Predictors of coronavirus disease 19 (COVID-19) pneumonitis outcome based on computed tomography (CT) imaging obtained prior to hospitalization: a retrospective study

Abstract

Purpose Computed tomography (CT) has been utilized as a diagnostic modality in the coronavirus disease 19 (COVID-19), while some studies have also suggested a prognostic role for it. This study aimed to assess the diagnostic and prognostic value of computed tomography (CT) imaging in COVID-19 patients. Methods This was a retrospective study of fifty patients with COVID-19 pneumonia. Twenty-seven patients survived, while 23 passed away. CT imaging was performed in all of the patients on the day of admission. Imaging findings were interpreted based on current guidelines by two expert radiologists. Imaging findings were compared between surviving and deceased patients. Lung scores were assigned to patients based on CT chest findings. Then, the receiver operating characteristic curve was used to determine cutoff values for lung scores. Results The common radiologic findings were ground-glass opacities (82%) and airspace consolidation (42%), respectively. Air bronchogram was more commonly seen in deceased patients (p = 0.04). Bilateral and multilobar involvement was more frequently found in deceased patients (p = 0.049 and 0.014, respectively). The mean number of involved lobes was 3.46 ± 1.80 lobes in surviving patients and 4.57 ± 0.60 lobes in the deceased patients (p = 0.009). The difference was statistically significant. The area under the curve for a lung score cutoff of 12 was 0.790. Conclusion Air bronchogram and bilateral and multilobar involvement were more frequently seen in deceased patients and may suggest a poor outcome for COVID-19 pneumonia.

Acute kidney injury in hospitalized COVID-19 patients in Iran; a systematic review and meta-analysis

“…The summary of characteristics extracted from studies is presented in Table 1. From 22 included studies, 9 were cohort (17,(19)(20)(21)(23)(24)(25)33,36), 8 were cross-sectional (26,(30)(31)(32)34,35,37,38), one was case-control (28), and 4 were trials (18,22,27,29). Nineteen studies had completed peer-review processes and were published from June to November 2020; while three studies (21,24,32) were not granted publication and peer-review at the time of inclusion.…”

“…While 12 articles reported results from more than 7 hospitals located in various districts of Tehran, Iran's capital and the most populous city (17-20, 23,26,27,30,33,34,36,37), other studies were carried out in Zahedan (the Southeast) (21,35), Abadan (the Southwest) ( 22), Qom (the center) (24), Urmia and Tabriz (the northwest) (25,29,31), Mazandaran and Rasht (the north) (28,38), and Nahavand (the west) (32). Three studies were conducted in a multi-center fashion (21,25,30), (17,20,23,24,27,36,38) which is an increase in serum creatinine by 0.3 mg/ dL within 48 hours or a 50% increase in serum creatinine from baseline within 7 days (39). One study outlined AKI as >30% decrease in eGFR estimated by MDRD equation (37), while AKI was defined roughly by serum creatinine cut-off if specified in other studies.…”

Occurrence of pulmonary residuals as one of the sequelae of COVID-19 and it's predictors among moderate and severe cases

“…Similarly those who have an already compromised functioning organ as in those with comorbid illness are not capable of maintaining proper viral response hence further loading the body and as COVID-19 disease progress body oxygen decreases, contributing to more organ dysfunction [ 19 ].…”

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

“…A dramatic outbreak of the novel Coronavirus disease 2019 (COVID-19) has already been recognized as a global alarming public healthcare distraction. COVID-19 was first identified in late 2019 in Wuhan, China [ 2 , 3 ], and unexpectedly spread to more than 200 countries; thus, the World Health Organization (WHO) announced a pandemic crisis immediately [ 4 ]. Due to strong communicable properties among human-to-human in close contact [ 5 ], healthcare professionals and policymakers have failed to control this dramatic pandemic outbreak, which resulted in rapidly killing people.…”

“…This study contributes to current literature in some aspects, which are summarized: First, we applied a newly adopted DenseNet-121 CNN technique with a data augmentation technique to classify and identify COVID-19 patients. While RT-PCR test results might be contributing to any limitations for the critical patients, the CT image classification technique might be a promising alternative for the detection of COVID-19 as this technique has an exceptionally high sensitivity in diagnosing the disease [ 2 ]. Second, we conducted an extensive application of experimental analyses and reported the performance of DenseNet-121 CNN on multiple indices, including sensitivity, specificity, and AUC.…”