Automated Notification of Relevant Expected or Incidental Findings in Imaging Exams in a Verticalized Healthcare System.

To describe the implementation of a standardized code system for notification of relevant expected or incidental findings in imaging exams and use of an automated textual mining tool of radiological report narratives, created to facilitate directing patients to specific lines of care, reducing the waiting time for interventions, consultations, and minimizing delays to treatment. We report our 12-month initial experience with the process. A standardized code was attached to every radiology report when a relevant finding was observed. On a daily basis, the notifications was sent to a dedicated medical team to review the notified abnormality and decide a proper action. Between October 1, 2020, and September 30, 2021, 40,296 sectional examinations (CT and MR scans) were evaluated in 35,944 patients. The main findings reported were calcified plaques on the trunk of the left coronary artery or trunk like, pulmonary nodule/mass and suspected liver disease. Data of follow-up was available in 10,019 patients. The age ranged from 24 to 101 years (mean of 71.3 years) and 6,626 were female (66.1%). In 2,548 patients a complementary study or procedure was indicated, and 3,300 patients were referred to a specialist. Customized database searches looking for critical or relevant findings may facilitate patient referral to specific care lines, reduce the waiting time for interventions or consultations, and minimize delays to treatment.

COVID-19 Infection in Octagenarian Patients: Imaging Aspects and Clinical Correlations.

INTRODUCTION: Computed tomography (CT) of the chest, although not a screening test or diagnosis of infection with the new coronavirus, has a fundamental role in assessing the extent of lung involvement and complications such as pleural effusion. Considering the higher morbidity and mortality of elderly patients due to this infection, the objective of this study was to evaluate the imaging aspects and clinical correlations of an extreme age (≥80 years) with a confirmed diagnosis for COVID-19. METHODS: This was a retrospective and single-center cohort study. CT scans were categorized qualitatively and quantitatively. In the first case, 3 descriptors were used to describe CT findings: "compatible" (findings of greater specificity for COVID-19: opacities with attenuation in ground glass with peripheral and bilateral distribution, with rounded morphology, with or without consolidations, crazy-pavement aspect, inverted halo sign, or organizing pneumonia findings), "doubtful" (findings not specific or unusual for COVID-19: opacities with attenuation in ground glass with nonrounded morphology, central, diffuse, or unilateral distribution, with or without consolidation, lobar or segmental consolidation without ground-glass opacity, small centrilobular nodules with the appearance of "tree-in-bud," excavations, pleural effusion, and thickening of interlobular septa), and "negative" (absence of pneumonia signs). For the quantitative assessment, which referred to the extent of pulmonary involvement, a tomographic severity classification was used: grade 1 (lung involvement ≤25%), grade 2 (pulmonary involvement between 26 and 50%), and grade 3 (pulmonary involvement >50%). RESULTS: A total of 138 patients were evaluated, with an average age of 86.2 years (84 women and 34 men). The mean time interval between onset of symptoms and tomography was 5.63 days. The most prevalent comorbidity was systemic arterial hypertension (81.2%). Compatible, doubtful, and negative tests were 117 (84.7%), 20 (14.4%), and 1 (0.7%), respectively. As for compatible exams, the most common findings were opacities in peripheral ground glass and rounded morphology, followed by crazy paving. The prevalence of pleural effusion was 28.2% and consolidation was 63.7%, and none of these findings were influenced by the duration of symptoms (p = 0.08 and p = 0.2, respectively). The exams classified as grade 1, grade 2, and grade 3 were 57 (41.6%), 46 (33.6%), and 34 (24.8%), respectively. There were statistically significant associations between the classification of tomographic severity and outcomes such as invasive ventilation (p = 0.004), admission to the intensive care unit (p < 0.001), and death (p < 0.001). DISCUSSION/CONCLUSION: Our results show that patients ≥80 years old present tomographic manifestations similar to those described for the general population (ground-glass opacities and "crazy paving") and that the extent of lung involvement is associated with the need for intensive care, invasive ventilation, and death. Although the literature describes an association between the stage of the disease and the appearance of consolidations and pleural effusion, this correlation was not observed in our study, which may suggest that this age-group is more predisposed to the appearance of such findings, typically described in the more advanced stages of infection.

Tomographic score (RAD-Covid Score) to assess the clinical severity of the novel coronavirus infection.

OBJECTIVES: The severity of pulmonary Covid-19 infection can be assessed by the pattern and extent of parenchymal involvement observed in computed tomography (CT), and it is important to standardize the analysis through objective, practical, and reproducible systems. We propose a method for stratifying the radiological severity of pulmonary disease, the Radiological Severity Score (RAD-Covid Score), in Covid-19 patients by quantifying infiltrate in chest CT, including assessment of its accuracy in predicting disease severity. METHODS: This retrospective, single-center study analyzed patients with a confirmed diagnosis of Covid-19 infection by real-time reverse-transcriptase polymerase chain reaction, who underwent chest CT at hospital admission between March 6 and April 6, 2020. CT scans were classified as positive, negative, or equivocal, and a radiological severity score (RAD-Covid Score) was assigned. Clinical severity was also assessed upon hospital admission. RESULTS: 658 patients were included. Agreement beyond chance (kappa statistic) for the RAD-Covid Score was almost perfect among observers (0.833), with an overall agreement of 89.5%. The RAD-Covid Score was positively correlated with clinical severity and death, i.e., the higher the RAD-Covid Score, the greater the clinical severity and mortality. This association proved independent of age and comorbidities. Accuracy of this score was 66.9%. CONCLUSIONS: The RAD-Covid Score showed good accuracy in predicting clinical severity at hospital admission and mortality in patients with confirmed Covid-19 infection and was an independent predictor of severity.

Tomographic score (RAD-Covid Score) to assess the clinical severity of the novel coronavirus infection

ABSTRACT Objectives: The severity of pulmonary Covid-19 infection can be assessed by the pattern and extent of parenchymal involvement observed in computed tomography (CT), and it is important to standardize the analysis through objective, practical, and reproducible systems. We propose a method for stratifying the radiological severity of pulmonary disease, the Radiological Severity Score (RAD-Covid Score), in Covid-19 patients by quantifying infiltrate in chest CT, including assessment of its accuracy in predicting disease severity. Methods: This retrospective, single-center study analyzed patients with a confirmed diagnosis of Covid-19 infection by real-time reverse-transcriptase polymerase chain reaction, who underwent chest CT at hospital admission between March 6 and April 6, 2020. CT scans were classified as positive, negative, or equivocal, and a radiological severity score (RAD-Covid Score) was assigned. Clinical severity was also assessed upon hospital admission. Results: 658 patients were included. Agreement beyond chance (kappa statistic) for the RAD-Covid Score was almost perfect among observers (0.833), with an overall agreement of 89.5%. The RAD-Covid Score was positively correlated with clinical severity and death, i.e., the higher the RAD-Covid Score, the greater the clinical severity and mortality. This association proved independent of age and comorbidities. Accuracy of this score was 66.9%. Conclusions: The RAD-Covid Score showed good accuracy in predicting clinical severity at hospital admission and mortality in patients with confirmed Covid-19 infection and was an independent predictor of severity.