000 ARs in order to test and validate the joint prediction capabilities of the two morphological parameters by applying the logistic regression machine learning method. Here, we confirm that the two parameters with their threshold values are, when applied together, good complementary predictors. Furthermore, the prediction probability of these predictor parameters is given at least 70% a day before.

Association of Urinary Matrix Metalloproteinase 7 Levels With Incident Renal Flare in Lupus Nephritis.

Flares of lupus nephritis (LN) are frequent and associated with impaired renal prognosis. One major management obstacle in LN flare is the lack of effective methods to identify at-risk patients earlier in their disease course. This study was undertaken to test the utility of measurement of urinary matrix metalloproteinase 7 (MMP-7) for the dynamic surveillance of renal disease activity and prediction of renal flares in LN. A prospective, 2-stage cohort study was performed in patients with LN. Urinary MMP-7 levels at the time of biopsy were evaluated in 154 patients with newly diagnosed LN in 2 independent cohorts. Urinary MMP-7 levels were assessed for correlation with renal histologic activity. Furthermore, after a minimum period of 12 months of renal disease remission, urinary MMP-7 levels were monitored bimonthly for 2 years in 65 patients with LN. The association between urinary MMP-7 levels and development of LN flare was analyzed. Urinary MMP-7 levels were elevated in patients with LN. A higher urinary MMP-7 level in LN was associated with greater renal histologic activity. As a marker for identifying LN patients with more severe renal histologic activity (i.e., a histologic activity index of ≥7), the level of urinary MMP-7 outperformed other clinical markers and improved their predictive performance, thus linking urinary MMP-7 levels to renal disease activity. Furthermore, among patients who had follow-up measurements of urinary MMP-7 after achievement of long-term remission of renal disease activity, an elevated urinary MMP-7 level during follow-up was independently associated with an increased risk of LN flare. This elevation in the urinary MMP-7 level hinted at the risk of an LN flare at an earlier time point prior to indications using conventional laboratory measures. Thus, use of the urinary MMP-7 level in conjunction with other clinical measures improved the prognostic value for prediction of an LN flare. Urinary MMP-7 levels in LN are correlated with renal histologic activity. An elevated urinary MMP-7 level detected after achievement of long-term renal disease remission is associated with a higher risk of incident renal flare in patients with LN.

The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data &amp; machine learning era

The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Predicting where and when space weather events such as solar flares and X-rays bursts are likely to occur in a specific area of interest constitutes a significant challenge in space weather research. Space weather scientists are, therefore, gradually exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of space weather events from past distribution patterns. As solar flares emit extreme ultraviolet and X-ray radiation, which leads to ionization effect in different layers of the ionosphere, most recent works related to solar flare predictions using machine learning (ML) techniques, focused on X-ray time series predictions. Here, we suggest using support vector machine for classifying subdaily and diurnal total electron content (TEC) spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M, and X-class solar flare events. This is done as opposed to predicting TEC time series using ML techniques. The predictions are estimated up to three days before each tested class events, along with different skill scores such as precision, recall, Heidke skill score (HSS), accuracy, and true skill statistics. The results indicate that the suggested approach has the ability to predict solar flare events of X and M-class 24 h prior to their occurrence with 91% and 76% HSS skill scores, respectively, which improves over most recent related works. However, for the small-size C and B-class flares, the suggested approach does not succeed in producing similar promising results.

