Statewide Hospital Admissions for Adult Survivors of Infant Surgical Diseases Over a 10-Year Period.

INTRODUCTION: The number of patients with congenital disease living to adulthood continues to grow. Often undergoing surgical correction in infancy, they continue to require lifelong care. Their numbers are largely unknown. We sought to evaluate hospital admissions of adult patients with esophageal atresia with tracheoesophageal fistula (EA/TEF), congenital diaphragmatic hernia (CDH), and Hirschsprung disease (HD). METHODS: The Florida Agency for Healthcare Administration inpatient database was merged with the Distressed Communities Index and Centers for Medicare and Medicaid Services Hospital and Physician Compare datasets. The dataset was queried for adult patients (≥18 y, born after 1970) with EA/TEF, CDH, and HD in their problem list from 2010 to 2020. Patient demographics, hospitalization characteristics, and discharge information were obtained. RESULTS: In total, 1140 admissions were identified (266 EA/TEF, 135 CDH, 739 HD). Patients were mostly female (53%), had a mean age of 31.6 y, and often admitted to an adult internist in a general hospital under emergency. Principal diagnoses and procedures (when performed) varied with diagnosis and age at admission. EA patients were admitted with dysphagia and foregut symptoms and often underwent upper endoscopy with dilation. CDH patients were often admitted for diaphragmatic hernias and underwent adult diaphragm repair. Hirschsprung patients were often admitted for intestinal obstructive issues and frequently underwent colonoscopy but trended toward operative intervention with increasing age. CONCLUSIONS: Adults with congenital disease continue to require hospital admission and invasive procedures. As age increases, diagnoses and performed procedures for each diagnoses evolve. These data could guide the formulation of multispecialty disease-specific follow-up programs for these patients.

Survival Tree Provides Individualized Estimates of Survival After Lung Transplant.

INTRODUCTION: Identifying contributors to lung transplant survival is vital in mitigating mortality. To enhance individualized mortality estimation and determine variable interaction, we employed a survival tree algorithm utilizing recipient and donor data. METHODS: United Network Organ Sharing data (2000-2021) were queried for single and double lung transplants in adult patients. Graft survival time <7 d was excluded. Sixty preoperative and immediate postoperative factors were evaluated with stepwise logistic regression on mortality; final model variables were included in survival tree modeling. Data were split into training and testing sets and additionally validated with 10-fold cross validation. Survival tree pruning and model selection was based on Akaike information criteria and log-likelihood values. Estimated survival probabilities and log-rank pairwise comparisons between subgroups were calculated. RESULTS: A total of 27,296 lung transplant patients (8175 single; 19,121 double lung) were included. Stepwise logistic regression yielded 47 significant variables associated with mortality. Survival tree modeling returned six significant factors: recipient age, length of stay from transplant to discharge, recipient ventilator duration post-transplant, double lung transplant, recipient reintubation post-transplant, and donor cytomegalovirus status. Eight subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves. CONCLUSIONS: Survival trees provide the ability to understand the effects and interactions of covariates on survival after lung transplantation. Individualized survival probability with this technique found that preoperative and postoperative factors influence survival after lung transplantation. Thus, preoperative patient counseling should acknowledge a degree of uncertainty given the influence of postoperative factors.

Machine Learning Analysis of Postlaparoscopy Hernias and "I'm Leaving You to Close" Strategy.

