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

Racial/ethnic disparities in patients undergoing transcatheter aortic valve replacement: Insights from the healthcare cost and utilization project\u27s national inpatient sample

PURPOSE: To identify racial/ethnic disparities in utilization rates, in-hospital outcomes and health care resource use among Non-Hispanic Whites (NHW), African Americans (AA) and Hispanics undergoing transcatheter aortic valve replacement (TAVR) in the United States (US). METHODS AND RESULTS: The National Inpatient Sample database was queried for patients ≥18 years of age who underwent TAVR from 2012 to 2014. The primary outcome was all-cause in hospital mortality. A total of 36,270 individuals were included in the study. The number of TAVR performed per million population increased in all study groups over the three years [38.8 to 103.8 (NHW); 9.1 to 26.4 (AA) and 9.4 to 18.2 (Hispanics)]. The overall in-hospital mortality was 4.2% for the entire cohort. Race/ethnicity showed no association with in-hospital mortality (P \u3e .05). Though no significant difference were found between AA and NHW in any secondary outcome, being Hispanic was associated with higher incidence of acute myocardial infarction (aOR = 2.02; 95% CI, 1.06-3.85; P = .03), stroke/transient ischemic attack (aOR = 1.81; 95% CI, 1.04-3.14; P = .04), acute kidney injury (aOR = 1.65; 95% CI, 1.23-2.21; P \u3c .01), prolonged length of stay (aOR = 1.18; 95% CI, 1.08-1.29; P \u3c .01) and higher hospital costs (aOR = 1.27; 95% CI, 1.18-1.36; P \u3c .01). CONCLUSION: There are significant racial disparities in patients undergoing TAVR in the US. Though in-hospital mortality was not associated with race/ethnicity, Hispanic patients had less TAVR utilization, higher in-hospital complications, prolonged length of stay and increased hospital costs

Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

OBJECTIVES: This study sought to develop and compare an array of machine learning methods to predict in-hospital mortality after transcatheter aortic valve replacement (TAVR) in the United States. BACKGROUND: Existing risk prediction tools for in-hospital complications in patients undergoing TAVR have been designed using statistical modeling approaches and have certain limitations. METHODS: Patient data were obtained from the National Inpatient Sample database from 2012 to 2015. The data were randomly divided into a development cohort (n = 7,615) and a validation cohort (n = 3,268). Logistic regression, artificial neural network, naive Bayes, and random forest machine learning algorithms were applied to obtain in-hospital mortality prediction models. RESULTS: A total of 10,883 TAVRs were analyzed in our study. The overall in-hospital mortality was 3.6%. Overall, prediction models\u27 performance measured by area under the curve were good (\u3e0.80). The best model was obtained by logistic regression (area under the curve: 0.92; 95% confidence interval: 0.89 to 0.95). Most obtained models plateaued after introducing 10 variables. Acute kidney injury was the main predictor of in-hospital mortality ranked with the highest mean importance in all the models. The National Inpatient Sample TAVR score showed the best discrimination among available TAVR prediction scores. CONCLUSIONS: Machine learning methods can generate robust models to predict in-hospital mortality for TAVR. The National Inpatient Sample TAVR score should be considered for prognosis and shared decision making in TAVR patients

Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States

BACKGROUND: Transcatheter mitral valve repair utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR. METHODS: Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers. RESULTS: A total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80-0.87), compared to 0.77 for logistic regression (95% CI, 0.58-0.95), 0.73 for an artificial neural network (95% CI, 0.55-0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47-0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality. CONCLUSIONS: We developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation

Initial Findings From the North American COVID-19 Myocardial Infarction Registry

The coronavirus disease 2019 (COVID-19) pandemic has impacted many aspects of ST-segment elevation myocardial infarction (STEMI) care, including timely access to primary percutaneous coronary intervention (PPCI). The goal of the NACMI (North American COVID-19 and STEMI) registry is to describe demographic characteristics, management strategies, and outcomes of COVID-19 patients with STEMI. A prospective, ongoing observational registry was created under the guidance of 3 cardiology societies. STEMI patients with confirmed COVID+ (group 1) or suspected (person under investigation [PUI]) (group 2) COVID-19 infection were included. A group of age- and sex-matched STEMI patients (matched to COVID+ patients in a 2:1 ratio) treated in the pre-COVID era (2015 to 2019) serves as the control group for comparison of treatment strategies and outcomes (group 3). The primary outcome was a composite of in-hospital death, stroke, recurrent myocardial infarction, or repeat unplanned revascularization. As of December 6, 2020, 1,185 patients were included in the NACMI registry (230 COVID+ patients, 495 PUIs, and 460 control patients). COVID+ patients were more likely to have minority ethnicity (Hispanic 23%, Black 24%) and had a higher prevalence of diabetes mellitus (46%) (all p < 0.001 relative to PUIs). COVID+ patients were more likely to present with cardiogenic shock (18%) but were less likely to receive invasive angiography (78%) (all p < 0.001 relative to control patients). Among COVID+ patients who received angiography, 71% received PPCI and 20% received medical therapy (both p < 0.001 relative to control patients). The primary outcome occurred in 36% of COVID+ patients, 13% of PUIs, and 5% of control patients (p < 0.001 relative to control patients). COVID+ patients with STEMI represent a high-risk group of patients with unique demographic and clinical characteristics. PPCI is feasible and remains the predominant reperfusion strategy, supporting current recommendations. [Display omitted

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially