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Patient-reported outcomes (PROs) are reports of a person’s health status that provide a global perspective of patient well-being. PROs can be classified into four primary domains: global, mental, physical, and social health. In this descriptive review, we focus on how PROs can be used in cardiac clinical trials, with an emphasis on cardiac surgical trials for patients with coronary heart disease and heart failure. We also highlight ongoing challenges and provide specific suggestions and novel opportunities to advance the field of cardiac clinical trials. Current challenges include the long-term measurement of PROs in clinical trials beyond one year, inconsistency in the choice of the outcome measures among studies, and the lack of measurement of PROs across multiple domains. Opportunities for advancement include measuring PROs using consumer health informatics tools, including returning information back to participants in formats that they can understand using visualization. Future opportunities include quantifying cohort-specific minimally clinically important differences for PROs.

Objectives An understanding of mental health symptoms during the coronavirus disease 2019 (COVID-19) pandemic is critical to ensure that health policies adequately address the mental health needs of people in the United States. The objective of this study was to examine mental health symptoms among US adults in an early stage of the COVID-19 pandemic. Methods We conducted a cross-sectional study in late March 2020 with a national sample of 963 US adults using an online research platform. Participants self-reported state of residence, psychosocial characteristics, and levels of anxiety, depression, anger, cognitive function, and fatigue in the context of COVID-19 using validated patient-reported outcomes scales in the Patient-Reported Outcome Measurement Information System measures. We used analysis of variance and multivariate linear regression to evaluate correlates of mental health symptoms. Results Overall, participants reported high levels of anxiety (mean [SD], 57.2 [9.3]) and depression (mean [SD], 54.2 [9.5]). Levels of anger, anxiety, cognitive function, depression, and fatigue were significantly higher among the Millennial Generation and Generation X (vs Baby Boomers), those with not enough or enough (vs more than enough) financial resources, females vs males), those with self-reported disability (vs no self-reported disability), and those with inadequate (vs adequate) health literacy. In adjusted models, being in Generation X and the Millennial Generation (vs Baby Boomer), having not enough or enough vs more than enough) financial resources, and having inadequate (vs adequate) health literacy were most strongly correlated with worse mental health symptoms. Conclusions Results suggest that mental health symptoms during the early stages of the COVID-19 pandemic were prevalent nationally, regardless of state of residence and especially among young, psychosocially vulnerable groups.

Aims We conducted a systematic review and meta-analysis to evaluate temporal trends in quality of life (QoL) after coronary artery bypass grafting (CABG) surgery in randomized clinical trials, and a quantitative comparison from before surgery to up to 5 years after surgery. Methods and results We searched MEDLINE, CINAHL, EMBASE, Cochrane Library, and PsycINFO from 2010 to 2020 to identify studies that included the measurement of QoL in patients undergoing CABG. The primary outcome was the Seattle Angina Questionnaire (SAQ), and secondary outcomes were the 36-item Short Form Health Survey (SF-36) and EuroQol Questionnaire (EQ-5D). We pooled the means and the weighted mean differences over the follow-up period. In the meta-analysis, 2586 studies were screened and 18 full-text studies were included. There was a significant trend towards higher QoL scores from before surgery to 1 year post-operatively for the SAQ angina frequency (AF), SAQ QoL, SF-36 physical component (PC), and EQ-5D, whereas the SF-36 mental component (MC) did not improve significantly. The weighted mean differences from before surgery to 1 year after was 24 [95% confidence interval (CI): 21.6–26.4] for the SAQ AF, 31 (95% CI: 27.5–34.6) for the SAQ QoL, 9.8 (95% CI: 7.1–12.8) for the SF-36 PC, 7.1 (95% CI: 4.2–10.0) for the SF-36 MC, and 0.1 (95% CI: 0.06–0.14) for the EQ-5D. There was no evidence of publication bias or small-study effect. Conclusion CABG had both short- and long-term improvements in disease-specific QoL and generic QoL, with the largest improvement in angina frequency.

Patient-reported outcomes provide key information about a patient’s health status and are increasingly being integrated into electronic health records as a means of engaging patients in their own care. However, there is a lack of research on how to effectively return this information in a clear and comprehensible way to a wide audience. We conducted a national survey, testing various formats of health information visualizations, and found that overall comprehension is high across all formats, particularly among line graphs, with variations based on literacy and numeracy. This study reinforces the importance of testing various formats among target populations when returning key health information to patients.

Aims The objective of the Randomized comparison of the Outcome of single vs. Multiple Arterial grafts: Quality of Life (ROMA:QOL) trial is to evaluate the impact of coronary artery bypass graft surgery (CABG) on quality of life (QOL). The primary hypothesis of ROMA:QOL is that participants in the multiple arterial graft (MAG) will report time-varying changes in QOL that will be lower in the post-operative recovery period and higher after 12 months compared to patients in the single arterial graft (SAG). The secondary hypotheses are that both groups will have improvements in symptoms at 12 months, and that compared to the SAG group, participants in the MAG group will experience better physical functioning and physical and mental health symptoms. Methods and Results An estimated 2111 participants will be enrolled from the parent ROMA trial from 13 countries. Outcome assessments include the Seattle Angina Questionnaire (SAQ) (primary outcome), Short Form-12v2, EuroQol-5D (EQ-5D)-5L, PROMIS-29, and PROMIS Neuropathic Pain measured at baseline, first post-operative visit, 6, 12, 24, 36, 48, and 60 months. The analysis for the primary outcome, the change in the SAQ from baseline to 12 months, will be compared across all time-points between the two treatment arms. Conclusion The ROMA:QOL trial will answer whether there are differences in QOL, physical and mental health symptoms overall for CABG, by MAG and SAG intervention arms, by sex, and between patients with and without diabetes.

