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Abstract Background Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program. Methods This analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model. Results A total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only. Conclusions Baseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction. Trial registration ClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

ABSTRACT Objective: Tofacitinib, an oral Janus kinase inhibitor, has been tested against a placebo in 289 patients with COVID-19 pneumonia. We analyzed the data from the tofacitinib- and placebo-treated patient cohorts to evaluate the laboratory profiles between baseline and day 7. Methods: We performed post hoc analyses on the following laboratory tests over time during the first 7 days after randomization: hemoglobin, leukocytes, neutrophils, lymphocytes, platelets, alanine aminotransferase, and aspartate aminotransferase. Results: Through the first 7 days after randomization, the levels of hemoglobin, white blood cells, neutrophils, and platelet counts were not significantly different between patients treated with tofacitinib or a placebo (all p>0.05). Non-significant differences were observed in aspartate aminotransferase levels over time between treatment groups, whereas alanine aminotransferase levels (U/L) were higher among tofacitinib-treated patients compared to placebo-treated patients (mean ratio, 1.30 [95% confidence interval (95%CI) = 1.14–1.48; p

Abstract Background and Aim The Global Ulcerative Colitis (UC) Narrative Survey aimed to evaluate the impact of UC, perceptions of UC burden, and management approaches. Here, we present data from patients and physicians in Australia. Methods Surveys, fielded by The Harris Poll, were completed by 215 patients with UC and 90 physicians, between August 2017 and February 2018. Surveys included questions on disease characteristics, impact on life, communication with physicians, and patient knowledge of UC. Results are presented descriptively from all respondents (with no imputation for missing data). Results Based on medication history, 84% of patients had moderate to severe UC. Diagnostic delay was on average 1.9 (SD 5.0) years and 48% of patients had waited ≥1 year for diagnosis. Nearly two‐thirds (65%) of patients considered themselves to be in remission, with 97% also reporting a flare in the past year. The majority (92%) of patients were satisfied with their UC medication and, if their treatment made them feel “good enough,” many (75%) would not consider an alternative. Most (90%) patients were satisfied with communication with their physician; however, only 48% felt comfortable raising emotional concerns. Both patients and physicians desired more time during routine appointments. Patients had gaps in their knowledge of UC, which physicians mostly recognized. Conclusions The Australian survey results highlighted the diagnostic delay and burden of UC patients' experience, gaps in patients' knowledge of UC, and challenges in patient–physician communication. Compared with the overall Global UC Narrative Survey, patients in Australia reported a high burden of disease.

Introduction: Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). Outcome prediction based on early treatment response, along with clinical and laboratory variables, would be very useful for clinical practice. The aim of this study was to determine early variables predictive of responder status in patients with UC treated with tofacitinib. Methods: Data were collected from patients treated with tofacitinib 10 mg twice daily in the OCTAVE Induction 1 and 2 studies (NCT01465763 and NCT01458951). Logistic regression and random forest analyses were performed to determine the power of clinical and/or laboratory variables to predict 2- and 3-point partial Mayo score responder status of patients at Weeks 4 or 8 after baseline. Results: From a complete list of variables measured in OCTAVE Induction 1 and 2, analyses identified partial Mayo score, partial Mayo subscore (stool frequency, rectal bleeding, and Physician Global Assessment), cholesterol level, and C-reactive protein level as sufficient variables to predict responder status. Using these variables at baseline and Week 2 predicted responder status at Week 4 with 84–87% accuracy and Week 8 with 74–79% accuracy. Variables at baseline, Weeks 2 and 4 could predict responder status at Week 8 with 85–87% accuracy. Conclusion: Using a limited set of time-dependent variables, statistical and machine learning models enabled early and clinically meaningful predictions of tofacitinib treatment outcomes in patients with moderately to severely active UC.

ABSTRACT: Background: Concerns have been raised that the risk of severe acute respiratory syndrome coronavirus 2 infection, or more severe or critical coronavirus disease 2019 (COVID-19), may be higher in immunocompromised individuals receiving immunomodulatory therapies compared with immunocompetent individuals. Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis, psoriatic arthritis, ulcerative colitis, and polyarticular course juvenile idiopathic arthritis. To date, data on tofacitinib treatment during the COVID-19 pandemic are limited. Objectives: To summarize current understanding of the use of tofacitinib in adults during the COVID-19 pandemic, and discuss research questions that are yet to be addressed, to further inform the safe and effective use of tofacitinib in clinical practice. Methods: We conducted a review of the literature (as of February 2021), to summarize the expert recommendations for the management of rheumatoid arthritis, psoriatic arthritis, and ulcerative colitis in the context of COVID-19, and to assess the current data regarding the use of tofacitinib in adult patients during the pandemic. Results: Current recommendations for rheumatoid arthritis, psoriatic arthritis, and ulcerative colitis state that tofacitinib treatment should be continued during the pandemic, except in cases of positive or presumed severe acute respiratory syndrome coronavirus 2 infection. However, limited data are available; analyses of data from international rheumatology and gastroenterology registries have suggested that tofacitinib may not be associated with an increased risk of hospitalization or treatment switching in adults with COVID-19. Conclusions: Further assessment of tofacitinib use in patients with rheumatoid arthritis, psoriatic arthritis, or ulcerative colitis will be required to elucidate and establish the benefit:risk profile of tofacitinib during the current COVID-19 pandemic.

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.

The development of UAV-based sensing systems for agronomic applications serves the improvement of crop management. The latter is in the focus of precision agriculture which intends to optimize yield, fertilizer input, and crop protection. Besides, in some cropping systems vehicle-based sensing devices are less suitable because fields cannot be entered from certain growing stages onwards. This is true for rice, maize, sorghum, and many more crops. Consequently, UAV-based sensing approaches fill a niche of very high resolution data acquisition on the field scale in space and time. While mounting RGB digital compact cameras to low-weight UAVs (< 5 kg) is well established, the miniaturization of sensors in the last years also enables hyperspectral data acquisition from those platforms. From both, RGB and hyperspectral data, vegetation indices (VIs) are computed to estimate crop growth parameters. In this contribution, we compare two different sensing approaches from a low-weight UAV platform (< 5 kg) for monitoring a nitrogen field experiment of winter wheat and a corresponding farmers’ field in Western Germany. (i) A standard digital compact camera was flown to acquire RGB images which are used to compute the RGBVI and (ii) NDVI is computed from a newly modified version of the Yara N-Sensor. The latter is a well-established tractor-based hyperspectral sensor for crop management and is available on the market since a decade. It was modified for this study to fit the requirements of UAV-based data acquisition. Consequently, we focus on three objectives in this contribution: (1) to evaluate the potential of the uncalibrated RGBVI for monitoring nitrogen status in winter wheat, (2) investigate the UAV-based performance of the modified Yara N-Sensor, and (3) compare the results of the two different UAV-based sensing approaches for winter wheat.