e 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 relevant—sensitivity (10) or specificity (11) (Figure 1 and Figure 2).
Less expensive and complex COVID-19 pneumonia diagnostic methods are urgently needed to facilitate timely intervention. Validated clinical prediction models to estimate the risk of SARS-CoV-2 infection are of value in managing limited hospital resources and aid in infection control. However, according to our research, various scores to estimate the probability of SARS-CoV-2 infection in inpatients with pneumonia have been proposed, but a small number have
Figure 1. Graphics (a) and (b) are informative on how the best values simultaneously for Sensitivity and Specificity, around 80%, were found, as function of the cut value of the probability to be positive. The selection of the best cut values of the probability providing the highest classification performance was based on the ROC curve (Graphic (c)), representing Sensitivity versus (1-Specificity).
Figure 2. Probability to be Positive versus total PSIC score. Two horizontal lines, representing two possible best cut values (0.10 and 0.15) for the probability are shown. The corresponding value for the total PSIC score is between 10 and 11.
L. Wynants et al.  published a metanalysis with critical approach to models published to support the diagnosis of COVID-19 in patients with suspected infection. One group of authors  developed the model “Suspected COVID-19 pneumonia Diagnosis Aid System”, which aims at predicting SARS-CoV-2 infection in patients admitted with fever. The authors developed diagnosis aid models based on machine-learning algorithms and clinical data derived from demographic, clinical signs and symptoms and laboratory tests. However, chest CT features were not included. On the other hand, another group  developed a diagnostic model to quickly identify COVID-19 pneumonia which mainly relies on chest CT findings, as they defend that according to clinical symptoms and signs and laboratory examinations, COVID-19 pneumonia is difficult to distinguish from other viral pneumonia.
The PSI