2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines.

AIM: The "2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease" provides recommendations to guide clinicians in the diagnosis, genetic evaluation and family screening, medical therapy, endovascular and surgical treatment, and long-term surveillance of patients with aortic disease across its multiple clinical presentation subsets (ie, asymptomatic, stable symptomatic, and acute aortic syndromes). METHODS: A comprehensive literature search was conducted from January 2021 to April 2021, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through June 2022 during the guideline writing process, were also considered by the writing committee, where appropriate. Structure: Recommendations from previously published AHA/ACC guidelines on thoracic aortic disease, peripheral artery disease, and bicuspid aortic valve disease have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with aortic disease have been developed. There is added emphasis on the role of shared decision making, especially in the management of patients with aortic disease both before and during pregnancy. The is also an increased emphasis on the importance of institutional interventional volume and multidisciplinary aortic team expertise in the care of patients with aortic disease.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.

Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes.

Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians. In this study, we developed a deep neural network model to predict outcomes following major cardiovascular procedures, using temporal image representation of past medical history as input. We created a novel explanation for the prediction of the model by defining impact scores that associate clinical observations with the outcome. For comparison, a logistic regression model was fitted to the same dataset. We compared the impact scores and log odds ratios by calculating three types of correlations, which provided a partial validation of the impact scores. The deep neural network model achieved an area under the receiver operating characteristics curve (AUC) of 0.787, compared to 0.746 for the logistic regression model. Moderate correlations were found between the impact scores and the log odds ratios. Impact scores generated by the explanation algorithm has the potential to shed light on the "black box" deep neural network model and could facilitate its adoption by clinicians.

Role of Nursing Informatics in the Automation of Pneumonia Quality Measure Data Elements.

Core measures are standard metrics to reflect the processes of care provided by hospitals. Hospitals in the United States are expected to extract data from electronic health records, automated computation of core measures, and electronic submission of the quality measures data. Traditional manual calculation processes are time intensive and susceptible to error. Automated calculation has the potential to provide timely, accurate information, which could guide quality-of-care decisions, but this vision has yet to be achieved. In this study, nursing informaticists and data analysts implemented a method to automatically extract data elements from electronic health records to calculate a core measure. We analyzed the sensitivity, specificity, and accuracy of core measure data elements extracted via SQL query and compared the results to manually extracted data elements. This method achieved excellent performance for the structured data elements but was less efficient for semistructured and unstructured elements. We analyzed challenges in automating the calculation of quality measures and proposed a rule-based (hybrid) approach for semistructured and unstructured data elements.

Generating disease-pertinent treatment vocabularies from MEDLINE citations.

OBJECTIVE: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. METHODS: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. RESULTS: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. CONCLUSIONS: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.

Veterans Like Me: Formative evaluation of a patient decision aid design.

Patient decision aids are tools intended to facilitate shared decision-making. Currently development of a patient decision aid is resource intensive: it requires a decision-specific review of the scientific literature by experts to ascertain the potential outcomes under different treatments. The goal of this project was to conduct a formative evaluation of a generalizable, scalable decision aid component we call Veterans Like Me (VLme). VLme mines EHR data to present the outcomes of individuals "like you" on different treatments to the user. These outcome are presented through a combination of an icon array and simulated narratives. Twenty-six patients participated in semi-structured interviews intended to elicit feedback on the tool's functional and interface design. The interview focused on the filters users desired with which to make cases similar to them, the kinds of outcomes they wanted presented, and their envisioned use of the tool. The interview also elicited participants information needs and salient factors related to the therapeutic decision. The interview transcripts were analyzed using an iteratively refined coding schema and content analysis. . Participants generally expressed enthusiasm for the tool's design and functionality. Our analysis identified desired filters for users to view patients like themselves, outcome types that should be included in future iterations of the tool (e.g. patient reported outcomes), and information needs that need to be addressed for patients to effectively participate in shared decision making. Implications for the integration of our findings into the design of patient decision aids are discussed.

Patient needs and preferences for herb-drug-disease interaction alerts: a structured interview study.