INTRODUCTION: Contributing factors to postlaparoscopy hernia are unknown. We hypothesized that postlaparoscopy incisional hernias are increased when the index surgery was performed in teaching hospitals. Laparoscopic cholecystectomy was chosen as the archetype for open umbilical access. MATERIALS AND METHODS: Maryland and Florida SID/SASD databases (2016-2019) wereused to track 1-year hernia incidence in both inpatient and outpatient settings, which was then linked to Hospital Compare, Distressed Communities Index (DCI), and ACGME. Postoperative umbilical/incisional hernia following laparoscopic cholecystectomy was identified using CPT and ICD-10. Propensity matching and eight machine learning modes were utilized including logistic regression, neural network, gradient boosting machine, random forest, gradient boosted trees, classification and regression trees, k nearest neighbors and support vector machines. RESULTS: Postoperative hernia incidence was 0.2% (total = 286; 261 incisional and 25 umbilical) in 117,570 laparoscopic cholecystectomy cases. Days to presentation (mean ± SD) were incisional 141 ± 92 and umbilical 66 ± 74. Logistic regression performed best (AUC 0.75 (95% ci 0.67-0.82) and accuracy 0.68 (95% ci 0.60-0.75) using 10-fold cross validation) in propensity matched groups (1:1; n = 279). Postoperative malnutrition (OR 3.5), hospital DCI of comfortable, mid-tier, at risk or distressed (OR 2.2 to 3.5), LOS >1 d (OR 2.2), postop asthma (OR 2.1), hospital mortality below national average (OR 2.0) and emergency admission (OR 1.7) were associated with increased hernias. A decreased incidence was associated with patient location of small metropolitan areas with <1 million residents (OR 0.5) and Charlson Comorbidity Index-Severe (OR 0.5). Teaching hospitals were not associated with postoperative hernia after laparoscopic cholecystectomy. CONCLUSIONS: Different patient factors as well as underlying hospital factors are associated with postlaparoscopy hernias. Performance of laparoscopic cholecystectomy at teaching hospitals is not associated with increased postoperative hernias.

Genomewide Association Studies of LRRK2 Modifiers of Parkinson's Disease.

OBJECTIVE: The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age-at-onset of Parkinson's disease. METHODS: We performed the first genomewide association study of penetrance and age-at-onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non-cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age-at-onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genomewide association study of Parkinson's disease was able to explain variability in penetrance and age-at-onset in LRRK2 mutation carriers. RESULTS: A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; p value = 2.5E-08, beta = 1.27, SE = 0.23, risk allele: C) met genomewide significance for the penetrance model. Co-immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these 2 proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: p value = 1.1E-07; age-at-onset top variant: p value = 9.3E-07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age-at-onset. INTERPRETATION: This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. ANN NEUROL 2021;90:82-94.

Safety comparison of minimally invasive abdomen-only esophagectomy versus minimally invasive Ivor Lewis esophagectomy: a retrospective cohort study.

BACKGROUND: We report mortality and post-operative complications from esophageal resection in the treatment of gastroesophageal adenocarcinoma or stricture, comparing a minimally invasive abdomen-only esophagectomy (MIAE) approach with a minimally invasive Ivor Lewis esophagectomy (MIILE) approach. METHODS: A single-center retrospective cohort study of patients with esophageal adenocarcinoma or stricture treated by either MIAE or MIILE was conducted. MIAE was offered for strictures less than five centimeters or cancers that were American Joint Committee on Cancer (AJCC) Stage ≤ T2 without lymphadenopathy. Patients treated with these surgical techniques were analyzed to assess pre-operative risk, intra and post-operative variables, adverse events, and overall survival. RESULTS: This study included 17 patients undergoing MIAE and 32 patients treated with MIILE. There were a fewer median number of lymph nodes resected (p < 0.001) and shorter operative duration (p < 0.001) for MIAE compared to MIILE. MIAE patients also had significantly higher Charlson Comorbidity Index scores and ACS National Surgical Quality Improvement Program (NSQIP) surgical risk values than MIILE patients (p < 0.05). There was no difference in median estimated blood loss, length of stay, pulmonary or cardiac complications between groups. There was no significant difference in 90-day survival. CONCLUSION: A minimally invasive abdomen-only approach in a specific patient population is comparable in safety to a minimally invasive Ivor Lewis approach, with associated shorter median operative duration. MIAE patients had significantly greater pre-operative comorbidities and higher calculated peri-operative risk of complication but demonstrated similar post-operative outcomes. This suggests that MIAE may be a suitable surgical approach for treating gastroesophageal adenocarcinoma or stricture in patients deemed unsuitable for MIILE.

