Demographic and Survivorship Disparities in Non–muscle-invasive Bladder Cancer in the United States

Objectives To examine survivorship disparities in demographic factors and risk status for non–muscle-invasive bladder cancer (NMIBC), which accounts for more than 75% of all urinary bladder cancers, but is highly curable with early identification and treatment. Methods We used the US National Cancer Institute’s Surveillance, Epidemiology, and End Results registries over a 19-year period (1988-2006) to examine survivorship disparities in age, sex, race/ethnicity, and marital status of patients and risk status classified by histologic grade, stage, size of tumor, and number of multiple primary tumors among NMIBC patients (n=29 326). We applied Kaplan-Meier (K-M) and Cox proportional hazard methods for survival analysis. Results Among all urinary bladder cancer patients, the majority of NMIBCs were in male (74.1%), non-Latino white (86.7%), married (67.8%), and low-risk (37.6%) to intermediate-risk (44.8%) patients. The mean age was 68 years. Survivorship (in median life years) was highest for non-Latino white (5.4 years), married (5.4 years), and low-risk (5.7 years) patients (K-M analysis, p<0.001). We found significantly lower survivorship for elderly, male (female hazard ratio [HR], 0.96), Latino (HR, 1.20), and unmarried (married HR, 0.93) patients. Conclusions Survivorship disparities were ubiquitous across age, sex, race/ethnicity, and marital status groups. Non-white, unmarried, and elderly patients had significantly shorter survivorship. The implications of these findings include the need for a heightened focus on health policy and more organized efforts to improve access to care in order to increase the chances of survival for all patients

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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)

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

BACKGROUND: 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. OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: These findings may be used to prevent CD in the outpatient setting by encouraging appropriate surveillance and management of CVH

Scholarship in Emergency Medicine: A Primer for Junior Academics Part I: Writing and Publishing

The landscape of scholarly writing, publishing, and university promotion can be complex and challenging. Mentorship may be limited. To be successful it is important to understand the key components of writing and publishing. In this article, we provide expert consensus recommendations on four key challenges faced by junior faculty: writing the paper; selecting contributors and the importance of authorship order; journal selection and indexing; and responding to critiques. After reviewing this paper, the reader should have an enhanced understanding of these challenges and strategies to successfully address them

Scholarship in Emergency Medicine: A Primer for Junior Academics Part I: Writing and Publishing

The landscape of scholarly writing, publishing, and university promotion can be complex and challenging. Mentorship may be limited. To be successful it is important to understand the key components of writing and publishing. In this article, we provide expert consensus recommendations on four key challenges faced by junior faculty: writing the paper; selecting contributors and the importance of authorship order; journal selection and indexing; and responding to critiques. After reviewing this paper, the reader should have an enhanced understanding of these challenges and strategies to successfully address them

Scholarship in Emergency Medicine: A Primer for Junior Academics Part III: Understanding Publication Metrics

There are approximately 78 indexed journals in the specialty of emergency medicine (EM), making it challenging to determine which is the best option for junior faculty. This paper is the final component of a three-part series focused on guiding junior faculty to enhance their scholarly productivity. As an EM junior faculty’s research career advances, the bibliometric tools and resources detailed in this paper should be considered when developing a publication submission strategy. The tenure and promotion decision process in many universities relies at least in part on these types of bibliometrics. This paper provides an understanding of new, alternative metrics that can be used to promote scientific progress in a transparent and timely manner

Measuring Scholarly Productivity: A Primer for Junior Faculty. Part III: Understanding Publication Metrics

There are approximately 78 indexed journals in the specialty of emergency medicine (EM), making it challenging to determine which is the best option for junior faculty. This paper is the final component of a three-part series focused on guiding junior faculty to enhance their scholarly productivity. As an EM junior faculty’s research career advances, the bibliometric tools and resources detailed in this paper should be considered when developing a publication submission strategy. The tenure and promotion decision process in many universities relies at least in part on these types of bibliometrics. This paper provides an understanding of new, alternative metrics that can be used to promote scientific progress in a transparent and timely manner