Pragmatic Recommendations for Identification and Triage of Patients with COVID-19 Disease in Low- and Middle-Income Countries

Implementation of a Triage Protocol Outside the Hospital Setting for Timely Referral During the COVID-19 Second Wave in Chennai, India

India experienced a surge in COVID-19 cases during the second wave in the period of April-June 2021. A rapid rise in cases posed challenges to triaging patients in hospital settings. Chennai, the fourth largest metropolitan city in India with an 8 million population, reported 7564 COVID-19 cases on May 12, 2021, nearly 3 times higher than the number of cases in the peak of COVID-19 in 2020. A sudden surge of cases overwhelmed the health system. We had established standalone triage centers outside the hospitals in the first wave, which catered to up to 2500 patients per day. In addition, we implemented a home-based triage protocol from May 26, 2021, to evaluate patients with COVID-19 who were aged ≤45 years without comorbidities. Among the 27,816 reported cases between May 26 and June 24, 2021, a total of 16,022 (57.6%) were aged ≤45 years without comorbidities. The field teams triaged 15,334 (55.1%), and 10,917 (39.2%) patients were evaluated at triage centers. Among 27,816 cases, 19,219 (69.1%) were advised to self-isolate at home, 3290 (11.8%) were admitted to COVID-19 care centers, and 1714 (6.2%) were admitted to hospitals. Only 3513 (12.7%) patients opted for the facility of their choice. We implemented a scalable triage strategy covering nearly 90% of the patients in a large metropolitan city during the COVID-19 surge. The process enabled early referral of high-risk patients and ensured evidence-informed treatment. We believe that the out-of-hospital triage strategy can be rapidly implemented in low-resource settings.

Lessons learnt from the first wave of COVID-19 in Damascus, Syria: a multicentre retrospective cohort study

OBJECTIVES

The decade-long Syrian war led to fragile health infrastructures lacking in personal and physical resources. The public health of the Syrian population was, therefore, vulnerable to the COVID-19 pandemic, which devastated even well-resourced healthcare systems. Nevertheless, the officially reported incidence and fatality rates were significantly lower than the forecasted numbers.

DESIGN

A retrospective cohort study.

SETTING

The four main responding hospitals in Damascus, which received most of the cases during the first pandemic wave in Syria (i.e., June-August 2020).

PARTICIPANTS

One thousand one hundred eighty-four patients who were managed as inpatient COVID-19 cases.

PRIMARY AND SECONDARY OUTCOME MEASURES

The records of hospitalised patients were screened for clinical history, vital signs, diagnosis modality, major interventions and status at discharge.

RESULTS

The diagnostic and therapeutic preparedness for COVID-19 was significantly heterogeneous among the different centres and depleted rapidly after the arrival of the first wave. Only 32% of the patients were diagnosed based on positive reverse transcription-PCR tests. Five hundred twenty-six patients had an indication for intensive care unit admission, but only 82% of them received it. Two hundred fifty-seven patients needed mechanical ventilation, but ventilators were not available to 14% of them, all of whom died. Overall mortality during hospitalisation reached 46% and no significant difference was found in fatality between those who received and did not receive these care options.

CONCLUSIONS

The Syrian healthcare system expressed minor resilience in facing the COVID-19 pandemic, as its assets vanished swiftly with a limited number of cases. This forced physicians to reserve resources (e.g., ventilators) for the most severe cases, which led to poor outcomes of in-hospital management and limited the admission capacity for milder cases. The overwhelmed system additionally suffered from constrained coordination, suboptimal allocation of the accessible resources and a severe inability to informatively report on the catastrophic pandemic course in Syria.

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study

BACKGROUND

Tools 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.

METHODS

An 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.

RESULTS

Of 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.

CONCLUSION

No 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.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting

Background

Uneven 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.

Methods

Routinely collected data for public hospitals in the Western Cape, South Africa from the 27 th August 2020 to 11 th March 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.

Results

We 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.

Conclusion

The 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 subject

Uneven 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 adds

The 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 ED.

How this study might affect research practice or policy

Use 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.

Antibiotic Stewardship in Disaster Situations: Lessons Learned in Lebanon

A post-prescription review and feedback program was implemented as an antimicrobial stewardship intervention in Lebanon as the country grappled with complete economic collapse, the COVID-19 pandemic, and a large blast in Beirut. We describe the implications of antimicrobial use in disaster preparedness and crisis situations, the sequelae related to increasing antimicrobial resistance, and our lessons learned in Lebanon. We explore opportunities and potential solutions for future disaster preparedness.

Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools

Rationale

This study was conducted to develop, validate, and compare prediction models for severe disease and critical illness among symptomatic patients with confirmed COVID-19.

Methods

For development cohort, 433 symptomatic patients diagnosed with COVID-19 between April 15th 2020 and June 30th, 2020 presented to Tawam Public Hospital, Abu Dhabi, United Arab Emirates were included in this study. Our cohort included both severe and non-severe patients as all cases were admitted for purpose of isolation as per hospital policy. We examined 19 potential predictors of severe disease and critical illness that were recorded at the time of initial assessment. Univariate and multivariate logistic regression analyses were used to construct predictive models. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration and goodness of fit of the models were assessed. A cohort of 213 patients assessed at another public hospital in the country during the same period was used to validate the models.