Contemporary utilization of the axillary artery in cardiac surgery.

INTRODUCTION: The axillary artery is a reliable inflow vessel when addressing pathology of the aortic root and aortic arch that may preclude standard central cannulation strategies. This narrative review examines the use of the axillary artery in cardiac surgery. Anatomy, indications for use, cannulation strategies, and potential complications will be discussed. METHODS: A comprehensive review of the current literature was performed using PubMed, Cochrane Review, and authoritative committee guidelines. A narrative review incorporating current available evidence was undertaken. COMMENT: Use of the axillary artery in select cardiac surgical cases is reliable, reproducible, and may be preferable in certain cases involving ascending aortic pathology, reoperative surgery, porcelain aorta, access for transcatheter valve therapies, and peripheral mechanical circulatory support.

A machine learning approach to high-risk cardiac surgery risk scoring.

INTRODUCTION: In patients undergoing high-risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision-making, a machine learning approach was used to determine weighted personalized factors contributing to mortality. METHODS: American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM-trained data were then used for local interpretable model-agnostic explanations (LIME) modeling to provide individual patient-specific mortality prediction. RESULTS: A total of 194 patient deaths among 1291 high-risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit. CONCLUSIONS: The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.

Regional Cerebral Oxygen Saturation Level Predicts 30-Day Mortality Rate After Left Ventricular Assist Device Surgery.

OBJECTIVE: Left ventricular assist device (LVAD) surgery is complex, high risk, and expensive. The authors' hypothesis is baseline regional cerebral oxygen saturation (rSO2) might be a predictor of postoperative clinical outcomes. DESIGN: Retrospective review of 210 consecutive continuous flow LVAD patients between 2008 and 2014. The primary measure is 30-day mortality rate and secondary measures include modified major adverse cardiocerebral events (MACE), length of stay (LOS), and intensive care unit (ICU) stay. Multiple logistic regression models were applied to examine if a binary outcome variable, such as 30-day mortality and MACE, is associated with rSO2 at baseline. Log-linear model was used to examine whether LOS or ICU stay hours is associated with rSO2 at baseline. SETTING: Single institution, academic hospital. PARTICIPANTS: Patients who received LVAD surgery ​at Jewish Hospital, Louisville, KY. INTERVENTIONS: All patients received LVAD surgery. Cerebral oximetry monitoring was used in both the preoperative and intraoperative periods. MEASUREMENTS AND MAIN RESULTS: The authors found that higher rSO2 at baseline is associated with lower 30-day mortality with an odds ratio of 0.94 and 95% confidence interval (0.888, 0.995) for every 1% increase of rSO2. For secondary outcomes, baseline rSO2 was not significantly associated with MACE, requirement for postoperative renal failure/dialysis, reoperation for bleeding, and LOS or ICU hours. CONCLUSIONS: Regional cerebral oxygen saturation levels at baseline are significantly associated with 30-day mortality after LVAD surgeries.

Survival trends in heart transplant patients supported on ECMO and IABP: A 10-year UNOS database analysis.

The United Network for Organ Sharing (UNOS) heart transplant allocation policy was changed in 2018. This study examines the impact of the change in UNOS heart transplant allocation policy on the use of temporary mechanical circulatory support (MCS) devices and post-transplant survival. The analysis included a total of 26,481 patients listed and transplanted between January 2013 and June 2022. The results showed a decrease in waiting time for transplant after the policy change, indicating a successful reduction in waitlist time for high-priority status patients. However, the length of hospital stays from transplant to discharge increased following the policy change. The study also found an increase in the frequency of ECMO and IABP use both at the time of listing and at the time of transplant following the policy change. Cumulative patient and graft survival at 1000 days decreased following the policy change (86.1 per cent versus 83.7 per cent at 1000 days, p = 0.002). However, the survival curves showed similar survival trends in the first 2 years, with late divergence in survival occurring after 2 years. In conclusion the latest UNOS heart transplant allocation policy change led to a decrease in waiting times and an increase in the use of temporary MCS devices. There was a decrease in cummulative survival at 1000 days following the policy change.

Clinical Applications of Machine Learning.

Objective: This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. Background: As machine learning, artificial intelligence, and generative artificial intelligence become increasingly utilized in clinical medicine, it is imperative that end users understand the underlying methodologies. Methods: This review describes publicly available datasets that can be used with interpretable predictive approaches, natural language processing, image recognition, and reinforcement learning models, outlines result interpretation, and provides references for in-depth information about each analytical framework. Results: This review introduces interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning methodologies. Conclusions: Interpretable predictive machine learning models, natural language processing, image recognition, and reinforcement learning are core machine learning methodologies that underlie many of the artificial intelligence methodologies that will drive the future of clinical medicine and surgery. End users must be well versed in the strengths and weaknesses of these tools as they are applied to patient care now and in the future.

Meta-Analysis in Surgical Research: Methodology and Statistical Application.

In evidence-based medicine, systematic review continues to carry the highest weight in terms of quality and reliability, synthesizing robust information from previously published cohort studies to provide a comprehensive overview of a topic. Meta-analysis provides further depth by allowing for comparative analysis between the studied intervention and the control group, providing the most up-to-date evidence on their characteristics and efficacy. We discuss the principles and methodology of meta-analysis, and its applicability to the field of surgical research. The clinical question is defined using PICO framework (Problem, Intervention, Comparison, Outcome). Then a systematic article search is performed across multiple medical databases using relevant search terms, which are then filtered out based on appropriate screening tools. Pertinent data from the selected articles are collected and undergo critical appraisal by at least two independent reviewers. Additional statistical tests may be performed to identify the presence of any significant bias. The data are then synthesized to perform comparative analysis between the intervention and comparison groups. In this article, we discuss specifically the usage of R software (R Foundation for Statistical Computing, Vienna, Austria) for data analysis and visualization. Meta-analysis results of the pooled data are presented using forest plots. Concerns for potential bias may be addressed through the creation of funnel plots. Meta-analysis is a powerful tool to provide highly reliable medical evidence. It may be readily performed by independent researchers with minimal need for funding or institutional approval. The ability to conduct such studies is an asset to budding medical scholars.

Is nonoperative management of appendicitis inferior in the COVID-19 era?

BACKGROUND: Previous studies on nonoperative management (NOM) of acute appendicitis (AA) indicated comparable outcomes to surgery, but the effect of COVID-19 infection on appendicitis outcomes remains unknown. Thus, we evaluate appendicitis outcomes during the COVID-19 pandemic to determine the effect of COVID-19 infection status and treatment modality. We hypothesized that active COVID-19 patients would have worse outcomes than COVID-negative patients, but that outcomes would not differ between recovered COVID-19 and COVID-negative patients. Moreover, we hypothesized that outcomes would not differ between nonoperative and operative management groups, regardless of COVID-19 status. METHODS: We queried the National COVID Cohort Collaborative from 2020 to 2023 to identify adults with AA who underwent operative or NOM. COVID-19 status was denoted as follows: COVID-negative, COVID-active, or COVID-recovered. Intention to treat was used for NOM. Propensity score-balanced analysis was performed to compare outcomes within COVID groups, as well as within treatment modalities. RESULTS: A total of 37,868 patients were included: 34,866 COVID-negative, 2,540 COVID-active, and 460 COVID-recovered. COVID-active and recovered less often underwent operative management. Unadjusted, there was no difference in mortality between COVID groups for operative management. There was no difference in rate of failure of NOM between COVID groups. Adjusted analysis indicated, compared with operative, NOM carried higher odds of mortality and readmission for COVID-negative and COVID-active patients. CONCLUSION: This study demonstrates higher odds of mortality among NOM of appendicitis and near equivalent outcomes for operative management regardless of COVID-19 status. We conclude that NOM of appendicitis is associated with worse outcomes for COVID-active and COVID-negative patients. In addition, we conclude that a positive COVID test or recent COVID-19 illness alone should not preclude a patient from appendectomy for AA. Surgeon clinical judgment of a patient's physiology and surgical risk should, of course, inform the decision to proceed to the operating room. LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

Artificial Intelligence in Surgical Research: Accomplishments and Future Directions.

