Causes and Outcomes of Intensive Care Admission Refusals: A Retrospective Audit from a Rural Teaching Hospital in Eastern Cape, South Africa.

(1) Background: Patients who deserve intensive care unit (ICU) admission may be denied due to a lack of resources, complicating ICU triage decisions for intensive care unit (ICU) clinicians. Among the resources that may be unavailable are trained personnel and monitored beds. In South Africa, the distribution of healthcare resources is reflected in the availability of ICU beds, with more ICU beds available in more affluent areas. Data on ICU refusal rates, reasons for refusal, patient characteristics, and outcomes are scarce in resource-constrained rural settings. Hence, this study sheds light on the ICU refusal rates, reasons for refusal, characteristics, and outcomes of refused patients at NMAH. (2) Methods: This was a three-month retrospective cross-sectional record review of refused and admitted patients from January to March 2022. COVID-19 patients and those younger than 13 years old were excluded. Refusal rates, reasons for refusal, characteristics, and outcomes of refused patients were analysed quantitatively using SPSS VS 20 software. Reasons for refusal were categorised as "too well", "too sick", and "suitable for admission but no resources". (3) Results: A total of 135 patients were discussed for ICU admission at NMAH during the study period; 73 (54.07%) were refused admission, and 62 (45.92%) were admitted. Being considered too sick to benefit from ICU was the most common reason for refusal (53.23%). Too well and no resources contributed 27.42% and 19.35%, respectively. Patients with poor functional status, comorbidities, medical diagnoses, and those referred from the ward or accident and emergency unit rather than the operating room were more likely to be refused ICU admission. Refused patients had a seven-day mortality rate of 47%. (4) Conclusions and recommendations: The study found an unmet need for critical care services at our institution, as well as a need for tools to help clinicians make objective triage decisions for critically ill patients. Therefore, the study suggests a need to improve the quality of services provided outside of the ICU, particularly for patients who were refused ICU admission, to improve their outcomes.

Prevalence and Causes of Elective Surgical Cancellations: Findings from a Rural Tertiary Hospital in the Eastern Cape, South Africa.

Background: Cancellations of elective surgeries adversely affect the patient, hospital staff, facility, and health system. Cancellations potentially result in hospital financial losses, theatre inefficiency, and substandard patient care. A common benchmark for the cancellation rate of elective surgeries is less than five percent, and most operating rooms fall short of this standard. There is a paucity of data on the rates and causes of elective surgical cancellations in rural, resource-limited settings. This study aimed to determine the prevalence of elective surgery cancellations, the causes for such cancellations, and the surgical disciplines most affected at Nelson Mandela Academic Hospital (NMAH). Methodology: This was an observational, descriptive, cross-sectional review of operating theatre records from January 2019 to July 2019. The prevalence and main causes of elective case cancellations were determined. The causes were classified, and the most affected surgical departments and patient characteristics were identified. Results: The prevalence of elective surgical case cancellations was 14.4% in our hospital, higher than the international benchmark of 5%. Patient-, facility-, and surgical-related factors were the leading causes of cancellations, and avoidable cancellations were mostly surgical- and anaesthetic-related. Ophthalmology was the most affected, followed by gynaecology and general surgery, with plastic surgery being the least affected. The most common patient-related factors were nonattendance and uncontrolled medical conditions, while overbooking was the most common surgical reason. Abnormal investigatory results and unfit status were the most common anaesthetic reasons. Facility-related issues included the lack of theatre time, equipment scarcity or malfunction, and staff unavailability. Most cancellations were unavoidable, but with careful planning, could be avoided. Conclusion and recommendations: This study identified challenges with theatre efficiency in a rural, resource-limited setting that call for the cooperation of multidisciplinary teams of surgeons, anaesthetists, nursing staff, and health care policymakers.

Determining the Minimum Dataset for Surgical Patients in Africa: A Delphi Study.

BACKGROUND: It is often difficult for clinicians in African low- and middle-income countries middle-income countries to access useful aggregated data to identify areas for quality improvement. The aim of this Delphi study was to develop a standardised perioperative dataset for use in a registry. METHODS: A Delphi method was followed to achieve consensus on the data points to include in a minimum perioperative dataset. The study consisted of two electronic surveys, followed by an online discussion and a final electronic survey (four Rounds). RESULTS: Forty-one members of the African Perioperative Research Group participated in the process. Forty data points were deemed important and feasible to include in a minimum dataset for electronic capturing during the perioperative workflow by clinicians. A smaller dataset consisting of eight variables to define risk-adjusted perioperative mortality rate was also described. CONCLUSIONS: The minimum perioperative dataset can be used in a collaborative effort to establish a resource accessible to African clinicians in improving quality of care.