Predictors and Outcomes of Flares in Chronic Graft-Versus-Host Disease

Introduction: Chronic Graft-versus-Host Disease (cGvHD) frequently requires prolonged immune suppressive therapy (IST) with &amp;gt; 50% still on IST at 5 years. The IST typically involves a slow taper of steroids often with flare of cGvHD, necessitating augmentation of previous therapy or addition of new IST. Studies describing cGvHD flares are limited. We analyzed patients with cGvHD who flared during the treatment with systemic IST, their overall survival (OS) and non-relapse mortality (NRM). Methods: This study included all adult patients with cGvHD (n=145) following an allogeneic transplant (2010 - 2017) from a matched sibling donor peripheral blood stem cell transplant (MSD, n=104 (72%) or double/single umbilical cord blood transplant (UCBT, n=41 (28%). The 2014 NIH Consensus Criteria were used to classify organ/overall cGvHD severity. Flare of cGvHD was defined as progression in cGvHD manifestations (after initial response), which was less severe than at diagnosis. Multivariate regression of flares was based on the Prentice, Williams and Peterson model for ordered multiple events (flares). Time-dependent effects on OS and NRM were analyzed by Cox and Fine and Gray regression with propensity scoring to control for confounding. Results: Flares occurred in 87 patients; the cumulative incidence of flares was 60% (95% CI: 51-70%) at a median of 188 days (range 16-751) after diagnosis of cGvHD. The median dose of prednisone was 1 mg/kg/day (range 0-4.2) at diagnosis of cGvHD. At the diagnosis of flare, 36 (41%) of the patients were off prednisone, 50 (57%) were receiving 0.1-0.5 mg/kg /day, and 2 patients &amp;gt; 0.5 mg/kg /day. Thirty two of the 87 (36%) patients experienced multiple flares (2 to 4). The most common organs involved at cGvHD flare were skin (n=45; 51%), mouth (n=27; 31%), GI tract (n=22; 25%) and liver (n=12; 14%); often in combinations of skin/mouth in 11 cases (13%), skin/GI in 6 (7%) and liver/mouth in 4 (5%) cases. Treatment for flare was mostly increase in dose of prednisone to 0.5 mg/kg/day (range 0.3-1.0) in 77 patients (88%) plus the addition of another line of IST in 48 patients (55%). In multiple regression analysis, only donor type was significant predictor of flare in cGvHD. UCBT was associated with 2-fold lower probability of flaring (HR 0.5; 95% CI: 0.3-0.9; p=0.03) compared to MSD. cGvHD severity, organ involvement, platelet count at diagnosis and type of onset were not significant predictors of cGvHD flares. At 2 years after the initial flare, the OS was 77% (95% CI: 66-84%) and NRM 19% (95% CI: 11-28%). Multiple regression analysis evaluating OS and NRM from onset of cGvHD comparing flare to non-flare were performed using flare as a time dependent variable. Compared to cGvHD patients without flare at 2 years, those with flare of cGvHD had a similar risk of NRM (HR 1.2; 95% CI: 0.2-6.1, p=0.86) and OS (HR 0.9; 95% CI: 0.4-2.3, p=0.85). At 2 years from cGvHD onset, the cumulative incidence of resolved cGvHD (durable discontinuation of steroids for ≥ 6 consecutive months) was 31% (95% CI: 21-41%) in those who flared vs. 86% (95% CI: 75-96%) in those without flare. Conclusions: Though cGvHD patients with flare had similar risk of NRM and OS as those without a flare, patients with flare required extended steroids, along with clinical monitoring and intensified IST. cGvHD after UCBT was associated with significantly lower risk of flaring compared to MSD. The ongoing burden of IST, risk of infection and morbidity of cGvHD is substantial and needs better approaches than chronic slow taper of steroids. Disclosures Weisdorf: Incyte: Research Funding; FATE Therapeutics: Consultancy. Wagner:Novartis: Research Funding; Rocket Pharmaceuticals, Inc.: Consultancy, Current equity holder in publicly-traded company; Magenta Therapeutics: Consultancy, Research Funding; BlueRock: Research Funding; Gadeta: Membership on an entity's Board of Directors or advisory committees. Blazar:Fate Therapeutics Inc.: Research Funding; Childrens' Cancer Research Fund: Research Funding; BlueRock Therapeutics: Research Funding; BlueRock Therapeuetic: Consultancy; Magenta Therapeutics: Consultancy; KidsFirst Fund: Research Funding; Tmunity: Other: Co-founder. MacMillan:Mesoblast: Consultancy; Angiocrine Biosciences, Inc.: Consultancy; Equillium, Inc.: Consultancy; Talaris Therapeutics, Inc: Consultancy; Fate Therapeutics, Inc.: Consultancy. Holtan:Generon: Consultancy; BMS: Consultancy; CSL Behring: Other: Clinical trial data adjudication; Incyte: Consultancy. Brunstein:AlloVir: Other: Advisory board; Gamida: Research Funding; Astex: Research Funding; Magenta: Research Funding. Betts:Patent Pending: Patents &amp; Royalties: Dr. Betts has a pending patent WO2017058950A1: Methods of treating transplant rejection. This includes the use of JAK inhibitors. Neither he nor his institution have received payment related to claims described in the patent.. Bachanova:FATE: Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Rashidi:Synthetic Biologics: Other: DSMC member (1 trial) and related honorarium. Arora:Fate Therapeutics: Consultancy; Kadmon: Research Funding; Pharmacyclics: Research Funding; Syndax: Research Funding.

