Deep Learning and Multivariable Models Select EVAR Patients for Short-Stay Discharge

An integrated vascular surgery residency program would increase interest among Saudi medical students in a career in vascular surgery

BACKGROUND AND OBJECTIVES

Considering the absence of integrated vascular surgery residency programs in Saudi Arabia, and the need for planning training pathways, we aim to identify how many medical students are interested in vascular surgery, and the factors affecting students' opinions on pursuing vascular surgery.

MATERIALS AND METHODS

A cross-sectional study was conducted using an online questionnaire that was distributed to medical students nationwide via social media and email. Data were collected from 13 February 2022 to 1 March 2022.

RESULTS

A total of 408 students participated. Among them, 152 students were interested in general surgery, of which 103 were considering vascular surgery as a possible future fellowship. However, only 29 out of 408 (7.1%) students picked vascular surgery as their 1st choice. The main motivating factors for students to pursue vascular surgery as a career were: an interest in vascular cases (cardiovascular science), the use of emerging technologies, and the endovascular capabilities of vascular surgeons. The negative factors were simply a preference for another specialty, followed by a lack of experience in vascular surgery.

CONCLUSION

This study reveals that only 7.1% of students consider vascular surgery their first choice. Both the lack of vascular surgeons and students' experience in vascular surgery affected awareness levels. Interaction with vascular surgeons through virtual rotations for under-served medical schools and the introduction of vascular sciences within the cardiology blocks during basic science years are recommended strategies.

Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis

BACKGROUND

Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.

DESIGN

Systematic review and meta-analyses.

PARTICIPANTS

Older adults (≥ 65 years) in any setting.

INTERVENTION

Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.

OUTCOME MEASURES

Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.

RESULTS

Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.

CONCLUSION

The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence

Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables

OBJECTIVES

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

METHODS

We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

RESULTS

We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

CONCLUSIONS

The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Artificial intelligence's role in vascular surgery decision-making

Artificial intelligence (AI) is the next great advance informing medical science. Several disciplines, including vascular surgery, use AI-based decision-making tools to improve clinical performance. Although applied widely, AI functions best when confronted with voluminous, accurate data. Consistent, predictable analytic technique selection also challenges researchers. This article contextualizes AI analyses within evidence-based medicine, focusing on "big data" and health services research, as well as discussing opportunities to improve data collection and realize AI's promise.

Prediction of thirty-day mortality risk after thoracic endovascular aortic repair for intact descending thoracic aortic aneurysms: Derivation of risk calculator in the Vascular Quality Initiative

OBJECTIVE

Thoracic endovascular aortic repair (TEVAR) for descending thoracic aortic aneurysm (DTAA) is associated with high perioperative survival, although mortality is a possible outcome. However, no risk score has been developed to predict mortality after TEVAR for intact DTAA to aid in risk discussion and preoperative patient selection. Our objective was to use a multi-institutional database to develop a 30-day mortality risk calculator for TEVAR after DTAA repair.

METHODS

The Vascular Quality Initiative database was queried for patients treated with TEVAR for intact DTAA between August 2014 and August 2020. Univariable and multivariable analyses aided in developing a 30-day mortality risk score. Internal validation was done with K-fold cross-validation and calibration curve analysis.

RESULTS

Of 2141 patients included in the analysis, 90 (4.2%) died within 30 days after the procedure. Clinically relevant variables identified to be independently associated with 30-day mortality and therefore used to derive the predictive model included age 75 years or greater (odds ratio [OR], 2.27; 95% confidence interval [CI], 1.50-3.44; P < .001), coronary artery disease (OR, 1.60; 95% CI, 1.03-2.47; P = .036), American Society of Anesthesiologists class IV/V (OR, 2.39; 95% CI, 1.39-4.10; P = .002), urgent vs elective procedure (OR, 3.47; 95% CI, 1.90-6.33; P < .001), emergent vs elective procedure (OR, 5.27; 95% CI, 2.36-11.75; P < .001), prior carotid revascularization (OR, 3.24; 95% CI, 1.64-6.39; P = .001), and proximal landing zone <3 (OR, 2.51; 95% CI, 1.65-3.81; P < .001). The model showed an area under the receiver operating characteristic curve of 0.75. Internal validation demonstrated a bias-corrected area under the receiver operating characteristic curve of 0.73 (95% CI, 0.66-0.79) and a calibration slope of 1.00 with a corresponding intercept of 0.00.

CONCLUSIONS

This study provides a novel clinically relevant risk prediction model to estimate 30-day mortality risk after TEVAR for DTAA. The TEVAR Mortality Risk Calculator provides useful prognostic information to guide patient selection and facilitate preoperative discussions and shared decision making. An easily accessible online version of the TEVAR Mortality Risk Score is available to facilitate ease of use.