A Phenominological Qualitative Study of Factors Influencing the Migration of South African Anaesthetists.

BACKGROUND: The anaesthetic workforce is a scarce resource in South Africa (SA), and the media frequently reports that anaesthetists are leaving the country in search of better opportunities in well-resourced countries. The aim of this study was to explore the factors influencing the migration intentions of South African anaesthetists. METHODS: This study utilised a qualitative methodology in the form of virtual interviews. A total of 23 interviews were conducted on purposefully selected participants using a snowball approach. The interviews were transcribed and coded into emerging themes using MAXQDA version 2022. Thematic content analysis was subsequently performed. RESULTS: The study found that all SA-based participants were considering emigrating, while those who had already emigrated had no plans to return to SA in the near future. Push factors prompted the consideration of emigration, and these were related to unsatisfactory living and working conditions in South Africa, such as a high level of crime and corruption, and the country's overall poor resources and infrastructure. Destination countries were chosen based on their pull factors such as better working conditions and opportunities for professional growth. These pull factors frequently outweighed South Africa's push factors and are therefore critical in the decision to emigrate. The main recommendations from the participants include facilitating collaboration between the public and private sectors, expanding the available sub-specialties in SA, and recognising fellowships in South African hospitals. The study participants were of the view that South African anaesthesiologists would be motivated to stay by a combination of patriotism and hope for the future. Conclusions and Policy Recommendations: The Anaesthetic workforce in South Africa is at critical risk and is unlikely to stabilise soon. Concerted efforts should be made by all concerned to explore ways of retaining the staff, considering the recommendations of the participants. Relevant key stakeholders in training and regulation of anaesthetics practice such as the Health Professions Council of South Africa, South African Society of Anaesthesiologists, and the Colleges of Medicine of South Africa should collaborate and prioritise mechanisms of monitoring emigration and intervening on modifiable professional and socio-political factors.

"Acute Kidney Injury predictive models: advanced yet far from application in resource-constrained settings."

Acute kidney injury (AKI) remains a major cause of morbidity and mortality in hospitalized patients, particularly critically ill patients. It poses a public health challenge in resource-constrained settings due to high administrative costs. AKI is commonly misdiagnosed due to its painless onset and late disruption of serum creatinine, which is the gold standard biomarker for AKI diagnosis. There is increasing research into the use of early biomarkers and the development of predictive models for early AKI diagnosis using clinical, laboratory, and imaging data. This field note provides insight into the challenges of using available AKI prediction models in resource-constrained environments, as well as perspectives that practitioners in these settings may find useful.

Development and validation of a clinical prediction model of acute kidney injury in intensive care unit patients at a rural tertiary teaching hospital in South Africa: a study protocol.

INTRODUCTION: Acute kidney injury (AKI) is a decline in renal function lasting hours to days. The rising global incidence of AKI, and associated costs of renal replacement therapy, is a public health priority. With the only therapeutic option being supportive therapy, prevention and early diagnosis will facilitate timely interventions to prevent progression to chronic kidney disease. While many factors have been identified as predictive of AKI, none have shown adequate sensitivity or specificity on their own. Many tools have been developed in developed-country cohorts with higher rates of non-communicable disease, and few have been validated and practically implemented. The development and validation of a predictive tool incorporating clinical, biochemical and imaging parameters, as well as quantification of their impact on the development of AKI, should make timely and improved prediction of AKI possible. This study is positioned to develop and validate an AKI prediction tool in critically ill patients at a rural tertiary hospital in South Africa. METHOD AND ANALYSIS: Critically ill patients will be followed from admission until discharge or death. Risk factors for AKI will be identified and their impact quantified using statistical modelling. Internal validation of the developed model will be done on separate patients admitted at a different time. Furthermore, patients developing AKI will be monitored for 3 months to assess renal recovery and quality of life. The study will also explore the utility of endothelial monitoring using the biomarker Syndecan-1 and capillary leak measurements in predicting persistent AKI. ETHICS AND DISSEMINATION: The study has been approved by the Walter Sisulu University Faculty of Health Science Research Ethics and Biosafety Committee (WSU No. 005/2021), and the Eastern Cape Department of Health Research Ethics (approval number: EC 202103006). The findings will be shared with facility management, and presented at relevant conferences and seminars.

