Essential Articles on Collaborative Care Models for the Treatment of Psychiatric Disorders in Medical Settings: A Publication by the Academy of Psychosomatic Medicine Research and Evidence-Based Practice Committee

Background: Collaborative care interventions for psychiatric disorders combine several components integrated into the medical setting: (1) systematic psychiatric assessment, (2) use of a non physician care manager to perform longitudinal symptom monitoring, treatment interventions, and care coordination, and (3) specialist-provided stepped-care recommendations. Collaborative care interventions have now been evaluated in a wide spectrum of care settings and offer great promise as a way of increasing quality of patient care, improving health of populations, and reducing health care costs. Methods: A systematic search of PubMedl MEDLINE databases was performed for publications between January 1970 and May 2013 to identify articles describing collaborative care and related interventions. Identified articles were then evaluated independently by multiple reviewers for quality and importance; additional articles were identified by searching reference lists and through recommendations of senior content-matter experts. The articles considered to be both of high quality and most important were then placed into categories and annotated reviews performed. Results: Over 600 articles were identified of which 67 were selectedfor annotated review. The results reported in these articles indicate that collaborative care interventions for psychiatric disorders have been consistently successful in improving key outcomes in both research and clinical intervention studies; cost analyses also suggest that this model is cost effective. Conclusions: Collaborative care models for psychiatric disorders are likely to serve an increasingly large role in health care given their effect on patient and population outcomes and their focus on integration of care

Assessment of psychiatric comorbidities and serotonergic or noradrenergic medication use on blood pressure using 24‐hour ambulatory blood pressure monitoring

Abstract In this study, the authors aimed to assess both nighttime and daytime blood pressure (BP) variability using 24‐hour ambulatory BP monitoring (ABPM) in persons with and without psychiatric conditions and with or without selective serotonin reuptake inhibitors (SSRIs) or serotonin‐norepinephrine reuptake inhibitors (SNRIs) treatment. In this retrospective study, patients who underwent psychiatric evaluation and ABPM within 6 months of each other between January 1, 2012 and December 31, 2017 were identified using billing data. Participants were divided into three groups—participants with no psychiatric diagnosis and no psychiatric medicine (−Diagnosis/−Medication), those with psychiatric diagnosis and on SSRIs/SNRIs (+Diagnosis/+Medication), and psychiatric diagnosis but no psychiatric medications (+Diagnosis/−Medication). Day and nighttime systolic and diastolic BPs were compared between groups controlling for relevant variables using multivariable linear regression models. A total of 475 participants met inclusion criteria including 135 in the −Diagnosis/−Medication group, 232 in the +Diagnosis/+Medication group, and 108 in the +Diagnosis/−Medication group. In adjusted multivariable analysis, the +Diagnosis/+Medication group had higher nighttime systolic BP (median 120 vs 110 mm (Hg); p = .01) and nighttime diastolic BP (median 68 vs 63 mm (Hg); p = .006) as compared to −Diagnosis/−Medication. No statistically significant differences in BPs between the −Diagnosis/−Medication and +Diagnosis/−Medication groups were observed, after adjustment. Use of SSRIs/SNRIs was associated with significantly higher nocturnal systolic and diastolic BP among patients with psychiatric diagnosis using SSRIs/SNRIs but not associated with psychiatric diagnosis without SSRI/SNRI use. SSRIs/SNRIs use may be associated with higher BP levels and this merits future prospective studies using ABPM to assess day and nighttime BP changes with SSRIs/SNRIs use

Neurophenotypes of COVID-19: Risk factors and recovery outcomes

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this “normal cognition” phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment