Paralization and online adaptation of an ongoing clinical trial in La Paz University Hospital (Madrid, Spain) during the COVID-19 worldwide pandemic

Introduction

Spain went into lockdown in March of 2020 due to the COVID-19 outbreak. We had to stop the third randomization of our ongoing clinical trial (Mediavilla et al., 2019), pausing weekly group psychotherapy for 12 people with a first episode of psychosis. Only 5 weekly sessions had been delivered, thus many were just starting to form a therapeutic link with the group. In a public health emergency context, psychotherapeutic groups are considered avoidable gatherings. However, stopping psychological therapy abruptly can make participants more vulnerable. The intervention groups were launched in an online format because we could not let anyone go without psychological support in such a difficult time.

Objectives

Communicate how we adapted an ongoing clinical trial to an online format during the lockdown in Spain.

Methods

In light of our participants’ needs and their acute deterioration the first two weeks of lockdown, we adapted our intervention. First, both arms (mindfulness-based v. psychoeducational multicomponent intervention) began online adaptations of the interventions. Second, a research assistant made weekly phone calls to provide basic psychosocial support, assure participants groups would continue, and later remind them of each online session. Third and last, the phoneline was accessible 24/7 (WhatsApp).

Results

The third randomization concluded in December. Six participants were lost in the transition to online groups. However, adherence was comparable to the previous two randomizations (4/12 completed the intervention).

Conclusions

Online psychotherapy may be used in emergencies such as a lockdown. However, the psychological mid- and long-term effects of a lockdown and online group therapy remain unknown.

Disclosure

No significant relationships.

Mental health service requirements after hospitalization due to COVID-19: a 1- year follow-up study

Introduction

Long-term COVID-19 effects has been recently described as persistent and prolonged symptoms after an acute and severe SARS-COV-2 (1). An important concern is that the sequelae of severe COVID-19 may suppose a substantial outpatient 's burden for the specialized services in reopening pandemic phase (2).

Objectives

To describe the frequency of mental health service use in COVID-19 hospitalized patients after discharge and to estimate the costs associated to the post- discharge consultations.

Methods

We used a 1-year follow-up cohort of 1455 COVID-19 inpatients hospitalized in La Paz University Hospital of Madrid, Spain between March 16th and April 15th, 2020. Data were retrieved from Psychiatry Service (PS) electronic health records and we described the frequency of mental health reason for consultation. We used information published by the Madrid health Office to estimate the cost of initial and following appointments.

Results

Our sample consisted of 1,455 patients admitted with a COVID-19 diagnosis between March 16th and April 15th, 2020, and then discharged. Roughly half of them were men (776, 53%), 238 (16%) had a prior history of mental health problems, and 44 (3%) died. 193 participants (13%) visited the mental health department after being discharged. The total cost was estimated in 103,581 USD, of which two-thirds corresponded to patients with prior history of mental health problems.

Conclusions

Our results indicate that the mental health burden of severe COVID-19 inpatient s after discharge was substantial during the first year of follow-up. This generate important economic impact to mental health providers and society at large.

Disclosure

No significant relationships.

Image estimation using doubly stochastic gaussian random field models

The two-dimensional (2-D) doubly stochastic Gaussian (DSG) model was introduced by one of the authors to provide a complete model for spatial filters which adapt to the local structure in an image signal. Here we present the optimal estimator and 2-D fixed-lag smoother for this DSG model extending earlier work of Ackerson and Fu. As the optimal estimator has an exponentially growing state space, we investigate suboptimal estimators using both a tree and a decision-directed method. Experimental results are presented.