Modelling of a triage scoring tool for SARS-COV-2 PCR testing in health-care workers: data from the first German COVID-19 Testing Unit in Munich

Abstract

Background: Numerous scoring tools have been developed for assessing the probability of SARS-COV-2 test positivity, though few being suitable or adapted for outpatient triage of health care workers.

Methods: We retrospectively analysed 3069 patient records of health care workers admitted to the COVID-19 Testing Unit of the Ludwig-Maximilians-Universität of Munich between January 27 and September 30, 2020, for real-time polymerase chain reaction analysis of naso- or oropharyngeal swabs. Variables for a multivariable logistic regression model were collected from self-completed case report forms and selected through stepwise backward selection. Internal validation was conducted by bootstrapping. We then created a weighted point-scoring system from logistic regression coefficients.

Results: 4076 (97.12%) negative and 121 (2.88%) positive test results were analysed. The majority were young (mean age: 38.0), female (69.8%) and asymptomatic (67.8%). Characteristics that correlated with PCR-positivity included close-contact professions (physicians, nurses, physiotherapists), flu-like symptoms (e.g., fever, rhinorrhoea, headache), abdominal symptoms (nausea/emesis, abdominal pain, diarrhoea), less days since symptom onset, and contact to a SARS-COV-2 positive index-case. Variables selected for the final model included symptoms (fever, cough, abdominal pain, anosmia/ageusia) and exposures (to SARS-COV-positive individuals and, specifically, to positive patients). Internal validation by bootstrapping yielded a corrected Area Under the Receiver Operating Characteristics Curve of 76.43%. We present sensitivity and specificity at different prediction cut-off points. In a subgroup with further workup, asthma seems to have a protective effect with regard to testing result positivity and measured temperature was found to be less predictive than anamnestic fever.

Conclusions: We consider low threshold testing for health care workers a valuable strategy for infection control and are able to provide an easily applicable triage score for the assessment of the probability of infection in health care workers in case of resource scarcity.

Keywords: COVID-19; Epidemiology; Germany; Munich; Prediction model; Public health; SARS-COV-2; Triage.

Fig. 1

7-day incidences per 100,000 inhabitants at the travel destination of tested returnees at the time of testing. Red lines: thresholds for public health measures in Germany

Fig. 2

Odds ratios (dark blue) plus 95% confidence interval (light blue) in univariable logistic regression of selected variables and COVID-19 test result with weekly growing dataset. For better readability, upper confidence interval values above 10 have been truncated (orange dots)

Fig. 3

Frequency of patients with positive and negative SARS-COV2 test result by triage score (red/blue bars) and sensitivity and specificity at score cut-offs (green/orange bars). N.B.: For better readability, the frequency of patients is shown in log scale. The red lines indicate the chosen cut-off values for risk group-classification (see below)