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

Comparison of gadobenate dimeglumine-enhanced dynamic MRI and 16-MDCT for the detection of hepatocellular carcinoma

OBJECTIVE: The objective of our study was to compare the diagnostic performance of gadobenate dimeglumine-enhanced MRI with that of 16-MDCT for the detection of hepatocellular carcinoma using receiver operating characteristic (ROC) curve analysis. MATERIALS AND METHODS: Thirty-one patients with 53 hepatocellular carcinomas underwent gadobenate dimeglumine-enhanced dynamic MRI and multiphasic CT using 16-MDCT within a mean interval of 5 days (range, 3-9 days). The dynamic MRI examination was performed using 3D fat-saturated volumetric interpolated imaging and sensitivity encoding on a 1.5-T unit. Both dynamic MRI and multiphasic MDCT included dual arterial phase images. Three observers independently interpreted the CT and MR images in random order, separately, and without patient identifiers. The diagnostic accuracy of each technique was evaluated using the alternative-free response ROC method. The sensitivity and positive predictive values were also calculated. RESULTS: The sensitivities of gadobenate dimeglumine-enhanced MRI for all observers were significantly higher than those of MDCT for all the lesions and for lesions 1.0 cm or smaller (p < 0.05); however, for lesions larger than 1.0 cm, the sensitivities of the two imaging techniques were similar. The mean area under the ROC curve (A(z)) of gadobenate dimeglumine-enhanced MRI (0.87 +/- 0.03 [SD]) was higher than that of MDCT (0.83 +/- 0.04), but no significant difference was found between them (p = 0.31). The number of false-positive findings on dynamic MRI was slightly higher than on MDCT, but no significant difference in the positive predictive value between the two imaging techniques was detected (observer 1, p = 0.06; observer 2, p = 0.13; observer 3, p = 1.00). CONCLUSION: Gadobenate dimeglumine-enhanced MRI has a higher sensitivity for small hepatocellular carcinomas (</= 1 cm) but a higher false-positive rate due to nonspecific enhancement of benign lesions, such as arterioportal shunt, leading to no significant difference of overall accuracy when compared with MDCT

Geriatric Fever Score: a new decision rule for geriatric care.

BackgroundEvaluating geriatric patients with fever is time-consuming and challenging. We investigated independent mortality predictors of geriatric patients with fever and developed a prediction rule for emergency care, critical care, and geriatric care physicians to classify patients into mortality risk and disposition groups.Materials and methodsConsecutive geriatric patients (≥65 years old) visiting the emergency department (ED) of a university-affiliated medical center between June 1 and July 21, 2010, were enrolled when they met the criteria of fever: a tympanic temperature ≥37.2°C or a baseline temperature elevated ≥1.3°C. Thirty-day mortality was the primary endpoint. Internal validation with bootstrap re-sampling was done.ResultsThree hundred thirty geriatric patients were enrolled. We found three independent mortality predictors: Leukocytosis (WBC >12,000 cells/mm3), Severe coma (GCS ≤ 8), and Thrombocytopenia (platelets ConclusionsWe found that the Geriatric Fever Score is a simple and rapid rule for predicting 30-day mortality and classifying mortality risk and disposition in geriatric patients with fever, although external validation should be performed to confirm its usefulness in other clinical settings. It might help preserve medical resources for patients in greater need

Independent mortality predictors identified using multivariate analysis of geriatric patients with fever in the Emergency Department.

<p>OR, odds ratio; CI, confidence interval; GCS, Glasgow coma scale; AUC, area under the curve.</p><p>Independent mortality predictors identified using multivariate analysis of geriatric patients with fever in the Emergency Department.</p

Univariate mortality predictors at <i>P</i><0.1 of geriatric patients with fever in the Emergency Department.

<p>GCS, Glasgow coma scale; SBP, systolic blood pressure; RR, respiratory rate; WBC, white blood cell count.</p><p>Univariate mortality predictors at <i>P</i><0.1 of geriatric patients with fever in the Emergency Department.</p

Patient enrollment plus flowchart of how the prediction rule was developed.

<p>Patient enrollment plus flowchart of how the prediction rule was developed.</p

A suggested disposition flowchart based on the Geriatric Fever Score for geriatric patients with fever in the emergency department (ED).

<p>A suggested disposition flowchart based on the Geriatric Fever Score for geriatric patients with fever in the emergency department (ED).</p