d 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 score was also significantly higher in the positive group (13.17 ± 3.72 vs 8.39 ± 3.67, p < 0.0001).
For Model I, the probability to be positive as a function of the PSIC score is given by the relation
and, for a cut value of 0.20, the calculated sensitivity and specificity achieved a value of 78.3% and 73.8%, respectively, with an overall predictive accuracy of 74.5%.
The most significant independent predictive variables of Model II were typical chest CT features (OR 12.16, 95% CI, 3.32 - 44.49) and exposure to a positive COVID-19 patient (OR 6.56, 95% CI, 1.33 - 32.30). For Model II, the probability to be positive as a function of two most significant independent variables is given
Table 2. Demographics and clinical characteristics of the validation cohort.
Abbreviation: SARS-CoV-2, Severe Acute Respiratory Syndrome-Coronavírus-2; LDH, lactate dehydrogenase; CT, Computed tomography. *Comorbidities included all those considered in the PSIC score such as: diabetes, COPD, cerebrovascular, oncologic and/or cardiovascular disease such as hypertension or ischemic cardiomyopathy.
Table 3. PSIC score variables frequency and total PSIC score according to SARS-Co-V-2 rRT-PCR result and groups’ comparison.
Abbreviation: CT, Computed tomography; LDH, lactate dehydrogenase; SARS-CoV-2, Severe Acute Respiratory Syndrome-Coronavírus-2. *Comorbidities included all those considered in the PSIC score such as: Diabetes, COPD, cerebrovascular, oncologic and/or cardiovascular disease such as hypertension or ischemic cardiomyopathy; +Including: Legionella pneumophila and Streptococcus pneumoniae urinary antigen tests.
by the relation
and for a cut value between 0.20 and 0.24, sensitivity and specificity were, 87.0% and 69.7%, respectively, with an overall classification performance of 72.4%. For all models, the AUC was greater than 0.8, meaning that their discriminating power can be considered as good.
Further analysis revealed that the best cut-off value for the PSIC score is 10 or 11. The selection of each one depends on what is more 