Results

One hundred and eighty-six patients were classified as severe while the remaining 247 were categorized as non-severe. For prediction of progression to severe disease, the three independent predictive factors were age, serum lactate dehydrogenase (LDH) and serum albumin (ALA model). For progression to critical illness, the four independent predictive factors were age, serum LDH, kidney function (eGFR), and serum albumin (ALKA model). The AUC for the ALA and ALKA models were 0.88 (95% CI, 0.86–0.89) and 0.85 (95% CI, 0.83–0.86), respectively. Calibration of the two models showed good fit and the validation cohort showed excellent discrimination, with an AUC of 0.91 (95% CI, 0.83–0.99) for the ALA model and 0.89 (95% CI, 0.80–0.99) for the ALKA model. A free web-based risk calculator was developed.

Conclusions

The ALA and ALKA predictive models were developed and validated based on simple, readily available clinical and laboratory tests assessed at presentation. These models may help frontline clinicians to triage patients for admission or discharge, as well as for early identification of patients at risk of developing critical illness.

COVID-19 in Low- and Middle-Income Countries (LMICs): A Narrative Review from Prevention to Vaccination Strategy

The management of the COVID-19 pandemic represents a challenging process, especially for low- and middle-income countries (LMICs) due to the serious economic and health resource problems it generates. In this article, we assess COVID-19 situation in LMICs and outline emerging problems and possible solutions. The prevention and control of COVID-19 would be based on focused tests exploiting those systems (e.g., GeneXpert^®) already used in other scenarios. This would be less stressful for the healthcare system in LMICs. Avoiding close contact with people suffering from acute respiratory infections, frequent handwashing, and avoiding unprotected contact with farm or wild animals are recommended infection control interventions. The appropriate use of personal protective equipment (PPE) is required, despite its procurement being especially difficult in LMICs. Patients’ triage should be based on a simple and rapid logarithm to decide who requires isolation and targeted testing for SARS-CoV-2. Being able to estimate which patients will develop severe disease would allow hospitals to better utilize the already limited resources more effectively. In LMICs, laboratories are often in the capital cities; therefore, early diagnosis and isolation become difficult. The number of ICU beds is often insufficient, and the equipment is often old and poorly serviced. LMICs will need access to COVID-19 treatments at minimal prices to ensure that all who need them can be treated. Year-to-date, different vaccines have been approved and are currently available. The main obstacle to accessing them is the limited ability of LMICs to purchase significant quantities of the vaccine.

Quality of and Recommendations for Relevant Clinical Practice Guidelines for COVID-19 Management: A Systematic Review and Critical Appraisal

Background: The morbidity and mortality of coronavirus disease 2019 (COVID-19) are still increasing. This study aimed to assess the quality of relevant COVID-19 clinical practice guidelines (CPGs) and to compare the similarities and differences between recommendations. Methods: A comprehensive search was conducted using electronic databases (PubMed, Embase, and Web of Science) and representative guidelines repositories from December 1, 2019, to August 11, 2020 (updated to April 5, 2021), to obtain eligible CPGs. The Appraisal of Guidelines for Research and Evaluation (AGREE II) tool was used to evaluate the quality of CPGs. Four authors extracted relevant information and completed data extraction forms. All data were analyzed using R version 3.6.0 software. Results: In total, 39 CPGs were identified and the quality was not encouragingly high. The median score (interquartile range, IQR) of every domain from AGREE II for evidence-based CPGs (EB-CPGs) versus (vs.) consensus-based CPG (CB-CPGs) was 81.94% (75.00-84.72) vs. 58.33% (52.78-68.06) in scope and purpose, 59.72% (38.89-75.00) vs. 36.11% (33.33-36.11) in stakeholder involvement, 64.58% (32.29-71.88) vs. 22.92% (16.67-26.56) in rigor of development, 75.00% (52.78-86.81) vs. 52.78% (50.00-63.89) in clarity of presentation, 40.63% (22.40-62.50) vs. 20.83% (13.54-25.00) in applicability, and 58.33% (50.00-100.00) vs. 50.00% (50.00-77.08) in editorial independence, respectively. The methodological quality of EB-CPGs were significantly superior to the CB-CPGs in the majority of domains (P < 0.05). There was no agreement on diagnosis criteria of COVID-19. But a few guidelines show Remdesivir may be beneficial for the patients, hydroxychloroquine +/- azithromycin may not, and there were more consistent suggestions regarding discharge management. For instance, after discharge, isolation management and health status monitoring may be continued. Conclusions: In general, the methodological quality of EB-CPGs is greater than CB-CPGs. However, it is still required to be further improved. Besides, the consistency of COVID-19 recommendations on topics such as diagnosis criteria is different. Of them, hydroxychloroquine +/- azithromycin may be not beneficial to treat patients with COVID-19, but remdesivir may be a favorable risk-benefit in severe COVID-19 infection; isolation management and health status monitoring after discharge may be still necessary. Chemoprophylaxis, including SARS-CoV 2 vaccines and antiviral drugs of COVID-19, still require more trials to confirm this.