Derivation and validation of a severity scoring tool for COVID-19 illness in low-resource setting

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

Validity and reliability of the South African Triage Scale in prehospital providers

Background The South African Triage Scale (SATS) is a validated in-hospital triage tool that has been innovatively adopted for use in the prehospital setting by Western Cape Government (WCG) Emergency Medical Services (EMS) in South Africa. The performance of SATS by EMS providers has not been formally assessed. The study sought to assess the validity and reliability of SATS when used by WCG EMS prehospital providers for single-patient triage. Methods This is a prospective, assessment-based validation study among WCG EMS providers from March to September 2017 in Cape Town, South Africa. Participants completed an assessment containing 50 clinical vignettes by calculating the three components — triage early warning score (TEWS), discriminators (pre-defined clinical conditions), and a final SATS triage color. Responses were scored against gold standard answers. Validity was assessed by calculating over- and under-triage rates compared to gold standard. Inter-rater reliability was assessed by calculating agreement among EMS providers’ responses. Results A total of 102 EMS providers completed the assessment. The final SATS triage color was accurately determined in 56.5%, under-triaged in 29.5%, and over-triaged in 13.1% of vignette responses. TEWS was calculated correctly in 42.6% of vignettes, under-calculated in 45.0% and over-calculated in 10.9%. Discriminators were correctly identified in only 58.8% of vignettes. There was substantial inter-rater and gold standard agreement for both the TEWS component and final SATS color, but there was lower inter-rater agreement for clinical discriminators. Conclusion This is the first assessment of SATS as used by EMS providers for prehospital triage. We found that SATS generally under-performed as a triage tool, mainly due to the clinical discriminators. We found good inter-rater reliability, but poor validity. The under-triage rate of 30% was higher than previous reports from the in-hospital setting. The over-triage rate of 13% was acceptable. Further clinically-based and qualitative studies are needed. Trial registration Not applicable

Developing prehospital clinical practice guidelines for resource limited settings: why re-invent the wheel?

ArticleMethods on developing new (de novo) clinical practice guidelines (CPGs) have received substantial attention. However, the volume of literature is not matched by research into alternative methods of CPG development using existing CPG documents—a specifc issue for guideline development groups in low- and middle-income countries. We report on how we developed a context specifc prehospital CPG using an alternative guideline development method. Difculties experienced and lessons learnt in applying existing global guidelines’ recommendations to a national context are highlighted

Pragmatic recommendations for identification and triage of patients with COVID-19 in low- And middle-income countries

Effective identification and prognostication of severe COVID-19 patients presenting to healthcare facilities are essential to reducing morbidity and mortality. Low- and middle-income country (LMIC) facilities often suffer from restrictions in availability of human resources, laboratory testing, medications, and imaging during routine functioning, and such shortages may worsen during times of surge. Low- and middle-income country healthcare providers will need contextually appropriate tools to identify and triage potential COVID-19 patients. We report on a series of LMIC-appropriate recommendations and suggestions for screening and triage of COVID-19 patients in LMICs, based on a pragmatic, experience-based appraisal of existing literature. We recommend that all patients be screened upon first contact with the healthcare system using a locally approved questionnaire to identify individuals who have suspected or confirmed COVID-19. We suggest that primary screening tools used to identify individuals who have suspected or confirmed COVID-19 include a broad range of signs and symptoms based on standard case definitions of COVID-19 disease. We recommend that screening include endemic febrile illness per routine protocols upon presentation to a healthcare facility. We recommend that, following screening and implementation of appropriate universal source control measures, suspected COVID-19 patients be triaged with a triage tool appropriate for the setting. We recommend a standardized severity score based on the WHO COVID-19 disease definitions be assigned to all suspected and confirmed COVID-19 patients before their disposition from the emergency unit. We suggest against using diagnostic imaging to improve triage of reverse transcriptase (RT)-PCR-confirmed COVID-19 patients, unless a patient has worsening respiratory status. We suggest against the use of point-of-care lung ultrasound to improve triage of RT-PCR-confirmed COVID-19 patients. We suggest the use of diagnostic imaging to improve sensitivity of appropriate triage in suspected COVID-19 patients who are RT-PCR negative but have moderate to severe symptoms and are suspected of a false-negative RT-PCR with high risk of disease progression. We suggest the use of diagnostic imaging to improve sensitivity of appropriate triage in suspected COVID-19 patients with moderate or severe clinical features who are without access to RT-PCR testing for SARS-CoV-2