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

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<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<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

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

A randomised controlled trial comparing quality of recovery between desflurane and isoflurane inhalation anaesthesia in patients undergoing ophthalmological surgery at a tertiary hospital in South Africa (DIQoR trial)

Background: The patient's experience of their postoperative recovery is an important perioperative outcome, with the 15-item quality of recovery scale (QoR-15) recommended as a standardised outcomes measure. Desflurane has a faster emergence from anaesthesia compared with other volatile anaesthetics, but it is uncertain whether this translates to better subjective quality of recovery. The hypothesis for this study is that patients receiving desflurane for maintenance of anaesthesia would have better postoperative quality of recovery than patients receiving isoflurane. Methods: Male and female adult patients undergoing ophthalmological surgery under general anaesthesia were randomly allocated to receive desflurane or isoflurane for maintenance of anaesthesia. The primary outcome was to compare postoperative QoR-15 scores. Secondary outcomes included comparing preoperative QoR-15 scores, volatile agent consumption, and time spent in the recovery room. Results: Data from 164 patients were analysed (80 desflurane, 84 isoflurane). Median (Q1, Q3) postoperative QoR-15 scores were not significantly different (desflurane: 145 [141, 148], isoflurane: 144 [139, 147], 95% confidence interval 0–3, P=0.176, minimal clinically important difference=8). Median (Q1, Q3) volatile agent consumption was 15.4 (12.5, 19.3) ml hr−1 in the desflurane group, and 7.4 (5.9, 9.7) ml hr−1 in the isoflurane group. Median (Q1, Q3) time spent in the recovery room was significantly shorter in the desflurane group (desflurane: 18 [13, 23]; isoflurane: 25 [19, 32], 95% confidence interval −10 to 5, P<0.001). Conclusions: This study found no difference in quality of recovery between patients who received desflurane or isoflurane for maintenance of general anaesthesia during ophthalmological surgery. A shorter time in the recovery room was not associated with improved QoR-15 scores. Clinical trial registration: NCT04188314

Critical care admission of South African (SA) surgical patients : results of the SA surgical outcomes study

CITATION: Skinner, D. L., et al. 2017. Critical care admission of South African (SA) surgical patients : results of the SA surgical outcomes Study. South African Medical Journal, 107(5):411-419, doi:10.7196/SAMJ.2017.v107i5.11455.The original publication is available at http://www.samj.org.za/index.php/samjBackground. 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<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<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).http://www.samj.org.za/index.php/samj/article/view/11880Publisher's versio

Maternal and neonatal outcomes after caesarean delivery in the African Surgical Outcomes Study : a 7-day prospective observational cohort study

CITATION: Bishop, D. et al. Maternal and neonatal outcomes after caesarean delivery in the African Surgical Outcomes Study : a 7-day prospective observational cohort study. The Lancet Global Health, 7(2):e513-e522. doi:10.1016/S2214-109X(19)30036-1The original publication is available at https://www.thelancet.com/journals/langlo/issue/vol7no2/PIIS2214-109X(19)X0002-9Background: Maternal and neonatal mortality is high in Africa, but few large, prospective studies have been done to investigate the risk factors associated with these poor maternal and neonatal outcomes. Methods: A 7-day, international, prospective, observational cohort study was done in patients having caesarean delivery in 183 hospitals across 22 countries in Africa. The inclusion criteria were all consecutive patients (aged ≥18 years) admitted to participating centres having elective and non-elective caesarean delivery during the 7-day study cohort period. To ensure a representative sample, each hospital had to provide data for 90% of the eligible patients during the recruitment week. The primary outcome was in-hospital maternal mortality and complications, which were assessed by local investigators. The study was registered on the South African National Health Research Database, number KZ_2015RP7_22, and on ClinicalTrials.gov, number NCT03044899. Findings: Between February, 2016, and May, 2016, 3792 patients were recruited from hospitals across Africa. 3685 were included in the postoperative complications analysis (107 missing data) and 3684 were included in the maternal mortality analysis (108 missing data). These hospitals had a combined number of specialist surgeons, obstetricians, and anaesthetists totalling 0·7 per 100000 population (IQR 0·2–2·0). Maternal mortality was 20 (0·5%) of 3684 patients (95% CI 0·3–0·8). Complications occurred in 633 (17·4%) of 3636 mothers (16·2–18·6), which were predominantly severe intraoperative and postoperative bleeding (136 [3·8%] of 3612 mothers). Maternal mortality was independently associated with a preoperative presentation of placenta praevia, placental abruption, ruptured uterus, antepartum haemorrhage (odds ratio 4·47 [95% CI 1·46–13·65]), and perioperative severe obstetric haemorrhage (5·87 [1·99–17·34]) or anaesthesia complications (11·47 (1·20–109·20]). Neonatal mortality was 153 (4·4%) of 3506 infants (95% CI 3·7–5·0). Interpretation: Maternal mortality after caesarean delivery in Africa is 50 times higher than that of high-income countries and is driven by peripartum haemorrhage and anaesthesia complications. Neonatal mortality is double the global average. Early identification and appropriate management of mothers at risk of peripartum haemorrhage might improve maternal and neonatal outcomes in Africa.https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(19)30036-1/fulltextPublisher’s versio