Loss-of-control (LOC) eating commonly develops during adolescence, and it predicts full-syndrome eating disorders and excess weight gain. Although negative emotions and emotion dysregulation are hypothesized to precede and predict LOC eating, they are rarely examined outside the self-report domain. Autonomic indices, including heart rate (HR) and heart rate variability (HRV), may provide information about stress and capacity for emotion regulation in response to stress.

We studied whether autonomic indices predict LOC eating in real-time in adolescents with LOC eating and body mass index (BMI) ⩾70th percentile. Twenty-four adolescents aged 12–18 (67% female; BMI percentile mean ± standard deviation = 92.6 ± 9.4) who reported at least twice-monthly LOC episodes wore biosensors to monitor HR, HRV, and physical activity for 1 week. They reported their degree of LOC after all eating episodes on a visual analog scale (0–100) using a smartphone.

Adjusting for physical activity and time of day, higher HR and lower HRV predicted higher self-reported LOC after eating. Parsing between- and within-subjects effects, there was a significant, positive, within-subjects association between pre-meal HR and post-meal LOC rating. However, there was no significant within-subjects effect for HRV, nor were there between-subjects effects for either electrophysiologic variable.

Findings suggest that autonomic indices may either be a marker of risk for subsequent LOC eating or contribute to LOC eating. Linking physiological markers with behavior in the natural environment can improve knowledge of illness mechanisms and provide new avenues for intervention.

To evaluate broad-spectrum intravenous antibiotic use before and after the implementation of a revised febrile neutropenia management algorithm in a population of adults with hematologic malignancies.

Quasi-experimental study.

Patients admitted between 2014 and 2018 to the Adult Malignant Hematology service of an acute-care hospital in the United States.

Aggregate data for adult malignant hematology service were obtained for population-level antibiotic use: days of therapy (DOT), C. difficile infections, bacterial bloodstream infections, intensive care unit (ICU) length of stay, and in-hospital mortality. All rates are reported per 1,000 patient days before the implementation of an febrile neutropenia management algorithm (July 2014–May 2016) and after the intervention (June 2016–December 2018). These data were compared using interrupted time series analysis.

In total, 2,014 patients comprised 6,788 encounters and 89,612 patient days during the study period. Broad-spectrum intravenous (IV) antibiotic use decreased by 5.7% with immediate reductions in meropenem and vancomycin use by 22 (P = .02) and 15 (P = .001) DOT per 1,000 patient days, respectively. Bacterial bloodstream infection rates significantly increased following algorithm implementation. No differences were observed in the use of other antibiotics or safety outcomes including C. difficile infection, ICU length of stay, and in-hospital mortality.

Reductions in vancomycin and meropenem were observed following the implementation of a more stringent febrile neutropenia management algorithm, without evidence of adverse outcomes. Successful implementation occurred through a collaborative effort and continues to be a core reinforcement strategy at our institution. Future studies evaluating patient-level data may identify further stewardship opportunities in this population.

To examine the factors that are associated with changes in depression in people with type 2 diabetes living in 12 different countries.

People with type 2 diabetes treated in out-patient settings aged 18–65 years underwent a psychiatric assessment to diagnose major depressive disorder (MDD) at baseline and follow-up. At both time points, participants completed the Patient Health Questionnaire (PHQ-9), the WHO five-item Well-being scale (WHO-5) and the Problem Areas in Diabetes (PAID) scale which measures diabetes-related distress. A composite stress score (CSS) (the occurrence of stressful life events and their reported degree of ‘upset’) between baseline and follow-up was calculated. Demographic data and medical record information were collected. Separate regression analyses were conducted with MDD and PHQ-9 scores as the dependent variables.

In total, there were 7.4% (120) incident cases of MDD with 81.5% (1317) continuing to remain free of a diagnosis of MDD. Univariate analyses demonstrated that those with MDD were more likely to be female, less likely to be physically active, more likely to have diabetes complications at baseline and have higher CSS. Mean scores for the WHO-5, PAID and PHQ-9 were poorer in those with incident MDD compared with those who had never had a diagnosis of MDD. Regression analyses demonstrated that higher PHQ-9, lower WHO-5 scores and greater CSS were significant predictors of incident MDD. Significant predictors of PHQ-9 were baseline PHQ-9 score, WHO-5, PAID and CSS.

This study demonstrates the importance of psychosocial factors in addition to physiological variables in the development of depressive symptoms and incident MDD in people with type 2 diabetes. Stressful life events, depressive symptoms and diabetes-related distress all play a significant role which has implications for practice. A more holistic approach to care, which recognises the interplay of these psychosocial factors, may help to mitigate their impact on diabetes self-management as well as MDD, thus early screening and treatment for symptoms is recommended.

Mood instability is an important problem but has received relatively little research attention. Natural language processing (NLP) is a novel method, which can used to automatically extract clinical data from electronic health records (EHRs).

To extract mood instability data from EHRs and investigate its impact on people with mental health disorders.

Data on mood instability were extracted using NLP from 27,704 adults receiving care from the South London and Maudsley NHS Foundation Trust (SLaM) for affective, personality or psychotic disorders. These data were used to investigate the association of mood instability with different mental disorders and with hospitalisation and treatment outcomes.

Mood instability was documented in 12.1% of people included in the study. It was most frequently documented in people with bipolar disorder (22.6%), but was also common in personality disorder (17.8%) and schizophrenia (15.5%). It was associated with a greater number of days spent in hospital (B coefficient 18.5, 95% CI 12.1–24.8), greater frequency of hospitalisation (incidence rate ratio 1.95, 1.75–2.17), and an increased likelihood of prescription of antipsychotics (2.03, 1.75–2.35).

Using NLP, it was possible to identify mood instability in a large number of people, which would otherwise not have been possible by manually reading clinical records. Mood instability occurs in a wide range of mental disorders. It is generally associated with poor clinical outcomes. These findings suggest that clinicians should screen for mood instability across all common mental health disorders. The data also highlight the utility of NLP for clinical research.

The authors have not supplied their declaration of competing interest.