Browsing by Author "wallis, Lee A"

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    Open Access
    Assessing use of the South African triage scale in the Western Cape government emergency medical services system
    (2022) Mould-Millman, Carl Nee-Kofi; wallis, Lee A
    Introduction: A critical concept underpinning emergency medicine is triage. Triage is the systematic process of sorting patients based on acuity and/or resource need, with the goal of getting the right person to the right place at the right time to receive the right level of care. Triage influences a patient's clinical trajectory, hence impacts both patient outcomes and health system resource utilization. Therefore, the consequences of triage are arguably even more critical in two scenarios: first, early on in the patient's care, such as the prehospital setting; and second, care in resource-constrained health systems, such as in Africa. Prehospital emergency care, delivered by Emergency Medical Services (EMS) providers, represents one of the earliest opportunities for emergency triage of the undifferentiated patient. We conducted a series of projects to, first, understand the current global scientific context of prehospital triage and, next, to better understand how the South Africa Triage Scale (SATS) is used by Western Cape EMS providers for prehospital triage. Findings may help enhance the application of SATS for prehospital triage in the Western Cape. Additionally, findings could provide evidence to encourage the adoption, or rejection, of SATS triage by other EMS systems in resource-constrained settings, especially in Africa. Methods: This project consisted of three distinct objectives which were investigated as separate, but interconnected, studies. The first objective was answered using a secondary research method (a scoping review) designed to discover and appraise existing prehospital triage tools across the world in an effort to better contextualise the specific role filled, and value added, by SATS. The second and third objectives were answered using a quantitative and qualitative approach, respectively, to assess the validity and reliability of SATS among EMS providers, and to understand EMS providers' experiences and perspectives using SATS. We converged the quantitative and qualitative data in a mixed methods analysis. Main results: In the scoping review, we screened 1521 unique articles and completed a full review of 55 articles. We reported that the majority of publications on prehospital triage tools were focused on stroke triage (35%) and trauma triage (35%). There were 15 (27%) publications, corresponding to 11 unique tools, relevant to prehospital triage of undifferentiated patients - overall, the tools had modest triage performance characteristics in high-income settings. However, we found no publications relevant to prehospital triage with SATS in the 2009 to 2019 study period, and no triage tools were studied in low- or middle-income countries. In the quantitative study, we conducted cognitive paper-based SATS triage assessments of 102 EMS providers of all qualifications within the Western Cape Government EMS system. We found a high rate (29.5%) of under-triage and an acceptable rate (13.1 %) of over-triage. Providers' use of the Triage Early Warning Score (TEWS) and the clinical discriminators were often incorrect in 41.4% and 41.2% of cases, respectively. In the qualitative assessment, we completed three focus group discussions with 15 diverse and representative providers from the Western Cape Government EMS system, and we achieved thematic saturation. Four major themes emerged from the discussions: Limited implementation and variable use of SATS; Prehospital effectiveness of SATS; Limitations of the discriminator; and, Special EMS considerations limiting SATS. In general, participants felt SATS was fairly easy to use and an asset in their patient care, explaining that it aided them clinically and with hospital communication. Participants, however, noted that the clinical discriminators were often challenging to apply in the prehospital setting, and the TEWS often did not reflect the patient's true or changing prehospital acuity. The qualitative findings both corroborated and helped explain some of the key quantitative results, with both suggesting that many clinical discriminators are problematic for prehospital use and manually calculating TEWS is an error-prone process for Western Cape EMS providers. Conclusion: SATS is being successfully and innovatively used in the prehospital triage of undifferentiated patients in the Western Cape of South Africa. Researching prehospital SATS in South Africa fills a global scientific gap given we found no reports of prehospital triage tools from low- or middle-income countries. Western Cape EMS participants reported that SATS was generally helpful and relatively easy to use, but reported challenges using TEWS and the clinical discriminators. SATS had good inter-rater reliability, but poor validity. The under-triage rate of 30% was high and attributable to misuse of TEWS and clinical discriminators. The over-triage rate of 13% was acceptable and confirmed by experiences recounted by the EMS participants. Modest adaptations of SATS by expert stakeholders could improve its prehospital performance and utility in the Western Cape Province. SATS for prehospital triage likely has good applicability and utility in other resource-constrained systems, but further adaptation and testing are warranted.
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    Building a model for development of a national trauma registry: designing and implementing standardised trauma form at regional hospitals in Tanzania
    (2021) Sawe, Hendry; wallis, Lee A; Coats, Timothy
    Background: Trauma registries are vital to a well-organized trauma system. However, registries are non-existent in most low and middle-income countries, largely due to the difficulty of reliably capturing patient-level data. The aim of this thesis was to develop and implement a context appropriate standardised trauma form incorporating the World Health Organization Data Set for Injury, for both clinical documentation and use in a trauma registry. Methods: This mixed methods participatory action research utilised Susman and Evered's approach to develop and implement a standardised trauma form, using its five steps: diagnosis, action planning, intervention, evaluation and specifying learning. In the diagnosis phase, an assessment of baseline documentation was performed. In the action-planning phase, focus group discussion revealed the barriers and facilitators to completing documentation. Then, in the actiontaking phase, semi structured interviews, training of health care providers, and feedback enabled the development, review, pilot, and implementation of a standardised trauma form. In the evaluation phase, we compared the number and types of variables captured after the form was implemented to the baseline collection. Finally, we specified learning to inform the next steps in the amplification of the observed impact. Results: The diagnosis phase established that many injury variables were not captured routinely at the participating regional hospitals. Analysis of barriers and facilitators and feedback on perceptions of providers toward using standardised documentation informed the development, piloting, modification, training of providers and implementation of a context appropriate standardised trauma documentation form for clinical charting and data capture. Implementation of the standardised trauma form was associated with improved capture of injury variables from baseline pre-implementation (33.6%), during 30-days initial pilot (86.4%) and after seven months post implementation (96.3%). The providers reported the form was user-friendly, resulted in less time documenting, and served as a guide to managing trauma patients. Conclusions: Through participatory action research a contextually appropriate, standardised trauma documentation form was successfully developed and implemented, yielding marked improvement in the capture of essential injury variables. This model can serve as a working guide to other low- and middle-income countries seeking to establish sustainable national injury registries.
