We performed a preimplementation assessment of workflows, resources, needs, and antibiotic prescribing practices of trainees and practicing dentists to inform the development of an ...antibiotic-stewardship clinical decision-support tool (CDST) for dentists.
We used a technology implementation framework to conduct the preimplementation assessment via surveys and focus groups of students, residents, and faculty members. Using Likert scales, the survey assessed baseline knowledge and confidence in dental providers' antibiotic prescribing. The focus groups gathered information on existing workflows, resources, and needs for end users for our CDST.
Of 355 dental providers recruited to take the survey, 213 (60%) responded: 151 students, 27 residents, and 35 faculty. The average confidence in antibiotic prescribing decisions was 3.2 ± 1.0 on a scale of 1 to 5 (ie, moderate). Dental students were less confident about prescribing antibiotics than residents and faculty (
< .01). However, antibiotic prescribing knowledge was no different between dental students, residents, and faculty. The mean likelihood of prescribing an antibiotic when it was not needed was 2.7 ± 0.6 on a scale of 1 to 5 (unlikely to maybe) and was not meaningfully different across subgroups (
= .10). We had 10 participants across 3 focus groups: 7 students, 2 residents, and 1 faculty member. Four major themes emerged, which indicated that dentists: (1) make antibiotic prescribing decisions based on anecdotal experiences; (2) defer to physicians' recommendations; (3) have limited access to evidence-based resources; and (4) want CDST for antibiotic prescribing.
Dentists' confidence in antibiotic prescribing increased by training level, but knowledge did not. Trainees and practicing dentists would benefit from a CDST to improve appropriateness of antibiotic prescribing.
Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are ...urgently needed. Predicting CD using electronic health record (EHR) data would allow for earlier diagnosis and treatment of the condition, thus improving overall cardiovascular outcomes. The Guideline Advantage (TGA) is an American Heart Association ambulatory quality clinical data registry of EHR data representing 70 clinics distributed throughout the US, and has been used to monitor outpatient prevention and disease management outcome measures across populations and for longitudinal research on the impact of preventative care. For this study, we represented all time-series cardiovascular health (CVH) measures and the corresponding data collection time points for each patient by numerical embedding vectors. We then employed a deep learning technique-long-short term memory (LSTM) model-to predict CD from the vector of time-series CVH measures by 5-fold cross validation and compared the performance of this model to the results of deep neural networks, logistic regression, random forest, and Naïve Bayes models. We demonstrated that the LSTM model outperformed other traditional machine learning models and achieved the best prediction performance as measured by the average area under the receiver operator curve (AUROC): 0.76 for LSTM, 0.71 for deep neural networks, 0.66 for logistic regression, 0.67 for random forest, and 0.59 for Naïve Bayes. The most influential feature from the LSTM model were blood pressure. These findings may be used to prevent CD in the outpatient setting by encouraging appropriate surveillance and management of CVH.
Highlights ► We assess intolerance of uncertainty scores across anxiety disorders and depression. ► We demonstrate invariant response patterns across anxiety disorders and depression. ► We provide ...robust support for a two-factor structure for the IUS-12.
To conduct a systematic review of methodologies, data sources, and best practices for identifying, calculating, and reporting recurrent firearm injury rates in the United States.
In accordance with ...PRISMA guidelines, we searched seven electronic databases on December 16, 2021, for peer-reviewed articles that calculated recurrent firearm injury in generalizable populations. Two reviewers independently assessed the risk of bias, screened the studies, extracted data, and a third resolved conflicts.
Of the 918 unique articles identified, 14 met our inclusion criteria and reported recurrent firearm injury rates from 1% to 9.5%. We observed heterogeneity in study methodologies, including data sources utilized, identification of subsequent injury, follow-up times, and the types of firearm injuries studied. Data sources ranged from single-site hospital medical records to comprehensive statewide records comprising medical, law enforcement, and social security death index data. Some studies applied machine learning to electronic health records to differentiate subsequent new firearm injuries from the index injury, while others classified all repeat firearm-related hospital admissions after variably defined cut-off times as a new injury. Some studies required a minimum follow-up observation period after the index injury while others did not. Four studies conducted survival analyses, albeit using different methodologies.
Variability in both the data sources and methods used to evaluate and report recurrent firearm injury limits individual study generalizability of individual and societal factors that influence recurrent firearm injury. Our systematic review highlights the need for development, dissemination, and implementation of standard practices for calculating and reporting recurrent firearm injury.
•The U.S. does not have a standardized data source for nonfatal firearm injuries.•There is variability in the methods used to calculate firearm reinjury rates.•Survival analysis can estimate the probability of recurrent firearm injury over time.•There is a need to standardize practices for calculating firearm reinjury rates.•This will facilitate violence intervention program outcome analyses across regions.
To evaluate a web-based breast reconstruction decision aid, BREASTChoice.
Although postmastectomy breast reconstruction can restore quality of life and body image, its morbidity remains substantial. ...Many patients lack adequate knowledge to make informed choices. Decisions are often discordant with patients' preferences.
Adult women with stages 0-III breast cancer considering postmastectomy breast reconstruction with no previous reconstruction were randomized to BREASTChoice or enhanced usual care (EUC).
Three hundred seventy-six patients were screened; 120 of 172 (69.8%) eligible patients enrolled. Mean age = 50.7 years (range 25-77). Most were Non-Hispanic White (86.3%) and had a college degree (64.3%). Controlling for health literacy and provider seen, BREASTChoice users had higher knowledge than those in EUC (84.6% vs. 58.2% questions correct; P < 0.001). Those using BREASTChoice were more likely to know that reconstruction typically requires more than 1 surgery, delayed reconstruction lowers one's risk, and implants may need replacement over time (all ps < 0.002). BREASTChoice compared to EUC participants also felt more confident understanding reconstruction information (P = 0.009). There were no differences between groups in decisional conflict, decision process quality, shared decision-making, quality of life, or preferences (all ps > 0.05). There were no differences in consultation length between BREASTChoice and EUC groups (mean = 29.7 vs. 30.0 minutes; P > 0.05). BREASTChoice had high usability (mean score = 6.3/7). Participants completed BREASTChoice in about 27 minutes.
