Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand

Abstract

Background Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios. Methods Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning. Results Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. Conclusions If predominantly respiratory symptoms are used for test-triaging, a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.

The predictive role of symptoms in COVID-19 diagnostic models: A longitudinal insight

“…Unlike many other studies [6–8, 10, 16], this research examined the number of days that specific symptoms are reported within an infection episode. We found that PCR+ participants reported a significantly longer duration of specific symptoms per episode, compared with those that were PCR−; cough had the longest duration followed by tiredness whilst runny nose had the longest duration among PCR− participants.…”

“…In the UK, PCR testing was initially prioritized for those presenting with a new (or worsening) cough, fever, or breathlessness [5]. However other symptoms such as altered or loss of smell (anosmia) or taste (ageusia), and gastrointestinal symptoms (such as loss of appetite and diarrhoea) have also been associated with COVID-19 [6][7][8]. In a Cochrane Review (2021), mainly based on more severely affected populations (e.g.…”

Ethno‐demographic disparities in humoral responses to the COVID‐19 vaccine among healthcare workers

“…MCA has been widely used in various studies, including cancer and COVID-19 studies. [19][20][21][22][23] The standardized score or z-score was calculated at each time point using the formula z = (x−μ)/σ, where x represents the raw value, μ represents the mean, and σ is the standard deviation.…”

Postacute Sequelae SARS-CoV-2 Infection by Vaccination Status: A Six-Month Latent Class Analysis

“…LCA identified acute respiratory distress syndrome (ARDS) phenotypes that respond to distinct treatments (Dahmer et al 2022, Famous et al 2017, Calfee et al 2014). Researchers have also used LCA to identify SARS-CoV-2 phenotypes in patients admitted to the ICU (Sinha et al 2021, Sigwadhi et al 2022), describe phenotypes specific to SARS-CoV-2 headaches (Karadaş et al 2021), predict hospitalization in confirmed SARS-CoV-2 cases (Wang et al 2021), describe unconfirmed SARS-CoV-2 sequalae in Brazil (Moreira 2021) and Iran, (Hosseinzadeh et al 2021), and predict SARS-CoV-2 test results based on patterns of symptoms (French et al 2021). The purpose of this research is to characterize PASC phenotypes by identifying symptom clusters over a six-month period following confirmed infection.…”