Factors Associated With Patient Engagement in Shared Decision-Making for Stroke Prevention Among Older Adults with Atrial Fibrillation

Objective: To examine the extent of, and factors associated with, patient engagement in shared decision-making (SDM) for stroke prevention among patients with atrial fibrillation (AF). Methods: We used data from the Systematic Assessment of Geriatric Elements-Atrial Fibrillation study which includes older ( \u3e /=65 years) patients with AF and a CHA2DS2-VASc \u3e /=2. Participants reported engagement in SDM by answering whether they actively participated in choosing to take an oral anticoagulant (OAC) for their condition. Multiple logistic regression was used to assess associations between sociodemographic, clinical, geriatric, and psychosocial factors and patient engagement in SDM. Results: A total of 807 participants (mean age 75 years; 48% female) on an OAC were studied. Of these, 61% engaged in SDM. Older participants ( \u3e /=80 years) and those cognitively impaired were less likely to engage in SDM, while those very knowledgeable of their AF associated stroke risk were more likely to do so than respective comparison groups. Conclusions: A considerable proportion of older adults with AF did not engage in SDM for stroke prevention with older patients and those cognitively impaired less likely to do so. Clinicians should identify patients who are less likely to engage in SDM, promote patient engagement, and foster better patient-provider communication which may enhance long-term patient outcomes

Characterizing Long COVID: Deep Phenotype of a Complex Condition

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411

Non-specific pain and 30-day readmission in acute coronary syndromes: findings from the TRACE-CORE prospective cohort

Abstract Background Patients with acute coronary syndromes often experience non-specific (generic) pain after hospital discharge. However, evidence about the association between post-discharge non-specific pain and rehospitalization remains limited. Methods We analyzed data from the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE) prospective cohort. TRACE-CORE followed patients with acute coronary syndromes for 24 months post-discharge from the index hospitalization, collected patient-reported generic pain (using SF-36) and chest pain (using the Seattle Angina Questionnaire) and rehospitalization events. We assessed the association between generic pain and 30-day rehospitalization using multivariable logistic regression (N = 787). We also examined the associations among patient-reported pain, pain documentation identified by natural language processing (NLP) from electronic health record (EHR) notes, and the outcome. Results Patients were 62 years old (SD = 11.4), with 5.1% Black or Hispanic individuals and 29.9% women. Within 30 days post-discharge, 87 (11.1%) patients were re-hospitalized. Patient-reported mild-to-moderate pain, without EHR documentation, was associated with 30-day rehospitalization (odds ratio [OR]: 2.03, 95% confidence interval [CI]: 1.14–3.62, reference: no pain) after adjusting for baseline characteristics; while patient-reported mild-to-moderate pain with EHR documentation (presumably addressed) was not (OR: 1.23, 95% CI: 0.52–2.90). Severe pain was also associated with 30-day rehospitalization (OR: 3.16, 95% CI: 1.32–7.54), even after further adjusting for chest pain (OR: 2.59, 95% CI: 1.06–6.35). Conclusions Patient-reported post-discharge generic pain was positively associated with 30-day rehospitalization. Future studies should further disentangle the impact of cardiac and non-cardiac pain on rehospitalization and develop strategies to support the timely management of post-discharge pain by healthcare providers

The Tripartite Model of Depression in Schizophrenia and Bipolar Disorder A Secondary Analysis

Abstract Models of affect, like the tripartite model, suggest that positive affect (PA) and negative affect (NA) are independent between subjects and negatively correlated within. Correlations may differ in bipolar disorder (BD) and schizophrenia. Using ecological momentary assessment (EMA) and clinical ratings, this secondary analysis evaluated the tripartite model by examining PA and NA. Two hundred eighty-one participants with BD or a psychotic disorder completed 30 days of EMA of PA and NA, and clinical raters assessed depression. PA and NA were more related between subjects and less related within subjects among participants with schizophrenia. In BD, lower momentary PA was positively associated with clinical ratings of depression, although greater momentary NA was not significantly associated with clinical ratings. In schizophrenia, the inverse was found. These results suggest that the tripartite model was not confirmed in people with schizophrenia or BD. However, PA and NA manifested associations in BD that were more congruent with population studies than in schizophrenia. These findings may have implications for clinical interventions targeting depression, PA, and NA in these populations