Prediction of Solar Flares and Background Fluxes of X-Ray Radiation According to Synoptic Ground-Based Observations Using Machine-Learning Models

The paper presents machine-learning models for predicting powerful solar flares and background X-ray fluxes in the range of 1–8 A. To predict solar flares for the next day, information was used on the current level of solar activity obtained from ground-based synoptic observations, such as characteristics of sunspots and radio fluxes at wavelengths of 10.7 and 5 cm, as well as the level of the background flux and the number of solar flares of the current day obtained from the GOES satellite. To predict the background fluxes of X-ray radiation, only data from ground-based telescopes were used. The high efficiency of the forecast for the next day is shown. The neural network was trained on data available since 2002.

Predictors of Flares in Patients with Rheumatoid Arthritis Who Exhibit Low Disease Activity: A Nationwide Cohort Study.

Using nationwide cohort data, this study evaluated predictors of flares in patients with rheumatoid arthritis (RA) who exhibit low disease activity (LDA) and the effects of flares on clinical outcomes. The Korean Observational Study Network for Arthritis (KORONA) registry is a nationwide Korean RA-specific cohort registry that collects data annually from 5.077 patients, with RA in 23 centers across South Korea. This study used data from 1.717 patients with RA who exhibited LDA [28–joint disease activity score (DAS28) < 3.2] at enrollment. Flares were defined as an increase in DAS28, compared with the previous value of > 1.2 or > 0.6, if the concurrent DAS28 was ≥ 3.2. Cox regression analysis was used to identify baseline predictors of flares. Of the 1.717 patients with RA, 566 (33.0%) experienced flares during the 2-year study period. An analysis of baseline characteristics of flare and non-flare groups revealed that more women and non-smokers were present in the flare group than in the non-flare group; the flare group also had higher scores on physician’s and patient’s pain and fatigue visual analogue scales (VAS) and the health assessment questionnaire (HAQ). In a multivariate analysis, physician’s VAS score, hemoglobin level, and HAQ score were significant predictors of flares. A high physician’s VAS score, low hemoglobin, and high HAQ score at baseline were significant predictors of flares in patients with RA who exhibited LDA.

Prediction performance of Hidden Markov modelling for solar flares

Solar flares are large explosions in the sun’s atmosphere. They can damage satellites and overload electrical systems. To manage that risk, finding methods of efficiently predicting future events is very important. In this paper we introduce a full-Sun flare prediction method based on the Hidden Markov modelling with two hidden states. We concentrate on the soft X-ray emission data near the minimum of solar cycle and consider two different driving dynamics for both states, namely the independent identically distributed (IID) random variables and the autoregressive (AR) processes, the latter introducing a memory structure. We compare prediction performance for the IID and AR approaches and also with a naive prediction equal to the last observation. The solar X-flux dynamics is predicted by using the day-ahead forecasts. We calculate point and interval forecasts and perform relevant statistical tests to choose the best method. It appears that the AR approach is clearly superior to the IID and naive both by means of point and interval forecasts. Moreover, it can well detect the higher state which can lead to very strong energy releases. Significant development of the model would be necessary to forecast solar flares over an entire solar cycle.

Update on the treatment of nonsystemic juvenile idiopathic arthritis including treatment-to-target: is (drug-free) inactive disease already possible?

This review concerns the outcome for nonsystemic juvenile idiopathic arthritis (JIA) with emphasis on treatment-to-target (T2T) and treatment strategies aiming at inactive disease by giving an overview of recent articles. More efficacious therapies and treatment strategies/T2T with inactive disease as target, have improved the outcome for JIA significantly. Recent studies regarding treatment strategies have shown 47-68% inactive disease after 1 year. Moreover, probability of attaining inactive disease at least once in the first year seems even higher in recent cohort-studies, reaching 80%, although these studies included relatively high numbers of oligoarticular JIA patients. However, 26-76% of patients flare upon therapy withdrawal and prediction of flares is still difficult. Remission can be achieved and sustained in (some) JIA patients, regardless of initial treatment. Cornerstone principles in the management of nonsystemic JIA treatment are early start of DMARD therapy, striving for inactive disease and T2T by close and repeated monitoring of disease activity. T2T and tight control appear to be more important than a specific drug in JIA. Next to inactive disease, it is important that patients/parents are involved in personal targets, like reduction of pain and fatigue. Future studies should focus on predictors (based on imaging-methods or biomarkers) for sustained drug-free remission and flare.

