Surgical Smoke and Biological Symptoms in Healthcare Professionals and Patients: A Systematic Review

PurposeThis study aimed to identify the evidence in the scientific literature between exposure to surgical smoke and biological symptoms in healthcare professionals and patients.DesignA systematic review.MethodsElectronic databases were searched, including vivo observational and experimental studies published until August 2020 in Portuguese, English, Spanish and French.FindingsWe identified 13 studies, with a predominance of cross-sectional (6; 46.15%), experimental laboratory (4; 30.76%) and cohort (3; 23.07%) studies. The main manifestations identified were related to respiratory tract and headache. There was identification of histopathological changes in the nasal mucosa of healthcare professionals and the presence of toxic substances from smoke identified in the urine of patients and healthcare professionals.ConclusionThe scientific literature on the biological symptoms of surgical smoke is mainly composed of observational studies with a reduced sample size, thus constituting aspects which limit a broader and long-term understanding of the biological effects of surgical smoke exposure in healthcare professionals and patients.     

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BackgroundPancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.MethodsWe conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.Results3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.ConclusionMost prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.