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Just-in-time adaptive interventions (JITAIs) are a novel approach to mobile health (mHealth) interventions, sending contextually tailored behavior change notifications to participants when they are more likely to engage, determined by data from wearable devices. We describe a community participatory approach to JITAI notification development for the myBPmyLife Project, a JITAI focused on decreasing sodium consumption and increasing physical activity to reduce blood pressure. Eighty-six participants were interviewed, 50 at a federally qualified health center (FQHC) and 36 at a university clinic. Participants were asked to provide encouraging physical activity and low-sodium diet notifications and provided feedback on researcher-generated notifications to inform revisions. Participant notifications were thematically analyzed using an inductive approach. Participants noted challenging vocabulary, phrasing, and culturally incongruent suggestions in some of the researcher-generated notifications. Community-generated notifications were more direct, used colloquial language, and contained themes of grace. The FQHC participants' notifications expressed more compassion, religiosity, and addressed health-related social needs. University clinic participants' notifications frequently focused on office environments. In summary, our participatory approach to notification development embedded a distinctive community voice within our notifications. Our approach may be generalizable to other communities and serve as a model to create tailored mHealth notifications to their focus population. [ABSTRACT FROM AUTHOR]

Fewer than 10% of individuals who suffer out-of-hospital cardiac arrest (OHCA) survive with good neurologic function. Bystander CPR more than doubles the chance of survival, and telecommunicator-CPR (T-CPR) during a 9-1-1 call substantially improves the frequency of bystander CPR. We examined the barriers to initiation of T-CPR. We analyzed the 9-1-1 call audio from 65 EMS-treated OHCAs from a single US 9-1-1 dispatch center. We initially conducted a thematic analysis aimed at identifying barriers to the initiation of T-CPR. We then conducted a conversation analysis that examined the interactions between telecommunicators and bystanders during the recognition phase (i.e., consciousness and normal breathing). We identified six process themes related to barriers, including incomplete or delayed recognition assessment, delayed repositioning, communication gaps, caller emotional distress, nonessential questions and assessments, and caller refusal, hesitation, or inability to act. We identified three suboptimal outcomes related to arrest recognition and delivery of chest compressions, which are missed OHCA identification, delayed OHCA identification and treatment, and compression instructions not provided following OHCA identification. A primary theme observed during missed OHCA calls was incomplete or delayed recognition assessment and included failure to recognize descriptors indicative of agonal breathing (e.g., "snoring", "slow") or to confirm that breathing was effective in an unconscious victim. We observed that modifiable barriers identified during 9-1-1 calls where OHCA was missed, or treatment was delayed, were often related to incomplete or delayed recognition assessment. Repositioning delays were a common barrier to the initiation of chest compressions. [ABSTRACT FROM AUTHOR]

Purpose: Distinguishing obstructive epicardial coronary artery disease (CAD) from microvascular dysfunction and diffuse atherosclerosis would be of immense benefit clinically. However, quantitative measures of absolute myocardial blood flow (MBF) integrate the effects of focal epicardial stenosis, diffuse atherosclerosis, and microvascular dysfunction. In this study, MFR and relative perfusion quantification were combined to create integrated MFR (iMFR) which was evaluated using data from a large clinical registry and an international multi-center trial and validated against invasive coronary angiography (ICA). Methods: This study included 1,044 clinical patients referred for 82Rb rest/stress positron emission tomography myocardial perfusion imaging and ICA, along with 231 patients from the Flurpiridaz 301 trial (clinicaltrials.gov NCT01347710). MFR and relative perfusion quantification were combined to create an iMFR map. The incremental value of iMFR was evaluated for diagnosis of obstructive stenosis, adjusted for patient demographics and pre-test probability of CAD. Models for high-risk anatomy (left main or three-vessel disease) were also constructed. Results: iMFR parameters of focally impaired perfusion resulted in best fitting diagnostic models. Receiver-operating characteristic analysis showed a slight improvement compared to standard quantitative perfusion approaches (AUC 0.824 vs. 0.809). Focally impaired perfusion was also associated with high-risk CAD anatomy (OR 1.40 for extent, and OR 2.40 for decreasing mean MFR). Diffusely impaired perfusion was associated with lower likelihood of obstructive CAD, and, in the absence of transient ischemic dilation (TID), with lower likelihood of high-risk CAD anatomy. Conclusions: Focally impaired perfusion extent derived from iMFR assessment is a powerful incremental predictor of obstructive CAD while diffusely impaired perfusion extent can help rule out obstructive and high-risk CAD in the absence of TID. [ABSTRACT FROM AUTHOR]