BREASTChoice can improve breast reconstruction decision quality by improving patients' knowledge and providing them with personalized risk estimates. More research is needed to facilitate point-of-care decision support and examine BREASTChoice's impact on patients' decisions over time.
Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better understand ...risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients. We used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups. Model performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings. Deep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers).
•DMN hypoconnectivity and salience network hyperconnectivity are present in obesity.•Aberrant seed-based connectivity found between various cognitive and limbic regions.•Decreased nodal efficiency, ...degree centrality and global efficiency seen in high BMI.•Rs-FC changes in obesity may reflect disrupted integrity of critical brain networks.
Obesity has been variously linked to differences in brain functional connectivity in regions associated with reward, emotional regulation and cognition, potentially revealing neural mechanisms contributing to its development and maintenance. This systematic review summarizes and critically appraises the existing literature on differences in resting state functional connectivity (Rs-FC) between overweight and individuals with obesity in relation healthy-BMI controls. Twenty-nine studies were identified and the results consistently support the hypothesis that obesity is associated with differences in Rs-FC. Specifically, obesity/overweight was consistently associated with (i) DMN hypoconnectivity and salience network hyperconnectivity; (ii) increased Rs-FC between the hypothalamus and reward, limbic and salience networks, and decreased Rs-FC between the hypothalamus and cognitive regions; (iii) increased power within regions associated with inhibition/emotional reasoning; (iv) decreased nodal efficiency, degree centrality, and global efficiency. Collectively, the results suggest obesity is associated with disrupted connectivity of brain networks responsible for cognition, reward, self-referential processing and emotional regulation.
Purpose
Individuals diagnosed with high survival cancers will often die of cardiovascular disease (CVD) rather than a recurrence of their cancer, yet CVD risk factors may be overlooked during ...survivorship care. We assess the prevalence of CVD risk factors among long-term cancer survivors and compare results to survey data from the general population in the same geographic region. We also characterize how often at-risk survivors discuss CVD-related health behaviors with their health care providers.
Methods
Survivors (
n
= 1,582) of breast, prostate, colorectal, and gynecologic cancers, 4–14 years after diagnosis, were recruited from two California cancer registries for a cross-sectional mail survey. We assessed CVD risk factors, including smoking, body mass index, physical inactivity, hypercholesterolemia, hypertension, and diabetes, as well as report of discussions with health care providers about diet, exercise, smoking, and lifestyle change assistance.
Results
With the exception of current smoking, CVD risk factors were more common among survivors than the general adult population. Of survivors, 62.0 % were overweight or obese, 55.0 % reported hypertension, 20.7 % reported diabetes, 18.1 % were inactive, and 5.1 % were current smokers. Compared to white, non-Hispanic survivors, Hispanic (
b
= 0.37,
p
= 0.007) and African-American (
b
= 0.66,
p
< 0.0001), but not Asian, survivors reported significantly more risk factors. One in three survivors with one or more risk factors for CVD did not report a health promotion discussion with their health care providers.
Conclusions
CVD risk factors are common among long-term survivors, but many at-risk survivors may not discuss lifestyle prevention with their health care team. Primary care and oncology should work together to deliver optimal survivorship care that addresses CVD risk factors, as well as prevalent disease.
Implications for cancer survivors
Cardiovascular disease may compromise cancer survivors’ long-term health and well-being, yet cardiovascular risk factors may be overlooked during survivorship care. We document that CVD risk factors are common among cancers survivors, yet nearly a third of survivors do not report health promotion discussions with their medical teams. Survivors should be aware of their cardiovascular risk factors and initiate discussions with their medical teams about health promotion topics, if appropriate.
Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and ...perspectives in community settings. We aimed to (1) Assess survivor CVH profiles, (2) compare self-reported and EHR-based categorization of CVH factors, and (3) describe perceptions regarding addressing CVH during oncology encounters.
This cross-sectional analysis utilized data from an ongoing NCI Community Oncology Research Program trial of an EHR heart health tool for cancer survivors (WF-1804CD). Survivors presenting for routine care after potentially curative treatment recruited from 8 oncology practices completed a pre-visit survey, including American Heart Association Simple 7 CVH factors (classified as ideal, intermediate, or poor). Medical record abstraction ascertained CVD risk factors and cancer characteristics. Likert-type questions assessed desired discussion during oncology care.
Of 502 enrolled survivors (95.6% female; mean time since diagnosis = 4.2 years), most had breast cancer (79.7%). Many survivors had common cardiovascular comorbidities, including high cholesterol (48.3%), hypertension or high BP (47.8%) obesity (33.1%), and diabetes (20.5%); 30.5% of survivors received high cardiotoxicity potential cancer treatment. Less than half had ideal/non-missing levels for physical activity (48.0%), BMI (18.9%), cholesterol (17.9%), blood pressure (14.1%), healthy diet (11.0%), and glucose/ HbA1c (6.0%). While > 50% of survivors had concordant EHR-self-report categorization for smoking, BMI, and blood pressure; cholesterol, glucose, and A1C were unknown by survivors and/or missing in the EHR for most. Most survivors agreed oncology providers should talk about heart health (78.9%).
Tools to promote CVH discussion can fill gaps in CVH knowledge and are likely to be well-received by survivors in community settings.
NCT03935282, Registered 10/01/2020.
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