MINI-ABSTRACT: The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data. OBJECTIVE: Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice. SUMMARY BACKGROUND DATA: The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies. METHODS: The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm. RESULTS AND CONCLUSIONS: The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.

Interpretable machine learning accurately reclassifies lobectomy surgical approaches by cost.

BACKGROUND: The volume of robotic lung resection continues to increase despite its higher costs and unproven superiority to video-assisted thoracoscopic surgery. We evaluated whether machine learning can accurately identify factors influencing cost and reclassify high-cost operative approaches into lower-cost alternatives. METHODS: The Florida Agency for Healthcare Administration and Centers for Medicare and Medicaid Services Hospital and Physician Compare datasets were queried for patients undergoing open, video-assisted thoracoscopic surgery and robotic lobectomy. K-means cluster analysis was used to identify robotic clusters based on total cost. Predictive models were built using artificial neural networks, Support Vector Machines, Classification and Regression Trees, and Gradient Boosted Machines algorithms. Models were applied to the high-volume robotic group to determine patients whose cost cluster changed if undergoing a video-assisted thoracoscopic surgery approach. A local interpretable model-agnostic explanation approach personalized cost per patient. RESULTS: Of the 6,618 cases included in the analysis, we identified 4 cost clusters. Application of artificial neural networks to the robotic subgroup identified 1,642 (65%) cases with no re-assignment of cost cluster, 583 (23%) with reduced costs, and 300 (12%) with increased costs if they had undergone video-assisted thoracoscopic surgery approach. The 5 overall highest cost predictors were patient admission from the clinic, diagnosis of metastatic cancer, presence of cancer, urgent hospital admission, and dementia. CONCLUSION: K-means cluster analysis and machine learning identify a patient population that may undergo video-assisted thoracoscopic surgery or robotic lobectomy without a significant difference in total cost. Local interpretable model-agnostic explanation identifies individual patient factors contributing to cost. Application of this modeling may reliably stratify high-cost patients into lower-cost approaches and provide a rationale for reducing expenditure.

Analysis of temporal trends in robotic surgery costs using the outpatient inguinal hernia repair model.

BACKGROUND: Robotic technology is increasingly utilized despite increased costs compared with laparoscopic procedures. As the robot is a fixed, indirect cost, we hypothesized increased volume of robotic procedures will decrease operative costs per patient. The model of same-day, unilateral, primary inguinal hernia surgery in males was chosen. METHODS: The Florida Agency for Health Care Administration database was queried for inguinal hernia repairs from 2015 to 2020. Inflation adjusted total and operative costs per patient were collected. Cost-over-time and change in cost-over-time were calculated for open, laparoscopic, and robotic cases. Linear regression using cost as the dependent variable generated predictive parameters. RESULTS: In the study, 36,393 cases (19,364 open, 12,322 laparoscopic, 4,707 robotic) among 86 hospitals were included. In addition, 18 hospitals were "high volume," defined as total robotic inguinal hernia volume of >100 (range, 107-368) during the study period, and included 8,604 cases (3,915 open, 1,786 laparoscopic, 2,903 robotic). Compared with laparoscopic, total robotic cost and cost over time were 1.22- (P < .001) and 1.5-fold higher (P < .002). The change in cost-over-time was increased significantly in robotic cases: 358, 420, 548, 691, and 1,542 cost-over-time for 2015 to 2020, respectively. Positive contributors to total hospital robotic costs were total robotic inguinal hernia volume (17.3), total laparoscopic inguinal hernia volume (12.6), and number of hospital beds (1.9). Total open inguinal hernia volume was a negative contributor (-10). CONCLUSION: We conclude, in the short term, robotic surgical costs are not behaving as traditional fixed costs in outpatient, unilateral inguinal hernia surgeries. Hospital methodology for cost assignment and increased robotic fixed costs such as purchase of additional instruments may explain these results.