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines recommend that older patients (65+) with mild cognitive impairment (MCI) and early-stage dementia receive similar guideline-concordant care after cardiovascular disease (CVD) events as those with normal cognition (NC). However, older patients with MCI and dementia receive less care for CVD and other conditions than those with NC. Whether physician recommendations for guideline-concordant treatments after two common CVD events, acute myocardial infarction (AMI) and acute ischemic stroke (stroke), differ between older patients with NC, MCI, and early-stage dementia is unknown. Objective: To test the influence of patient cognitive status (NC, MCI, early-stage dementia) on physicians' recommendations for guideline-concordant treatments for AMI and stroke. Design: We conducted two parallel, randomized survey studies for AMI and stroke in the US using clinical vignettes where the hypothetical patient's cognitive status was randomized between physicians. Participants: The study included cardiologists, neurologists, and generalists who care for most patients hospitalized for AMI and stroke. Main Measures: The primary outcome was a composite quality score representing the number of five guideline-concordant treatments physicians recommended for a hypothetical patient after AMI or stroke. Key Results: 1,031 physicians completed the study (58.5% response rate). Of 1,031 respondents, 980 physicians had complete information. After adjusting for physician factors, physicians recommended similar treatments after AMI and stroke in hypothetical patients with pre-existing MCI (adjusted ratio of expected composite quality score, 0.98 [95% CI, 0.94, 1.02]; P = 0.36) as hypothetical patients with NC. Physicians recommended fewer treatments to hypothetical patients with pre-existing early-stage dementia than to hypothetical patients with NC (adjusted ratio of expected composite quality score, 0.90 [0.86, 0.94]; P < 0.001). Conclusion: In these randomized survey studies, physicians recommended fewer guideline-concordant AMI and stroke treatments to hypothetical patients with early-stage dementia than those with NC. We did not find evidence that physicians recommend fewer treatments to hypothetical patients with MCI than those with NC. [ABSTRACT FROM AUTHOR]

Rationale: Acute pulmonary embolism is a leading cause of cardiovascular death. There are limited data on the national mortality trends from pulmonary embolism. Understanding these trends is crucial for addressing the mortality and associated disparities associated with pulmonary embolism. Objectives: To analyze the national mortality trends related to acute pulmonary embolism and determine the overall age- adjusted mortality rate (AAMR) per 100,000 population for the study period and assess changes in AAMR among different sexes, races, and geographic locations. Methods: We conducted a retrospective cohort analysis using mortality data of individuals aged ≥15 years with pulmonary embolism listed as the underlying cause of death in the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database from January 2006 to December 2019. These data are produced by the National Center for Health Statistics. Results: A total of 109,992 pulmonary embolism-related deaths were noted in this dataset nationwide between 2006 and 2019. Of these, women constituted 60,113 (54.7%). The AAMR per 100,000 was not significantly changed, from 2.84 in 2006 to 2.81 in 2019 (average annual percentage change [AAPC], 0.2; 95% confidence interval [CI], -0.1 to 0.5; P = 0.15). AAMR increased for men throughout the study period compared with women (AAPC, 0.7 for men; 95% CI, 0.3 to 1.2; P = 0.004 vs. AAPC, -0.4 for women; 95% CI, -1.1 to 0.3; P = 0.23, respectively). Similarly, AAMR for pulmonary embolism increased for Black compared with White individuals, from 5.18 to 5.26 (AAPC, 0.4; 95% CI, 0.0 to 0.7; P = 0.05) and 2.82 to 2.86 (AAPC, 0.0; 95% CI, -0.6 to 0.6; P = 0.99), respectively. Similarly, AAMR for pulmonary embolism was higher in rural areas than in micropolitan and large metropolitan areas during the study period (4.07 [95% CI, 4.02 to 4.12] vs. 3.24 [95% CI, 3.21 to 3.27] vs. 2.32 [95% CI, 2.30-2.34], respectively). Conclusions: Pulmonary embolism mortality remains high and unchanged over the past decade, and enduring sex, racial and socioeconomic disparities persist in pulmonary embolism. Targeted efforts to decrease pulmonary embolism mortality and address such disparities are needed. [ABSTRACT FROM AUTHOR]

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, −7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users. [ABSTRACT FROM AUTHOR]

Rationale: Nearly 3 in 5 in-hospital cardiac arrests (IHCAs) occur in the intensive care unit (ICU), yet large-scale data on the outcomes of in-ICU cardiac arrests have not been published for over a decade. Objectives: We sought to examine outcomes of in-ICU cardiac arrests, evaluating both achievement of return of spontaneous circulation (ROSC) and subsequent survival to hospital discharge and how these have changed over time and by type of cardiac arrest. Methods: This was an observational study using the Get With The Guidelines-Resuscitation registry, an American Heart Association-sponsored, prospective, multisite registry of IHCAs in the United States, including adults 18 years of age and older with a confirmed initial cardiac arrest occurring in the ICU who underwent resuscitation. Outcomes included achievement of ROSC and survival to hospital discharge. Multivariable hierarchical logistic regression adjusting for patient-level factors and hospitals as random effects was used to evaluate ROSC and survival. Results: A total of 114,371 adult, in-ICU IHCAs from January 2006 to December 2018 were studied. The mean age was 63.8 years, 41.3% were women, and 82.1% had a nonshockable initial rhythm. Of the 114,371 ICU cardiac arrests, 70,610 (61.7%) achieved ROSC, and 21,747 (19.0%) survived until hospital discharge. The rate of ROSC improved from 2006 to 2018 (unadjusted rate, 55.0-65.4%; adjusted odds ratio [OR] per year, 1.04; 95% confidence interval [CI], 1.03-1.05). There was an increase in overall survival to discharge during this time (unadjusted rate, 16.7-20.5%; adjusted OR per year, 1.03; 95% CI, 1.03-1.04). The survival to discharge rate of the 70,610 patients who achieved ROSC increased slightly (unadjusted rate, 30.3-31.4%; adjusted OR per year, 1.02; 95% CI, 1.01, 1.02). Conclusions: There is an increase in survival to discharge for patients who experienced a cardiac arrest in the ICU between 2006 and 2018. There is an increase in achievement of ROSC and post-ROSC survival to discharge, although the increase in achievement of ROSC was greater than the increase in post-ROSC survival. [ABSTRACT FROM AUTHOR]

Background: Older patients (65+) with mild cognitive impairment (MCI) receive less guideline-concordant care for cardiovascular disease (CVD) and other conditions than patients with normal cognition (NC). One potential explanation is that patients with MCI want less treatment than patients with NC; however, the treatment preferences of patients with MCI have not been studied. Objective: To determine whether patients with MCI have different treatment preferences than patients with NC. Design: Cross-sectional survey conducted at two academic medical centers from February to December 2019 Participants: Dyads of older outpatients with MCI and NC and patient-designated surrogates. Main Measures: The modified Life-Support Preferences-Predictions Questionnaire score measured patients' preferences for life-sustaining treatment decisions in six health scenarios including stroke and acute myocardial infarction (range, 0–24 treatments rejected with greater scores indicating lower desire for treatment). Key Results: The survey response rate was 73.4%. Of 136 recruited dyads, 127 (93.4%) completed the survey (66 MCI and 61 NC). The median number of life-sustaining treatments rejected across health scenarios did not differ significantly between patients with MCI and patients with NC (4.5 vs 6.0; P=0.55). Most patients with MCI (80%) and NC (80%) desired life-sustaining treatments in their current health (P=0.99). After adjusting for patient and surrogate factors, the difference in mean counts of rejected treatments between patients with MCI and patients with NC was not statistically significant (adjusted ratio, 1.08, 95% CI, 0.80–1.44; P=0.63). Conclusion: We did not find evidence that patients with MCI want less treatment than patients with NC. These findings suggest that other provider and system factors might contribute to patients with MCI getting less guideline-concordant care. [ABSTRACT FROM AUTHOR]

