Potential solutions for screening, triage, and severity scoring of suspected COVID-19 positive patients in low-resource settings: a scoping review

Hirner, Sarah; Pigoga, Jennifer L; Naidoo, Antoinette Vanessa; Hynes, Emilie J Calvello; Omer, Yasein; Wallis, Lynne; Bills, Corey B

doi:10.1136/bmjopen-2020-046130

Cited by 11 publications

(21 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The models developed in this study establish how one can differentiate COVID-19 from influenza, albeit only if a computerized decision aid could interview the patient and calculate the probability of a likely infection. Such method of assessment and triage would be helpful if access to at-home COVID-19 tests were limited, as experienced in the United States for some time [16], and as it continues to be limited in many low-and moderate-income countries [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of COVID-19 and influenza

Alemi

Vang²,

Wojtusiak³

et al. 2022

PLOS Glob Public Health

View full text Add to dashboard Cite

This study uses two existing data sources to examine how patients’ symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

Differential diagnosis of COVID-19 and influenza

Alemi

Vang²,

Wojtusiak³

et al. 2022

PLOS Glob Public Health

View full text Add to dashboard Cite

show abstract

“…For application in low-and middle-income settings, as rapid COVID tests are not available, triage tools must be applied to patients with suspected COVID and must use easily obtainable clinical factors, as opposed to laboratory and other investigations. 1,4 Prognostic research to date has largely been conducted in inpatient and highincome settings on patients with confirmed COVID-19. [4][5][6] Acuity scores, including the UK Royal College of Physicians National Early Warning Score, version 2 (NEWS2), COVID specific Pandemic Respiratory Infection Emergency System Triage (PRIEST) score and Quick COVID-19 Severity Index have shown good prediction of adverse outcomes and been suggested as a way to risk-stratify patients with suspected COVID-19 in the Emergency Department.…”

Section: Introductionmentioning

confidence: 99%

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

Marincowitz

Sbaffi

Hodkinson

et al. 2022

Preprint

View full text Add to dashboard Cite

Study Objective Tools 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. Methods An 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. Results Of 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. Conclusion Use 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.

show abstract

“…This study was conducted in the United Arab Emirates (UAE), which hosts over 200 nationalities, and about 88.5% of the population are expatriates and immigrants from other Middle Eastern and South Asian countries ( 9 ). Secondly, a recent critical appraisal and systemic analysis revealed that most published predictive models have significant selection bias and poorly defined predictors and outcomes, as well as lacking calibration and validation, making their generalizability and implementation across different settings and populations questionable ( 10 , 11 ). Our predictive models were developed in accordance with the TRIPOD statement's recommendations with complete data on disease progression and clearly defined final outcomes ( 12 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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

show abstract

Potential solutions for screening, triage, and severity scoring of suspected COVID-19 positive patients in low-resource settings: a scoping review

Cited by 11 publications

References 26 publications

Differential diagnosis of COVID-19 and influenza

Differential diagnosis of COVID-19 and influenza

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

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

Contact Info

Product

Resources

About