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Poursamimi, Javad, Saravani, Solyman, Hossein Beigi, Roghayeh, Rashki Ghalenoo, Sara, Ghafari, Mehrangiz, Valizadeh, Majid. (1404). Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia. سامانه مدیریت نشریات علمی, 80(5), 1199-1208. doi: 10.66224/ARI.80.5.3389
Javad Poursamimi; Solyman Saravani; Roghayeh Hossein Beigi; Sara Rashki Ghalenoo; Mehrangiz Ghafari; Majid Valizadeh. "Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia". سامانه مدیریت نشریات علمی, 80, 5, 1404, 1199-1208. doi: 10.66224/ARI.80.5.3389
Poursamimi, Javad, Saravani, Solyman, Hossein Beigi, Roghayeh, Rashki Ghalenoo, Sara, Ghafari, Mehrangiz, Valizadeh, Majid. (1404). 'Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia', سامانه مدیریت نشریات علمی, 80(5), pp. 1199-1208. doi: 10.66224/ARI.80.5.3389
Poursamimi, Javad, Saravani, Solyman, Hossein Beigi, Roghayeh, Rashki Ghalenoo, Sara, Ghafari, Mehrangiz, Valizadeh, Majid. Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia. سامانه مدیریت نشریات علمی, 1404; 80(5): 1199-1208. doi: 10.66224/ARI.80.5.3389

Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia

Archives of Razi Institute
مقاله 14، دوره 80، شماره 5، آذر و دی 2025، صفحه 1199-1208    اصل مقاله (965.92 K)
نوع مقاله: Original Articles
شناسه دیجیتال (DOI): 10.66224/ARI.80.5.3389
نویسندگان
Javad Poursamimi* 1؛ Solyman Saravani2؛ Roghayeh Hossein Beigi3؛ Sara Rashki Ghalenoo4؛ Mehrangiz Ghafari5؛ Majid Valizadeh6
1Department Immunology, Faculty of Medicine, Zabol University of Medical Sciences, Zabol, Iran.
2Department of Laboratory Sciences, School of Paramedical Sciences, Zabol University of Medical Sciences, Zabol, Iran.
3Department of Internal Medicine, School of Medicine, Zabol University of Medical Sciences, Zabol, Iran.
4Department of Cardiology, School of Medicine, Zabol University of Medical, Sciences, Zabol, Iran.
5Department of Pathology, School of Medicine, Zabol University of Medical Sciences, Zabol, Iran.
6Department of Basic Sciences, School of Medicine, Zabol University of Medical Sciences, Zabol, Iran.
چکیده
Introduction: The clinical symptoms of COVID-19 and non-COVID-19 pulmonary infections are very similar. This study aimed to differentiate between these groups of patients by evaluating laboratory criteria and abnormalities in CT scans.
Materials & Methods: The medical records of 200 patients referred to Amir Hospital in Zabol were analyzed between February 2020 to February 2021. Some of our findings in the COVID-19 group, compared to the non-COVID-19 group, included increase in red blood cell counts (RBCs), mean corpuscular hemoglobin concentration (MCHC), mean hematocrit (HCT), erythrocyte sedimentation rate (ESR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR).
Results: The COVID-19 group had a lower mean corpuscular volume (MCV) of 80 femtoliters (fL) and mean cell hemoglobin (MCH) below 36. The symptoms of pulmonary infection were mostly bilateral in the COVID-19 group, whereas in the non-COVID-19 group, they were predominantly unilateral. In total, 21.6% of patients had 5 to 10 lesions, while 24.7% of the non-COVID-19 group had fewer than 3 lesions. The COVID-19 group showed a distribution of both peripheral and diffuse lesions, whereas the non-COVID-19 group had a predominantly peripheral distribution. Linear opacity and ground-glass opacity (GGO) were observed in 10(6.2%) and 40(24.7%) individuals in the COVID-19 group, and in 13(8%) and 32(19.8) individuals in the non-COVID-19 group, respectively. Twenty-one (13%) COVID-19 patients and 16(9.9%) non-COVID-19 patients exhibited septal thickening. Moreover, fine reticular opacities, crazy paving patterns, and pleural effusion were observed in 6(3.7%), 19(11.7%), and 8(4.9%) of the COVID-19 patients, and in 20(12.3%), 24(14.8%), and 18(11.1%) of the non-COVID-19 patients, respectively.
Conclusion: Finally, this study concluded that laboratory indices such as MCV, and CT scan findings such as septal thickening are very beneficial for distinguishing between these two groups.
کلیدواژه‌ها
Blood cell count؛ Computed tomography (CT) scan)؛ COVID-19؛ Diagnosis؛ Hematologic tests؛ Symptoms
عنوان مقاله [English]
مطالعه آینده نگر در مورد ارتباط بین تست‌های هماتولوژیک و اسکن‌های توموگرافی کامپیوتری ریه (CT Scan) در بیماران مبتلا به عفونت‌های ریوی حاد COVID-19 و غیرCOVID-19
چکیده [English]
علائم بالینی ناشی از عفونت های ریوی Covid 19 و Non-Covid 19 بسیار بهم شبیه بوده و جهت تمایز این دو گروه از تست مولکولی RT-PCR استفاده می شود. هدف از این مطالعه ارزیابی معیارهای آزمایشگاهی و تصاویر CT در تمییز این دو گروه بیماران بود. ۲۰۰ پرونده بیمار مبتلا به پنومونی حاد در بازه زمانی فوریه 2020 تا فوریه 2021 ارزیابی شد برخی از یافته‌های ما در گروه COVID-19 در مقایسه با گروه غیر COVID-19 شامل افزایش تعداد گلبول‌های قرمز (RBCs)، غلظت هموگلوبین سلولی (MCHC)، میانگین هماتوکریت (HCT)، میزان رسوب گلبول‌های قرمز (ESR) بود. ، نسبت نوتروفیل به لنفوسیت (NLR) و نسبت پلاکت به لنفوسیت (PLR). علاوه بر این، گروه COVID-19 دارای میانگین حجم بدنی کمتر (MCV) 80 فمتولیتر (fL) و میانگین هموگلوبین سلولی (MCH) زیر 36 بود. علائم عفونت ریوی عمدتاً در گروه COVID-19 دو طرفه بود، در حالی که در گروه گروه غیر کووید-19، عمدتاً یک طرفه بودند.
21.6 درصد از بیماران 5 تا 10 ضایعه داشتند، در حالی که 24.7 درصد از گروه غیر کووید-19 کمتر از 3 ضایعه داشتند. گروه COVID-19 توزیعی از ضایعات محیطی و منتشر را نشان داد، در حالی که گروه غیر COVID-19 عمدتاً توزیع محیطی داشتند. کدورت خطی و تیرگی شیشه ای (GGO) در 10 (6.2٪) و 40 (24.7٪) از افراد در گروه COVID-19، و 13 (8٪) و 32 (19.8) نفر در گروه غیر کووید-19 مشاهده شد. به ترتیب 19 گروه. بیست و یک (13%) بیمار COVID-19 و 16 (9.9%) بیمار غیر COVID-19 شاخص ضخیم شدن سپتوم را نشان دادند. علاوه بر این، شاخص کدورت شبکه ای ظریف، الگوهای سنگ فرش و افیوژن پلور در 6 (3.7%)، 19 (11.7%) و 8 (4.9%) از بیماران COVID-19 و 20 (12.3%)، 24 مورد مشاهده شد. به ترتیب (14.8%) و 18 (11.1%) از بیماران غیر کووید-19. در نهایت، این مطالعه به این نتیجه رسید که شاخص های آزمایشگاهی مانند MCV و یافته های سی تی اسکن مانند ضخیم شدن سپتوم برای تمایز بین این دو گروه بسیار مفید است.
کلیدواژه‌ها [English]
COVID-19, SARS-CoV-2, آزمایشات هماتولوژیک, اسکن توموگرافی کامپیوتری (CT اسکن)
سایر فایل های مرتبط با مقاله
مراجع

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