Clinical and imaging features predict mortality in COVID-19 infection in Iran

Abstract

The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptasepolymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.

Chest radiograph characteristics in COVID-19 infection and their association with survival

“…2.5 on average (P = 0.0002), and likely can capture more subtle severity changes. These findings agree with many severity scores in the literature with the Brixia and RALE score being found to be significantly higher in non-survivors [ [15] , [16] , [17] ] and another study with a score similar to Brixia, but a 0–24 score, found it to be significantly higher in non-survivors (20.3 v.s 19.1, P = 0.038) [ 18 ]. Our Mild/ Moderate/ Severe and 0−8 score have the advantage of being more straightforward to calculate than other scores in the literate, and therefore easier to implement clinically, but may not be able to detect small differences in severity and does do not take shadowing density into consideration (like the RALE score).…”

“…Other CXR grading systems have been used in the literature a common example being the Radiographic Assessment of Lung Edema (RALE) score, where the lungs are split into quadrants, given an involvement and density score, the quadrant scores are multiplied and then summed and is a 0–48 score [ 15 ], a simplified version of the RALE score has additionally been used, where each lung is given a score of 0–4 proportional to the amount of lung affected [ 5 ]. Another common score is the Brixia score [ 16 , 17 ], which is 0−18, where the left and right upper, middle and lower zones are each given a 0–3 score proportional to the amount of lung involvement, there is another similar score, but it is scored 0–4 in each zone and is therefore a 0–24 score [ 18 ].…”

The effect of cardiovascular disease and acute cardiac injury on fatal COVID-19: a meta-analysis

“…Among these articles, 7 articles overlapped study data with other articles, and 185 articles met the other exclusion criteria. Finally, 56 articles remained [ [2] , [3] , [4] , 9 , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] ].

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Evaluation of performance of chest x-ray in distinguishing intensıve care unit need among COVID-19 patients

“…Presence of pleural effusion was also noted. Afterwards, the Observer 1 used a radiographic assessment of lung edema (RALE) score [14][15][16] for each CXR in range of 0 (no pathological abnormality) to 48 (complete pathological involvement of both lungs) to assess the disease severity quantitatively (Figures 1 and 2). To assess the reproducibility of the RALE scores, a second radiologist (Observer 2) who had 9 years of experience in CXR interpretation evaluated all CXRs for only RALE score assessment in a separate session independently.…”