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Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient's severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening.
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Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
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BACKGROUND/OBJECTIVE: It is important to predict the COVID-19 patient's prognosis, particularly in countries with lack or deficiency of medical resource for patient's triage management. Currently, WHO guideline suggests using chest imaging in addition to clinicolaboratory evaluation to decide on triage between home-discharge versus hospitalization. We designed our study to validate this recommendation to guide clinicians. This study providing some suggestions to guide clinicians for better decision making in 2020. METHODS: In this retrospective study, patients with RT-PCR confirmed COVID-19 (N = 213) were divided in different clinical and management scenarios: home-discharge, ward hospitalization and ICU admission. We reviewed the patient's initial chest CT if available. We evaluated quantitative and qualitative characteristics of CT as well as relevant available clinicolaboratory data. Chi-square, One-Way ANOVA and Paired t-test were used for analysis. RESULTS: The finding showed that most patients with mixed patterns, pleural effusion, 5 lobes involved, total score ≥10, SpO2% ≤ 90, ESR (mm/h) ≥ 60 and WBC (103/µL) ≥ 8000 were hospitalized. Most patients with Ground-glass opacities only, ≤3 lobes involvement, peripheral distribution, SpO2% ≥ 95, ESR (mm/h) < 30 and WBC(103/µL) < 6000 were home-discharged. CONCLUSIONS: This study suggests the use of initial chest CT (qualitative and quantitative evaluation) in addition to initial clinicolaboratory data could be a useful supplementary method for clinical management and it is an excellent decision making tool (home-discharge versus ICU/Ward admission) for clinicians.