Objective: To describe how much of the recent increase in hospital-cardiologist integration has come from acquisitions of physician practices compared to individual employment decisions. While the role of physician practice acquisitions has received considerable attention in the news, integration may also be driven by individual physicians accepting employment at hospital-based practices.Data Sources: American Medical Association Physician Masterfile and Medicare data.Study Design: Analysis of changes in hospital-cardiologist integration from 2011 to 2018. We measured increases in integration and changes in the number of independent and hospital-owned practices.Data Collection/extraction Methods: Not applicable.Principal Findings: In 2011, 18% of cardiologists were integrated, rising to 25% in 2016. Of this rise, 48% occurred with no acquisitions. Physicians who had completed residencies in the past 5 years (early career physicians) had higher rates of integration that also increased over time: the percentage of early career physicians joining hospital systems rose from 25% to 32%, indicating rapid growth in the number of physicians who began their careers working in hospital-based sites.Conclusions: A large and growing portion of hospital-cardiologist integration came from hospital employment at the individual physician level. Future policies focused on preserving competition and affordability may benefit from better understanding this form of consolidation. [ABSTRACT FROM AUTHOR]

Background: Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.Methods: This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.Results: Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67-0.78.Conclusion: The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias. [ABSTRACT FROM AUTHOR]

Background: Transcatheter aortic valve replacement (TAVR) has made palliation from aortic stenosis more broadly available to populations previously thought to be too high risk for surgery, such as those with Alzheimer's disease and related dementias (ADRD); however, its safety and effectiveness in this context are uncertain. Methods: We performed a retrospective cohort study of national Medicare beneficiaries, aged 66 and older with Parts A and B, between 2010 and 2016. Patients undergoing AVR were identified, and follow‐up was available through 2017. Multivariable regression was used to measure the independent association between having a diagnosis of ADRD at the time of AVR, stratified by TAVR and surgery, and outcomes (mortality and Medicare institutional days at 1 year after AVR). Results: The average rate of increase in AVR per year was 17.5 cases per 100,000 ADRD and 8.4 per 100,000 non‐ADRD beneficiaries, largely driven by more rapid adoption of TAVR. Adjusted mortality following AVR declined significantly between those treated in 2010 and 2016, from 13.5% (95% CI 10.2%–17.7%) to 6.3% (95% CI 5.2%–7.6%) and from 13.7% (95% CI 12.7%–14.7%) to 6.3% (95% CI 5.8%–6.9%) in those with and without ADRD, respectively. The sharpest decline was noted for patients undergoing TAVR between 2011 and 2016, with adjusted mortality declining from 19.9% (95% CI 11.2%–32.8%) to 5.2% (95% CI 4.1%–6.5%) and from 12.2% (95% CI 9.3%–15.8%) to 5.0% (95% CI 4.4%–5.6%) in patients with and without ADRD, respectively. Similar declines were evident for Medicare institutional days in the year after AVR in both patient groups. Conclusions: Rates of AVR in those with ADRD increased during the past decade largely driven by the diffusion of TAVR. The use of TAVR in this vulnerable population did not come at the expense of increasing Medicare institutional days or mortality at 1‐year. [ABSTRACT FROM AUTHOR]

Objective: To identify modifiers of emergency medical services (EMS) oversight quality, including facilitators and barriers, and inform best practices and policy related to EMS oversight and system performance. Methods: We used a qualitative design, including 4 focus groups and 10 in-depth, 1-on-1 interviews. Primary data were collected from EMS stakeholders in Michigan from June to July 2016. Qualitative data were analyzed using the rapid assessment technique. Results: Emergent themes included organizational structure, oversight and stakeholder leadership, interorganizational communication and relationships, competition or collaboration among MCA stakeholders, quality improvement practices, resources, and needs specific to rural communities. Conclusions: EMS is a critical component of disaster response. This study revealed salient themes and modifiers, including facilitators and barriers, of EMS oversight quality. These findings were evaluated in the context of current evidence and informed state policy to improve the quality of EMS oversight and prehospital care for both routine and disaster settings. Some were particular to geographic regions and communities, whereas others were generalizable. [ABSTRACT FROM AUTHOR]

Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow. [ABSTRACT FROM AUTHOR]

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53-75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19. [ABSTRACT FROM AUTHOR]

Using Michigan public health data, we assessed geographical access to specialist providers for hepatitis C virus (HCV) treatment in urban and rural areas in Michigan and explored correlates of HCV in these areas to help inform HCV elimination planning and resource allocations. We found higher HCV incidence in urban areas, lower treatment specialist access in rural areas, but few correlates of HCV across adult populations in both areas. State and local HCV elimination planning should include population‐based screening among all adults and address geographical barriers to care. [ABSTRACT FROM AUTHOR]

Background: Community-engaged research engages the community in a collaborative manner to improve the local setting and has been increasingly applied to health services research. These complex studies require collaboration and team building among clinical experts, methodologists, and community stakeholders, yet few examples of this process have been published. Aim: Through a study of community emergency medical services, our aim was to understand team building among content experts and methodologists to advance the framework for adaptive community-engaged research. Methods: We collected process field notes and transcripts from the project kick-off meetings and analyzed the data using an immersion/crystallization process, which is an exploratory qualitative analysis characterized by continual review of the data collected. All 25 members of the study's research team, who are both local to Michigan and reside out-of-state, were invited to attend. Results: We describe the process of the kick-off event for this community-engaged study and report major themes: collaborating throughout each stage of the kick-off meeting, building trusting relationships by challenging each other's perspectives and sharing expertise, and gaining shared experiences of learning and understanding of study content and goals. Conclusion: Team-building in community engaged health research requires thoughtful effort, and planned kick-off meeting can be a useful strategy to build a shared vision among content experts and methodologists. [ABSTRACT FROM AUTHOR]

Background: In response to the development of highly effective but expensive new medications, policymakers, payors, and health systems are considering novel and pragmatic ways to provide these medications to patients. One approach is to target these treatments to those most likely to benefit. However, to maximize the fairness of these policies, and the acceptance of their implementation, the values and beliefs of patients should be considered. The provision of treatments for chronic hepatitis C (CHC) in the resource-constrained context of the Veterans Health Administration (VHA) offered a real-world example of this situation, providing the opportunity to test the value of using Democratic Deliberation (DD) methods to solicit the informed opinions of laypeople on this complex issue.Methods: We recruited Veterans (n = 30) from the VHA to attend a DD session. Following educational presentations from content experts, participants engaged in facilitated small group discussions to: 1) identify strategies to overcome CHC treatment barriers and 2) evaluate, vote on, and modify/improve two CHC treatment policies - "first come, first served" (FCFS) and "sickest first" (SF). We used transcripts and facilitators' notes to identify key themes from the small group discussions. Additionally, participants completed pre- and post-DD surveys.Results: Most participants endorsed the SF policy over the FCFS policy, emphasizing the ethical and medical appropriateness of treating the sickest first. Concerns about SF centered on the difficulty of implementation (e.g., how is "sickest" determined?) and unfairness to other Veterans. Proposed modifications focused on: 1) the need to consider additional health factors, 2) taking behavior and lifestyle into account, 3) offering education and support, 4) improving access, and 5) facilitating better decision-making.Conclusions: DD offered a robust and useful method for addressing the allocation of the scarce resource of CHC treatment. Participants were able to develop a modified version of the SF policy and offered diverse recommendations to promote fairness and improve quality of care for Veterans. DD is an effective approach for incorporating patient preferences and gaining valuable insights for critical healthcare policy decisions in resource-limited environments. [ABSTRACT FROM AUTHOR]

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation. [ABSTRACT FROM AUTHOR]