Do chest CT findings predict the outcome in pregnant patients with COVID-19 pneumonia? A cross-sectional study

Do chest CT findings predict the outcome in pregnant patients with COVID-19 pneumonia? 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A cross-sectional study Saeid Esmaeilian, Arash Teimouri, Meisam Hoseinyazdi, Elham Mohajeri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3840586/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: COVID-19 is a novel infectious disease that poses significant challenges for the management of pregnant patients, especially regarding the use and interpretation of chest CT scans. This study aimed to evaluate the clinical significance and radiological features of chest CT scans in pregnant women with COVID-19. Methods: The authors conducted a cross-sectional, multi-center study of 53 pregnant women with confirmed COVID-19 by RT-PCR testing who underwent chest CT scans in three hospitals in Iran. They collected and analyzed data on demographic, clinical, laboratory, and radiological variables, as well as maternal and neonatal outcomes. They used low-dose chest CT protocol and abdominal shields to minimize radiation exposure and assessed the interobserver reliability of the chest CT diagnosis using Cohen’s kappa coefficient. Results: The study included 41 pregnant women with COVID-19 and available chest CT scans. The mean maternal age was 34.6±7.60 years and the mean gestational age was 28.73±5.66 weeks. Dyspnea was the most common symptom (85.4%), followed by fever (68.3%) and cough (51.2%). Asthma (p=0.001) and diabetes (p=0.001) were significantly associated with worse outcomes and abnormal chest CT scans. Myalgia (p=0.001), respiratory distress (p=0.001), admission O2 saturation (p=0.001), leukopenia (p=0.001), lymphocytosis (p=0.001), and high CRP levels (p=0.001) were also correlated with adverse outcomes and abnormal chest CT scans. Normal chest scans were observed in 25 patients (61%), while 16 patients (39%) had abnormal CT scan findings. Ground glass opacity (81.3%) and consolidation (68.8%) were the most common radiological features on chest CT scans. Four maternal deaths occurred due to delayed referral and underlying comorbidities such as heart failure or renal failure. Conclusion: The percentage of lung involvement and the presence of consolidation on chest CT scans could predict the prognosis of pregnant women with COVID-19. Chest CT scans could be a useful tool for the diagnosis and management of COVID-19 in pregnancy, especially in cases of severe symptoms or comorbidities. Infectious Diseases lung CT scan Imaging COVID-19 pregnancy outcome Figures Figure 1 Introduction The emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, towards the conclusion of 2019 has prompted inquiries into its pathogenesis, therapy, and clinical presentations across various health conditions( 1 ). Previous research indicated that elderly individuals exhibited a heightened vulnerability to experiencing severe infection and consequences arising from COVID-19( 2 ). The reverse transcription polymerase chain reaction (RT-PCR) test is extensively employed as a diagnostic modality for COVID-19; nevertheless, it is associated with a notable incidence of false negative results. Hence, radiographic imaging techniques, including chest CT and chest radiography (CXR), play a crucial role in the diagnosis and assessment of pregnant individuals exhibiting symptoms indicative of COVID-19 infection( 3 ). Pregnancy is characterized by a distinct immunological state that heightens the mother's susceptibility to a range of viral infections( 4 , 5 ). Based on prior encounters with Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS), both of which were induced by different strains of coronaviruses, it has been observed that pregnant women constitute a population at elevated risk and necessitate additional preventive measures( 6 ). Nevertheless, there is ongoing debate over the effects of COVID-19 on pregnancy and the outcomes of newborns. Previous studies conducted through meta-analytic approaches have indicated that pregnant women may experience more severe manifestations of the condition. However, the available information does not provide sufficient support for the occurrence of vertical transmission( 7 – 9 ). Insufficient data exists about the lung CT scan characteristics of pregnant individuals affected with COVID-19, potentially impacting their prognostic outcomes. The objective of this study was to ascertain the lung CT scan features observed in pregnant Iranian patients with COVID-19 and explore any potential correlation with the clinical outcome. Material and Methods This cross-sectional, multi-center study collected data from 53 pregnant patients with confirmed COVID-19 via PCR testing. These patients were referred to Faghihi and Namazi General Teaching Hospitals in Shiraz, Iran, and Rask General Hospital in Rask City, Sistan VA Baluchistan province, Iran between March 2020 and September 2022. The study included pregnant patients with positive findings for COVID-19 via RT-PCR testing of respiratory secretions obtained through nasopharyngeal or oropharyngeal swabs and who had available chest CT scans in the Picture Archiving and Communication Systems (PACS). Patients who were uncooperative, lost to follow-up, had incomplete clinical and chest CT data, or had other lung infections were excluded from the study. Demographic data, medical history, admission time complaints, O2 saturation, vital signs at the time of admission, and laboratory data (CBC, diff, ESR, CRP) were collected using a pre-designed checklist by a blinded trained research group. A second blinded trained research group reviewed final maternal records after delivery to extract data on stillbirth/miscarriage, preterm birth, IUGR, NICU admission or neonate death after admission about COVID. Chest CT scans were performed using 16-MDCT Philips