Journal of Thoracic and Cardiovascular Surgery最新文献

Objective: Intensive care unit length of stay (ICU LOS) accounts for a large percent of inpatient cost following cardiac surgery. The Society of Thoracic Surgeons (STS) risk calculator predicts total LOS but does not discriminate between ICU and non-ICU time. We sought to develop a predictive model of prolonged ICU LOS.

Methods: Adult patients undergoing STS index operations within a regional collaborative (2014-2021) were included. Prolonged ICU LOS was defined as ICU care for ≥72 hours postoperatively. A logistic regression model was utilized to develop a prediction model for the prolonged ICU LOS with pre-specified risk factors identified from our previous single center study. Internal prediction model validation was determined by bootstrapping resampling method. The prediction model performance was assessed by measures of discrimination and calibration.

Results: We identified 37,519 patients that met inclusion criteria with 11,801 (31.5%) patients experiencing prolonged ICU stay. From the logistic regression model, there were significant associations between prolonged ICU LOS and all pre-specified factors except sleep apnea (all p<0.05). MELD, preoperative intra-aortic balloon pump use, and procedure types were the most significant predictors of prolonged ICU LOS (all p<0.0001). Our prediction model had not only a good discrimination power (bootstrapped-corrected C-index=0.71) but also excellent calibration (bootstrapped-corrected mean absolute error=0.005).

Conclusions: Prolonged ICU stay following cardiac surgery can be predicted with good predictive accuracy utilizing preoperative data and may aid in patient counseling and resource allocation. Through use of a state-wide database, the application of this model may extend to other practices.

Background: Esophagectomy is central to curative therapy for esophageal cancer (EC). Perioperative outcomes affect both disease-free and overall survival in oncological esophageal surgery. The adoption of robotic techniques may improve surgical outcome. However, the complex nature of perioperative outcomes is not adequately captured by individual quality measures.

Methods: All patients after minimally invasive esophagectomy (MIE) or robotic-assisted MIE (RAMIE) junction between 2015 and 2022 were included. Textbook outcome was defined as negative resection margins (R0), retrieval of >20 lymph nodes, no major complications, reinterventions, ICU readmission, 30-day readmission or mortality and hospital stay <21 days. Individual propensity scores were calculated using a logistic regression model. Factors affecting TO were evaluated using a logistic regression model while multivariate Cox proportional-hazards model was used to evaluate TO and survival.

Results: Of 236 patients included in this study, 106 (44.91%) achieved TO. TO was achieved in 71 cases after MIE (n=71 (41.21%)) and 31 after RAMIE (n=31 (57.41%)(p=0.036). RAMIE was associated with achievement of TO (OR: 2.01; 95% CI: 1.07-3.80; p=0.031) in the overall cohort. Achievement of TO was due to a reduction in major complications in the RAMIE group. Patients with perioperative TO had a higher 3 year disease-free survival (DFS) rate and overall survival rate (UV: HR 2.49, 95% CI: 1.18-5.26, p=0.016; MV: HR 4.30, 95% CI: 1.60-11.55, p=0.004) compared to those without perioperative TO and disease-free survival (UV: HR 2.28, 95% CI: 1.24-4.19, p=0.008; MV: HR 2.82, 95% CI: 1.26-6.32, p=0.011) after 2 year follow up.

Conclusions: RAMIE is associated with an increased TO achievement. Achieving TO is associated with enhanced long-term survival in esophageal cancer patients and warrants continued emphasis on surgical quality improvement.

Objective: Chatbot use has developed a presence in medicine and surgery and has been proposed to help guide clinical decision making. However, the accuracy of information provided by artificial intelligence (AI) platforms has been questioned. We evaluated the performance of 4 popular chatbots on a board-style examination and compared results with a group of board-certified thoracic surgeons.