Determining the value proposition of surgical care in CMS star rated hospitals.

BACKGROUND: CMS Hospital Quality Star ratings reflect the quality of care given to patients. It is hypothesized that increased Star-rating is associated with higher cost and that the value proposition is diminished. METHODS: This study used the Florida AHCA inpatient dataset, CY2019. Partial colectomy was selected as a representative inpatient surgical procedure. Analysis was performed on this data to compare high and low Star-rated hospitals. RESULTS: Total costs were equivalent among all Star levels on initial analysis. In a propensity matched comparison with 1 Star, 5 Star hospitals had significantly lower length-of-stay and ICU, anesthesia, radiology and lab costs, and conversely, had higher total (+2%), operating room and med-surg supply costs. CONCLUSIONS: These results demonstrate that total colectomy costs are functionally equivalent among the CMS 1- and 5- Star categories. The results indicate that higher CMS Star ratings fulfill the value proposition and indeed offer higher quality without significantly increased cost.

Recipient Survival after Orthotopic Liver Transplantation: Interpretable Machine Learning Survival Tree Algorithm for Patient-Specific Outcomes.

BACKGROUND: Elucidating contributors affecting liver transplant survival is paramount. Current methods offer crude global group outcomes. To refine patient-specific mortality probability estimation and to determine covariate interaction using recipient and donor data, we generated a survival tree algorithm, Recipient Survival After Orthotopic Liver Transplantation (ReSOLT), using United Network Organ Sharing (UNOS) transplant data. STUDY DESIGN: The UNOS database was queried for liver transplants in patients ≥18 years old between 2000 and 2021. Preoperative factors were evaluated with stepwise logistic regression; 43 significant factors were used in survival tree modeling. Graft survival of <7 days was excluded. The data were split into training and testing sets and further validated with 10-fold cross-validation. Survival tree pruning and model selection was achieved based on Akaike information criterion and log-likelihood values. Log-rank pairwise comparisons between subgroups and estimated survival probabilities were calculated. RESULTS: A total of 122,134 liver transplant patients were included for modeling. Multivariable logistic regression (area under the curve = 0.742, F1 = 0.822) and survival tree modeling returned 8 significant recipient survival factors: recipient age, donor age, recipient primary payment, recipient hepatitis C status, recipient diabetes, recipient functional status at registration and at transplantation, and deceased donor pulmonary infection. Twenty subgroups consisting of combinations of these factors were identified with distinct Kaplan-Meier survival curves (p < 0.001 among all by log rank test) with 5- and 10-year survival probabilities. CONCLUSIONS: Survival trees are a flexible and effective approach to understand the effects and interactions of covariates on survival. Individualized survival probability following liver transplant is possible with ReSOLT, allowing for more coherent patient and family counseling and prediction of patient outcome using both recipient and donor factors.

Nonelective coronary artery bypass graft outcomes are adversely impacted by Coronavirus disease 2019 infection, but not altered processes of care: A National COVID Cohort Collaborative and National Surgery Quality Improvement Program analysis.

