Haemodynamic monitoring in patients undergoing high-risk surgery: a survey of current practice among anaesthesiologists at the University of the Witwatersrand

Background: Haemodynamic monitoring and optimisation in high-risk surgery patients improve postoperative outcomes. High-income countries (HICs) have reviewed their haemodynamic monitoring and management practices. There is, however, a paucity of literature in low- and middle-income countries (LMICs) in this regard. The aim of this study was to describe the current haemodynamic monitoring practice in high-risk surgery patients among anaesthesiologists at the University of the Witwatersrand.

Methods: A survey was conducted among anaesthesiologists at the University of the Witwatersrand using a convenience sampling method by means of an adapted questionnaire from previous research done on this topic.

Results: A total of 64 out of 76 questionnaires were analysed, attaining a response rate of 84%. Ninety-seven per cent of the respondents either provided or directly supervised anaesthesia for high-risk surgery patients. Ninety-seven per cent of them frequently monitored invasive arterial blood pressure (IABP), 68.8% monitored stroke volume variation (SVV) and 53% monitored cardiac output (CO). The most frequently optimised parameter was IABP (68.8%); while CO was optimised by only 39.1% of the respondents. The VigileoTM monitor was the most frequently used CO device (84.4%). The main reason for not monitoring CO was the use of dynamic parameters of fluid responsiveness as a surrogate for CO (57.8%). Seventy-five per cent of the respondents used SVV as a diagnostic indicator for volume expansion, but the haemodynamic effects of volume expansion were frequently assessed using change in heart rate (78.1%) and blood pressure (76.6%). Most of the respondents (98.4%) believed that their haemodynamic management practice could be improved.

Conclusion: Anaesthesiologists at the University of the Witwatersrand frequently monitored and optimised IABP rather than CO in high-risk surgery patients. The respondents used dynamic parameters of fluid responsiveness as a surrogate for CO monitoring and as an indicator for volume expansion. Most of the respondents believed that their current haemodynamic management practice in this setting could be improved.

The impact of the Fundamental Critical Course on knowledge acquisition in Rwanda

Background. Emerging critical care systems have gained little attention in low- and middle-income countries. In sub-Saharan Africa, only 4% of the healthcare workforce is trained in critical care, and mortality rates are unacceptably high in this patient population.
Aim. We sought to retrospectively describe the knowledge acquisition and confidence improvement of practitioners who attend the Fundamental Critical Care Support (FCCS) course in Rwanda.
Methods. We conducted a retrospective study in which we assessed survey data and multiple-choice question data that were collected before and after course delivery. The purpose of these assessments at the time of delivery was to evaluate participants’ perception and acquisition of critical care knowledge.
Results. Thirty-six interprofessional clinicians completed the training. Performance on the multiple-choice questions improved overall after the course (mean score pre-course of 56.5% to mean score post-course of 65.8%,p-value <0.001) and improved in all content areas with the exception of diagnosis and management of acute coronary syndrome and acute respiratory failure/mechanical ventilation. Both physicians and nurses improved their scores significantly (68.9% to 75.6%,p-value = 0.031 and 52.0% to 63.5%,p-value <0.001, respectively). Self-reported
confidence in level of knowledge also increased in all areas. Survey respondents indicated on open-answer questions that they would like the course offerings at least annually, and that further dissemination of the course in Rwanda was warranted.
Conclusion. Deploying the established FCCS course improved Rwandan healthcare provider knowledge and confidence across most critical care content areas. Therefore, this course represents a good first step in bridging the gaps noted in emerging critical care systems.

Essential Emergency and Critical Care as a health system response to critical illness and the COVID19 pandemic: What does it cost?

Essential Emergency and Critical Care (EECC) is a novel approach to the care of critically ill patients, focusing on first-tier, low-cost care and designed to be feasible even in low-resourced and low-staffed settings. This is distinct from advanced critical care, usually conducted in ICUs with specialised staff, facilities and technologies. This paper estimates the incremental cost of EECC and advanced critical care for the planning of care for critically ill patients in low resource settings with Kenya and Tanzania as case studies.

The incremental costing took a health systems perspective. A normative approach based on the ingredients defined through the recently published global consensus on EECC was used. The setting was a district hospital in which the patient is provided with the definitive care typically provided at that level for their condition. Quantification of resource use was based on COVID-19 as a tracer condition using clinical expertise. Local prices were used where available, and all costs were converted to USD2020.

The costs per patient day of EECC is estimated to be 1.01 USD, 10.83 USD and 32.84 USD in Tanzania and 1.76 USD, 14.86 USD and 37.43 USD in Kenya, for moderate, severe and critical COVID-19 patients respectively. The cost per patient day of advanced critical care is estimated to be 13.11 USD and 17.33 USD for severe and 297.30 USD and 369.64 USD for critical COVID-19 patients in Tanzania and Kenya, respectively.

EECC, an approach of providing the essential care to all critically ill patients, is low-cost. The components of EECC are basic and universal and, when assessed against the existing gaps in critical care coverage and costs of advanced critical care, suggest that it should be a priority area of investment for health systems around the globe.

Cost of postoperative sepsis in Vietnam

Despite improvements in medical care, the burden of sepsis remains high. In this study, we evaluated the incremental cost associated with postoperative sepsis and the impact of postoperative sepsis on clinical outcomes among surgical patients in Vietnam. We used the national database that contained 1,241,893 surgical patients undergoing seven types of surgery. We controlled the balance between the groups of patients using propensity score matching method. Generalized gamma regression and logistic regression were utilized to estimate incremental cost, readmission, and reexamination associated with postoperative sepsis. The average incremental cost associated with postoperative sepsis was 724.1 USD (95% CI 553.7–891.7) for the 30 days after surgery, which is equivalent to 28.2% of the per capita GDP in Vietnam in 2018. The highest incremental cost was found in patients undergoing cardiothoracic surgery, at 2,897 USD (95% CI 530.7–5263.2). Postoperative sepsis increased patient odds of readmission (OR = 6.40; 95% CI 6.06–6.76), reexamination (OR = 1.67; 95% CI 1.58–1.76), and also associated with 4.9 days longer of hospital length of stay among surgical patients. Creating appropriate prevention strategies for postoperative sepsis is extremely important, not only to improve the quality of health care but also to save health financial resources each year.

Prospective, observational study of perioperative critical incidents, anaesthesia and mortality in elective paediatric surgical patients at a national referral hospital in Niger

Aims: To describe perioperative critical incidents, the conduct of anaesthesia and perioperative mortality in elective paediatric surgery patients in a national referral hospital in Niger.

Methods: This is a prospective, observational study conducted from January to March 2018. All paediatric patients 15 years an younger, who underwent elective surgery in the Niamey National Hospital were included. The following variables were studied: age, sex, type of surgery, American Society of Anesthesiologists physical status (ASA PS) classification, monitoring system, anaesthesia technique, critical incidents, blood transfusion, analgesia, qualification of the anaesthesia practitioner, postoperative destination and mortality. Data were analysed with Excel 2007 and Epi Info 6™ (Centers for Disease Control and Prevention Atlanta, GA). The chi2 test was used for univariate associations with critical incidents. Statistical significance was considered if p < 0.05. Results: There were 231 (27.2%) paediatric patients of 849 surgical patients during the study period. Within the paediatric group, the mean age was 6 ± 4 years. The male:female sex ratio was 1.65. A full blood count was completed preoperatively in all patients. Three per cent of the patients received a preoperative blood transfusion. The most frequently performed surgery was abdominal (42.4%). Most patients were classified as ASA PS I (55%) and ASA PS II (45%). General anaesthesia was performed in 96.1% of cases and spinal anaesthesia in 3.9%. The median duration of general anaesthesia was 63 (interquartile range 45–90) minutes. There were 27 reported critical incidents (11.7%), ten of which occurred during induction (4.9%), five intraoperatively (2.2%) and 12 postoperatively (5.2%). Multimodal postoperative analgesia was used in 33.8% of these patients. One patient died in the postoperative period (0.43%). Conclusion: Perioperative critical incidents in paediatric surgical patients in Niger remain high. To improve this situation requires paediatric training of anaesthetic staff, and improved paediatric monitoring and the use of safer anaesthesia agents.

