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 and means of two groups were also performed, assuming, respectively, Bernoulli and Normal populations with unknown equal variances. All the analysis was carried out using the software IBM SPSS Statistics 25.
A total of 207 patients with pneumonia were admitted to the COVID-19 Department. The validation cohort included 145 patients, and 62 (30.0%) were excluded due to lack of information on PSIC score variables such as: chest CT (n = 20), procalcitonin level (n = 40), urinary antigen tests (n = 15), LDH level (n = 3) and information regarding exposure to a positive SARS-CoV-2 patient (n = 1).
The mean age was 69.90 ± 17.83 years and 81 (55.9%) were males. Thirty-seven (25.5%) patients were active or ex-smokers and most (87.6%) presented comorbidities, including cardiovascular disease (n = 108, 74.5%), diabetes (n = 45, 31.0%), COPD (n = 34, 23.4%), oncologic (n = 24, 16.6%) and/or cerebrovascular disease (n = 21, 14.5%).
Ninety-eight (67.6%) patients presented a CRP > 5 mg/dL, 93 (64.1%) PCT < 0.5 ng/mL, 77 (53.1%) lymphopenia (<1200/uL), and 74 (51.0%) LDH level > 250 U/L. Sixty-one (42.1%) patients presented a PSIC score ≥ 10.
Twenty-three (15.9%) patients tested positive in the SARS-CoV-2 rRT-PCR assay, 20 of whom (86.9%) obtained a PSIC score ≥ 10. Of note, 3 of the 62 excluded patients tested positive in the SARS-CoV-2 rRT-PCR assay. Two of these patients did not perform chest CT and one did not include information in the clinical file regarding exposure status to a positive SARS-CoV-2 patient. The total PSIC score of these patients were 8, 10 and 13, respectively.
Demographics and clinical characteristics of patients in the validation cohort are provided in Table 2.
PSIC score variables and mean total PSIC scores were compared between the positive and negative COVID-19 groups (Table 3). Typical chest CT features, exposure to a positive COVID-19 patient and PCT < 0.5 ng/mL were significantly more prevalent in the positive SARS-CoV-2 rRT-PCR group (p < 0.0001, <0.0001 and 0.044, respectively). The mean PSIC scor