Predicting Unplanned Trauma ICU Admissions for Initial Nonoperative, Non-ICU Patients.

INTRODUCTION: Unplanned intensive care unit (ICU) admissions are associated with increased morbidity and mortality. This study uses interpretable machine learning to predict unplanned ICU admissions for initial nonoperative trauma patients admitted to non-ICU locations. METHODS: TQIP (2020-2021) was queried for initial nonoperative adult patients admitted to non-ICU locations. Univariable analysis compared patients who required an unplanned ICU admission to those who did not. Using variables that could be known at hospital admission, gradient boosting machines (CatBoost, LightGBM, XGBoost) were trained on 2021 data and tested on 2020 data. SHapley Additive exPlanations (SHAP) were used for interpretation. RESULTS: The cohort had 1,107,822 patients; 1.6% had an unplanned ICU admission. Unplanned ICU admissions were older (71 [58-80] vs. 61 [39-76] years, p < 0.01), had a higher Injury Severity Score (ISS) (9 [8-13] vs. 9 [4-10], p < 0.01), longer length of stay (11 [7-17] vs. 4 [3-6] days, p < 0.01), higher rates of all complications and most comorbidities and injuries (p < 0.05). All models had an AUC of 0.78 and an F1 score of 0.12, indicating poor performance in predicting the minority class. Mean absolute SHAP values revealed ISS (0.46), age (0.29), and absence of comorbidities (0.16) as most influential in predictions. Dependency plots showed greater SHAP values for greater ISS, age, and presence of comorbidities. CONCLUSIONS: Machine learning may outperform prior attempts at predicting the risk of unplanned ICU admissions in trauma patients while identifying unique predictors. Despite this progress, further research is needed to improve predictive performance by addressing class imbalance limitations.

OnetoMap Meta-Data: Healthcare Analytics Through Research.

INTRODUCTION: Big Data has revolutionized healthcare research through the three Vs: volume, veracity, and variety. This study introduces the OnetoMap meta-data repository, a centralized inventory developed in collaboration with the University of South Florida's Department of Surgery. METHODS: The repository offers extensive details about each database, including its primary purpose, available variables, and examples of high-impact research utilizing these databases. It aims to create a centralized inventory, enabling researchers to locate and link relevant datasets efficiently. Each dataset is described using standardized criteria to ensure clarity and usability, such as data type, source, collection methods, and potential linkages to other datasets.  Results: Currently, the OnetoMap repository contains descriptions of 49 datasets, with ongoing updates to include new datasets and additional data years. These datasets include a range of data types, including cross-sectional and longitudinal, gathered through claims, registries, electronic health records, and surveys. The repository is hosted on GitHub, enabling version control, collaboration, and open access. Effective search functionalities and descriptive categorization enhance the findability of datasets. DISCUSSION: The data repository includes comprehensive records of patient health statuses, socioeconomic profiles, hospital structures, and physician practices, enabling nuanced interventions and addressing complex healthcare needs. It also promotes interdisciplinary research and accelerates novel discoveries by providing a centralized source of diverse data and facilitating collaboration among research teams. CONCLUSION: The OnetoMap meta-data repository represents a significant advancement in healthcare research by providing a centralized, detailed, and easily accessible repository of clinical research databases. Future directions include implementing automatic annual updates of datasets, exploring automatic dataset linkage, providing monthly updates on published research, creating a user chat space for enhanced collaboration, and developing code applets for simplified data analysis. These efforts will ensure that the repository remains current, functional, and accessible, ultimately facilitating new discoveries and insights in healthcare outcomes research.

Hospitals with decreasing cost-to-charge ratios bill greater surgical charges for similar outcomes.