Brilliance (Philips Healthcare, United States) in Shiraz and 16-MDCT Aquilion 16- Canon in Rask with low-dose chest CT protocol without contrast. Patients were scanned in the supine position during a breath-hold after inhalation and abdominal shields were used for all patients. Three clinical radiologists independently reviewed the chest CT scans of included patients using a pre-designed checklist that included consolidation, density, ground glass opacity, pulmonary infiltration, possible nodules or halo sign, bronchiectasis, lymphadenopathy, consolidation, cystic change, pleural or pericardial effusion, and air-bronchogram. Inter-observer reliability was assessed using Cohen’s kappa calculation and final CT results were determined by consensus discussion. Data were analyzed using SPSS software (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp). Continuous variables were presented as mean and standard deviation while categorical variables were presented as counts and percentages. Interobserver reliability on CT diagnosis of COVID-19 was evaluated using Cohen’s kappa calculation. Quantitative variables were checked for normality using the Kolmogorov–Smirnoff test. Fisher’s exact test was used for comparing categorical variables and Pearson’s test was used for assessing the correlation between numerical variables. Chi2 and T-tests (or their non-parametric tests) were used for analysis. Results This cross-sectional, multi-center study comprised a cohort of 41 individuals (Fig. 1). Out of the total number of cases, 23 individuals (58.5%) were directed to Faghihi Hospital located in Shiraz, while the remaining 18 individuals (41.5%) were referred to the clinic at Velayat Rask Hospital. Table 1 provides a description of the demographic characteristics. The average mother age was 34.6 ± 7.60 years, with a range of 17 to 49 years. Similarly, the average gestational age was 28.73 ± 5.66 weeks, with a range of 21 to 34 weeks. Out of the total number of patients, 29 individuals (70.73%) received outpatient care, while the remaining patients were admitted to either respiratory wards or respiratory ICUs. The duration of hospitalization for admitted patients had a mean of 5.32 ± 3.13 days, with a range of 1–16 days. Out of the overall sample size, 37 patients, accounting for 90.24% of the cohort, exhibited favorable prognoses without requiring intubation. Conversely, 4 patients, constituting 9.75% of the population, underwent intubation but unfortunately succumbed thereafter. The prevalent underlying disorders seen in the study population were diabetes mellitus (14.6%), asthma (12.19%), heart failure (4.87%), and renal failure (2.43%). The PCR results following delivery were negative for all neonates. The prevalence of dyspnea as a symptom was found to be 70.73%, making it the most often reported symptom among the study participants. Conversely, cough was reported by only 14.63% of the participants, indicating that it was the least commonly experienced symptom. The average oxygen saturation at arrival was 91.83 ± 5.96%. The prevailing observations among all patients were leukopenia (60.97%) and respiratory distress (23.66 ± 9.98). Table 1 presents additional information pertaining to the investigations. The study found a strong association between the presence of asthma and diabetes, myalgia, respiratory distress, admission O2 saturation, leukopenia, lymphocytosis, and high CRP levels with outcomes and abnormal chest CT scans (P-value < 0.001). These characteristics were identified as the most critical risk factors for adverse outcomes, as shown in Table-2. In our analysis, the most prevalent neonatal and fetal problems were intrauterine growth restriction (IUGR) with a frequency of 19.51% and pre-term birth with a frequency of 9.75%. Notably, no instances of stillbirths, miscarriages, or neonate fatalities were observed in our research. The Cohen's kappa coefficient yielded a score of 0.82, which signifies a high level of interobserver agreement in the computed tomography (CT) diagnosis of COVID-19. The study revealed that the majority of chest scans were within normal parameters, as indicated by 16 patients (43.24%) exhibiting aberrant CT scan results. The average extent of lung involvement, as determined by the visualized score, was found to be 57.66 ± 7.64%. The predominant radiological abnormalities observed on CT scans were ground glass opacity (24.39%) and consolidation (21.95%), as indicated in Table 3 . The results of the study indicate that the existence of consolidation and the percentage of lung involvement were shown to have a statistically significant association with patient outcomes, as evidenced by a P-value of less than 0.001. There were no statistically significant differences seen in the prevalence of halo sign (5.40%), nodule (10.81%), fibrotic band (2.70%), sub-pleural spare (10.81%), or atoll sign (10.81%) in relation to patient outcomes, as shown in Table-3. In the present study, we documented a total of four maternal fatalities. Among these cases, two individuals exhibited near-complete consolidation of the lungs, while one had approximately 85% consolidation. All three patients were admitted to Rask Hospital and were infected with the Delta variant of the coronavirus. Additionally, these individuals had pre-existing conditions of heart failure or renal failure. The fourth patient displayed near-80% ground-glass opacity (GGO) involvement in the lungs, was admitted to Faghihi Hospital, and was infected with the Wuhan variant of COVID-19. Notably, all four patients sought medical attention more than five days after experiencing dyspnea. Upon admission, the average oxygen saturation level was measured at 84%. Table 1 description of important demographic findings Shiraz study(N = 23) Rask study(N = 18) Total (N = 41) Maternal Age (Years) 32.34 ± 8.06 36.39 ± 7.15 34.6 ± 7.60 Gestational Age (weeks) 26.14 ± 6.19 31.3 ± 5.13 28.73 ± 