An evaluation of personality traits associated with job satisfaction among South African anaesthetists using the Big Five Inventory

BACKGROUND : Job satisfaction is a vital contributor to occupational well-being and may be instrumental in mitigating stress and the adverse effects thereof. This is particularly pertinent in anaesthesiology, which is an inherently stressful field. There are myriad factors, including personality traits, shown to influence job satisfaction. Personality testing is conducted in many industries prior to recruitment; however, this is not the case in medicine. Currently the prevailing tool for the aforementioned purpose is the Big Five Inventory based on the well-described Five Factor Model of personality. METHODS : A cross-sectional survey was utilised with electronic questionnaires distributed to all 1 509 members of the South African Society of Anaesthesiologists in 2016. Specialists, registrars, diploma-qualified and full-time general practitioner anaesthetists working in both the private and public sectors were included. RESULTS : A response rate of 31% was achieved. Statistical analysis demonstrated that Neuroticism was the strongest and most consistent negative correlate of job satisfaction, while Agreeableness was positively associated with job satisfaction. Encouragingly, a mean of 65.6% was recorded for job satisfaction using a visual analogue scale. Socio-demographic variables positively associated with job satisfaction included increasing age, male gender, private practice and specialist/diploma qualification. CONCLUSIONS : Information gleaned from this study may prove useful in vocational counselling with the aim of improving occupational well-being, thereby reducing burnout and maladaptive behaviour among South African anaesthetists.http://www.sajaa.co.za/index.php/sajaaam2018Anaesthesiolog

Development of a clinical prediction model for high hospital cost in patients admitted for elective non-cardiac surgery to a private hospital in South Africa

INTRODUCTION : Clinicians may find early identification of patients at risk for high cost of care during and after surgery useful, to prepare for focused management that results in optimal clinical outcome. The aim of the study was to develop a clinical prediction model to identify high and low hospital cost outcome after elective non-cardiac surgery using predictors identified from a preoperative self-assessment questionnaire. METHODS : Data to develop a clinical prediction model were collected for this purpose at a private hospital in South Africa. Predictors were defined from a preoperative questionnaire. Cost of hospital admission data were received from hospital administration, which reflected the financial risk the hospital carries and which could be reasonably attributed to a patient’s individual clinical risk profile. The hospital cost excluded fees charged (by any healthcare provider), and cost of prosthesis and other consignment items that are related to the type of procedure. The cost outcome measure was described as cost per total Work Relative Value Units (Work RVUs) for the procedure, and dichotomised. Variables that were associated with the outcome during univariate analysis were subjected to a forward stepwise regression selection technique. The prediction model was evaluated for discrimination and calibration, and internally validated. RESULTS : Data from 770 participants were used to develop the prediction model. The number of participants with the outcome of high cost were 142/770 (18.4%). The predictors included in the full prediction model were type of surgery, treatment for chronic pain with depression, and activity status. The area under the receiver operating curve (AUROC) for the prediction model was 0.83 (95% confidence interval [CI]: 0.79 to 0.86). The Hosmer–Lemeshow indicated goodness-of-fit (p = 0.967). The prediction model was internally validated using bootstrap resampling from the development cohort, with a resultant AUROC of 0.86 (95% CI: 0.82 to 0.89). CONCLUSION : The study describes a clinical risk prediction model developed using easily collected patient-reported variables and readily available administrative information. The prediction model should be validated and updated using a larger dataset, and used to identify patients in which cost-effective care pathways can add value.Supplement 1: Patient information and self-assessment questionnaire.Supplement 2: Binary outcome definition.Supplement 3: Table – Use of self-assessment questions to define predictor variables.Supplement 4: Table – Information on cases with extreme values excluded from derivation cohort.The South African Society of Anaesthesiologists (SASA) Jan Pretorius Research Fund; University of Pretoria, Faculty of Health Sciences, School of Medicine – research assistant grant; The SASA Acacia Branch Committee.http://www.sajaa.co.zadm2022Anaesthesiolog

The association between preoperative anemia and postoperative morbidity in pediatric surgical patients : a secondary analysis of a prospective observational cohort study