BACKGROUND: While complementary and alternative medicine (CAM) is commonly used in the United States and elsewhere, and hazardous interactions with prescription drugs can occur, patients do not regularly communicate with physicians about their CAM use. The objective of this study was to discover patient information needs and preferences for herb-drug-disease interaction alerts. METHODS: We recruited 50 people from several locations within the University of Utah Hospital to participate in this structured interview study. They were asked to provide their preferences for the herb-drug-disease interaction alerts. Qualitative methods were used to reveal the themes that emerged from the interviews. RESULTS: Most participants reported they had previously used, or they were currently using, CAM therapies. The majority had made the effort to inform their healthcare provider(s) about their CAM usage, although some had not. We found that most respondents were interested in receiving alerts and information about potential interactions. Many preferred to receive the alerts in a variety of ways, both in person and electronically. CONCLUSIONS: In addition to conventional medicine, many patients regularly use complementary and alternative therapies. And yet, communication between patients and providers about CAM use is not consistent. There is a demand for interventions in health care that provide timely, integrative communication support. Delivering the herb-drug-disease alerts through multiple channels could help meet critical patient information needs.

Changes in Oral Anticoagulant Treatment Rates in Atrial Fibrillation before and after the Introduction of Direct Oral Anticoagulants.

BACKGROUND: Direct oral anticoagulants (DOACs) have the potential to improve stroke prevention among atrial fibrillation (AF) patients. We sought to determine if oral anticoagulation (OAC) treatment rates have increased since the approval of DOACs. METHODS: We identified 6,688 patients with AF at an academic medical center from January 2008 to June 2015. We examined OAC prescription rates over time and according to CHA2DS2VASc score using multivariable Poisson regression models, with an interaction term between risk score and year of AF diagnosis. RESULTS: Among 6,688 AF patients, 78% had CHA2DS2VASc scores ≥2, 51.6% of whom received an OAC prescription within 90 days of diagnosis. The OAC prescription rate was 47.8% in the pre-DOAC era and peaked at 56.4% in 2014. Relative to the pre-DOAC era, prescription rates increased in 2012 and leveled off thereafter. The prescription rate for the highest risk group was 58.5%, compared with 45.0% in patients with a CHA2DS2VASc score of 2 (p < 0.01). In the adjusted analysis, prescription rates were higher for the higher risk group (adjusted relative risk 1.24 for CHA2DS2VASc score 7-9 vs. 2, 95% CI 1.09-1.40). CONCLUSIONS: OAC treatment rates have increased since DOAC introduction, but substantial treatment gaps remain, specifically among the higher risk patients.

Assessing Pictograph Recognition: A Comparison of Crowdsourcing and Traditional Survey Approaches.

BACKGROUND: Compared to traditional methods of participant recruitment, online crowdsourcing platforms provide a fast and low-cost alternative. Amazon Mechanical Turk (MTurk) is a large and well-known crowdsourcing service. It has developed into the leading platform for crowdsourcing recruitment. OBJECTIVE: To explore the application of online crowdsourcing for health informatics research, specifically the testing of medical pictographs. METHODS: A set of pictographs created for cardiovascular hospital discharge instructions was tested for recognition. This set of illustrations (n=486) was first tested through an in-person survey in a hospital setting (n=150) and then using online MTurk participants (n=150). We analyzed these survey results to determine their comparability. RESULTS: Both the demographics and the pictograph recognition rates of online participants were different from those of the in-person participants. In the multivariable linear regression model comparing the 2 groups, the MTurk group scored significantly higher than the hospital sample after adjusting for potential demographic characteristics (adjusted mean difference 0.18, 95% CI 0.08-0.28, P<.001). The adjusted mean ratings were 2.95 (95% CI 2.89-3.02) for the in-person hospital sample and 3.14 (95% CI 3.07-3.20) for the online MTurk sample on a 4-point Likert scale (1=totally incorrect, 4=totally correct). CONCLUSIONS: The findings suggest that crowdsourcing is a viable complement to traditional in-person surveys, but it cannot replace them.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

BACKGROUND: The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. METHODS: Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. RESULTS: The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. CONCLUSION: The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.