5.66 Vaccination (%) 0 2(11.11) 2 (4.87) Hospitalization Duration (days) 6.13 ± 4.07 4.51 ± 2.19 5.32 ± 3.13 Underlying disease Asthma (%) 3(13.04) 2(11.11) 5(12.19) DM (%) 1(4.34) 5(27.77) 6(14.63) Heart Failure (%) 0 2(11.11) 2 (4.87) Kidney Dysfunction (%) 0 1(5.55) 1(2.43) Symptoms Cough (%) 5 (21.7) 1(5.55) 6(14.63) Dyspnea (%) 15 (65.2) 14(77.77) 29(70.73) Fever (%) 14 (60.9) 11 (61.11) 25 (60.97) Myalgia (%) 2 (8.70) 7(38.88) 9(21.95) Investigations Leukopenia (%) 11(47.80) 14(77.77) 25(60.97) Lymphocytosis (%) 3(13.04) 1(5.55) 4(9.75) Positive CRP (%) 4(17.39) 2(11.11) 6(14.63) Positive ESR (%) 1(4.34) 0 1(2.43) O2 saturation (%) 93.52 ± 4.59 90.15 ± 7.34 91.83 ± 5.96 Respiratory rate 26.14 ± 6.19 21.19 ± 7.59 23.66 ± 9.98 Maternal complication OPD follow up (%) 19(82.60) 10(55.55) 29(70.73) Ward Admission (%) 4(17.39) 5(27.77) 9(21.95) ICU Admission (%) 0 3(16.66) 3(7.31) Death (%) 1(4.34) 3(16.66) 4(9.75) Fetal complication IUGR (%) 3(13.04) 5(27.77) 8(19.51) Preterm Birth (%) 1(4.34) 3(16.66) 4(9.75) Stillbirth/miscarriage (%) 0 0 0 Neonate complication Neonate death (%) 0 0 0 NICU Admission (%) 1(4.34) 3(16.66) 4(9.75) Table 2 Compare important demographic findings with Maternal final status and their Chest Ct scan results Variable Maternal Status p-value Chest CT Imaging p-value total Alive Death Normal Abnormal Age yr (M ± SD) 29.83 ± 4.51 39.37 ± 5.48 < 0.001 22.76 ± 9.1 46.44 ± 7.61 < 0.001 34.6 ± 7.60 GA weeks (M ± SD) 25.43 ± 3.24 32.03 ± 2.65 < 0.001 24.36 ± 6.37 33.1 ± 1.53 < 0.001 28.73 ± 5.66 Hospitalization Duration (days) (M ± SD) 2.73 ± 4.29 7.91 ± 6.67 < 0.001 1.943.13 8.70 ± 3.13 < 0.001 5.32 ± 3.13 Asthma (%) 2(5.40) 3 (75.0) < 0.001 1(4) 4 (25) < 0.001 5(12.19) DM (%) 4(10.81) 2(50) < 0.001 3(12) 3(18.75) 0.912 6(14.63) Cough (%) 5(13.51) 1(25.0) 0.20 2(8) 4 (25) 0.02 6(14.63) Dyspnea (%) 25(67.56) 4(100) < 0.001 16(64) 13(81.25) 0.82 29(70.73) Fever (%) 22(59.45) 3(75) 0.62 19(76) 6(37.5) 0.86 25 (60.97) Myalgia (%) 5(13.51) 4(100) < 0.001 4(16) 5(31.25) 0.03 9(21.95) Leukopenia (%) 21(56.75) 4(100) < 0.001 16(64) 9(56.25) < 0.001 25(60.97) Lymphocytosis (%) 3(8.10) 1(25) 0.08 2(8) 2(12.5) 0.04 4(9.75) CRP (%) 2(5.40) 4(100) < 0.001 1(4) 5(31.25) < 0.001 6(14.63) O2 saturation % (M ± SD) 99.09 ± 9.35 84.57 ± 2.56 < 0.001 96.32 ± 8.62 87.343.48 < 0.001 91.83 ± 5.96 Respiratory rate(M ± SD) 15.62 ± 7.94 31.7 ± 3.29 < 0.001 18.62 ± 9.86 28.9 ± 4.35 < 0.001 23.66 ± 9.98 OPD follow up (%) 29(78.37) 0 ---- 21(84) 8(50) < 0.001 29(70.73) Ward Admission (%) 8(21.62) 1(25) 0.91 0 9(56.25) 0.001 1(4) 3(18.75) 0.02 3(7.31) IUGR (%) 8(19.51) ----- ----- 6(37.5) 2(12.5) 0.04 8(19.51) Preterm Birth (%) 4(10.81) ----- ----- 1(4) 3(18.75) 0.01 4(9.75) NICU admission (%) 4(10.81) ----- ----- 3(12) 1(6.25) 0.07 4(9.75) Table 3 description and compare of Chest CT scan findings with Final maternal status Status p-value Total Live Death Chest CT Findings Normal 25(67.56) 0 < 0.001 25(60.97) Abnormal 12(32.43) 4(100) 16(43.24) Lung’s involvement % (M ± SD) 28.67 ± 4.26 86.66 ± 23.3 < 0.001 57.66 ± 7.64 Ground Glass Opacity (%) 9(24.32) 1(25) 0.80 10(24.39) Consolidation 6(16.21) 3(75) < 0.001 9(21.95) Halo Sign (%) 2(5.40) 0 ---- 2(4.87) Nodule (%) 4(10.81) 0 ---- 4(9.75) Fibrotic Band (%) 1(2.70) 0 ---- 1(2.43) Sub-pleural Spare (%) 3(8.10) 1(25) 0.06 4(9.75) Atoll Sign (%) 4(10.81) 0 ---- 4(9.75) Discussion Assessing the mortality rate of COVID-19 in pregnant individuals serves as a valuable approach for ascertaining the extent of the disease's impact. The present investigation reveals that the fatality rate among expectant individuals with confirmed positive polymerase chain reaction (PCR) outcomes for COVID-19 was 9.75%. The prevalence of this rate was documented as 2.2% in Mexico in the year 2020( 9 ), but a another study conducted in Mexico in 2021 found a prevalence of 18.5%( 10 ). According to a comprehensive review, the mortality rate among pregnant individuals varied from 8.5% in Kenya to 61.5% in Uganda( 11 ). The death rate in the general population exhibited a considerable variation, with reported figures ranging from 0.1% in Burundi to 18.1% in Yemen( 12 ).The observed variations in death rates could potentially be attributed to disparities in the size of the tested population, reporting methodologies employed by healthcare systems, and unidentified variables. Prior to the advent and widespread adoption of polymerase chain reaction (PCR) assays for the diagnosis of COVID-19, lung computed tomography (CT) scans served as the primary diagnostic modality, particularly in Iran( 13 ). As a result of the pronounced ionization effect associated with CT scans, the majority of pregnant patients exhibiting minor symptoms were not subjected to this imaging technique. Instead, CT scans were reserved for those individuals presenting with notable dyspnea or other symptomatic manifestations. Consequently, the death rate among pregnant individuals exhibited a greater incidence compared to that observed across the broader population. One potential factor contributing to the elevated mortality rate among pregnant individuals is the heightened cardiac burden experienced during pregnancy, perhaps rendering the lungs more vulnerable to the development of acute respiratory distress syndrome (ARDS). In the present study, it was observed that 25 patients, accounting for 56.75% of the total sample, exhibited normal chest CT scans. Conversely, a smaller proportion of patients, namely 16 individuals (43.24%), displayed abnormal chest CT scans. The most frequently observed radiological abnormalities on chest CT scans were ground glass opacity (GGO) (24.39%) and consolidation (21.95%). Our investigation also revealed the significance of lung involvement in pregnant patients, since the average extent of lung involvement in cases resulting in maternal death was notably greater compared to the surviving groups. The sole radiological indicator that exhibited