BACKGROUND: The cost-to-charge ratio reflects the markup of hospital services. A lower cost-to-charge ratio indicates lower costs and/or greater charges. This study examines factors associated with cost-to-charge ratio trends to determine whether decreasing cost-to-charge ratio is associated with worse surgical outcomes. METHODS: The Florida Agency for Healthcare Administration Inpatient database (2018-2020) was queried for common surgical procedures and linked to the Distressed Communities Index, RAND Corporation Hospital data, Center for Medicare Services Cost Reports, and American Hospital Association data. Only hospitals with monotonically increasing or decreasing cost-to-charge ratio were included in the study. Univariable analysis compared these hospitals. Using patient-level data, interpretable machine learning predicted cost-to-charge ratio trend while identifying influential factors. RESULTS: The cohort had 67 hospitals (27 increasing cost-to-charge ratio and 40 decreasing cost-to-charge ratio) with 35,661 surgeries. Decreasing cost-to-charge ratio hospitals were more often proprietarily owned (78% vs 33%, P = .01) and had greater mean total charges ($134,349 ± $114,510 vs $77,185 ± $82,027, P < .01) with marginally greater mean estimated costs ($14,863 ± $12,343 vs $14,458 ± $15,440, P < .01). Patients from decreasing cost-to-charge ratio hospitals had greater rates of most comorbidities (P < .05) but no difference in mortality or overall complications. Machine-learning models revealed charges rather than clinical factors as most influential in cost-to-charge ratio trend prediction. CONCLUSIONS: Decreasing cost-to-charge ratio hospitals charge vastly more despite minimally greater estimated costs and no difference in outcomes. Although differences in case-mix existed, charges were the predominant differentiators. Patient clinical factors had far less of an impact.

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.

For the Love of the Game: Calculating the Premium Associated With Academic Surgical Practice.

OBJECTIVE: We sought to determine the premium associated with a career in academic surgery, as measured by compensation normalized to the work relative value unit (wRVU). BACKGROUND: An academic surgical career embodying innovation and mentorship offers intrinsic rewards but is not well monetized. We know compensation for academic surgeons is less than their nonacademic counterparts, but the value of clinical effort, as normalized to the wRVU, between academic and nonacademic surgeons has not been well characterized. Thus, we analyzed the variations in the valuation of academic and nonacademic surgical work from 2010 to 2022. METHODS: We utilized Medical Group Management Association Provider Compensation data from 2010, 2014, 2018, and 2022 to compare academic and nonacademic surgeons. We analyzed raw total cash compensation (TCC), wRVU, TCC per wRVU (TCC/wRVU), and TCC to collections (TCCtColl). We calculated collections per wRVU (Coll/wRVU). We adjusted TCC and TCCtColl for inflation using the Consumer Price Index. Linear modeling for trend analysis was performed. RESULTS: Compared with nonacademic, academic surgeons had lower TCC (2010: $500,415.0±23,666 vs $631,515.5±23,948.2, -21%; 2022: $564,789.8±23,993.9 vs $628,247.4±15,753.2, -10%), despite higher wRVUs (2022: 9109.4±474.9 vs 8062.7±252.7) and higher Coll/wRVU (2022: 76.68±8.15 vs 71.80±6.10). Trend analysis indicated that TCC will converge in 2038 at an estimated $660,931. CONCLUSIONS: In 2022, academic surgeons had more clinical activity and superior organizational revenue capture, despite less total and normalized clinical compensation. On the basis of TCC/wRVUs, academia charges a premium of 16% over nonacademic surgery. However, trend analysis suggests that TCC will converge within the next 20 years.

Lower Socioeconomic Status Predicts Greater Obstacles to Care: Using Outpatient Cholecystectomy as a Model Cohort.

BACKGROUND: Patients with low socioeconomic status (SES) are disadvantaged in terms of access to health care. A novel metric for SES is the Distressed Communities Index (DCI). This study evaluates the effect of DCI on hospital choice and distance traveled for surgery. METHODS: A Florida database was queried for patients with symptomatic cholelithiasis or chronic cholecystitis who underwent an outpatient cholecystectomy between 2016 and 2019. Patients' DCI was compared with hospital ratings, comorbidities, Charlson Comorbidity Index, and distance traveled for surgery. Stepwise logistic regression was used to determine which factors most influenced distance traveled for surgery. RESULTS: There were 54,649 cases-81 open, 52,488 laparoscopic, and 2,080 robotic. There was no difference between surgical approach and patient's DCI group (p = 0.12). Rural patients traveled the farthest for surgery (avg 21.29 miles); urban patients traveled the least (avg 5.84 miles). Patients from distressed areas more often had surgery at one- or two-star hospitals than prosperous patients (61% vs 36.3%). Regression indicated distressed or at-risk areas predicted further travel for rural/small-town patients, while higher hospital ratings predicted further travel for suburban/urban patients. DISCUSSION: Compared to prosperous areas, patients from distressed areas have surgery at lower-rated hospitals, travel further if they live in rural/small-town areas, but travel less if they live in suburban areas. We postulate that farther travel in rural areas may be explained by a lack of health care resources in poor, rural areas, while traveling less in suburban areas may be explained by personal lack of resources for patients with low SES.

Expanded analysis for patients with acute cholecystitis indicates outcomes vary based on COVID-19 status and treatment modality.

BACKGROUND: The impact of different phases of COVID-19 infection on outcomes from acute calculous cholecystitis (ACC) is not well understood. Therefore, we examined outcomes of acute cholecystitis during the COVID-19 pandemic, comparing the effect of different treatment modalities and COVID-19 infection status. We hypothesized that patients with acute COVID-19 would have worse outcomes than COVID-negative patients, but there would be no difference between COVID-negative and COVID-recovered patients. METHODS: We used 2020-2023 National COVID Cohort Collaborative data to identify adults with ACC. Treatment (antibiotics-only, cholecystostomy tube, or cholecystectomy) and COVID-19 status (negative, active, or recovered) were collected. Treatment failure of nonoperative managements was noted. Adjusted analysis using a series of generalized linear models controlled for confounders (age, sex, body mass index, Charlson comorbidity index, severity at presentation, and year) to better assess differences in outcomes among treatment groups, as well as between COVID-19 groups. RESULTS: In total, 32,433 patients (skewed count) were included: 29,749 COVID-negative, 2112 COVID-active, and 572 (skewed count) COVID-recovered. COVID-active had higher rates of sepsis at presentation. COVID-negative more often underwent cholecystectomy. Unadjusted, COVID-active had higher 30-day mortality, 30-day complication, and longer length of stay than COVID-negative and COVID-recovered. Adjusted analysis revealed cholecystectomy carried lower odds of mortality for COVID-active and COVID-negative patients than antibiotics or cholecystostomy. COVID-recovered patients' mortality was unaffected by treatment modality. Treatment failure from antibiotics was more common for COVID-negative patients. CONCLUSION: Acute cholecystitis outcomes are affected by phase of COVID-19 infection and treatment modality. Cholecystectomy does not lead to worse outcomes for COVID-active and COVID-recovered patients than nonoperative treatments; thus, these patients can be considered for cholecystectomy if their physiology is not prohibitive.

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.

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.

The continued financial effect of COVID: Increasing costs for non-elective major lower extremity amputations.

Background: The COVID-19 pandemic necessitated changes in processes of care, which significantly impacted surgical care. This study evaluated the impact of these changes on patient outcomes and costs for non-elective major lower extremity amputations (LEA). Methods: The 2019-2021 Florida Agency for Health Care Administration database was queried for adult patients who underwent non-elective major LEA. Per-patient inflation-adjusted costs were collected. Patient cohorts were established based on Florida COVID-19 mortality rates: COVID-heavy (CH) included nine months with the highest mortality, COVID-light (CL) included nine months with the lowest mortality, and pre-COVID (PC) included nine months before COVID (2019). Outcomes included in-hospital patient outcomes and hospitalization cost. Results: 6132 patients were included (1957 PC, 2104 CH, and 2071 CL). Compared to PC, there was increased patient acuity at presentation, but morbidity (31%), mortality (4%), and length of stay (median 12 [8-17] days) were unchanged during CH and CL. Additionally, costs significantly increased during the pandemic; median total cost rose 9%, room costs increased by 16%, ICU costs rose by 15%, and operating room costs rose by 15%. When COVID-positive patients were excluded, cost of care was still significantly higher during CH and CL. Conclusions: Despite maintaining pre-pandemic standards, as evidenced by unchanged outcomes, the pandemic led to increased costs for patients undergoing non-elective major LEA. This was likely due to increased patient acuity, resource strain, and supply chain shortages during the pandemic. Key message: While patient outcomes for non-elective major lower extremity amputations remained consistent during the COVID-19 pandemic, healthcare costs significantly increased, likely due to increased patient acuity and heightened pressures on resources and supply chains. These findings underscore the need for informed policy changes to mitigate the financial impact on patients and healthcare systems for future public health emergencies.

Cirrhosis Increases the Rate of Failure of Nonoperative Management in Blunt Liver Injuries.

Pre-existing cirrhosis is associated with increased mortality in blunt liver injury. Despite widespread use of nonoperative management (NOM) for blunt liver injury, there is a relative paucity of data regarding how pre-existing cirrhosis impacts the success of NOM. Herein, we perform a retrospective cohort study using ACS TQIP 2017-2020 data to assess the relationship between cirrhosis and failure of NOM for adult patients with blunt liver injury. 37,176 patients were included (342 cirrhosis and 36,834 without cirrhosis). After propensity-score matching, patients with pre-existing cirrhosis had higher rates of failure of NOM (32.2 vs 14.1%, p < 0.01) and in-hospital mortality (36.3 vs 10.8%, p < 0.01) than patients without cirrhosis. Hesitancy to operate on patients with pre-existing cirrhosis and trauma, as well as significant underlying coagulopathy, may explain these findings.

Transition to Permitless Open Carry and Association with Firearm-Related Suicide.

BACKGROUND: Firearm-related death rates continue to rise in the US. As some states enact more permissive firearm laws, we sought to assess the relationship between a change to permitless open carry (PLOC) and subsequent firearm-related death rates, a currently understudied topic. STUDY DESIGN: Using state-level data from 2013 to 2021, we performed a linear panel analysis using a state fixed-effects model. We examined total firearm-related death, suicide, and homicide rates separately. If a significant association between OC law and death rate was found, we then performed a difference-in-difference (DID) analysis to assess for a causal relationship between changing to PLOC and increased death rate. For significant DID results, we performed confirmatory DID separating firearm and nonfirearm death rates. RESULTS: Nineteen states maintained a no OC or permit-required law, whereas 5 changed to permitless and 26 had a PLOC before 2013. The fixed-effects model indicated more permissive OC law that was associated with increased total firearm-related deaths and suicides. In DID, changing law to PLOC had a significant average treatment effect on the treated of 1.57 (95% CI 1.05 to 2.09) for total suicide rate but no significant average treatment effect for the total firearm-related death rate. Confirmatory DID results found a significant average treatment effect on the treated of 1.18 (95% CI 0.90 to 1.46) for firearm suicide rate. CONCLUSIONS: OC law is associated with total firearm-related death and suicide rates. Based on our DID results, changing to PLOC is indeed strongly associated with increased suicides by firearm.

Examining surgeon stress in robotic and laparoscopic surgery.

Robotic surgery may decrease surgeon stress compared to laparoscopic. To evaluate intraoperative surgeon stress, we measured salivary alpha-amylase and cortisol. We hypothesized robotic elicited lower increases in surgeon salivary amylase and cortisol than laparoscopic. Surgical faculty (n = 7) performing laparoscopic and robotic operations participated. Demographics: age, years in practice, time using laparoscopic vs robotic, comfort level and enthusiasm for each. Operative data included operative time, WRVU (surgical "effort"), resident year. Saliva was collected using passive drool collection system at beginning, middle and end of each case; amylase and cortisol measured using ELISA. Standard values were created using 7-minute exercise (HIIT), collecting saliva pre- and post-workout. Linear regression and Student's t test used for statistical analysis; p values < 0.05 were significant. Ninety-four cases (56 robotic, 38 laparoscopic) were collected (April-October 2022). Standardized change in amylase was 8.4 ± 4.5 (p < 0.001). Among operations, raw maximum amylase change in laparoscopic and robotic was 23.4 ± 11.5 and 22.2 ± 13.4; raw maximum cortisol change was 44.21 ± 46.57 and 53.21 ± 50.36, respectively. Values normalized to individual surgeon HIIT response, WRVU, and operative time, showing 40% decrease in amylase in robotic: 0.095 ± 0.12, vs laparoscopic: 0.164 ± 0.16 (p < 0.02). Normalized change in cortisol was: laparoscopic 0.30 ± 0.44, robotic 0.22 ± 0.4 (p = NS). On linear regression (p < 0.001), surgeons comfortable with complex laparoscopic cases had lower change in normalized amylase (p < 0.01); comfort with complex robotic was not significant. Robotic may be less physiologically stressful, eliciting less increase in salivary amylase than laparoscopic. Comfort with complex laparoscopic decreased stress in robotic, suggesting laparoscopic experience is valuable prior to robotic.

The downtrending cost of robotic bariatric surgery: a cost analysis of 47,788 bariatric patients.