The structure, function and implementation of an outcomes database at a Ugandan secondary hospital: the Mbarara Surgical Services Quality Assurance Database

The Mbarara Surgical Services Quality Assurance Database (Mbarara SQUAD) is an outcomes database of surgical, obstetric and anaesthetic/critical care at Mbarara Regional Referral Hospital, a secondary referral hospital in southwestern Uganda. The primary scope of SQUAD is the assessment of the outcomes of care. The primary outcome is mortality. The aim is to improve the quality of care, guide allocation of resources and provide a platform for research. The target population includes all inpatients admitted for treatment to the surgery service, the obstetrics and gynaecology services, and the intensive care unit (ICU). Data collection was initiated in 2013 and closed in 2018. Data were extracted from patient charts and hospital logbooks. The database has over 50 000 patient encounters, including over 20 000 obstetrics and gynaecology admissions, 15 000 surgical admissions and 16 000 otolaryngology outpatient visits. Entries are coded using the International Classification of Diseases, Tenth Revision (ICD-10) for diagnoses, and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for procedures. The completeness and accuracy of the data entry and the coding were validated. Governance of data use is by a local steering committee in Mbarara. The structure, function and implementation of this database may be relevant for similar hospital databases in low-income countries.

The impact of COVID-19 on healthcare-associated infections in intensive care units in low and middle income countries: International Nosocomial Infection Control Consortium (INICC) findings

Background
: This study examines the impact of the COVID-19 pandemic on healthcare-associated infection (HAI) incidence in low-to-middle-income countries (LMICs).

Methods
: Patients from 7 LMICs were followed during hospital intensive care unit (ICU) stays throughout January 2019 to May 2020. HAI rates were calculated using the INICC Surveillance Online System applying CDC-NHSN criteria. Pre-COVID-19 rates for 2019 were compared to COVID-19 era rates for 2020 for central line associated bloodstream infections (CLABs), catheter associated urinary tract infections (CAUTIs), ventilator associated events (VAEs), mortality and lengths of stay (LOS).

Results
: 7,775 patients were followed for 49,506 bed-days. 2019 to 2020 rate comparisons: 2.54 and 4.73 CLABSIs per 1,000 central line days (RR=1.85, p = 0.0006), 9.71 and 12.58 VAEs per 1,000 mechanical ventilator days (RR=1.29, p = 0.10), 1.64 and 1.43 CAUTIs per 1,000 urinary catheter days (RR=1.14; p = 0.69). Mortality rates were 15.2% and 23.2% for 2019 and 2020 (RR=1.42; p < 0.0001). Mean LOS were 6.02 and 7.54 days (RR=1.21, p < 0.0001). Discussion : This report documents a rise in HAI rates in 7 LMICs during the first 5 months of the COVID-19 pandemic and highlights the need to reprioritize and return to conventional infection prevention practices.

Lessons learnt from emergency medicine services during the COVID-19 pandemic: A case study of India and the United States

India and the United States have both witnessed a high burden of COVID-19 infections since the pandemic was declared in early 2020. However, the COVID-19 restrictions have met with mixed responses in India and the US. Despite recommendations to continue social isolation and personal hygiene measures, India has not been able to curb the rise in daily cases. Our findings demonstrate the difference in the manner by which India and the US differ in their emergency handling of patients. We conducted a thorough review of the existing protocols and data concerning emergency responses in India and the US. The triage and care of suspected COVID-19 positive patients is different across India and the US. We find that there is a shortage of oxygenation, vaccination and other essential supplies in India. Further, the US is able to triage patients through telemedicine and EMS before suspected COVID-19 patients arrive, which is less prevalent in India. Our study identifies the importance of the emergency department (ED) as a critical contributor to the prevention and care of suspected and confirmed COVID-19 patients. Hospitals in India have been struggling to accommodate a huge influx of patients during its second wave with the ED playing a key link in their COVID-19 response.