a substantial correlation with unfavorable prognosis was the existence of consolidation. There was a strong association observed between low oxygen saturation, increased C-reactive protein (CRP) levels, and leukopenia and abnormal chest computed tomography (CT) scans. In the present investigation, a total of four instances of maternal mortality were documented. Among these cases, two individuals exhibited near-complete consolidation of the lungs, while one individual displayed around 85% consolidation of the lungs. Additionally, one case shown near-80% ground-glass opacity involvement in the lungs. Each of the four patients had been referred to the hospital more than five days following the initial manifestation of symptoms. The most prevalent symptoms seen among patients were dyspnea and fever. However, it was determined that fever did not exhibit a statistically significant correlation with survival status, as indicated by a p-value greater than 0.05. Several factors were found to be predictive of poor prognosis in pregnant individuals with COVID-19, including advanced maternal age, prolonged gestational age, the presence of underlying medical conditions, low oxygen saturation levels upon admission, experiencing dyspnea (shortness of breath), leukopenia (low white blood cell count), and elevated levels of C-reactive protein (CRP). Based on the results of our study, it is recommended that pregnant individuals exhibiting admission O2 saturation values below 90%, considerable discomfort, leukopenia, and positive C-reactive protein (CRP) levels should be promptly subjected to early computed tomography (CT) scan imaging upon admission. Early CT scan imaging may be beneficial for patients who meet these criteria, and those who have symptoms lasting for a prolonged duration may also derive benefits from early CT scan imaging upon admission. Additional research is required to establish appropriate guidelines for the utilization of CT scan imaging in pregnant individuals. This intervention has the potential to assist hospital bed managers and physicians in making informed decisions on the allocation of ICU or isolated beds at the time of patient arrival. By doing so, it has the potential to contribute to a reduction in maternal mortality rates and the timely discharge of patients who no longer require hospitalization. The study conducted by Kuzan et al. revealed that fever and dry cough were the prevailing symptoms observed in pregnant individuals affected with COVID-19. Likewise, in the research conducted by Liu et al., the primary clinical manifestations encompassed cough, fever, and nasal congestion( 4 , 10 , 11 ). The study conducted by Kuzan et al. involved a retrospective analysis of clinical, biochemical, and radiological characteristics in a group of fifty-five pregnant women who exhibited symptoms suggestive of COVID-19. Among these patients, 9.1% required admission to the intensive care unit (ICU), with three individuals developing acute respiratory distress syndrome (ARDS) and one unfortunate fatality occurring( 14 ). The findings of chest CT scans frequently revealed bilateral involvement (88.2%), multilobe distribution (100%) with both peripheral and central involvement (70.6%), patchy morphology (94.1%), and ground-glass opacity (94.1%)( 4 ). Certain medical professionals have posited that the clinical trajectory and ultimate result of COVID-19 in expectant individuals may not be more severe compared to the broader populace, a hypothesis that has been deliberated upon in the majority of analogous investigations( 15 ). Prior studies have indicated that the majority of pregnant individuals diagnosed with COVID-19 were in the third trimester of pregnancy and experienced symptoms of mild to moderate severity( 3 , 14 , 15 ). Nevertheless, our research demonstrated a positive correlation between advancing maternal age and an elevated rate of mortality. There is evidence to suggest that the presence of COVID-19 during pregnancy is linked to worse maternal outcomes, such as increased morbidity, preterm birth, and a heightened likelihood of requiring critical care. In the present investigation, the most prevalent neonatal and fetal problems seen were intrauterine growth restriction (IUGR) with a frequency of 19.51% and pre-term birth with a frequency of 9.75%. Notably, no instances of stillbirths, miscarriages, or neonate fatalities were recorded. The study conducted by Liu et al. examined clinical and CT data from a sample of 21 pregnant women and 19 non-pregnant women of similar age who were diagnosed with COVID-19 pneumonia. The findings revealed that the rate of preterm birth among pregnant women was 41.2%. Out of the total sample size of 55 pregnant women, a proportion of five individuals (9.1%) required admission to the critical care unit. Among these cases, three individuals experienced the onset of acute respiratory distress syndrome, while one unfortunate individual succumbed to the condition( 10 ). The study involved 23 pregnant patients who were hospitalized and diagnosed with COVID-19, as conducted by Wu et al. The retrospective collection of clinical presentations encompassed many components, such as laboratory testing, chest CT imaging, and symptoms, as documented in the records. In this particular study, it is noteworthy that all participants had been discharged from the hospital. However, it is important to highlight that the median duration of hospitalization was found to be 17 days, with a range spanning from 6 to 31 days( 11 ). Another significant point of worry is the vertical spread of infection. In the present investigation, all infants subjected to polymerase chain reaction (PCR) testing exhibited negative results for the SARS-CoV-2 virus, hence indicating a diminished occurrence of vertical transmission. This observation aligns with prior research, wherein a small proportion of babies exhibited positive results for SARS-CoV-2 