The effect of different anaesthetic mask shapes on the anatomical dead space using infant, child and adult part-task trainers

Dead space is the portion of tidal volume that does not participate in alveolar gas exchange. The purpose of this study was to compare the dead space contribution of differently shaped masks, of the same size, by measuring the volume of each mask. The study was conducted in the Clinical Simulation Unit of the School of Medicine, University of the Free State (UFS) using formed masks with inflatable polyvinylchloride (PVC) cuffs and rounded masks with non-inflatable PVC cuffs. The masks were placed on the faces of the infant, child and adult part-task trainers as well as on a flat surface. The cuffs of the formed masks were inflated to 5 cm water and 70 cm water. Masks were filled with water and the volume was measured.The volumes (ml) of the masks on the flat surface were significantly larger than those measured on the part-task trainers’ faces. The volume of the rounded masks was greater than the volume of the formed masks. The amount of cuff inflation pressure (5 cm water vs. 70 cm water) did not lead to a significant change in mask volume: 102.3 ml (standard deviation [SD] 75.9) vs. 110.2 ml (SD 82.3), averaged for all sizes.Formed masks contribute less to anatomical dead space than rounded masks and are thus possibly the better choice. Cuff inflation pressure has insignificant influence on dead space volume.Keywords: anaesthesia, anatomical dead space, inhalation, masks, pulmonary ventilation, respirator

An evaluation of severe anesthetic-related critical incidents and risks from the South African paediatric surgical outcomes study : a 14-day prospective, observational cohort study of pediatric surgical patients

SUPPLEMENTARY MATERIAL 1 : The South African Paediatric Surgical Outcomes Study (SAPSOS): A 14-day prospective, observational cohort study of paediatric surgical patients.SUPPLEMENTARY MATERIAL 2 : South African Paediatric Surgical Outcomes Study (SAPSOS) : Operating Room case record form.SUPPLEMENTARY MATERIAL 3 : Supplemental Tables and Figures.BACKGROUND : Severe anesthetic-related critical incident (SARCI) monitoring is an essential component of safe, quality anesthetic care. Predominantly retrospective data from low- and middle-income countries (LMICs) report higher incidence but similar types of SARCI compared to high-income countries (HIC). The aim of our study was to describe the baseline incidence of SARCI in a middle-income country (MIC) and to identify associated risk for SARCI. We hypothesized a higher incidence but similar types of SARCI and risks compared to HICs. METHODS : We performed a 14-day, prospective multicenter observational cohort study of pediatric patients (aged <16 years) undergoing surgery in government-funded hospitals in South Africa, a MIC, to determine perioperative outcomes. This analysis described the incidence and types of SARCI and associated perioperative cardiac arrests (POCAs). We used multivariable logistic regression analysis to identify risk factors independently associated with SARCI, including 7 a priori variables and additional candidate variables based on their univariable performance. RESULTS : Two thousand and twenty-four patients were recruited from May 22 to August 22, 2017, at 43 hospitals. The mean age was 5.9 years (±standard deviation 4.2). A majority of patients during this 14-day period were American Society of Anesthesiologists (ASA) physical status I (66.4%) or presenting for minor surgery (54.9%). A specialist anesthesiologist managed 59% of cases. These patients were found to be significantly younger (P < .001) and had higher ASA physical status (P < .001). A total of 426 SARCI was documented in 322 of 2024 patients, an overall incidence of 15.9% (95% confidence interval [CI], 14.4–17.6). The most common event was respiratory (214 of 426; 50.2%) with an incidence of 8.5% (95% CI, 7.4–9.8). Six children (0.3%; 95% CI, 0.1–0.6) had a POCA, of whom 4 died in hospital. Risks independently associated with a SARCI were age (adjusted odds ratio [aOR] = 0.95; CI, 0.92–0.98; P = .004), increasing ASA physical status (aOR = 1.85, 1,74, and 2.73 for ASA II, ASA III, and ASA IV–V physical status, respectively), urgent/emergent surgery (aOR = 1.35, 95% CI, 1.02–1.78; P = .036), preoperative respiratory infection (aOR = 2.47, 95% CI, 1.64–3.73; P < .001), chronic respiratory comorbidity (aOR = 1.75, 95% CI, 1.10–2.79; P = .018), severity of surgery (intermediate surgery aOR = 1.84, 95% CI, 1.39–2.45; P < .001), and level of hospital (first-level hospitals aOR = 2.81, 95% CI, 1.60–4.93; P < .001). CONCLUSIONS : The incidence of SARCI in South Africa was 3 times greater than in HICs, and an associated POCA was 10 times more common. The risk factors associated with SARCI may assist with targeted interventions to improve safety and to triage children to the optimal level of care.The Jan Pretorius Research Fund, South African Society of Anaesthesiologists; Discipline of Anaesthesiology and Critical Care, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal; Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital and University of Cape Town; Department of Anaesthesia, University of the Witwatersrand; and Paediatric Anaesthesia Community of South Africa.https://journals.lww.com/anesthesia-analgesia/pages/default.aspxhj2023Anaesthesiolog

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran