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BackgroundUneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic.MethodsRoutinely collected data for public hospitals in the Western Cape, South Africa from the 27thAugust 2020 to 11thMarch 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort.ResultsWe analysed 305,564, derivation 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment.ConclusionThe LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.What is already known on this subjectUneven vaccination in low- and middle-income countries (LMICs) coupled with less resilient health care provision mean that emergency health care systems in LMICs may still be at risk of being overwhelmed during periods of increased COVID-19 infection.Risk-stratification scores may help rapidly triage need for hospitalisation. However, those proposed for use in the ED for patients with suspected COVID-19 have been developed and validated in high-income settings.What this study addsThe LMIC-PRIEST score has been robustly developed using a large routine dataset from the Western Cape, South Africa and is directly applicable to existing triage practices in LMICs.External validation across both income settings and COVID-19 variants showed good discrimination and high sensitivity (at lower thresholds) to a composite outcome indicating need for inpatient admission from the EDHow this study might affect research, practice or policyUse of the LMIC-PRIEST score at thresholds of three or less would allow identification of very low-risk patients (negative predictive value ≥0.99) across all settings assessedDuring periods of increased demand, this could allow the rapid identification and discharge of patients from the ED using information collected at initial assessment.

Study ObjectiveTools proposed to triage acuity in suspected COVID-19 in the ED have been derived and validated in higher-income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa.MethodsAn observational cohort study using routinely collected data from EDs across the Western Cape, from the 27th of August 2020 to 11th March 2022 was conducted to assess performance of the PRIEST tool, NEWS2, TEWS, the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS in suspected COVID-19. The primary outcome was death or ICU admission.ResultsOf 446,084 patients, 15,397 patients (3.45%, 95% CI:34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST tool algorithm identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.47 (NEWS2) to 0.65 (PRIEST tool). Use of the tools at recommended thresholds would have more than doubled admissions with only a 0.01% reduction in false negative triage.ConclusionUse of the PRIEST score, NEWS2 and PMEWS at a threshold of a point higher would achieve similar accuracy to current clinical admission decision, with possible gains in transparency and speed of decision-making.

BackgroundTools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa.MethodsAn observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days.ResultsOf the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage.ConclusionNo risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

Aims, Objectives and BackgroundUneven vaccination in low- and middle-income settings and less resilient health care provision mean that emergency health care systems may still be at risk of being overwhelmed during periods of increased COVID-19 infection. Risk stratification tools proposed to allow rapid triage of need for admission in ED settings have almost exclusively been developed and validated in high-income settings during early waves of the pandemic.Our study aimed to estimate the accuracy of risk-stratification tools recommended to predict severe illness in adults with suspected COVID-19 infection in the Western Cape of South Africa.Method and DesignAn observational cohort study using routinely electronically collected clinical information in all state-run hospitals in the Western Cape between 27th August 2020 and 11th March 2022 was conducted to assess performance of the PRIEST tool, NEWS2, the WHO algorithm, CRB-65, TEWS, Quick Covid Severity Index and PMEWS in patients with suspected COVID-19. The primary outcome was death, respiratory support or ICU admission.Abstract 1482 Figure 1Performance of tools predicting composite primary outcome for total study periodAbstract 1482 Figure 2Performance of tools predicting composite primary outcome for the Omicron periodAbstract 1482 Table 1Triage tool diagnostic accuracy statistics (95% CI) for predicting any adverse outcome (entire study period)ToolN*C-statisticThresholdN (%) above thresholdSensitivitySpecificityPPVNPVCRB-65432,5840.70(0.70, 0.71)>0102,964 (23.8%)0.61(0.61, 0.61)0.78(0.77, 0.78)0.09(0.09, 0.09)0.98(0.98, 0.98)NEWS2433,1010.80(0.79, 0.80)>1178835 (41.3%)0.83(0.83, 0.83)0.6(0.6,0.6)0.07(0.07–0.07)0.99(0.99, 0.99)PMEWS438,8100.79(0.79, 0.79)>2199,386 (45.4%)0.85(0.85, 0.85)0.56(0.56, 0.56)0.06(0.06, 0.07)0.99 (0.99,0.99)PRIEST438,8800.82(0.82, 0.82)>4158,893 (36.2%)0.83(0.83, 0.83)0.65 (0.65,0.66)0.08(0.08, 0.08)0.99(0.99, 0.99)WHO437,8500.71(0.71, 0.72)>0235,775 (53.8%)0.82(0.81, 0.82)0.47(0.47, 0.47)0.05(0.05, 0.05)0.99(0.99, 0.99)TEWS432,6120.72(0.71, 0.72)>2134,097 (31%)0.62(0.62, 0.62)0.70(0.70, 0.70)0.07(0.07, 0.07)0.98(0.98, 0.98)Quick COVID446,0880.70(0.69, 0.70)>335,145 (7.9%)0.33(0.33, 0.33)0.93(0.93, 0.93)0.14(0.14, 0.14)0.98(0.98, 0.98)*Patients with Abstract 1482 Table 2Triage tool diagnostic accuracy statistics (95% CI) for predicting any adverse outcome (Omicron period)ToolN*C-statisticThresholdN (%) above thresholdSensitivitySpecificityPPVNPVCRB-65136,9610.69(0.68, 0.70)>031,373 (22.9%)0.59(0.59, 0.59)0.78(0.78, 0.78)0.05(0.05, 0.05)0.99(0.99, 0.99)NEWS2137,1250.77(0.76, 0.78)>176,183 (55.6%)0.87(0.87, 0.87)0.45(0.45, 0.45)0.03(0.03, 0.03)0.99(0.99, 0.99)PMEWS138,9540.76(0.75, 0.76)>259,876 (43.1%)0.80(0.80, 0.80)0.58(0.58, 0.58)0.04(0.04, 0.04)0.99(0.99, 0.99)PRIEST158,8930.78(0.77, 0.79)>446,529 (33.5%)0.75(0.75, 0.75)0.67(0.67, 0.67)0.04(0.04, 0.04)0.99(0.99, 0.99)WHO138,6660.62(0.61, 0.63)>072,599 (52.4%)0.70(0.70, 0.70)0.48(0.48, 0.48)0.03(0.03, 0.03)0.99(0.99, 0.99)TEWS136,9670.73(0.72, 0.74)>239,509 (28.8%)0.64(0.64, 0.64)0.72(0.72, 0.72)0.04(0.04, 0.04)0.99(0.99, 0.99)Quick COVID1405200.61(0.60, 0.63)>38,210(6.4%)0.17(0.17, 0.17)0.94(0.94, 0.94)0.06(0.06, 0.06)0.98(0.98, 0.98)*Patients with Results and ConclusionOf the 446,084 patients, 15,397 patients (3.45%, 95% CI:34% to 35.1%) experienced the primary outcome. Figure 1 presents the ROC curves for the triage tools for the total study period and figure 2 for the period of the Omicron wave. NEWS2, PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.47 (NEWS2) to 0.65 (PRIEST tool). The low prevalence of the primary outcome, especially in the Omicron period, meant use of these tools would have more than doubled admissions with only a small reduction in risk of false negative triage.Triage tools developed specifically in low- and middle-income settings may be needed to provide accurate risk prediction. Existing triage tools may need to be used at varying thresholds to reflect different baseline-line risks of adverse outcomes in different settings.

