key: cord-349198-mx5pu9fq authors: Sverzellati, Nicola; Milanese, Gianluca; Milone, Francesca; Balbi, Maurizio; Ledda, Roberta E.; Silva, Mario title: Integrated Radiologic Algorithm for COVID-19 Pandemic date: 2020-04-07 journal: J Thorac Imaging DOI: 10.1097/rti.0000000000000516 sha: doc_id: 349198 cord_uid: mx5pu9fq nan Still, we are well aware of the diagnostic limits of CT, including the following (and they are): Atypical patterns of COVID-19: it can be seen when other lung disorders (eg, extensive emphysema, lung fibrosis) are present, or in immune deficiency, the latter being known to determine atypical pulmonary CT findings. 11 Temporal evolution of disease: early negative CT can be interpreted as absence of pneumonia, but does not tell anything about the presence of infection from SARS-CoV-2. 3 Furthermore, subjects with normal CT may develop severe COVID-related pneumonia shortly (eg, in 2 days). 12 On the basis of scientific data, knowledge in thoracic radiology, and our Institutional intensive experience with COVID-19 epidemic, we drew and adapted an integrated radiologic algorithm based on the first 702 cases of patients who referred to dedicated COVID-19 radiology protocol after first-level clinical triage in a dedicated emergency unit ( Fig. 2A) . This finding might be extremely subtle on radiography or even overlooked (especially with suboptimal quality of radiography, for instance anteroposterior projection). Of note, COVID-19-related abnormalities may be asymmetrical in distribution, making radiography diagnosis even more uncertain. . Flowchart A, CT diagnostic categories in the COVID-19 protocol. Radiologic integrated contribution is established with 3 major categories that specifically refer to COVID-19: category 1 (green border) encompasses both normal CT and CT with signs of exclusive non-COVID-19 disease; category 2 (yellow border) is meant as indeterminate category that includes 2 main complex scenarios with COVID-19 as alternative or overlapping disease; category 3 encompasses typical patterns recently associated with COVID-19. Both category 2 and 3 are also scored with a CT severity index for description of disease extent, in the attempt of assisting clinicians with the most complete information. Bottom boxes show the proportion of subjects who are discarded from the COVID-19 protocol on the basis of integrated radiologic algorithm (29%), thus reducing the number of patients who are kept in COVID-19 protocol for further clinical assessment. Flowchart B, Integrated COVID-19 protocol with flowchart showing the patient path from the admission to the respiratory triage, selection of those who are to undergo radiology integration based on clinical parameters, CT assessment based on the 3 defined radiologic categories of COVID-19 protocol, and subsequent clinical assessment for appropriate hospitalization or tentative discharge. The basic rationale of this radiologically integrated algorithm is the use of CT to safely triage the massive load of acute respiratory referral during a specific period of pandemic, now called COVID-19. This algorithm is based on practical experience in this contingency and absolutely does not find application out of the aforementioned dramatic scenario. The classification of CT findings was defined by 3 major categories: (1) Negative for COVID-19. (2) Indeterminate for COVID-19. (3) Typical pattern of COVID-19. 13 Category 1 includes both normal CT and CT signs certainly attributable to a specific disease, with the aim of ruling out from the dedicated COVID-19 protocol. This category was meant and assigned with absolute caution, to minimize false negatives. We observed 16% (111/702) cases with normal CT despite decent clinical symptoms and blood gas abnormality. Further, 13% (90/702) cases were highly consistent with other disorders (Fig. 3) . Overall, category 1 allowed to exclude 29% of referrals from the COVID-19 protocol: a substantial help in the process of patient selection for dedicated respiratory care. Among those with completely negative CT, we report 5% (6/111) later onset of CT findings of COVID-19 (within 1 wk, CT performed for further clinical worsening), which is an important limitation of the different clinical-radiologic phases of this disease (Fig. 4) . The amount of negative CT might be explained by diseases that are not visible at CT (eg, acute exacerbation of chronic obstructive pulmonary disease). This specific aspect of the current algorithm will be thoroughly analyzed in due time. Category 2 included 2 subsets of CT indeterminate scenarios: Differential diagnosis between COVID-19 OR other disease (Fig. 5 ). Potential overlap of COVID-19 AND other disease (Fig. 6 ). This category includes any CT finding that does not safely suggest exclusive alternative diagnosis. We observed 10% (72/702) patients with differential diagnosis and 9% (66/ 702) patients with potential overlap. Overall, category 2 was assigned in 19% of cases. In our experience, this category ranged through the historical period, with less indeterminate