Daily Telephone Call During the COVID-19 Pandemic: Perceptions of Families and Providers.

BACKGROUND: In intensive care units (ICUs), the quality of communication with families is a key point in the caregiver-patient-family relationship. During the COVID-19 pandemic, hospital visits were prohibited, and many ICUs implemented a daily telephone call strategy to ensure continuity of communication with patients' families. OBJECTIVE: To assess how family members and health care providers perceived this communication strategy. METHODS: The study was conducted in a 45-bed ICU during the COVID-19 pandemic. Communication with families consisted of a single daily telephone call from the senior physician in charge of the patient to the patient's surrogate decision maker. Satisfaction was qualitatively assessed via an anonymous online questionnaire with open-ended questions. RESULTS: Participants completed 114 questionnaires. Forty-six percent of surrogate decision makers stated that the key medical messages were understandable, but 57% of other family members expressed that the frequency of information delivery was insufficient. Fifty-six percent of the physicians described the practice as functional for the organization of the unit. Among health care providers other than physicians, 55% felt that not having to interact with families decreased their emotional load and 50% mentioned saving time and the absence of task interruptions as positive aspects. CONCLUSION: Fixed-time, daily telephone calls in the ICU allowed satisfactory transmission of information between physicians and surrogate decision makers, as perceived by both parties. However, the telephone-based communication strategy could still be improved.

Corrigendum: Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues.

[This corrects the article DOI: 10.3389/fpsyt.2021.644980.].

Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues.

Introduction: Individual participant data meta-analyses (IPD-MAs) include the raw data from relevant randomised clinical trials (RCTs) and involve secondary analyses of the data. Performed since the late 1990s, ~50 such meta-analyses have been carried out in psychiatry, mostly in the field of treatment. IPD-MAs are particularly relevant for three objectives: (1) evaluation of the average effect of an intervention by combining effects from all included trials, (2) evaluation of the heterogeneity of an intervention effect and sub-group analyses to approach personalised psychiatry, (3) mediation analysis or surrogacy evaluation to replace a clinical (final) endpoint for the evaluation of new treatments with intermediate or surrogate endpoints. The objective is to describe the interest and the steps of an IPD-MA method applied to the field of psychiatric therapeutic research. Method: The method is described in three steps. First, the identification of the relevant trials with an explicit description of the inclusion/exclusion criteria for the RCT to be incorporated in the IPD-MA and a definition of the intervention, the population, the context and the relevant points (outcomes or moderators). Second, the data management with the standardisation of collected variables and the evaluation and the assessment of the risk-of-bias for each included trial and of the global risk. Third, the statistical analyses and their interpretations, depending on the objective of the meta-analysis. All steps are illustrated with examples in psychiatry for treatment issues, excluding study protocols. Conclusion: The meta-analysis of individual patient data is challenging. Only strong collaborations between all stakeholders can make such a process efficient. An "ecosystem" that includes all stakeholders (questions of interest prioritised by the community, funders, trialists, journal editors, institutions, …) is required. International medical societies can play a central role in favouring the emergence of such communities.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

OBJECTIVES: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. METHOD: We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. RESULT: We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. CONCLUSIONS: Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.