using PCR testing. The origins of these infections are not well established, as they could have arisen during the process of vaginal delivery or subsequent to birth through transmission from an asymptomatic infected mother or hospital personnel. The presence of heightened concentrations of immunoglobulin (Ig) M and IgG specific to SARS-CoV-2 in neonates born to women with confirmed infection indicates the potential occurrence of vertical transmission( 16 ). The results of our investigation indicate that the administration of hydroxychloroquine did not result in a decrease in the length of hospitalization for pregnant individuals diagnosed with COVID-19. The inclusion or exclusion of this medicine in worldwide therapeutic guidelines has been subject to occasional changes since the initial identification of the condition( 17 ). Nevertheless, subsequent clinical trials have provided evidence contradicting the purported clinical advantages of hydroxychloroquine in treating patients with COVID-19( 18 ). Numerous clinical trials have been undertaken thus far in the pursuit of identifying an efficacious intervention for COVID-19, with the majority of these studies omitting individuals who are pregnant( 19 ). Additional investigation is required in order to furnish comprehensive data pertaining to therapeutic alternatives for expectant individuals. The current investigation exhibits a number of constraints. The retrospective nature of the study led to the exclusion of valuable data, and adherence to the ALARA principle resulted in a limited number of CT scans being performed on pregnant women, hence reducing the sample size. Furthermore, the presence of diverse geographical areas, distinct subtypes of COVID-19, and variations in the timeframe of the investigation constituted additional constraints. Conclusion our study revealed that the degree of lung involvement and the identification of consolidation on computed tomography (CT) scans can serve as prognostic indicators for pregnant individuals with COVID-19. Pregnant individuals exhibiting admission oxygen saturation levels below 90%, experiencing notable distress, displaying leukopenia, and exhibiting positive C-reactive protein (CRP) levels were found to have a higher probability of presenting abnormal chest computed tomography (CT) scans. Declarations Competing interests The authors state no conflict of interest. Funding The authors received no funds for this project. Availability of data and materials. The data supporting this manuscript's findings are not publicly available due to Ethical restrictions. Data are, however, available from the authors upon reasonable request and with permission of Shiraz University of Sciences. Ethics Statements Ethical approval to report this study was obtained from the Ethics Committee of Shiraz University of Sciences. (Approval No. IR.SUMS.REC.1399.042). References Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. 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Dong L, Tian J, He S, Zhu C, Wang J, Liu C, et al. Possible vertical transmission of SARS-CoV-2 from an infected mother to her newborn. Jama. 2020;323(18):1846–8. Lamontagne F, Agoritsas T, Macdonald H, Leo Y-S, Diaz J, Agarwal A, et al. A living WHO guideline on drugs for covid-19. bmj. 2020;370. Omrani AS, Pathan SA, Thomas SA, Harris TR, Coyle PV, Thomas CE, et al. Randomized double-blinded placebo-controlled trial of hydroxychloroquine with or without azithromycin for virologic cure of non-severe Covid-19. EClinicalMedicine. 2020;29:100645. Whitehead CL, Walker SP. Consider pregnancy in COVID-19 therapeutic drug and vaccine trials. The Lancet. 2020;395(10237):e92. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3840586","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265619281,"identity":"8d7fc79a-1325-4948-b3ee-a093793b87bd","order_by":0,"name":"Saeid Esmaeilian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACCRDBU/FfDkQfeEC8ljPMxmAtCURr4W1jTmwAMYjSIjnt+OMPb86wpc8PO/wQaIudnG4DAS3S0jlmknMqeHI33k4zAGpJNjY7QECLnHQOGzPPGYncjbMTQFoOJG4jrCX98WfeNoN0w9npH4jTIi2dYCDN25aQIC+dQ6QtkrNBfjlzwHCDdE7BgQQDIvwicTsdGGIVB+TlZ6dv/vChwk6OoBY4MACrNCBWOQjIN5CiehSMglEwCkYUAADq3kXcPIyuSwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4368-3772","institution":"Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran","correspondingAuthor":true,"prefix":"","firstName":"Saeid","middleName":"","lastName":"Esmaeilian","suffix":""},{"id":265619282,"identity":"978ca8fd-b458-442e-8d98-13e74a95239b","order_by":1,"name":"Arash Teimouri","email":"","orcid":"","institution":"Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran","correspondingAuthor":false,"prefix":"","firstName":"Arash","middleName":"","lastName":"Teimouri","suffix":""},{"id":265619283,"identity":"93153d7b-efb2-42ab-b249-b6efc4c5ae37","order_by":2,"name":"Meisam Hoseinyazdi","email":"","orcid":"","institution":"Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, Maryland, USA","correspondingAuthor":false,"prefix":"","firstName":"Meisam","middleName":"","lastName":"Hoseinyazdi","suffix":""},{"id":265619284,"identity":"e35f26ca-419a-4c95-91ee-301fc244f468","order_by":3,"name":"Elham Mohajeri","email":"","orcid":"","institution":"Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Mohajeri","suffix":""},{"id":265619285,"identity":"3b389757-31d5-497d-b949-d4df3f698691","order_by":4,"name":"Sedighe Hooshmandi","email":"","orcid":"","institution":"Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran","correspondingAuthor":false,"prefix":"","firstName":"Sedighe","middleName":"","lastName":"Hooshmandi","suffix":""}],"badges":[],"createdAt":"2024-01-06 20:28:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3840586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3840586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49540062,"identity":"382a29e6-d906-41b2-b113-bf26f5498656","added_by":"auto","created_at":"2024-01-12 