Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Osteopontin—A Master Regulator of Epithelial-Mesenchymal Transition

Osteopontin (OPN) plays an important functional role in both physiologic and pathologic states. OPN is implicated in the progression of fibrosis, cancer, and metastatic disease in several organ systems. The epithelial-mesenchymal transition (EMT), first described in embryology, is increasingly being recognized as a significant contributor to fibrotic phenotypes and tumor progression. Several well-established transcription factors regulate EMT and are conserved across tissue types and organ systems, including TWIST, zinc finger E-box-binding homeobox (ZEB), and SNAIL-family members. Recent literature points to an important relationship between OPN and EMT, implicating OPN as a key regulatory component of EMT programs. In this review, OPN’s interplay with traditional EMT activators, both directly and indirectly, will be discussed. Also, OPN’s ability to restructure the tissue and tumor microenvironment to indirectly modify EMT will be reviewed. Together, these diverse pathways demonstrate that OPN is able to modulate EMT and provide new targets for directing therapeutics

Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer

Abstract Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for preoperative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I–III gastric cancer patients undergoing gastrectomy between 2004 and 2016. 26 features were used to train predictive models using H2O.ai AutoML. Performance on validation cohort was measured. In 39,108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for preoperative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73–0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented preoperatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care

Surgical Eligibility Does Not Imply Surgical Equity: Recommendations for Curative Treatment in Patients with Stage I/II Pancreatic Head Adenocarcinoma Differ by Age and Race

OBJECTIVEWe sought to characterize differences in pancreatectomy recommendation rates to surgically eligible patients with pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head across age and racial groups. BACKGROUNDPancreatectomy is not recommended in almost half of otherwise healthy patients with stage I/II pancreatic ductal adenocarcinoma (PDAC) lacking a surgical contraindication. We characterized differences in pancreatectomy recommendation among surgically eligible patients across age and racial groups. METHODSNon-Hispanic White (NHW) and Non-Hispanic Black (NHB) patients were identified in the National Cancer Database with clinical stage I/II pancreatic head adenocarcinoma, Charlson Comorbidity Index of 0-1, and age 40-89 years. Rates of surgery recommendation and overall survival (OS) by age and race were compared. A Pancreatectomy Recommendation Equivalence Point (PREP) was defined as the age at which the rate of not recommending surgery matched the rate of recommending and completing surgery. Marginal standardization was used to identify association of age and race with recommendation. OS was compared using Kaplan-Meier and Cox regression models. RESULTSAmong 40,866 patients, 36,133 (88%) were NHW and 4,733 (12%) were NHB. For the entire cohort, PREP was 79 years. PREP was 5 years younger in NHB patients than in NHW patients (75 vs 80 years). Adjusted rates of not recommending surgery were significantly higher for NHB than for NHW patients in each age group. After adjusting for surgery recommendation, we found no difference in OS between NHW and NHB patients (hazard ratio 0.98 [95% CI 0.94-1.02]). CONCLUSIONSPREP of NHB patients was 5 years younger than NHW patients, and in every age group, the rate of not recommending pancreatectomy was higher in NHB patients. Age and race disparities in treatment recommendations may contribute to shorter longevity of NHB patients

Outcomes of Mixed Pathologic Response in Patients with Multiple Colorectal Liver Metastases Treated with Neoadjuvant Chemotherapy and Liver Resection

Background Pathologic response to preoperative chemotherapy predicts survival in patients with colorectal liver metastases (CLMs) who undergo hepatectomy. In multiple CLMs, mixed pathologic response, wherein tumors exhibit different degrees of treatment response, is possible. We sought to evaluate survival outcomes of mixed response in patients with multiple CLMs. Methods We conducted a retrospective cohort study using a single-institution database of patients with two or more CLMs who underwent preoperative chemotherapy and hepatectomy (2010-2018). Pathologic response of each tumor was measured on pathology. Patients were stratified by pathologic response as complete (pCR) = 0-1% viability; major (pMajR) = 2-49% viability; minor (pMinR) = 50-99% viability; or mixed (pMixR) = at least one pCR/MajR tumor and one pMinR. Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and adjusted risk of death was evaluated using Cox regression. Results Among 444 patients, 6% had pCR, 34% had pMajR, 36% had pMinR, and 24% had pMixR. Median and 5-year RFS for patients with pMixR was 10.4 months and 16%, respectively, compared with pMajR (11.3 months and 18%, respectively), pMinR (7.7 months and 13%, respectively), and pCR (23.1 months and 38%, respectively) [log-rank p < 0.001]. Median and 5-year OS for patients with pMixR was 77.4 months and 60%, respectively, compared with pMajR (80.5 months and 63%, respectively), pMinR (49.9 months and 39%, respectively), and pCR (median OS not reached; median follow-up of 37.1 months and 5-year OS of 65%) [log-rank p = 0.002]. pMixR was associated with a 52% risk of death reduction (hazard ratio 0.48, 95% confidence interval 0.30-0.78 vs. pMinR). Conclusions One-quarter of patients with multiple CLMs have pMixR following preoperative chemotherapy and hepatectomy. OS and RFS for patients with pMixR mirror those of pMajR rather than pMinR, suggesting the greatest response achieved in any metastasis best predicts survival

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): A feasibility trial design

Background: The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. Objectives: To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. Design: This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of \u3e 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors\u27 medication use and imaging surveillance recommendations aligned with current medical guidelines. Trial registration: ClinicalTrials.Gov Identifier: NCT05377320