This White Paper has been formally accepted for support by the International Federation for Emergency Medicine (IFEM) and by the World Federation of Intensive and Critical Care (WFICC), put forth by a multi-specialty group of intensivists and emergency medicine providers from low- and low-middle-income countries (LMICs) and high-income countries (HiCs) with the aim of 1) defining the current state of caring for the critically ill in low-resource settings (LRS) within LMICs and 2) highlighting policy options and recommendations for improving the system-level delivery of early critical care services in LRS. LMICs have a high burden of critical illness and worse patient outcomes than HICs, hence, the focus of this White Paper is on the care of critically ill patients in the early stages of presentation in LMIC settings. In such settings, the provision of early critical care is challenged by a fragmented health system, costs, a health care workforce with limited training, and competing healthcare priorities. Early critical care services are defined as the early interventions that support vital organ function during the initial care provided to the critically ill patient—these interventions can be performed at any point of patient contact and can be delivered across diverse settings in the healthcare system and do not necessitate specialty personnel. Currently, a single “best” care delivery model likely does not exist in LMICs given the heterogeneity in local context; therefore, objective comparisons of quality, efficiency, and cost-effectiveness between varying models are difficult to establish. While limited, there is data to suggest that caring for the critically ill may be cost effective in LMICs, contrary to a widely held belief. Drawing from locally available resources and context, strengthening early critical care services in LRS will require a multi-faceted approach, including three core pillars: education, research, and policy. Education initiatives for physicians, nurses, and allied health staff that focus on protocolized emergency response training can bridge the workforce gap in the short-term; however, each country’s current human resources must be evaluated to decide on the duration of training, who should be trained, and using what curriculum. Understanding the burden of critical Illness, best practices for resuscitation, and appropriate quality metrics for different early critical care services implementation models in LMICs are reliant upon strengthening the regional research capacity, therefore, standard documentation systems should be implemented to allow for registry use and quality improvement. Policy efforts at a local, national and international level to strengthen early critical care services should focus on funding the building blocks of early critical care services systems and promoting the right to access early critical care regardless of the patient’s geographic or financial barriers. Additionally, national and local policies describing ethical dilemmas involving the withdrawal of life-sustaining care should be developed with broad stakeholder representation based on local cultural beliefs as well as the optimization of limited resources.