BACKGROUND : The prevalence of anemia in the South African pediatric surgical population is unknown. Anemia may be associated with increased postoperative complications. We are unaware of studies documenting these findings in patients in low- and middle-income countries (LMICs). AIM : The primary aim of this study was to describe the association between preoperative anemia and 26 defined postoperative complications, in noncardiac pediatric surgical patients. Secondary aims included describing the prevalence of anemia and risk factors for intraoperative blood transfusion. METHOD : This was a secondary analysis of the South African Paediatric Surgical Outcomes Study, a prospective, observational surgical outcomes study. Inclusion criteria were all consecutive patients aged between 6 months and <16 years, presenting to participating centers during the study period who underwent elective and nonelective noncardiac surgery and had a preoperative hemoglobin recorded. Exclusion criteria were patients aged <6 months, undergoing cardiac surgery, or without a preoperative Hb recorded. To determine whether an independent association existed between preoperative anemia and postoperative complications, a hierarchical stepwise logistic regression was conducted. RESULTS : There were 1094 eligible patients. In children in whom a preoperative Hb was recorded 46.2% had preoperative anemia. Preoperative anemia was independently associated with an increased risk of any postoperative complication (odds ratio 2.0, 95% confidence interval: 1.3-3.1, P = .002). Preoperative anemia (odds ratio 3.6, 95% confidence interval: 1.8-7.1, P < .001) was an independent predictor of intraoperative blood transfusion. CONCLUSION : Preoperative anemia had a high prevalence in a LMIC and was associated with increased postoperative complications. The main limitation of our study is the ability to generalize the results to the wider pediatric surgical population, as these findings only relate to children in whom a preoperative Hb was recorded. Prospective studies are required to determine whether correction of preoperative anemia reduces morbidity and mortality in children undergoing noncardiac surgery.http://wileyonlinelibrary.com/journal/panhj2021Anaesthesiolog

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

Critical care admission of South African (SA) surgical patients: Results of the SA Surgical Outcomes Study

Background. Appropriate critical care admissions are an important component of surgical care. However, there are few data describing postoperative critical care admission in resource-limited low- and middle-income countries.Objective. To describe the demographics, organ failures, organ support and outcomes of non-cardiac surgical patients admitted to critical care units in South Africa (SA).Methods. The SA Surgical Outcomes Study (SASOS) was a 7-day national, multicentre, prospective, observational cohort study of all patients ≥16 years of age undergoing inpatient non-cardiac surgery between 19 and 26 May 2014 at 50 government-funded hospitals. All patients admitted to critical care units during this study were included for analysis.Results. Of the 3 927 SASOS patients, 255 (6.5%) were admitted to critical care units; of these admissions, 144 (56.5%) were planned, and 111 (43.5%) unplanned. The incidence of confirmed or strongly suspected infection at the time of admission was 35.4%, with a significantly higher incidence in unplanned admissions (49.1 v. 24.8%, p&lt;0.001). Unplanned admission cases were more frequently hypovolaemic, had septic shock, and required significantly more inotropic, ventilatory and renal support in the first 48 hours after admission. Overall mortality was 22.4%, with unplanned admissions having a significantly longer critical care length of stay and overall mortality (33.3 v. 13.9%, p&lt;0.001).Conclusion. The outcome of patients admitted to public sector critical care units in SA is strongly associated with unplanned admissions. Adequate ‘high care-dependency units’ for postoperative care of elective surgical patients could potentially decrease the burden on critical care resources in SA by 23%. This study was registered on ClinicalTrials.gov (NCT02141867)

Development of a clinical prediction model for in-hospital mortality from the South African cohort of the African surgical outcomes study

BACKGROUND : Data on the factors that influence mortality after surgery in South Africa are scarce, and neither these data nor data on risk-adjusted in-hospital mortality after surgery are routinely collected. Predictors related to the context or setting of surgical care delivery may also provide insight into variation in practice. Variation must be addressed when planning for improvement of risk-adjusted outcomes. Our objective was to identify the factors predicting in-hospital mortality after surgery in South Africa from available data. METHODS : A multivariable logistic regression model was developed to identify predictors of 30-day in-hospital mortality in surgical patients in South Africa. Data from the South African contribution to the African Surgical Outcomes Study were used and included 3800 cases from 51 hospitals. A forward stepwise regression technique was then employed to select for possible predictors prior to model specification. Model performance was evaluated by assessing calibration and discrimination. The South African Surgical Outcomes Study cohort was used to validate the model. RESULTS : Variables found to predict 30-day in-hospital mortality were age, American Society of Anesthesiologists Physical Status category, urgent or emergent surgery, major surgery, and gastrointestinal-, head and neck-, thoracic- and neurosurgery. The area under the receiver operating curve or c-statistic was 0.859 (95% confidence interval: 0.827–0.892) for the full model. Calibration, as assessed using a calibration plot, was acceptable. Performance was similar in the validation cohort as compared to the derivation cohort. CONCLUSION : The prediction model did not include factors that can explain how the context of care influences post-operative mortality in South Africa. It does, however, provide a basis for reporting risk-adjusted perioperative mortality rate in the future, and identifies the types of surgery to be prioritised in quality improvement projects at a local or national level.http://link.springer.com/journal/268hj2022AnaesthesiologyMaxillo-Facial and Oral SurgerySurger

South African Paediatric Surgical Outcomes Study : a 14-day prospective, observational cohort study of paediatric surgical patients