Multivariate time series dataset for space weather data analytics

We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.

Preflare very long-periodic pulsations observed in Hα emission before the onset of a solar flare

Context. Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-ray (SXR) flux. They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. Aims. In this Letter, we report a preflare-VLP event prior to the onset of an M1.1 circular-ribbon flare on 2015 October 16. It was simultaneously observed in Hα, SXR, and extreme ultraviolet (EUV) wavelengths. Methods. The SXR fluxes in 1−8 Å and 1−70 Å were recorded by the Geostationary Operational Environmental Satellite (GOES) and Extreme Ultraviolet Variability Experiment, respectively; the light curves in Hα and EUV 211 Å were integrated over a small local region, which were measured by the 1 m New Vacuum Solar Telescope and the Atmospheric Imaging Assembly (AIA), respectively. The preflare-VLP is identified as the repeat and quasi-periodic pulses in light curves during preflare phase. The quasi-periodicity can be determined from the Fourier power spectrum with Markov chain Monte Carlo-based Bayesian. Results. Seven well-developed pulses are found before the onset of an M1.1 circular-ribbon flare. They are firstly seen in the local light curve in Hα emission and then discovered in full-disk SXR fluxes in GOES 1−8 Å and ESP 1−70 Å, as well as the local light curve in AIA 211 Å. These well-developed pulses can be regarded as the preflare-VLP, which might be modulated by LRC-circuit oscillation in the current-carrying plasma loop. The quasi-period is estimated to be ∼9.3 min. Conclusions. We present the first report of a preflare-VLP event in the local Hα line and EUV wavelength, which could be considered a precursor of a solar flare. This finding should therefore prove useful for the prediction of solar flares, especially for powerful flares.

Solar Flare Forecasting Using Time Series and Extreme Gradient Boosting Ensembles

Space weather events may cause damage to several fields, including aviation, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are one of the most significant events, and refer to sudden radiation releases that can affect the Earth's atmosphere within a few hours or minutes. Therefore, it is worth designing high-performance systems for forecasting such events. Although in the literature there are many approaches for flare forecasting, there is still a lack of consensus concerning the techniques used for designing these systems. Seeking to establish some standardization while designing flare predictors, in this study we propose a novel methodology for designing such predictors, further validated with extreme gradient boosting tree classifiers and time series. This methodology relies on the following well-defined machine learning based pipeline: (i) univariate feature selection; (ii) randomized hyper-parameter optimization; (iii) imbalanced data treatment; (iv) adjustment of cut-off point of classifiers; and (v) evaluation under operational settings. To verify our methodology effectiveness, we designed and evaluated three proof-of-concept models for forecasting $\geq C$ class flares up to 72 hours ahead. Compared to baseline models, those models were able to significantly increase their scores of true skill statistics (TSS) under operational forecasting scenarios by 0.37 (predicting flares in the next 24 hours), 0.13 (predicting flares within 24-48 hours), and 0.36 (predicting flares within 48-72 hours). Besides increasing TSS, the methodology also led to significant increases in the area under the ROC curve, corroborating that we improved the positive and negative recalls of classifiers while decreasing the number of false alarms.

Elevated Urinary Neutrophil Gelatinase-Associated Lipocalin Is a Biomarker for Lupus Nephritis: A Systematic Review and Meta-Analysis.

Objective Lupus nephritis (LN) is a major and severe complication of systemic lupus erythematosus (SLE). Neutrophil gelatinase-associated lipocalin (NGAL), as a promising next-generation biomarker in clinical nephrology, has received extensive attention. However, its diagnostic performance in LN has high variability. Therefore, we performed an updated meta-analysis to further evaluate the diagnostic accuracy of urinary NGAL (uNGAL). Materials and Methods PubMed, Embase, and Cochrane Library were searched from inception to October 27, 2019. Meta-analysis was performed with a bivariate random effects model. Additionally, the summary receiver operating characteristic (SROC) curves were established. The sources of heterogeneity were explored by meta-regression, subgroup analysis, and sensitivity analysis. Publication bias was assessed using the Deeks test. Results 19 articles consisting of 21 eligible studies were included. In diagnosing LN, the estimates (95% confidence interval (CI)) were as follows: sensitivity, 0.84 (0.71-0.91); specificity, 0.91 (0.70-0.98); and the SROC-AUC value, 0.92 (0.90-0.94). In identifying active LN, the estimates were as follows: sensitivity, 0.72 (0.56-0.84); specificity, 0.71 (0.51-0.84); and the AUC value, 0.77 (0.74-0.81). With respect to predicting renal flare, the estimates were as follows: sensitivity, 0.80 (0.57-0.92); specificity, 0.67 (0.58-0.75); and the AUC value, 0.74 (0.70-0.78). For the studies to distinguish proliferative LN, the estimates were as follows: sensitivity, 0.87 (0.66-0.97), and specificity, 0.69 (0.39-0.91). Deeks' funnel plot suggested that there was no significant publication bias. Conclusions Our meta-analysis indicates that uNGAL was a useful biomarker for diagnosis, estimation of activity, and prediction of renal flare of LN. In addition, the usefulness of uNGAL to distinguish pathological types of LN needs to be further investigated.

Global Energetics of Solar Flares. XI. Flare Magnitude Predictions of the GOES Class

Abstract In this study we determine scaling relationships of observed solar flares that can be used to predict upper limits of the Geostationary Orbiting Earth Satellite (GOES)–class magnitude of solar flares. The flare prediction scheme is based on the scaling of the slowly varying potential energy E p (t), which is extrapolated in time over an interval of Δt ≤ 24 hr. The observed scaling of the dissipated energy E diss scales with the potential field energy as . In addition, the observed scaling relationship of the flare volume, , the multi-thermal energy, E th ∝ V 0.76, the flare emission measure , the EM-weighted temperature T w , and the GOES flux, , allows us then to predict an upper limit of the GOES-class flare magnitude in the extrapolated time window. We find a good correlation (cross-correlation coefficient (CCC) ≈ 0.7) between the observed and predicted GOES-class flare magnitudes (in 172 X- and M-class events). This is the first algorithm that employs observed scaling laws of physical flare parameters to predict GOES flux upper limits, an important capability that complements previous flare prediction methods based on machine-learning algorithms used in space-weather forecasting.

SAT0163 IMMUNOSUPPRESSANT WITHDRAWAL AFTER REMISSION ACHIEVEMENT IN LUPUS NEPHRITIS: EFFECT ON FLARE OCCURRENCE

Background:Whether and when immunosuppressive therapy may be safely withdrawn in patients with lupus nephritis (LN) is still poorly defined. Indeed, there is no clear agreement about the optimal duration of maintenance treatment.Objectives:We aimed at assessing the rate and predictors of flare after IS withdrawal in patients with LN in remission.Methods:Patients with systemic lupus erythematosus (SLE) (ACR criteria) and biopsy-proven LN diagnosed between 1990 and 2019, ever treated with IS and currently in follow-up were considered. IS discontinuation was defined as the complete withdrawal of any immunosuppressive drug in patients in remission. Remission was defined as normal serum creatinine, proteinuria &lt;0.5 g/24h, inactive urine sediment, and no extra-renal SLE activity (clinical SLE Disease Activity Index [c-SLEDAI]-2K=0) on a stable immunosuppressive and/or antimalarial therapy and/or on prednisone ≤5 mg/day. Flares were defined according to SLEDAI Flare Index; renal flare was defined as an increase of proteinuria &gt;0.5 g/24h requiring an increase in corticosteroid therapy or the reintroduction of IS. Predictors of a subsequent flare were analyzed by multivariate logistic regression analysis.Results:Out of 456 SLE patients regularly followed-up, 206 (45.1%) had LN and were considered in our study. Eighty-three patients (40.3%) discontinued IS after remission achievement (Table 1). After stopping therapy, patients were followed for a mean±SD of 99±77 months (range 12-378). Nineteen patients (22.8%) developed a flare after IS discontinuation, after a mean±SD follow-up of 78±68 months (range 7-312), and were re-treated; among them, 6 patients (7.2%) experienced a renal and 13 (15.6%) an extra-renal flare. Compared to patients who flared, patients in persistent IS-free remission had longer remission before IS withdrawal (51.2±31.5 vs. 29.3±16.5 months, p&lt;0.001), and continued antimalarials after IS discontinuation (p=0.005). No differences in flare occurrence according to the type of IS discontinued were found. At multivariate analysis, therapy with antimalarials was the strongest protective factor against disease flare (OR 0.06, 95% CI 0.11-0.41, p=0.004) (Table 2). At last follow-up, mean±SD SLEDAI-2K was 3.1±2.8 and 1.5±1.6 in patients who experienced or not a flare after IS discontinuation, respectively (p=0.058), Indeed, 10/19 patients (52.5%) who developed a flare re-achieved remission.Table 1.Characteristics of 83 patients with LN in remission who discontinued immunosuppressive therapy, overall and according to flare occurrenceTotal patients (83)Patients with flare (19)Patients without flare (64)P valueFemale, N(%)72 (88.7)16 (84.2)56 (87.5)nsAge at 2019, years43±1139±11.545±10.40.049SLE duration at 2019, years18±916.7±9.018.6±8.6nsSLE duration at IS discontinuation, years9.7±7.67.1±6.110.5±7.8nsTime to achieve remission, months27±3722.1±35.628.5±37.6nsRemission duration at IS discontinuation, months46±3029 ±16.551±31.5&lt;0.001IS therapy duration, years6.7±4.35.2±3.87.1±4.20.061Anti dsDNA, N(%)65 (78.3)18 (95)47 (73)0.059HCQ after IS discontinuation, N(%)67 (80.7)12 (63.1)55 (85.9)0.005IS, immunosuppressant; HCQ, hydroxychloroquineTable 2.Multivariate logistic regression: predictors of flare occurrenceDependent variable: flare occurrenceOR95% CIp valueNumber of ISs, ever3.2641.030-10.3420.044HCQ therapy after IS discontinuation0.0960.014-0.6520.017Remission duration at IS discontinuation0.9540.912-0.9970.037Cyclophosphamide, ever0.0650.008-0.5480.012IS, immunosuppressant; HCQ, hydroxychloroquineConclusion:Based on our experience, withdrawal of IS is feasible in selected patients with LN, i.e. patients who achieved stable remission and received maintenance therapy with antimalarials. Patients who experience new flares can re-achieve remission with an appropriate treatment.Disclosure of Interests: :Margherita Zen Speakers bureau: BMS, Ely Lilly, Janssen, GSK, Mariele Gatto Speakers bureau: GSK, Francesco Benvenuti: None declared, Francesca Saccon: None declared, Maddalena Larosa: None declared, Luca Iaccarino Speakers bureau: GSK, Pfizer, Janssen, Novartis, Andrea Doria Consultant of: GSK, Pfizer, Abbvie, Novartis, Ely Lilly, Speakers bureau: UCB pharma, GSK, Pfizer, Janssen, Abbvie, Novartis, Ely Lilly, BMS

SAT0162 WITHDRAWAL OF LOW-DOSE STEROIDS IN SYSTEMIC LUPUS ERYTHEMATOSUS IN REMISSION: PREDICTORS OF FLARES AND DIFFERENCE IN OUTCOMES IN SEROLOGICALLY ACTIVE CLINICALLY QUIESCENT PATIENTS

