Quality of YouTube® videos on thrombolysis and thrombectomy for deep venous thrombosis.

OBJECTIVE: YouTube® has gained popularity as an unofficial educational resource for surgical trainees, but its content's quality and educational value remain to be evaluated. The aim of this study is to analyze the current content on these techniques for lower extremity DVT (LEDVT) on YouTube®. METHODS: A search was performed on YouTube® using 13 search terms in August 2022 on a clear-cached browser. Open-access videos focusing on the surgical techniques of venous thrombolysis or thrombectomy for LEDVT were included. Quality and educational value were assessed and graded based on metrics for accountability (4 items), content (13 items), and production (9 items). RESULTS: Out of 138 videos regarding LEDVT oriented towards medical professionals, only 14 met inclusion criteria. Videos ran for a median of 3.4 min (range 0.37-35.6 min) with a median of 941 views (range 106-54624). Videos scored a median of 5.5 (range 1.0-8.0) out of 11 for content, a median of 2.0 out of 6.0 (range 0.0-2.0) for accountability, and a median of 5.5 out of 9.0 (range 3.0-9.0) for production. CONCLUSION: Few YouTube® videos focus on the technical aspects of DVT thrombolysis/thrombectomy, and they vary significantly in content with overall poor accountability and production quality.

Surgical Outcomes of Vertical Rectus Abdominis Myocutaneous Flap Pelvic Reconstruction.

BACKGROUND: The vertical rectus abdominis myocutaneous (VRAM) flap has emerged as the workhorse flap for perineal and pelvic reconstruction. We aimed to evaluate outcomes of VRAM flap over a 20-year period and the role of mesh abdominal wall reinforcement following VRAM flap-based reconstruction. METHODS: We conducted a retrospective review of all consecutive patients who underwent pelvic reconstruction with a VRAM flap between January 2001 and March 2021. Our primary outcome measure included recipient and donor surgical site occurrences (SSOs). RESULTS: We identified a total of 546 patients (55% females) with a mean age was 58 years and mean BMI was 27 kg/m 2. Mesh was used at the time of VRAM reconstruction to reinforce the abdominal donor site in 36% of patients. Recipient-site SSOs occurred in 38% of patients while donor-site SSOs occurred in 17% of patients. Hernia was identified in 9.9% of patients while bulge developed in 6.4% of patients. Cox proportional hazards regression model for hernia occurrence identified age, BMI, tobacco use (HR 2.03, 95% CI [1.02 - 4.04]) and use as an extended VRAM (HR 2.13, 95% CI [1.04 - 4.36]). The use of mesh or component separation were not independent protective factors for hernia occurrence. CONCLUSION: The pedicled VRAM flap is versatile and is our preferred flap for reconstruction of pelvic and perineal defects. The use of mesh/component separation to allow for fascial closure was not shown to reduce donor site hernia occurrence.

Evaluation of modified frailty index for predicting post-operative outcomes after lower-extremity free-flap reconstruction.

BACKGROUND: Risk stratification for complex procedures such as microsurgical reconstruction of the lower extremities is an important part of preoperative planning and counseling. The aim of this study was to determine the effectiveness of the modified five-item frailty index (5-mFI) score, a validated tool for assessing risk in surgical patients, in predicting postoperative complications after lower extremity (LE) free flap reconstruction. METHODS: A retrospective review of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was conducted from 2010 to 2020 on patients who underwent LE free-flap reconstruction. 5-mFI scores were calculated and patients were categorized as 5-mFI ≥2 or <2. The primary endpoint was the presence of 30-day overall complications. The secondary endpoints included 30-day readmission, need for reoperation, and need to discharge to a care facility. Comparisons were made using one-way analysis of variances, Pearson's chi-squared test, or Fisher's extract test. Multivariable logistic regression models were performed for sex, age, BMI, smoker status, operative time, and ASA classification. RESULTS: Total of 294 (61.6% males) patients were identified. Univariate analysis showed 5-mFI ≥2 had higher rates of overall complications (p = .043) and hematologic complications (p = .033). In this population, there were also higher rates of reoperation (p = .003) and discharge to care facility (p < .001). Multivariable regression models further substantiated that 5-mFI ≥2 was independently associated with increased overall complications [2.46, CI: 1.10-5.59, p = .031], hematologic complications [2.55, 1.02-6.35, p = .046], reoperation [4.55, 1.54-13.3, p = .006], and discharge to facility [2.86, 1.27-6.45, p = .011]. CONCLUSIONS: There is a strong association of 5-mFI ≥2 with adverse post-operative outcomes in male patients undergoing LE free-flap reconstruction. This can be a valuable adjunct in the counsel of patients for whom lower extremity salvage is feasible.