BACKGROUND : Children comprise a large proportion of the population in sub-Saharan Africa. The burden of paediatric surgical disease exceeds available resources in Africa, potentially increasing morbidity and mortality. There are few prospective paediatric perioperative outcomes studies, especially in low- and middle-income countries (LMICs). METHODS : We conducted a 14-day multicentre, prospective, observational cohort study of paediatric patients (aged <16 yrs) undergoing surgery in 43 government-funded hospitals in South Africa. The primary outcome was the incidence of in-hospital postoperative complications. RESULTS : We recruited 2024 patients at 43 hospitals. The overall incidence of postoperative complications was 9.7% [95% confidence interval (CI): 8.4–11.0]. The most common postoperative complications were infective (7.3%; 95% CI: 6.2–8.4%). In-hospital mortality rate was 1.1% (95% CI: 0.6–1.5), of which nine of the deaths (41%) were in ASA physical status 1 and 2 patients. The preoperative risk factors independently associated with postoperative complications were ASA physcial status, urgency of surgery, severity of surgery, and an infective indication for surgery. CONCLUSIONS : The risk factors, frequency, and type of complications after paediatric surgery differ between LMICs and high-income countries. The in-hospital mortality is 10 times greater than in high-income countries. These findings should be used to develop strategies to improve paediatric surgical outcomes in LMICs, and support the need for larger prospective, observational paediatric surgical outcomes research in LMICs. CLINICAL TRIAL REGISTRATION : NCT03367832.Jan Pretorius Research Fund; 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 the Paediatric Anaesthesia Community of South Africa (PACSA).https://bjanaesthesia.org2020-02-01gl2019Anaesthesiolog

Clinical prediction models for risk-adjusted outcomes in South African surgical patients

Background National clinical data on perioperative care in South Africa are scarce. There is both an urgent need, and the imminent opportunity, to increase the body of evidence necessary to inform on initiatives to improve safety, affordability and access to surgical- and anaesthesia care in this country. Clinical prediction models are a useful way to present factors that predict a specific endpoint, and the relationships of these factors in influencing the endpoint. Such summarised information is important for perioperative clinicians and teams to understand how their circumstances and their practice influence a patient’s outcome after surgery, and how this influence, and the outcome, compare to teams in different circumstances or institutions. Developing clinical prediction models is an exercise in defining and identifying predictors and endpoints that should form part of a core set of measures for research on perioperative care. It is crucial to validate clinical prediction models in settings other than where it was developed before it is implemented. Prediction models may require updating before it can be generalisable. The aim of this thesis is to report on clinical prediction model development in two surgically heterogeneous South African cohorts: i) a public sector cohort; the South African dataset from the African Surgical Outcomes Study (ASOS); and ii) a private sector cohort, from data gathered for the purpose of model development, in patients presenting for elective non-cardiac surgery in a single private hospital. Methods Data from two cohorts of patients that differ with regards to the sample population, and the healthcare sector, were used to develop two separate clinical prediction models. A clinical prediction model with in-hospital mortality as endpoint was developed in the public sector cohort. A prediction model with healthcare resource use as endpoint was developed from a self-assessment questionnaire in the private sector cohort. Using clinical judgement, predictors for the prediction models were identified from univariate regression analysis and subsequent forward stepwise regression techniques. The prediction models were assessed for performance regarding calibration, discrimination and clinical usefulness, and were internally validated by fitting to a bootstrap sample. The prediction model that was developed from the ASOS South Africa cohort was validated in the cohort of patients participating in the South African Surgical Outcomes Study, which is a temporally separate dataset containing data collected in 2014. The possibility of validating the Surgical Outcomes Risk Tool, an established prediction tool, in the ASOS South Africa cohort, was investigated. There is currently no data available for external validation of the prediction model developed in the private sector cohort. Results During prediction model development, important variables (predictors and endpoints) were identified that should form part of a core dataset. The ASOS South Africa prediction model was developed with postoperative in-hospital mortality, censored at thirty days, as the endpoint. The predictors included in the prediction model were largely related to the risk inherent to the urgency, severity and type of surgical procedure. The private sector prediction model was developed with the cost of hospital admission, excluding fees, as endpoint. The predictors included were the type of surgery and predictors defined from patient-reported information. Although both prediction models performed fairly well with regard to calibration, discrimination and clinical usefulness, the prediction models will require validation in cohorts of patients representing a different South African population. It is expected that the prediction models will require adjustment or updating after external validation. The definitions of predictors will also have to be reconsidered when validating these prediction models in cohorts from other settings. Conclusion During the development of the clinical prediction models, predictor definitions were investigated. Variables (predictors and endpoints) should be defined in such a way as to align with international classification systems, since these are used to ‘code’ variables in electronic health information systems to enable aggregation of data. The advantages of external validation of clinical prediction models, and the subsequent prediction model updating, would be: the opportunity to further refine the definitions of candidate predictors to enable international comparisons; the potential to include health economic measures to inform on the cost-effectiveness of surgery; and the chance to define and include patient-reported measures in the core data set. The result may well be that evidence gathered in this way would assist in developing strategies for optimal delivery of perioperative care to the entire South African population. Doctors, and their patients, will have to voluntarily participate in national multicentre research projects to gather evidence on perioperative care in South Africa. One has to consider the additional burden the collection of perioperative data would entail, and how such ‘citizen scientists’ would be motivated to participate.Thesis (DMed)--University of Pretoria, 2019.AnaesthesiologyDMedUnrestricte