Background:According to the recent recommendations for Systemic Lupus Erythematosus (SLE), a progressive tapering until withdrawal of glucocorticoids (GC) is considered one of the main goals of SLE management (1). However, which patient may be a candidate for safe GC withdrawal has not been determined yet and a proportion of patients are kept on long-term low-dose prednisone despite clinical remission.Objectives:to evaluate the rate of low-dose GC withdrawal in SLE patients in remission and to identify predictors of flares.Methods:Eligible patients were SLE patients according to the ACR criteria (2) who were in prolonged clinical remission defined by a cSLEDAI=0 for at least 2 years and on a stable SLE treatment (immunosuppressive drugs and/or hydroxychloroquine (HCQ) and daily 5 mg prednisone). A SACQ period was defined as at least 1-year period with persistent serologic activity without clinical manifestations. Flares were defined by SELENA-SLEDAI Flare Index (3). Damage was assessed by SLICC damage index (SDI). Data were compared by the unpaired student’s t test or chi-squared test as appropriate. Predictors of flares after GC withdrawal were analyzed by Cox regression.Results:Out of 246 SLE patients registered in the Naples Lupus Clinic database, 132 eligible patients were identified. Among them, we selected 57 (43%) patients in whom a GC withdrawal was attempted. 75 (57%) patients were in the prednisone maintenance group.There were no significant differences between the two treatment groups (table 1). The proportion of patients experiencing a flare was not significantly lower in the maintenance group than in the withdrawal group (15/75 vs 16/57; p=0.28). Moreover, the proportion of patients who had an increase in the SDI at the end of follow up was similar between the two groups (14/75 vs 8/57; p=0.48). However, among the withdrawal group, the rate of flares was significantly higher in SACQ patients (10/22 vs 6/35; p=0.02), while the majority of serologically inactive patients (82%) successfully stopped GCs without subsequent flares. At Cox regression analysis (Table 2), duration of HCQ therapy and &gt;4 year remission at withdrawal were protective factors, while a SACQ disease and history of lupus nephritis (LN) increased the risk of disease flare.Table 1.Baseline characteristics of 132 patients at study entryCharacteristicsWithdrawal group (n=57)Maintenance group (n=75)p-valueFemale, no. (%)54 (94)70 (93)0.73Age, years26.7±10.128.5±11.70.37Disease duration, years8.5±2.99.1±12.90.73History of lupus nephritis, no. (%)13(22)22(29)SDI score0.40±0.70.57±0.80.26Immunosuppressive drugs, no. (%)31 (54)33(44)0.16HCQ, no. (%)52 (91)66 (88)0.06Low C3, no. (%)28 (49)41(54)0.52Increased dsDNA Ab, no. (%)11 (19)19 (25)0.41Table 2.Factors predicting lupus flares during follow-up at Cox regression analysisVariablesHR95% CIp valueSACQ2.991.08 – 8.250.03Age0.970.93 – 1.020.29Disease duration0.990.94 – 1.040.84History of LN3.381.22-9.330.01SDI score1.130.63 – 2.010.66Immunosuppressive drugs2.390.86 – 6.620.09HCQ, ever2.920.43 – 35.20.95Duration of HCQ0.840.72 – 0.980.035years remission0.120.04 – 0.390.0003Conclusion:GC withdrawal is an achievable target in SLE and may be attempted in patients in complete remission. In SACQ patients, maintenance of 5mg prednisone is superior to its withdrawal in order to prevent flares. Long-term HCQ therapy and prolonged remission can significantly reduce the risk of disease relapse after GC withdrawal.

Solar Flare Prediction Using Magnetic Field Diagnostics above the Photosphere

Abstract In this article, we present the application of the weighted horizontal gradient of magnetic field (WG M ) flare prediction method to three-dimensional (3D) extrapolated magnetic configurations of 13 flaring solar active regions (ARs). The main aim is to identify an optimal height range, if any, in the interface region between the photosphere and lower corona, where the flare onset time prediction capability of WG M is best exploited. The optimal height is where flare prediction, by means of the WG M method, is achieved earlier than at the photospheric level. 3D magnetic structures, based on potential and nonlinear force-free field extrapolations, are constructed to study a vertical range from the photosphere up to the low corona with a 45 km step size. The WG M method is applied as a function of height to all 13 flaring AR cases that are subject to certain selection criteria. We found that applying the WG M method between 1000 and 1800 km above the solar surface would improve the prediction of the flare onset time by around 2–8 hr. Certain caveats and an outlook for future work along these lines are also discussed.

AB0819 FLARES AMONG PATIENTS WITH PSORIATIC ARTHRITIS (PsA) - FREQUENCY AND IMPACT ON PATIENT OUTCOMES: REAL-WORLD SURVEY IN THE US AND EUROPE