Objective: The effects of Coronavirus disease 2019 (COVID-19) infection and altered processes of care on nonelective coronary artery bypass grafting (CABG) outcomes remain unknown. We hypothesized that patients with COVID-19 infection would have longer hospital lengths of stay and greater mortality compared with COVID-negative patients, but that these outcomes would not differ between COVID-negative and pre-COVID controls. Methods: The National COVID Cohort Collaborative 2020-2022 was queried for adult patients undergoing CABG. Patients were divided into COVID-negative, COVID-active, and COVID-convalescent groups. Pre-COVID control patients were drawn from the National Surgical Quality Improvement Program database. Adjusted analysis of the 3 COVID groups was performed via generalized linear models. Results: A total of 17,293 patients underwent nonelective CABG, including 16,252 COVID-negative, 127 COVID-active, 367 COVID-convalescent, and 2254 pre-COVID patients. Compared to pre-COVID patients, COVID-negative patients had no difference in mortality, whereas COVID-active patients experienced increased mortality. Mortality and pneumonia were higher in COVID-active patients compared to COVID-negative and COVID-convalescent patients. Adjusted analysis demonstrated that COVID-active patients had higher in-hospital mortality, 30- and 90-day mortality, and pneumonia compared to COVID-negative patients. COVID-convalescent patients had a shorter length of stay but a higher rate of renal impairment. Conclusions: Traditional care processes were altered during the COVID-19 pandemic. Our data show that nonelective CABG in patients with active COVID-19 is associated with significantly increased rates of mortality and pneumonia. The equivalent mortality in COVID-negative and pre-COVID patients suggests that pandemic-associated changes in processes of care did not impact CABG outcomes. Additional research into optimal timing of CABG after COVID infection is warranted.

Disparities in coronary artery bypass grafting between high- and low-volume surgeons and hospitals.

Background: High-volume surgeons and hospitals performing coronary artery bypass grafting have been associated with improved patient outcomes. However, patients of increased socioeconomic distress may have worse outcomes because of health care disparities. We sought to identify trends and outcomes in patients of elevated distress undergoing bypass grafting. Methods: The Florida Agency for Healthcare Administration administrative data set was merged with Centers for Medicare and Medicaid Services Physician and Hospital Compare and Economic Innovation Group Distressed Community Index data sets to build a comprehensive database. The data set was queried to identify patients undergoing coronary artery bypass procedures between 2016 and 2020. High- and low-volume hospitals and surgeons were compared. Patient and hospital demographics, comorbidities, length of stay, and postoperative complications were analyzed by χ2 and t test where appropriate. Results: A total of 41,571 coronary artery bypass grafting procedures were performed by 174 surgeons at 67 Florida hospitals. Low- and high-volume hospitals did not differ with respect to hospital ownership, overall star rating, national comparisons of mortality, readmission, or cost effectiveness. Patients from at-risk and distressed communities were more likely to undergo surgery at low-volume hospitals. Hospital length of stay was increased for low-volume hospitals (10.2 vs 9.4 days, P < .05). Postoperative complications including pneumonia, arrhythmia, respiratory failure, acute renal failure, shock, pleural effusion, and sepsis were more frequent at low-volume hospitals and for low-volume surgeons. Conclusion: High-volume hospitals and surgeons have improved postoperative outcomes and hospital length of stay when compared to low-volume hospitals and surgeons performing coronary artery bypass grafting. At-risk and distressed populations are more likely to undergo bypass surgery at low-volume hospitals, potentially contributing to worse patient outcome. Efforts should be made to mitigate the potential impact of low socioeconomic status to improve outcomes in this population.

Adopting robotic thoracic surgery impacts hospital overall lung resection case volume.

PURPOSE: We sought to evaluate the role of robotic-assisted lung surgery on hospital volume using difference in difference (DID). We propose hospital adoption of robotic thoracic technology increases total volume of specific procedures as compared to non-robotic hospitals. METHODS: The 2010-2015 Florida Agency for Health Care Administration dataset was queried for open, video-assisted thoracoscopic, and robotic-assisted thoracic surgeries. Incident Rate Ratios (IRR) from DID analysis determined the significance of robotic technology. For each technique, length of stay and elements of charges were compared to determine statistical significance. RESULTS: A total of 28,484 lung resection procedures performed at 162 hospitals, 65 of which had robotic capabilities were included. Robotic hospitals experienced an 85% increase in total lung surgical volume (IRR 1.85, p-value <0.001). This increase in volume was consistent for each lung resection procedure separately. CONCLUSION: Hospital adoption of robotic technology significantly increases the overall lung surgical volume for select lung resection procedures.