suspicion. Since few data is available on validated scores predictive of SARS-CoV-2 infection, the aim of this study was to validate the PSIC clinical risk score, applied to inpatients with pneumonia, in predicting a higher probability of COVID-19, based on a single-center department cohort, while assessing the significance of the independent PSIC score variables on the probability of a positive SARS-CoV-2 rRT-PCR test.
This retrospective study was conducted in a COVID-19 dedicated Department, at Centro Hospitalar de Setúbal, E.P.E. (CHS), in a community hospital in Setúbal, Portugal, with the approval of the hospital institutional review board. As only retrospective, non-identifying patient data were collected, informed consent was not applied. Access to clinical file was performed through hospital intranet software namely SClinic.
All patients with pneumonia admitted to the ward between 20/03 and 20/05/2020 were included. Patients without all the required variables to the score calculus were excluded.
Each case of the data set is characterized by 12 independent/predictor variables (including age, gender, comorbidities, symptoms, laboratory and imaging findings as well as exposure as described in the PSIC score), 10 of which are categorical variables with two classes (Yes = 1/No = 0). The dependent variable is a binary variable coded as 0 (Test Negative) and 1 (Test Positive). The independent categorical variables are the 10 score variables proposed by the PSIC codified in binary values considering the presence or absence of male gender, comorbidities (diabetes, COPD, cerebrovascular, oncologic and/or cardiovascular disease such as hypertension or ischemic cardiomyopathy), exposure to a positive SARS-CoV-2 patient, fever and cough at presentation, lymphopenia (<1200/µL), reactive C-protein (CRP) > 5 mg/dL, procalcitonin (PCT) < 0.5 ng/mL, negative respiratory viruses other than SARS-CoV-2 test as well as Legionella pneumophila and Streptococcus pneumoniae urinary antigen tests, lactate dehydrogenase (LDH) > 250 U/L and typical chest CT features. Typical CT features were defined as uni-/bilateral ground glass opacities/consolidation or crazy-paving patterns . Clinical characteristics outside the scope of the PSIC score were not considered. The total PSIC score was calculated for each patient.
All patients were tested for SARS-CoV-2 infection by rRT-PCR assay using nasal and oropharyngeal swab specimens. The number of samples collected during admission was variable, based on individual clinical characteristics. Patients were then classified as COVID-19 positive or negative.
Data were collected based on patients’ clinical file and were analyzed in May 2020. A team of experienced respiratory clinicians reviewed, abstracted and cross-checked the data.
The logistic regression modeling approach was selected considering the binary nature of the dependent variable. The selection of significant independent variables, with predictive power, was done by using the Forward Stepwise (Likelihood Ratio) method. The coefficients adjustment of the retained independent variables was done by maximizing the log likelihood function using a nonlinear optimization algorithm. The use of the logistic regression model as a classifier tool (Positive group versus Negative group result) requires a cut value for the computed probability. The best cut value was obtained with the help of the Receiver Operating Characteristic (ROC) curve together with the evaluation of the discriminatory power of the model through the Area Under the Curve (AUC). Since the total PSIC score was calculated for each patient and this was also included on our database, in our study two candidate logistic regression models were developed. The probability of a positive SARS-CoV-2 rRT-PCR test was computed. Model I considered the total PSIC score of each patient and Model II considered all the PSIC score variables regardless total score.
Descriptive statistics were performed for the basic analysis of data. Hypothesis tests of significance of the differences between proportions an