Primary Gastrointestinal Diffuse Large B-cell Lymphoma Presenting as Ileal Perforation.

Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma and can rarely present as a primary gastrointestinal malignancy. Primary gastrointestinal lymphoma (PGIL) is associated with a significant risk of perforation and peritonitis, with high rates of mortality. Here we describe a case of a newly diagnosed PGIL in a previously healthy 22-year-old male presenting for new-onset abdominal pain with diarrhea. Early hospital course was characterized by peritonitis and severe septic shock. Despite multiple surgical interventions and resuscitative efforts, the patient's condition continued to deteriorate until cardiac arrest and death on hospital day five. A diagnosis of DLBCL of the terminal ileum and cecum was made by pathology post-mortem. The prognosis for these patients can be improved through early intervention with chemotherapy regimens and surgical resection of the malignant tissue. This report highlights DLBCL as a rare cause of gastrointestinal perforation that can culminate in precipitous multiorgan failure and death.

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review.

Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.

The Virtual Interview Experience for MATCH 2021: A Pilot Survey of General Surgery Residency Program Directors.

BACKGROUND: Virtual interviews (VIs) for the 2020 residency application season were mandated as a result of the COVID-19 pandemic. We aimed to determine the perspectives of general surgery (GS) program directors (PDs) on the benefits and drawbacks of VIs. METHODS: A 14-item survey was emailed to all GS PDs from programs identified on the American Council for Graduate Medical Education website. Program directors were asked about the cost-time benefit of VIs, its ability to assess candidates, and their thoughts on the future of VIs for evaluating residency applicants. RESULTS: 60 PDs responded corresponding to a response rate of 21%. While 93% agreed/strongly agreed that VIs were less expensive, only 35% found VIs to be less time-consuming. 75% and 67%, respectively, disagreed/strongly disagreed that VIs allowed for an easier assessment of an applicant's fit, and personality and communication skills. Almost one-half of our survey respondents suggested that VIs made the selection committee rely more heavily on objective applicant data. Almost two-thirds of GS PDs suggested that they would adopt both VI and in-person interview formats for future application cycles. The median [interquartile range] cost saved through the implementation of VIs was US$ 4500 [1625 - 10 000]. CONCLUSION: Remarkably, VIs have been swiftly imbibed by all residency programs and many aspects of the VI experience were positive. While MATCH 2021 has definitely proved to be one of its kind, the implementation of VIs has been met with overall broad success and a promising future awaits this novel modality of resident selection to GME programs in the United States.

Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.

Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.

Deep Learning Applications in Surgery: Current Uses and Future Directions.

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.

A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models.

Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.

Predicting Patient-Reported Outcomes Following Surgery Using Machine Learning.

Patient-reported outcomes (PROs) enable providers to identify differences in treatment effectiveness, postoperative recovery, quality of life, and patient satisfaction. By allowing a shift from disease-specific factors to the patient perspective, PROs provide a tailored patient-centric approach to shared decision-making. Artificial intelligence (AI) and machine learning (ML) techniques can facilitate such shared decision-making and improve patient outcomes by accurate prediction of PROs. This article aims to provide a comprehensive review of the use of AI and ML models in predicting PROs following surgery through an overview of common predictive algorithms and modeling techniques, as well as current applications and limitations in the surgical field.

A Synopsis of Artificial Intelligence and its Applications in Surgery.

Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.

Artificial Intelligence and Machine Learning in Surgery.

In the present era, the technology of artificial intelligence has started to rapidly gain popularity as a revolutionary innovation in healthcare. The following article serves as the introduction to our symposium on artificial intelligence in surgery.

New Frontiers of Natural Language Processing in Surgery.

The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.

Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training.

The technology of artificial intelligence (AI) has made significant in-roads into the field of medicine over the last decade. With surgery being a discipline where repetition is the key to mastery, the scope of AI presents enormous potential for resident education through the analysis of technique and delivery of structured feedback for performance improvement. In an era marred by a raging pandemic that has decreased exposure and opportunity, AI offers an attractive solution towards improving operating room efficiency, safe patient care in the hands of supervised residents and can ultimately culminate in reduced health care costs. Through this article, we elucidate the current adoption of the artificial intelligence technology and its prospects for advancing surgical education.

Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery.

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.

Did the COVID-19 Pandemic Impact the Caliber of Trainees Taken During Match 2021? A Survey of General Surgery Residency Program Directors.