17:16:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310160,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3840586/v1/1a310ef586c9435818e7eb82.jpg"},{"id":49541418,"identity":"b889d352-aef9-4187-b893-99f4500f6e0a","added_by":"auto","created_at":"2024-01-12 17:24:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":372222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3840586/v1/fc6a5b9b-3dcd-450d-bc94-f5dfc41d0a8f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDo chest CT findings predict the outcome in pregnant patients with COVID-19 pneumonia? A cross-sectional study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, towards the conclusion of 2019 has prompted inquiries into its pathogenesis, therapy, and clinical presentations across various health conditions(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Previous research indicated that elderly individuals exhibited a heightened vulnerability to experiencing severe infection and consequences arising from COVID-19(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The reverse transcription polymerase chain reaction (RT-PCR) test is extensively employed as a diagnostic modality for COVID-19; nevertheless, it is associated with a notable incidence of false negative results. Hence, radiographic imaging techniques, including chest CT and chest radiography (CXR), play a crucial role in the diagnosis and assessment of pregnant individuals exhibiting symptoms indicative of COVID-19 infection(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePregnancy is characterized by a distinct immunological state that heightens the mother's susceptibility to a range of viral infections(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Based on prior encounters with Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS), both of which were induced by different strains of coronaviruses, it has been observed that pregnant women constitute a population at elevated risk and necessitate additional preventive measures(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, there is ongoing debate over the effects of COVID-19 on pregnancy and the outcomes of newborns. Previous studies conducted through meta-analytic approaches have indicated that pregnant women may experience more severe manifestations of the condition. However, the available information does not provide sufficient support for the occurrence of vertical transmission(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsufficient data exists about the lung CT scan characteristics of pregnant individuals affected with COVID-19, potentially impacting their prognostic outcomes. The objective of this study was to ascertain the lung CT scan features observed in pregnant Iranian patients with COVID-19 and explore any potential correlation with the clinical outcome.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThis cross-sectional, multi-center study collected data from 53 pregnant patients with confirmed COVID-19 via PCR testing. These patients were referred to Faghihi and Namazi General Teaching Hospitals in Shiraz, Iran, and Rask General Hospital in Rask City, Sistan VA Baluchistan province, Iran between March 2020 and September 2022. The study included pregnant patients with positive findings for COVID-19 via RT-PCR testing of respiratory secretions obtained through nasopharyngeal or oropharyngeal swabs and who had available chest CT scans in the Picture Archiving and Communication Systems (PACS). Patients who were uncooperative, lost to follow-up, had incomplete clinical and chest CT data, or had other lung infections were excluded from the study.\u003c/p\u003e \u003cp\u003eDemographic data, medical history, admission time complaints, O2 saturation, vital signs at the time of admission, and laboratory data (CBC, diff, ESR, CRP) were collected using a pre-designed checklist by a blinded trained research group. A second blinded trained research group reviewed final maternal records after delivery to extract data on stillbirth/miscarriage, preterm birth, IUGR, NICU admission or neonate death after admission about COVID.\u003c/p\u003e \u003cp\u003eChest CT scans were performed using 16-MDCT Philips Brilliance (Philips Healthcare, United States) in Shiraz and 16-MDCT Aquilion 16- Canon in Rask with low-dose chest CT protocol without contrast. Patients were scanned in the supine position during a breath-hold after inhalation and abdominal shields were used for all patients. Three clinical radiologists independently reviewed the chest CT scans of included patients using a pre-designed checklist that included consolidation, density, ground glass opacity, pulmonary infiltration, possible nodules or halo sign, bronchiectasis, lymphadenopathy, consolidation, cystic change, pleural or pericardial effusion, and air-bronchogram. Inter-observer reliability was assessed using Cohen\u0026rsquo;s kappa calculation and final CT results were determined by consensus discussion.\u003c/p\u003e \u003cp\u003eData were analyzed using SPSS software (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp). Continuous variables were presented as mean and standard deviation while categorical variables were presented as counts and percentages. Interobserver reliability on CT diagnosis of COVID-19 was evaluated using Cohen\u0026rsquo;s kappa calculation. Quantitative variables were checked for normality using the Kolmogorov\u0026ndash;Smirnoff test. Fisher\u0026rsquo;s exact test was used for comparing categorical variables and Pearson\u0026rsquo;s test was used for assessing the correlation between numerical variables. Chi2 and T-tests (or their non-parametric tests) were used for analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis cross-sectional, multi-center study comprised a cohort of 41 individuals (Fig.\u0026nbsp;1). Out of the total number of cases, 23 individuals (58.5%) were directed to Faghihi Hospital located in Shiraz, while the remaining 18 individuals (41.5%) were referred to the clinic at Velayat Rask Hospital. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a description of the demographic characteristics. The average mother age was 34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60 years, with a range of 17 to 49 years. Similarly, the average gestational age was 28.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66 weeks, with a range of 21 to 34 weeks. Out of the total number of patients, 29 individuals (70.73%) received outpatient care, while the remaining patients were admitted to either respiratory wards or respiratory ICUs. The duration of hospitalization for admitted patients had a mean of 5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13 days, with a range of 1\u0026ndash;16 days. Out of the overall sample size, 37 patients, accounting for 90.24% of the cohort, exhibited favorable prognoses without requiring intubation. Conversely, 4 patients, constituting 9.75% of the population, underwent intubation but unfortunately succumbed thereafter. The prevalent underlying disorders seen in the study population were diabetes mellitus (14.6%), asthma (12.19%), heart failure (4.87%), and renal failure (2.43%). The PCR results following delivery were negative for all neonates.\u003c/p\u003e \u003cp\u003eThe prevalence of dyspnea as a symptom was found to be 70.73%, making it the most often reported symptom among the study participants. Conversely, cough was reported by only 14.63% of the participants, indicating that it was the least commonly experienced symptom. The average oxygen saturation at arrival was 91.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96%. The prevailing observations among all patients were leukopenia (60.97%) and respiratory distress (23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents additional information pertaining to the investigations.\u003c/p\u003e \u003cp\u003eThe study found a strong association between the presence of asthma and diabetes, myalgia, respiratory distress, admission O2 saturation, leukopenia, lymphocytosis, and high CRP levels with outcomes and abnormal chest CT scans (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These characteristics were identified as the most critical risk factors for adverse outcomes, as shown in Table-2. In our analysis, the most prevalent neonatal and fetal problems were intrauterine growth restriction (IUGR) with a frequency of 19.51% and pre-term birth with a frequency of 9.75%. Notably, no instances of stillbirths, miscarriages, or neonate fatalities were observed in our research.\u003c/p\u003e \u003cp\u003e The Cohen's kappa coefficient yielded a score of 0.82, which signifies a high level of interobserver agreement in the computed tomography (CT) diagnosis of COVID-19. The study revealed that the majority of chest scans were within normal parameters, as indicated by 16 patients (43.24%) exhibiting aberrant CT scan results. The average extent of lung involvement, as determined by the visualized score, was found to be 57.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64%. The predominant radiological abnormalities observed on CT scans were ground glass opacity (24.39%) and consolidation (21.95%), as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of the study indicate that the existence of consolidation and the percentage of lung involvement were shown to have a statistically significant association with patient outcomes, as evidenced by a P-value of less than 0.001. There were no statistically significant differences seen in the prevalence of halo sign (5.40%), nodule (10.81%), fibrotic band (2.70%), sub-pleural spare (10.81%), or atoll sign (10.81%) in relation to patient outcomes, as shown in Table-3.\u003c/p\u003e \u003cp\u003eIn the present study, we documented a total of four maternal fatalities. Among these cases, two individuals exhibited near-complete consolidation of the lungs, while one had approximately 85% consolidation. All three patients were admitted to Rask Hospital and were infected with the Delta variant of the coronavirus. Additionally, these individuals had pre-existing conditions of heart failure or renal failure. The fourth patient displayed near-80% ground-glass opacity (GGO) involvement in the lungs, was admitted to Faghihi Hospital, and was infected with the Wuhan variant of COVID-19. Notably, all four patients sought medical attention more than five days after experiencing dyspnea. Upon admission, the average oxygen saturation level was measured at 84%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003edescription of important demographic findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShiraz study(N\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRask study(N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal Age (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational Age (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVaccination (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization Duration (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eUnderlying disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsthma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(12.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(27.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeart Failure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney Dysfunction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(5.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSymptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCough (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(5.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDyspnea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(77.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(70.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFever (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (61.