Brief App-Based Cognitive Behavioral Therapy for Anxiety Symptoms in Psychiatric Inpatients: Feasibility Randomized Controlled Trial.

BACKGROUND: Psychiatric inpatients often have limited access to psychotherapeutic education or skills for managing anxiety, a common transdiagnostic concern in severe and acute mental illness. COVID-19-related restrictions further limited access to therapy groups on inpatient psychiatric units. App-based interventions may improve access, but evidence supporting the feasibility of their use, acceptability, and effectiveness in psychiatric inpatient settings is limited. MindShift CBT is a free app based on cognitive behavioral therapy principles with evidence for alleviating anxiety symptoms in the outpatient setting. OBJECTIVE: We aimed to recruit 24 participants from an acute general psychiatric inpatient ward to a 1-month randomized control study assessing the feasibility and acceptability of providing patients with severe and acute mental illness access to the MindShift CBT app for help with managing anxiety symptoms. METHODS: Recruitment, data collection, analysis, and interpretation were completed collaboratively by clinician and peer researchers. Inpatients were randomized to two conditions: treatment as usual (TAU) versus TAU plus use of the MindShift CBT app over 6 days. We collected demographic and quantitative data on acceptability and usability of the intervention. Symptoms of depression, anxiety, and psychological distress were measured in pre- and poststudy surveys for preliminary signals of efficacy. We conducted individual semistructured interviews with participants in the MindShift CBT app group at the end of their trial period, which were interpreted using a standardized protocol for thematic analysis. RESULTS: Over 4 weeks, 33 inpatients were referred to the study, 24 consented to participate, 20 were randomized, and 11 completed the study. Of the 9 randomized participants who did not complete the study, 7 were withdrawn because they were discharged or transferred prior to study completion, with a similar distribution among both conditions. Among the enrolled patients, 65% (13/20) were admitted for a psychotic disorder and no patient was admitted primarily for an anxiety disorder. The average length of stay was 20 days (SD 4.4; range 3-21) and 35% (7/20) of patients were involuntarily admitted to hospital. Small sample sizes limited accurate interpretation of the efficacy data. Themes emerging from qualitative interviews included acceptability and usability of the app, and patient agency associated with voluntary participation in research while admitted to hospital. CONCLUSIONS: Our study benefitted from collaboration between peer and clinician researchers. Due to rapid patient turnover in the acute inpatient setting, additional flexibility in recruitment and enrollment is needed to determine the efficacy of using app-based psychotherapy on an acute psychiatric ward. Despite the limited sample size, our study suggests that similar interventions may be feasible and acceptable for acutely unwell inpatients. Further study is needed to compare the efficacy of psychotherapeutic apps with existing standards of care in this setting. TRIAL REGISTRATION: ClinicalTrials.gov NCT04841603; https://clinicaltrials.gov/ct2/show/NCT04841603.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Effect of the Coronavirus Disease 2019 Pandemic on the Quality of Stroke Care in Stroke Units and Alternative Wards: A National Comparative Analysis.

BACKGROUND AND PURPOSE: Changes to hospital systems were implemented from March 2020 in Australia in response to the coronavirus disease 2019 pandemic, including decreased resources allocated to stroke units. We investigate changes in the quality of acute care for patients with stroke or transient ischemic attack during the pandemic according to patients' treatment setting (stroke unit or alternate ward). METHODS: We conducted a retrospective cohort study of patients admitted with stroke or transient ischemic attack between January 2019 and June 2020 in the Australian Stroke Clinical Registry (AuSCR). The AuSCR monitors patients' treatment setting, provision of allied health and nursing interventions, prescription of secondary prevention medications, and discharge destination. Weekly trends in the quality of care before and during the pandemic period were assessed using interrupted time series analyses. RESULTS: In total, 18,662 patients in 2019 and 8,850 patients in 2020 were included. Overall, 75% were treated in stroke units. Before the pandemic, treatment in a stroke unit was superior to alternate wards for the provision of all evidence-based therapies assessed. During the pandemic period, the proportion of patients receiving a swallow screen or assessment, being discharged to rehabilitation, and being prescribed secondary prevention medications decreased by 0.58% to 1.08% per week in patients treated in other ward settings relative to patients treated in stroke units. This change represented a 9% to 17% increase in the care gap between these treatment settings during the period of the pandemic that was evaluated (16 weeks). CONCLUSIONS: During the first 6 months of the pandemic, widening care disparities between stroke units and alternate wards have occurred.

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19.

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January-September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7-7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7-10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19-25%), cerebrovascular diseases (24%, 13-35%), nontraumatic intracranial hemorrhage (34%, 20-50%), encephalitis and/or myelitis (37%, 17-60%) and myopathy (72%, 67-77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.

COVID-19 Pandemic Impact on Care for Stroke in Australia: Emerging Evidence From the Australian Stroke Clinical Registry.

We present information on acute stroke care for the first wave of the COVID-19 pandemic in Australia using data from the Australian Stroke Clinical Registry (AuSCR). The first case of COVID-19 in Australia was recorded in late January 2020 and national restrictions to control the virus commenced in March. To account for seasonal effects of stroke admissions, patient-level data from the registry from January to June 2020 were compared to the same period in 2019 (historical-control) from 61 public hospitals. We compared periods using descriptive statistics and performed interrupted time series analyses. Perceptions of stroke clinicians were obtained from 53/72 (74%) hospitals participating in the AuSCR (80% nurses) via a voluntary, electronic feedback survey. Survey data were summarized to provide contextual information for the registry-based analysis. Data from the registry covered locations that had 91% of Australian COVID-19 cases to the end of June 2020. For the historical-control period, 9,308 episodes of care were compared with the pandemic period (8,992 episodes). Patient characteristics were similar for each cohort (median age: 75 years; 56% male; ischemic stroke 69%). Treatment in stroke units decreased progressively during the pandemic period (control: 76% pandemic: 70%, p < 0.001). Clinical staff reported fewer resources available for stroke including 10% reporting reduced stroke unit beds. Several time-based metrics were unchanged whereas door-to-needle times were longer during the peak pandemic period (March-April, 2020; 82 min, control: 74 min, p = 0.012). Our data emphasize the need to maintain appropriate acute stroke care during times of national emergency such as pandemic management.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.