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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.

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Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.

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INTRODUCTION: Tofacitinib is an oral small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). This post hoc analysis assessed whether various statistical techniques could predict outcomes of tofacitinib maintenance therapy in patients with UC. METHODS: Data from patients who participated in a 52-week, phase III maintenance study (OCTAVE Sustain) and an open-label long-term extension study (OCTAVE Open) were included in this analysis. Patients received tofacitinib 5 or 10 mg twice daily (BID) or placebo (OCTAVE Sustain only). Logistic regression analyses were performed to generate models using clinical and laboratory variables to predict loss of responder status at week 8 of OCTAVE Sustain, steroid-free remission (defined as a partial Mayo score of 0-1 in the absence of corticosteroid use) at week 52 of OCTAVE Sustain, and delayed response at week 8 of OCTAVE Open. Furthermore, differences in loss of response/discontinuation patterns between treatment groups in OCTAVE Sustain were established. RESULTS: The generated prediction models demonstrated insufficient accuracy for determining loss of response at week 8, steroid-free remission at week 52 in OCTAVE Sustain, or delayed response in OCTAVE Open. Both tofacitinib doses demonstrated comparable response/remission patterns based on visualizations of disease activity over time. The rectal bleeding subscore was the primary determinant of disease worsening (indicated by an increased total Mayo score), and the endoscopy subscore was the primary determinant of disease improvement (indicated by a decreased total Mayo score). CONCLUSION: Visualizations of disease activity subscores revealed distinct patterns among patients with UC that had disease worsening and disease improvement. The statistical models assessed in this analysis could not accurately predict loss of responder status, steroid-free remission, or delayed response to tofacitinib. Possible reasons include the small sample size or missing data related to yet unknown key variables that were not collected during these trials.

Doctors use tofacitinib (Xeljanz^®) to treat people with moderate to severe ulcerative colitis. Patients who respond to (have improved symptoms following) treatment with tofacitinib 10 mg twice a day for 8 weeks, or up to 16 weeks if they do not respond initially (known as induction treatment), can receive tofacitinib treatment at the lowest effective dose to sustain their response (called maintenance treatment). Predicting how patients respond to tofacitinib maintenance treatment may help clinicians work out the lowest effective dose for each patient. In this study, data from the tofacitinib clinical trials were used to assess the ability to predict maintenance therapy response or failure in patients with ulcerative colitis. Differences between patients who received tofacitinib 5 or 10 mg twice a day and who either stopped responding to treatment or stopped taking treatment were looked at. The study could not accurately predict which patients would experience disease worsening, steroid-free remission (very mild or no symptoms, and not taking steroids), or take longer to respond following tofacitinib maintenance treatment. Patterns of patients who had stopped responding to treatment, or stopped taking treatment, were similar between patients who received tofacitinib 5 or 10 mg twice daily. When reviewed using doctor- and patient-reported scores that measure ulcerative colitis disease activity, different factors were important in patients with disease worsening compared with disease improvement. The results suggest that further research is needed to more accurately predict how patients with ulcerative colitis will respond to tofacitinib maintenance treatment.

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In March 2020, the COVID-19 virus turned into a pandemic that hit organizations globally. This pandemic qualifies as an exogenous shock. Based on the threat-rigidity hypothesis, we hypothesize that this shock led to an increase in directive leadership behavior. We also argue that this relationship depends on the magnitude of the crisis and on well-learned responses of managers. In our empirical analysis we employ a differences-in-differences design with treatment intensity and focus on the period of the first lockdown, March until June 2020. Using a dataset covering monthly data for almost 27,000 managers across 48 countries and 32 sectors for January 2019 to December 2020, we find support for the threat-rigidity hypothesis. During the first lockdown, directive leadership increased significantly. We also find that this relationship is moderated by COVID-19 deaths per country, the sectoral working from home potential, and the organizational level of management. Our findings provide new evidence how large exogenous shocks like COVID-19 can impact leadership behavior.

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The behavioral approach to leadership, which has introduced leadership styles, has been of great importance to the leadership field. Despite its importance, scholars have recently argued and demonstrated that these styles have various conceptual, methodological, and empirical limitations that could hamper further development of the leadership field. Consequently, they have called for alternative approaches to study leadership. We argue that taking a configurational or person-oriented approach to leadership behavior, which focuses on ideal-type configurations of leadership behaviors to identify leadership archetypes, offers such an alternative. We demonstrate the potential of such an approach via the use of archetypal analysis, for a dataset of 46 behaviors across 6 leadership styles, including more than 150,000 respondents. Our results offer a clear indication for the existence of archetypes of leadership. We also suggest how the resulting archetypes can get a meaningful interpretation, and discuss implications for future research.

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Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

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