Building an emotion regulation recommender algorithm for socially anxious individuals using contextual bandits.

OBJECTIVES: Poor emotion regulation (ER) has been implicated in many mental illnesses, including social anxiety disorder. To work towards a scalable, low-cost intervention for improving ER, we developed a novel contextual recommender algorithm for ER strategies. DESIGN: N = 114 socially anxious participants were prompted via a mobile app up to six times daily for five weeks to report their emotional state, use of 19 different ER strategies (or no strategy), physical location, and social context. Information from passive sensors was also collected. METHODS: Given the large number of ER strategies, we used two different approaches for variable reduction: (1) grouping ER strategies into categories based on a prior meta-analysis, and (2) considering only the ten most frequently used strategies. For each approach, an algorithm that recommends strategies based on one's current context was compared with an algorithm that recommends ER strategies randomly, an algorithm that always recommends cognitive reappraisal, and the person's observed ER strategy use. Contextual bandits were used to predict the effectiveness of the strategies recommended by each policy. RESULTS: When strategies were grouped into categories, the contextual algorithm was not the best performing policy. However, when the top ten strategies were considered individually, the contextual algorithm outperformed all other policies. CONCLUSIONS: Grouping strategies into categories may obscure differences in their contextual effectiveness. Further, using strategies tailored to context is more effective than using cognitive reappraisal indiscriminately across all contexts. Future directions include deploying the contextual recommender algorithm as part of a just-in-time intervention to assess real-world efficacy. PRACTITIONER POINTS: Emotion regulation strategies vary in their effectiveness across different contexts. An algorithm that recommends emotion regulation strategies based on a person's current context may one day be used as an adjunct to treatment to help dysregulated individuals optimize their in-the-moment emotion regulation. Recommending flexible use of emotion regulation strategies across different contexts may be more effective than recommending cognitive reappraisal indiscriminately across all contexts.

Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia.

Neutropenia is a common side effect of myelosuppressive chemotherapy and is associated with adverse outcomes. Early Warning Scores are used to identify at-risk patients and facilitate rapid clinical interventions. Since few Early Warning Scores have been validated in patients with neutropenia, we aimed to create predictive models and nomograms of fever, ICU transfer, and mortality in hospitalized neutropenic patients. DESIGN: Development of statistical prediction models and nomograms using data from a retrospective cohort study of hospitalized patients with neutropenia. SETTING: University of Virginia Medical Center, a tertiary-care academic medical center in Charlottesville, VA. PATIENTS: The derivation and validation cohorts included hospitalized adult patients with neutropenia who were admitted to the inpatient wards between October 2010 and January 2015, and April 2017 and April 2020, respectively. We defined neutropenia as an absolute neutrophil count of less than 500 cells/mm3. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The derivation cohort included 1,531 hospital admissions in patients with neutropenia. Fever, ICU transfer, and in-hospital mortality occurred in 955 admissions (62%), 297 admissions (19%), and 147 admissions (10%), respectively. In the derivation cohort, the internally validated area under the curves with 95% CI for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.74%20(0.67-0.84),%200.77"0.74 (0.67-0.84), 0.77 (0.67-0.86), and HYPERLINK "callto:0.95%20(0.0.87-1.0"0.95 (0.0.87-1.0), respectively. The validation cohort included 1,250 admissions in patients with neutropenia. In the validation cohort, the area under the curve (95% CI) for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.70%20(0.67-0.73),%200.78"0.70 (0.67-0.73), 0.78 (0.72-0.84), and HYPERLINK "callto:0.91%20(0.88-0.94"0.91 (0.88-0.94), respectively. Using these models, we developed clinically applicable nomograms which detected adverse events a median of 4.0-11.4 hours prior to onset. CONCLUSIONS: We created predictive models and nomograms for fever, ICU transfer, and mortality in patients with neutropenia. These models could be prospectively validated to detect high-risk patients and facilitate early clinical intervention to improve patient outcomes.