cases in the early days after COVID-19 outbreak (5/40 patients, namely 13% of all patients undergoing CT), then progressively increasing with more days into the epidemic of this area (11/51 patients, namely 22%). Again, several epidemiological considerations of these categories are strictly applicable in the epidemiological setting of such specific contingency. Comparison with prior CT will be very helpful in defining the differential with overlapping patterns and prompt appropriate classification, especially in oncologic patients and patients with immune deficiency. Category 3 includes a number of typical patterns of COVID-19 with a range of disease phases and severity. Category 3 was assigned in 52% (363/702) of cases, and it always prompted clinical assessment for further stratification of risk and optimal management, within the available facilities of COVID-19 protocol (over 350 extra/ converted beds). CT pattern was included in the attempt of depicting different phases of the disease and, hopefully, to assist the clinician in recognizing early and potentially evolving scenarios from more mature and potentially more stable scenarios. We observed classical peripheral ground-glass opacity and crazy paving pattern, also recognizing the spectrum of the DAD-related initial phases at CT, essentially exudative (Fig. 7) , or organizing (Fig. 8) . Furthermore, in keeping with previous findings, we observed multiple groundglass nodules (of variable size) randomly distributed in the lungs. CT severity index of COVID-19 was elaborated to stratify among subjects with category 2 or 3 disease. Both morphology and extent were categorized. The degree of lung parenchymal involvement (defined as "extent") was included in the attempt to assist the clinician in the hospitalization, and was classified into mild, moderate, and severe (Table 1, Fig. 9 ). The supervening massive situation ought to try and define some very low-risk strata among category 2 and 3 patients by definition of extremely small findings on CT to be integrated with clinical parameters and to allow early discharge from hospital with peripheral follow-up (general practitioners are involved in peripheral monitoring of COVID-19) (Fig. 2B) . Mild extent in both category 2 and 3 was based on the subjective experience of the first 702 cases, with the aim of stratifying a group of low-risk patients to discharge and FIGURE 6. CT image in axial (A, B) and coronal (C) plane showing ground-glass opacities with heterogeneous distribution on axial plane (both central and subpleural), as well as some perilobular distribution, associated with massive bilateral pleural effusion. Prospective reporting was category 2 for potential overlapping of COVID-19 and supervening heart failure. One day after CT, the swab was positive, and heart failure was confirmed by evolution after diuretic therapy. Noteworthy, we observed elderly and cardiopathic patients displaying overlapping of COVID-19 and heart failure with potential overlapping of ground-glass opacities from infection and edema. potentially further carving the number of patients admitted in the dedicated COVID-19 respiratory protocol for respiratory assistance. If the reporting radiologist is undecided between mild and moderate disease, multidisciplinary discussion is suggested. These codes allow very quick reporting time by the thoracic radiologist, notably well below 2 minutes in the majority of cases, with some extra time needed when atypical cases are read (observed 19% in overall workload). The number of dedicated CT scanners will depend on the size of the center (24/7 service granted by 2 dedicated scanners in our center, with further 4 scanners for non-COVID-19 clinical activity). Further details of CT workflow management will apply, including times for sanitation of the CT room and any room where patients are supposed to wait to supply a continuous flow. 14 We are sharing this algorithm to let colleagues know, discuss, and prepare in advance with a strategy that we would have never thought about until 4 weeks ago, but now we see it as one good option to manage the humongous wave of admittance for acute respiratory symptoms and the consecutive overload of the intensive care unit. Now, we are using these diagnostic categories and integrating them with clinical data to face the admittance overflow and to control hospitalization, potentially selecting for discharge those with limited disease. Future studies will clarify the prognostic value of this integrated algorithm. Ground-glass admixed with perilobular opacities or consolidation with signs of distortion † Severe *Differential between "moderate" and "severe" is entirely subjective and will not impact on the decision for hospitalization (Fig. 8) . †This category was chosen in the presence of conspicuous organized consolidation, despite the predominant pattern still being ground-glass. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia Responding to Covid-19-a once-in-a-century pandemic? 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