Methods: Clinical scenarios were developed within domains based on the ABTS Qualifying Exam. Each scenario included three stems written with the Key Feature methodology related to diagnosis, evaluation, and treatment. Ten scenarios were presented to ChatGPT-4, Bard (now Gemini), Perplexity, and Claude 2, as well as randomly selected ABTS-certified surgeons. The maximum possible score was 3 points per scenario. Critical failures were identified during exam development; if they occurred in any of the 3 stems the entire question received a score of 0. The Mann-Whitney U test was used to compare surgeon and chatbot scores.

Results: Examinations were completed by 21 surgeons, the majority of whom (14; 66%) practiced in academic or university settings. The median score per scenario for chatbots was 1.06compared to 1.88 for surgeons (difference 0.66, p=0.019). Surgeon median scores were better than chatbot median scores for all except two scenarios. Chatbot answers were significantly more likely to be deemed critical failures compared to those provided by surgeons (median 0.50 per chatbot/scenario vs. 0.19 per surgeon/scenario; p=0.016).

Conclusions: Four popular chatbots performed at a significantly lower level than board-certified surgeons. Implementation of AI should be undertaken with caution in clinical decision making.

Objective: Thoracic surgery program directors (PDs) are key mentors for the next generation of cardiothoracic surgeons and surgeon-scientists. This study evaluates the industry payments to PDs compared to other practicing thoracic surgeons to assess potential conflicts of interest and their influence.

Methods: PDs were identified using the Electronic residency application system (ERAS) and their National provider identifiers (NPIs). Industry payments from 2015 to 2021 were extracted from the OpenPayments (cms.gov) database. We analyzed the number and types of payments, including consulting fees, speaking engagements, and research-related contributions. Payments were compared by PD characteristics such as gender, specialty, and academic metrics using the Chi-square test and Kruskal-Wallis test.

Results: All 77 identified PDs (66 males and 11 females) received industry payments, totaling $14,094,422 across 8,028 transactions. The median payment was $16716, with maximum reaching $2,661,001. Male PDs and those in cardiac specialties received significantly higher payments. Research funding for PDs was predominantly awarded to males (100%). Significant disparities were observed in industry payments to PDs compared to non-PDs, especially in food and beverage, and educational activities. PDs initially received more than double the research payments compared to non-PDs in the early years. This disparity reduced significantly by 2021, reflecting a move towards more equitable research funding.

Conclusion: The substantial payment to PDs reflects their significant role in thoracic surgery, yet raises concerns regarding potential influence on trainees. These findings underscore the need for increased transparency and measures to address disparities in industry support, particularly concerning gender and specialty.

Background: The current study aims to report the presentation of the malperfusion syndrome in patients with acute Type A aortic dissection admitted to surgery and its impact on mortality.

Methods: Data were retrieved from the multicenter European Registry of Type A Aortic Dissection (ERTAAD). The Penn classification was utilized to categorize malperfusion syndromes. A machine learning algorithm was applied to assess the multivariate interaction's importance regarding in-hospital mortality.

Results: A total of 3,902 consecutive patients underwent repair for Acute Type A Aortic Dissection. Local malperfusion syndrome occurred in 1,584 (40.58%) patients. Multi-organ involvement occurred in 582 patients (36.74%) whereas 1,002 patients (63.26%) had single-organ malperfusion. The prevalence was the highest for cerebral (21.27%) followed by peripheral (13.94%), myocardial (9.7%), renal (9.33%), mesenteric (4.15%), and spinal malperfusion (2.10%). Multi-organ involvement predominantly occurred in organs perfused by the downstream aorta. Malperfusion significantly increased mortality risk (p < 0.001, OR 1.95 ± 0.29). The Boruta machine learning algorithm identified the Penn classification as significantly associated with in-hospital mortality (p< 0.0001, variable importance = 7.91), however, 8 other variables yielded higher prediction importance. According to the Penn classification mortality rates were for Penn A = 12.38%, Penn B = 20.71% Penn C = 28.90%, and Penn BC = 31.84% respectively.

Conclusions: Nearly half of the examined cohort presented with signs of malperfusion syndrome predominantly due to local involvement. More than one-third of patients with local malperfusion syndrome had a multivessel involvement. Furthermore, different levels of Penn classification can be used only as a first tool for preliminary stratification of early mortality risk.