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 PSIC proposed a risk model that confers a relative score (grade) to ten variables, including demographic, clinical, analytical and imaging data. The score’s main objective is to determine which patients with lower airway infection present a higher probability of SARS-CoV-2 infection. However, this score has not yet been validated. This study aimed at validating this clinical risk score for SARS-CoV-2 infection, applicable to inpatients with pneumonia. Most patients admitted for pneumonia were males, with a mean age of 69.90 ± 17.83 years old, and most (87.6%) presented comorbidities which is concordant with the epidemiological COVID-19 studies’ findings, suggesting a higher incidence of hospital admission in this subgroup of patients   . However, other respiratory pathogens can also present a higher incidence of severe disease in the elderly subpopulations with comorbidities  . Indeed, when comparing positive and negative SARS-CoV-2 rRT-PCR subgroups, male gender and comorbidities were equally found in both conditions.
According to recent publications, the most common comorbidities in COVID-19 patients are hypertension, obesity and diabetes   , which is also in agreement with our findings. Of note, obesity was not analyzed in this study since it was not included in the PSIC score.
When comparing positive and negative SARS-CoV-2 rRT-PCR subgroups, typical chest CT features, exposure to a positive SARS-CoV-2 patient and PCT < 0.5 ng/mL were significantly more prevalent in the positive SARS-CoV-2 rRT-PCR subgroup.
The logistic regression analysis revealed that typical chest CT features and contact with a positive case were the most significant independent variables in predicting a positive rRT-PCR-SARS-CoV-2 test, which partially agrees with the relative scoring system proposed by the PSIC. This is also concordant with other studies’ findings, which have demonstrated that typical chest CT features present higher sensitivity in the early detection of COVID-19 pneumonia . Interestingly, 37 (30.3%) patients in the negativ