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.

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.

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.

The SAPS 3 score as a predictor of hospital mortality in a South African tertiary intensive care unit: A prospective cohort study.

BACKGROUND: No African countries were included in the development of the Simplified Acute Physiology Score 3 (SAPS 3). This study aimed to assess the performance of the SAPS 3 as a predictor of hospital mortality in patients admitted to a multi-disciplinary tertiary intensive care unit (ICU) in South Africa. METHODS: A prospective cohort study was undertaken in a tertiary single-centre closed multidisciplinary ICU with 16 beds over 12 months in 2017. First time admissions 12 years and over were included. Exclusions were patients who died within six hours of admission, incomplete data sets and unknown outcome after ICU discharge. Demographic data, clinical admission data and co-morbidities were recorded. The SAPS 3 score was calculated within the first hour of ICU admission. The highest Sequential Organ Failure Assessment score, vasopressor use, mechanical ventilation requirements and details of acute kidney injury, if present, were recorded. Discrimination of the model was evaluated using an area under the receiver operating characteristic curve (AUROC) and calibration by the Hosmer-Lemeshow (HL) Goodness of Fit Test (C and H statistic). The observed versus the SAPS 3 model predicted mortality ratios were compared and the standardized mortality ratio (SMR) was calculated. RESULTS: A total of 829 admissions with a mean SAPS 3 (SD) of 48.1 (16) were included. Of patients with a known human immunodeficiency virus (HIV) status, 32,4% were positive. The ICU and hospital mortality rates were 13.3% and 21.4% respectively. The SAPS 3 model had a AUROC of 0.796 and HL C and H statistics were 12.1 and 11.8 (p-values 0.15 and 0.16). The SMR for the model was 1.002 (95%CI: 0.91-1.10). The mortality of 41% for the subgroup with sepsis/septic shock was higher than predicted with a SMR of 1.24 (95% CI 1.11-1.37). CONCLUSIONS: The SAPS 3 model showed good calibration and fair discrimination when applied to the cohort. The SAPS 3 model can be used to describe the case mix in this African ICU with a high incidence of HIV. Ongoing efforts should be made to improve outcomes of septic patients.

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.

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).

A meta-analysis of the efficacy of preoperative surgical safety checklists to improve perioperative outcomes.

BACKGROUND: Meta-analyses of the implementation of a surgical safety checklist (SSC) in observational studies have shown a significant decrease in mortality and surgical complications. OBJECTIVE: To determine the efficacy of the SSC using data from randomised controlled trials (RCTs). METHODS: This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered with PROSPERO (CRD42015017546). A comprehensive search of six databases was conducted using the OvidSP search engine. RESULTS: Four hundred and sixty-four citations revealed three eligible trials conducted in tertiary hospitals and a community hospital, with a total of 6 060 patients. All trials had allocation concealment bias and a lack of blinding of participants and personnel. A single trial that contributed 5 295 of the 6 060 patients to the meta-analysis had no detection, attrition or reporting biases. The SSC was associated with significantly decreased mortality (risk ratio (RR) 0.59, 95% confidence interval (CI) 0.42 - 0.85; p=0.0004; I2=0%) and surgical complications (RR 0.64, 95% CI 0.57 - 0.71; p<0.00001; I2=0%). The efficacy of the SSC on specific surgical complications was as follows: respiratory complications RR 0.59, 95% CI 0.21 - 1.70; p=0.33, cardiac complications RR 0.74, 95% CI 0.28 - 1.95; p=0.54, infectious complications RR 0.61, 95% CI 0.29 - 1.27; p=0.18, and perioperative bleeding RR 0.36, 95% CI 0.23 - 0.56; p<0.00001. CONCLUSIONS: There is sufficient RCT evidence to suggest that SSCs decrease hospital mortality and surgical outcomes in tertiary and community hospitals. However, randomised evidence of the efficacy of the SSC at rural hospital level is absent.