Introduction: Symptom self-management is important in heart failure (HF) but challenging given the high prevalence of associated cognitive impairment. Visualizations may support symptom self-management by improving recognition of meaningful changes in symptoms over time. The purpose of this study was to evaluate whether visualizations of self-reported symptom status were associated with recognition of worsening symptoms (risk perception) and intention to act on worsening symptoms (behavioral intention) . Methods: We recruited hospitalized, English-speaking adults with HF from 2 inpatient cardiac units at an urban academic medical center. A professional designer developed 4 visualizations of simulated changes in self-reported symptoms (e.g., fatigue; Figure 1 ) using best practices for displaying health information to adults with cognitive impairment. Using the participants’ favorite visualization of the 4, we evaluated risk perception and behavioral intention using validated scales. We also collected demographics, health literacy, and cognitive status using the Montreal Cognitive Assessment (MoCA). Results: Participants (n=40) had an average age of 61.3 years (±12.5) and were 22% female, 52% White, and 38% Latino. More than half (55%) had low health literacy. Most (88%) had mild/moderate cognitive impairment (MoCA score < 26). The favorite visualization (selected by 42%) was the number line. Regarding risk perception, 70% reported it was very/extremely likely their HF was getting worse and 54% reported they were very/extremely worried about their HF getting worse based on the visualization. Regarding behavioral intention, most (82%) were very/extremely likely to act based on the visualization. Conclusion: Visualizations of symptoms over time communicated risk to most patients who in turn reported being more likely to act, and may be an effective tool to support symptom self-management among HF patients with mild/moderate cognitive impairment.

Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting “current” suicidal ideation from unstructured clinical notes in electronic health record (EHR) systems. Weakly supervised machine learning methods leverage imperfect labels for training, alleviating the burden of creating a large manually annotated dataset. After identifying a cohort of 600 patients at risk for suicidal ideation, we used a rule-based natural language processing approach (NLP) approach to label the training and validation notes (n = 17,978). Using this large corpus of clinical notes, we trained several statistical machine learning models—logistic classifier, support vector machines (SVM), Naive Bayes classifier—and one deep learning model, namely a text classification convolutional neural network (CNN), to be evaluated on a manually-reviewed test set (n = 837). The CNN model outperformed all other methods, achieving an overall accuracy of 94% and a F1-score of 0.82 on documents with “current” suicidal ideation. This algorithm correctly identified an additional 42 encounters and 9 patients indicative of suicidal ideation but missing a structured diagnosis code. When applied to a random subset of 5,000 clinical notes, the algorithm classified 0.46% (n = 23) for “current” suicidal ideation, of which 87% were truly indicative via manual review. Implementation of this approach for large-scale document screening may play an important role in point-of-care clinical information systems for targeted suicide prevention interventions and improve research on the pathways from ideation to attempt.

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.

Introduction Peripheral edema is a common precursor to decompensation for patients with heart failure (HF), and commonly measured with the pitting test. Video recordings of the pitting peripheral edema test can capture details of skin topography and time-evolution of tissue recovery with much greater precision than a human eye. Video-based deep learning can potentially overcome the subjectivity of the peripheral edema test and assist patients in identifying objective changes in their peripheral edema. We have previously shown that our deep learning model can accurately estimate peripheral edema grade with 98% accuracy using physical edema models. The objective of this study was to evaluate feasibility of collecting videos of peripheral edema from hospitalized patients with heart failure who have various skin colors and features for the purpose of refining the deep learning model for automated edema classification. Methods We enrolled 18 patients from February-April, 2019 from cardiac medical, surgical, and intensive care units at an urban university medical center. Patients were first screened for peripheral edema through the inpatient electronic medical record system. After meeting enrollment criteria and signing informed consent, trained personnel recorded 10 videos of the pitting test on patients with peripheral edema each day of their hospitalization using a Samsung Galaxy S9 phone. The personnel used one index finger to make a circular pit on the left and right side of three bony prominences (foot, ankle, and shin). Videos were taken freehand, under diffuse indoor light conditions and were 5 seconds to 2 minutes long, depending on the time it took for the skin/tissue to fully rebound from the pitting test. Each video was labeled independently with the edema grade by a research assistant and nurse, and downloaded from a secure endpoint (phone) to a central database for analysis. Results We collected 376 videos from 18 patients over an average of three days. Overall, the sample of participants was 16% female, 33% Black, 11% Hispanic and had grades of edema that ranged from grade 0 (12%), grade 1 (51%), grade 2 (18%), grade 3 (9%), and grade 4 (10%). The videos contained a wide variability of diverse skin tones and features to ensure that the method is robust and generalizable to a multiracial patient population (Figure 1). Discussion We established the feasibility of collecting videos of peripheral edema from hospitalized patients with HF. Deep learning holds promise for the automatic classification and objective measurement of clinical signs captured with video data.