BACKGROUND: The cancellation of clinical rotations (CRs) and implementation of virtual interviews (VIs) profoundly affected the residency selection process leading up to the 2021 NRMP Match. The authors investigated how these changes influenced the caliber of applicants taken by general surgery (GS) residency programs from the perspectives of program directors (PDs). METHODS: A 14 question, web-based electronic survey was emailed to PDs of ACGME-accredited GS residency programs. Questions sought program characteristics and PDs' perspectives regarding potential differences in subjective characteristics and clinical skills demonstrated by their 2021 Match class relative to previous resident classes. RESULTS: A total of 75 PDs (27.2%) responded to our survey. Most respondents observed no changes in residents' fit with their program (72.0%), communication skills (68.0%), responsiveness to clinical instruction and feedback (73.3%), work ethic (73.3%), and rotation evaluations (68.0%). Only 21.3% of PDs believed that VIs negatively impacted their ability to accurately assess applicant intangibles. Conversely, 56.0% of PDs reported that the cancellation of CRs in 2020 negatively affected residents' clinical competency at the start of residency. At 1-year following the 2021 NRMP Match, 30.7% of PDs reported that the clinical skills exhibited by their 2021 Match class were poorer than previous resident classes. DISCUSSION: Our findings suggest that VIs limited selection committees' ability to accurately assess applicant's subjective characteristics to a lesser degree than previously described in the literature. Canceled CRs adversely affected the 2021 Match Class's clinical skills at the start of residency and at 1 year following the 2021 NRMP Match.

Obesity Stratification Predicts Short-Term Complications After Parastomal Hernia Repair.

INTRODUCTION: While previous studies have documented adverse outcomes among obese patients undergoing ventral and inguinal hernia repairs, there is a lack of literature regarding the impact of obesity on parastomal hernia (PSH) repair. This retrospective study aims to determine the value of obesity stratification in predicting postoperative complications in patients undergoing PSH repair. MATERIALS AND METHODS: Outcomes of elective PSH repairs from 2010 to 2020 in the American College of Surgeons National Surgical Quality Improvement Program database were analyzed. Patient demographics, preoperative characteristics, and postoperative outcomes were compared using bivariate analysis and multivariable regression models. RESULTS: A total of 2972 patients were retrospectively analyzed. Multivariable regression found, compared to nonobese patients, patients of obesity class ≥ II were 1.37 times more likely to develop complications overall (P = 0.006) and 1.55 times more likely to develop wound complications (P < 0.001). This group also yielded a 1.60 times higher risk of developing superficial wound infection (P = 0.007) and a 1.63 times greater risk of developing postoperative sepsis (P = 0.044). Total length of stay was longer for patients of obesity class ≥ II but not for obesity class I when compared to patients with body mass index <30.0 kg/m2. CONCLUSIONS: Patients with a body mass index ≥35.0 kg/m2 are more susceptible to an increased rate of complications after PSH repairs. The findings of this study will allow surgeons to stratify obese patients who would benefit from preoperative weight loss interventions prior to PSH repair and discuss associated risks with patients to facilitate informed consent.

Impact of the Virtual Format on Plastic Surgery Residency and Fellowship Interviews: A National Cross-Sectional Study.

BACKGROUND: The 2020 to 2021 residency and fellowship application cycles were profoundly affected by the introduction of virtual interviews. The authors investigated the impact the virtual format had on plastic surgery residency and fellowship interviews from the perspectives of program directors. METHODS: Surveys were sent to program directors of integrated plastic surgery residency and fellowship programs to ascertain their perspectives regarding the virtual format's impact on residency and fellowship interviews. Program directors were stratified into residency and fellowship cohorts, and comparative analysis was performed. RESULTS: Ninety-two program directors, 28 from integrated plastic surgery residency programs and 64 from fellowship programs, completed our survey (35 percent). Compared to in-person interviews, virtual interviews were reported to be more economical and time efficient by program directors of residency (100 percent and 46 percent, respectively) and fellowship programs (97 percent and 48 percent, respectively). Consequentially, 36 percent and 47 percent of residency and fellowship programs were able to interview more applicants, respectively. Program directors of residency and fellowship programs reported that virtual interviews hindered their ability to assess applicants' fit with the program (75 percent and 63 percent, respectively), personality and communication skills (75 percent and 64 percent, respectively), and commitment to the field, along with their ability to function as a trainee (57 percent and 50 percent, respectively). Overall, 71 percent of program directors of residency and 58 percent of program directors of fellowship programs preferred in-person interviews. The majority of residency (71 percent) and fellowship (56 percent) program directors intend to conduct both in-person and virtual interviews in future application cycles ( p = 0.12). CONCLUSIONS: Despite preferring in-person interviews, program directors intend to host both in-person and virtual interviews in future application cycles. It remains to be seen how virtual interviews will be used moving forward.