Barriers to obtaining diagnostic testing for coronary artery disease among veterans

Validation of an Integrated Genetic-Epigenetic Test for the Assessment of Coronary Heart Disease

BACKGROUND

Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD.

METHODS AND RESULTS

In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence-guided, clinically implementable method that uses input from 6 methylation-sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches.

CONCLUSIONS

We conclude that this scalable integrated genetic-epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation-sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

OBJECTIVE

Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives.

DESIGN

Retrospective hospital registry study.

SETTING

University-affiliated hospitals network (NY, USA).

PATIENTS

246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort.

MEASUREMENTS

Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery.

MAIN RESULTS

8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively.

CONCLUSIONS

We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

Transportation Barriers to Health Care in the United States: Findings From the National Health Interview Survey, 1997-2017

Objectives. To quantify the number of people in the US who delay medical care annually because of lack of available transportation and to examine the differential prevalence of this barrier for adults across sociodemographic characteristics and patient populations.Methods. We used data from the National Health Interview Survey (1997-2017) to examine this barrier over time and across groups. We used joinpoint regression analysis to identify significant changes in trends and multivariate analysis to examine correlates of this barrier for the year 2017.Results. In 2017, 5.8 million persons in the United States (1.8%) delayed medical care because they did not have transportation. The proportion reporting transportation barriers increased between 2003 and 2009 with no significant trends before or after this window within our study period. We found that Hispanic people, those living below the poverty threshold, Medicaid recipients, and people with a functional limitation had greater odds of reporting a transportation barrier after we controlled for other sociodemographic and health characteristics.Conclusions. Transportation barriers to health care have a disproportionate impact on individuals who are poor and who have chronic conditions. Our study documents a significant problem in access to health care during a time of rapidly changing transportation technology.

No-shows in appointment scheduling - a systematic literature review

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

"You Need to Be a Good Listener": Recruiters' Use of Relational Communication Behaviors to Enhance Clinical Trial and Research Study Accrual

Medical and research professionals who discuss clinical trials and research studies with potential participants face an often daunting challenge, particularly when recruiting from minority and underserved populations. This study reports on findings from a focus group study of 63 research coordinators, study nurses, professional recruiters, and other professionals in Indianapolis, IN and Miami, FL who work to recruit from minority and underserved populations. These professionals discussed the importance of creating a sense of connection with potential participants as part of the recruitment and retention process. Building a relationship, however fleeting, involved a number of concrete behaviors, including listening to personal information, expressing empathy, and then providing reciprocal self-disclosures; having repeated contact, usually by working in the same environment over an extended period of time; demonstrating respect through politeness and the use of honorifics; going the extra mile for participants; offering flexibility in scheduling follow-up appointments; and creating a sense of personal and community trust by being truthful. The implications of these findings for clinical trial and research study accrual are discussed.

Large-Scale No-Show Patterns and Distributions for Clinic Operational Research

Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75-79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others.

Elective versus routine postoperative clinic appointments after circumcisions performed under local anesthesia

OBJECTIVE

To evaluate a model of elective postoperative clinic appointments after a minor urological procedure and to compare it to the traditional practice of routine appointments.

METHODS

A total of 104 consecutive patients undergoing adult circumcision under local anesthesia were divided into 2 groups; group A (n = 45) received routine postoperative clinic appointments and group B (n = 59) were given the option to make an appointment on an as-needed basis. Both groups received detailed postoperative instructions on the early signs of symptoms of potential adverse events. The 2 groups were compared regarding demographics, clinical profile, postoperative recovery, and outcome.

RESULTS

Group A patients ("routine appointments") were younger (51 vs 60 years, P <.0001) and included fewer African Americans (57.8% vs 78.0%, P <.03) compared to group B patients ("elective appointments"). Postoperative clinic appointments were categorized as unnecessary in 84.4% (38/45) and 71.1% (42/59) of the patients in groups A and B, respectively. Of the remaining 17 patients in group B who elected to make an appointment, only 1 patient (1.7%) had a true procedure-related issue that justified the visit and required management. Overall, there was no statistical difference between the 2 groups with regard to the number of patients with perceived postoperative issues (P = .36).

CONCLUSION

The traditional practice of routine clinic appointments after uncomplicated adult circumcision is medically unnecessary and provides little value in the majority of cases. The practice of open access elective postoperative evaluation based on clearly defined clinical criteria is efficacious, safe, convenient, and enhances resource utilization.