The surgical robot is assumed to be a fixed, indirect cost. We hypothesized rising volume of robotic bariatric procedures would decrease cost per patient over time. Patients who underwent elective, initial gastric bypass (GB) or sleeve gastrectomy (SG) for morbid obesity were selected from Florida Agency for Health Care Administration database from 2017 to 2021. Inflation-adjusted cost per patient was collected. Cost-over-time ($/patient year) and change in cost-over-time were calculated for open, laparoscopic, and robotic cases. Linear regression on cost generated predictive parameters. Density plots utilizing area under the curve demonstrated cost overlap. Among 76 hospitals, 11,472 bypasses (223 open, 6885 laparoscopic, 4364 robotic) and 36,316 sleeves (26,596 laparoscopic, 9724 robotic) were included. Total cost for robotic was approximately 1.5-fold higher (p < 0.001) than laparoscopic for both procedures. For GB, laparoscopic had lower total ($15,520) and operative ($6497) average cost compared to open (total $17,779; operative $9273) and robotic (total $21,756; operative $10,896). For SG, laparoscopic total cost was significantly less than robotic ($10,691 vs. $16,393). Robotic GB cost-over-time increased until 2021, when there was a large decrease in cost (-$944, compared with 2020). Robotic SG total cost-over time fluctuated, but decreased significantly in 2021 (-$490 compared with 2020). While surgical costs rose significantly in 2020 for bariatric procedures, our study suggests a possible downward trend in robotic bariatric surgery as total and operative costs are decreasing at a higher rate than laparoscopic costs.

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.

Corrigendum to "Association of state-level factors with rate of firearm-related deaths" [Surg. Open Sci. vol. 14 (2023) 114-119].

[This corrects the article DOI: 10.1016/j.sopen.2023.07.011.].

Patient outcomes and cost in robotic emergency general surgery.

The use of robotic technology in general surgery continues to increase, though its utility for emergency general surgery remains under-studied. This study explores the current trends in patient outcomes and cost of robotic emergency general surgery (REGS). The Florida Agency for Healthcare Administration database (2018-2020) was queried for adult patients undergoing intra-abdominal emergency general surgery within 24 h of admission and linked to CMS Cost Reports/Hospital Compare, American Hospital Association, and Rand Corporation Hospital datasets. Patients from the four most common REGS procedures were propensity matched to laparoscopic equivalents for hospital cost analysis. A telephone survey was performed with the top 10 REGS hospitals to identify key qualities for successful REGS programs. 181 hospitals (119 REGS, 62 non-REGS) performed 60,733 emergency surgeries. Six-percent were REGS. The most common REGS were cholecystectomy, appendectomy, inguinal and ventral hernia repairs. Before and after propensity matching, total cost for these four procedures were significantly higher than their laparoscopic equivalents, which was due to higher surgical cost as the non-operative costs did not differ. There were no differences in mortality, individual complications, or length of stay for most of the four procedures. REGS volume significantly increased each year. The survey found that 8/10 hospitals have robotic-trained staff available 24/7. Although REGS volume is increasing in Florida, cost remains significantly higher than laparoscopy. Given higher costs and lack of significantly improved outcomes, further study should be undertaken to better inform which specific patient populations would benefit from REGS.

Association of state-level factors with rate of firearm-related deaths.

Background: Over 48,000 people died by firearm in the United States in 2021. Firearm violence has many inciting factors, but the full breadth of associations has not been characterized. We explored several state-level factors including factors not previously studied or insufficiently studied, to determine their association with state firearm-related death rates. Methods: Several state-level factors, including firearm open carry (OC) and concealed carry (CC) laws, state rank, partisan lean, urbanization, poverty rate, anger index, and proportion of college-educated adults, were assessed for association with total firearm-related death rates (TFDR). Secondary outcomes were firearm homicide (FHR) and firearm suicide rates (FSR). Exploratory data analysis with correlation plots and ANOVA was performed. Univariable and multivariable linear regression on the rate of firearm-related deaths was also performed. Results: All 50 states were included. TFDR and FSR were higher in permitless OC and permitless CC states. FHR did not differ based on OC or CC category. Open carry and CC were eliminated in all three regression models due to a lack of significance. Significant factors for each model were: 1) TFDR - partisan lean, urbanization, poverty rate, and state ranking; 2) FHR - poverty rate; 3) FSR - partisan lean and urbanization. Conclusions: Neither open nor concealed carry is associated with firearm-related death rates when socioeconomic factors are concurrently considered. Factors associated with firearm homicide and suicide differ and will likely require separate interventions to reduce firearm-related deaths. Key message: Neither open carry nor concealed carry law are associated with total firearm-related death rate, but poverty rate, urbanization, partisan lean, and state ranking are associated. When analyzing firearm homicide and suicide rates separately, poverty rate is strongly associated with firearm homicide rate, while urbanization and partisan lean are associated with firearm suicide rate.