Performance in mortality prediction of SAPS 3 And MPM-III scores among adult patients admitted to the ICU of a private tertiary referral hospital in Tanzania: a retrospective cohort study

Background
Illness predictive scoring systems are significant and meaningful adjuncts of patient management in the Intensive Care Unit (ICU). They assist in predicting patient outcomes, improve clinical decision making and provide insight into the effectiveness of care and management of patients while optimizing the use of hospital resources. We evaluated mortality predictive performance of Simplified Acute Physiology Score (SAPS 3) and Mortality Probability Models (MPM0-III) and compared their performance in predicting outcome as well as identifying disease pattern and factors associated with increased mortality.

Methods
This was a retrospective cohort study of adult patients admitted to the ICU of the Aga Khan Hospital, Dar- es- Salaam, Tanzania between August 2018 and April 2020. Demographics, clinical characteristics, outcomes, source of admission, primary admission category, length of stay and the support provided with the worst physiological data within the first hour of ICU admission were extracted. SAPS 3 and MPM0-III scores were calculated using an online web-based calculator. The performance of each model was assessed by discrimination and calibration. Discrimination between survivors and non–survivors was assessed by the area under the receiver operator characteristic curve (ROC) and calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test.

Results
A total of 331 patients were enrolled in the study with a median age of 58 years (IQR 43-71), most of whom were male (n = 208, 62.8%), of African origin (n = 178, 53.8%) and admitted from the emergency department (n = 306, 92.4%). In- hospital mortality of critically ill patients was 16.1%. Discrimination was very good for all models, the area under the receiver-operating characteristic (ROC) curve for SAPS 3 and MPM0-III was 0.89 (95% CI [0.844–0.935]) and 0.90 (95% CI [0.864–0.944]) respectively. Calibration as calculated by Hosmer-Lemeshow goodness-of-fit test showed good calibration for SAPS 3 and MPM0-III with Chi- square values of 4.61 and 5.08 respectively and P–Value > 0.05.

Conclusion
Both SAPS 3 and MPM0-III performed well in predicting mortality and outcome in our cohort of patients admitted to the intensive care unit of a private tertiary hospital. The in-hospital mortality of critically ill patients was lower compared to studies done in other intensive care units in tertiary referral hospitals within Tanzania.

Resource Use, Availability and Cost in the Provision of Critical Care in Tanzania: A Systematic Review

Introduction

Critical care is essential in saving lives of critically ill patients, however, provision of critical care across lower resource settings can be costly, fragmented and heterogenous. Despite the urgent need to scale-up the provision of critical care, little is known about its availability and cost. Here, we aim to systematically review and identify reported resource use, availability and costs for the provision of critical care and the nature of critical care provision in Tanzania.

Methods

The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines; PROSPERO registration number: CRD42020221923. We searched Medline, Embase and global health databases. We included studies that reported on provision of critical care, cost and availability of resources used in the provision of critical care published after 2010. Costs were adjusted and reported in 2019 USD and TZS using the world bank GDP deflators.

Results

A total 31 studies were found to fulfil the inclusion and exclusion criteria. Critical care identified in Tanzania was categorised into: ICU delivered critical care and non-ICU critical care. The availability of ICU delivered critical care was limited to urban settings whereas non-ICU critical care was found in rural and urban settings. 15 studies reported on the costs of services related to critical care yet no study reported an average or unit cost of critical care. Costs of medication, equipment (e.g. oxygen, PPE), services, and human resources were identified as inputs to specific critical care services in Tanzania.

Conclusion

There is limited evidence on the resource use, availability and costs of critical care in Tanzania. There is a strong need for further empirical research on critical care resources availability, utilization and costs across specialties and hospitals of different level in LMICs like Tanzania to inform planning, priority setting and budgeting for critical care services.