Background:Flares in PsA, presenting as periods of acute disease activity, are thought to negatively impact patients’ lives. This has not been extensively studied in a real-world setting.Objectives:Describe flares, assess impact on quality of life and work productivity, and explore predictors.Methods:A cross-sectional survey among patients with PsA recruited by rheumatologists and dermatologists was conducted in France, Germany, Italy, Spain, UK and US. Data were collected Jun-Aug 2018 via patient record forms and patient self-complete forms. Physicians recorded flare status (in flare currently/flared in last 12 mo/longer than 12 mo or never), demographics, physician perceived severity and clinical outcomes. Patients reported quality of life [QoL] (EQ5D-5L), work productivity (WPAI), disability (HAQ-DI), pain (PsAID12 pain scale). Patients were compared by flare status using parametric or non-parametric tests. Logistic regression explored predictors of flare. Multivariate regression explored the impact of flare status on patient reported outcomes (PRO). The model was adjusted for gender, age, BMI, physician speciality.Results:Data were collected for 2,238 patients (586 US, 1,652 EU). Mean age was 48.7 years (13.2 SD), 53.8% were male. Physicians reported 7.5% were currently in flare and 22.0% had flared in the last 12 mo. Patients had experienced 2.2 mean flares in the last 12 mo (4.9 SD), lasting a mean 16.4 days (16.2 SD). Patients in flare were comparable demographically with those not; however, those in flare were less likely to work full time (43.6 vs. 59.3%, p&lt;0.01). Patients not in flare had clinically active disease (Table 1).Table 1.Clinical characteristics of patients by flare statusCurrently in flare (n=168)Flared in last 12 mo(n=492)Not flared in last 12 mo/never flared(n=1578)p-valueIn remission, n (%)4 (2.4)157 (33.2)799 (53.5)&lt;0.01*Current BSA affected, mean (SD)10.3 (12.0)6.5 (7.6)5.0 (7.7)&lt;0.01*66 SJC, mean (SD)5.4 (4.6)4.5 (7.3)2.4 (6.9)&lt;0.01*68 TJC, mean (SD)7.8 (5.8)5.5 (8.3)3.1 (5.6)&lt;0.01Physician-perceived severity, n (%)Mild35 (20.8)346 (70.3)1297 (82.2)&lt;0.01Moderate101 (60.1)139 (28.3)261 (16.5)Severe32 (19.0)7 (1.4)20 (1.3)*Calculated on available data: Total base sizes: BSA=1665; SJC=514; TJC=493; Satisfaction=931Results showed that flare status significantly impacted QoL, work productivity, disability, and pain (Table 2). Exploring predictors of flare in the last 12 mo in un-adjusted analyses showed that demographic characteristics were not predictive of flare status, however patients presenting as moderate or severe at diagnosis were at greater risk of flare. Patients who were prescribed a bDMARD at diagnosis were at lower risk (Figure 1).Table 2.Impact of flare status on PROsCurrent flare statusChange in predicted PRO valuesP valueEQ5D utility (n=933)Not in flare (ref)In flare0.83-0.23&lt;0.01EQ5D VAS (n=946)Not in flareIn flare76.1-21.3&lt;0.01WPAI % overall work impairment (n=496)Not in flareIn flare20.1+28.5&lt;0.01HAQ-DI (n=901)Not in flareIn flare0.4+0.6&lt;0.01PsAID12 pain score (n=922)Not in flareIn flare2.5+3.0&lt;0.01PRO key for worse outcome (range): EQ5D utility (0-1.0) = lower; EQ5D VAS (1-100) = lower; WPAI (0-100) = higher; HAQ-DI (0-3) = higher; PsAID12 pain (0-10) = higherFigure 1.Predictors of flaring in last 12 monthsConclusion:One third of patients surveyed were either currently in flare or had flared in the last 12 mo. Being in flare adversely impacted QoL, disability and work productivity. Flare may be predicted by overall physician-reported PsA disease severity at diagnosis.Disclosure of Interests:Ana-Maria Orbai Grant/research support from: Abbvie, Eli Lilly and Company, Celgene, Novartis, Janssen, Horizon, Consultant of: Eli Lilly; Janssen; Novartis; Pfizer; UCB. Ana-Maria Orbai was a private consultant or advisor for Sun Pharmaceutical Industries, Inc, not in her capacity as a Johns Hopkins faculty member and was not compensated for this service., William Tillett Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, UCB, Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, UCB, Suzanne Grieb Grant/research support from: Janssen, Steve Peterson Employee of: Janssen Research &amp; Development, LLC, Elizabeth Holdsworth Employee of: Adelphi Real World, Sophie Meakin Employee of: Adelphi Real World, Sara Bruce Wirta Employee of: Janssen-Cilag Sweden AB, Soumya D Chakravarty Shareholder of: Johnson &amp; Johnson, Employee of: Janssen Scientific Affairs, LLC, Laure Gossec Grant/research support from: Lilly, Mylan, Pfizer, Sandoz, Consultant of: AbbVie, Amgen, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sandoz, Sanofi-Aventis, UCB

A framework for designing and evaluating solar flare forecasting systems

ABSTRACT Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth’s atmosphere, thus leading researchers to drive efforts on their forecasting. The related literature is comprehensive and holds several systems proposed for flare forecasting. However, most techniques are tailor-made and designed for specific purposes, not allowing researchers to customize them in case of changes in data input or in the prediction algorithm. This paper proposes a framework to design, train, and evaluate flare prediction systems which present promising results. Our proposed framework involves model and feature selection, randomized hyperparameters optimization, data resampling, and evaluation under operational settings. Compared to baseline predictions, our framework generated some proof-of-concept models with positive recalls between 0.70 and 0.75 for forecasting ≥M class flares up to 96 h ahead while keeping the area under the ROC curve score at high levels.