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    Derivation and validation of a severity scoring tool for COVID-19 illness in low-resource setting
    (2021) Pigoga, Jennifer L; wallis, Lee A
    Background The COVID-19 pandemic has profoundly impacted some of the most vulnerable populations in lowresource settings (LRS) across the globe. These settings tend to have underdeveloped healthcare systems that are exceptionally vulnerable to the strain of an outbreak such as SARS-CoV-2. LRS-based clinicians are in need of effective and contextually appropriate triage and assessment tools that have been purpose-designed to aid in evaluating the severity of potential COVID-19 patients. In the context of the COVID-19 crisis, a low-input severity scoring tool could be a cornerstone of ensuring timely access to appropriate care and justified use of critically limited resources. Aim and objectives The aim of this research was to develop and validate a tool to assist frontline providers in rapidly predicting severe COVID-19 disease in LRS. To achieve this aim, the following objectives were defined: identify existing methods of risk stratification of suspected COVID-19 patients worldwide; establish predictors of severe COVID-19 illness measurable in LRS; derive a risk stratification tool to assist facility-based healthcare providers in LRS in evaluating in-hospital mortality risk; and validate tool SST in the African setting using real-world data. Methods To achieve the aim of this dissertation, quantitative and review methodologies were employed across four studies. First, a scoping review was conducted to identify all studies describing screening, triage, and severity scoring of suspected COVID-19 patients worldwide. These tools were then compared to usability and feasibility standards for LRS emergency units, to determine viable tool options for such settings. Following this, a systematic review and meta-analysis were undertaken to evaluate existing literature for associations between COVID-19 illness severity, and historical characteristics, clinical presentations, and investigations measurable in LRS. Three online databases were searched to identify all studies assessing potential associations between clinical characteristics and investigations, and COVID-19 illness severity. Data for all variables that were statistically analysed in relation to COVID19 disease severity were extracted and a meta-analysis was conducted to generate pooled odds ratios for individual variables' predictive abilities. In the third study, machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients to derive the AFEM COVID-19 Mortality Score (AFEM-CMS), a contextually appropriate mortality index for COVID-19. Following this, a fourth study was conducted with a more recent Sudanese dataset to validate the tool. Results The scoping review identified COVID-19 risk stratification 23 tools with potential feasibility for use in LRS. Of these, none had been validated in LRS. The systematic review then identified 79 eligible articles, including data from 27713 individual patients with laboratory-confirmed COVID-19. A total of 202 features were studied in relation to COVID-19 severity across these articles, of which 81 were deemed feasible for assessment in LRS. Meta-analysis of two demographic features, 21 comorbidities, and 21 presenting signs and symptoms with appropriate data available identified 19 significant predictors of severe COVID-19, including: past medical history of stroke (pOR: 3.08 (95% CI [1.95, 4.88])), shortness of breath (pOR: 2·78 (95% CI [2·24-3·46])), chronic kidney disease (pOR: 2.55 (95% CI [1.52-4.29])), and presence of any comorbidity (pOR: 2.41 (95% CI [2.01-2.89])). These significant predictors of severe COVID-19 were then considered for inclusion in the AFEM-CMS. Data from 467 COVID-19 patientsin Sudan were used to derive two versions of the tool. Both include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings with pulse oximetry, oxygen saturation is included and, in settings without access, heart rate is included. The AFEM-CMS showed good discrimination: The model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737-0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678- 0.760). The tool was then validated against a second set of data from Sudan and found to once again have reasonable discriminatory power in identifying those at greatest risk of death from COVID-19: The model including pulse oximetry had a C-statistic of 0.732 (95% CI: 0.687-0.777) and the model excluding pulse oximetry had a C-statistic of 0.696 (0.645-0.747). Conclusions and relevance This dissertation establishes what is, to our knowledge, the first COVID-19 mortality prediction tool intentionally designed for frontline providers in LRS and validated in such a setting. The derivation and validation of the AFEM-CMS highlight the feasibility and potential impact of real-time development of clinical tools to improve patient care, even in times of surge in LRS. This study is just one of hundreds of efforts across all resource levels suggesting that rapid use of machine learning methodologies holds promise in improving responses to pandemics and other emergencies. It is our hope that, in future health crises, LRS-based clinicians and researchers can refer to these techniques to inform contextually and situationally appropriate clinical tools and reduce morbidity and mortality.