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (60.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMyalgia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(38.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(21.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eInvestigations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeukopenia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(47.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(77.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(60.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphocytosis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(5.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive CRP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive ESR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO2 saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMaternal complication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPD follow up (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(82.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(55.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(70.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWard Admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(27.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(21.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU Admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(16.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(7.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(16.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFetal complication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIUGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(27.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(19.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreterm Birth (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(16.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStillbirth/miscarriage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNeonate complication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeonate death (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNICU Admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(16.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompare important demographic findings with Maternal final status and their Chest Ct scan results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMaternal Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eChest CT Imaging\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge yr (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.76\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA weeks (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization Duration (days) (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.943.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5(12.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(13.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(67.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29(70.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(59.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 (60.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyalgia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(13.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9(21.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukopenia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(56.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(56.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25(60.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytosis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(14.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2 saturation % (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.09\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.32\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.343.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate(M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPD follow up (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(78.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29(70.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWard Admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(21.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(56.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9(21.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3(7.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIUGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(19.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8(19.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm Birth (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICU admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003edescription and compare of Chest CT scan findings with Final maternal status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChest CT Findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(67.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25(60.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(32.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(43.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLung\u0026rsquo;s involvement % (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.66\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGround Glass Opacity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(24.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(24.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eConsolidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(16.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(21.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHalo Sign (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(4.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNodule (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFibrotic Band (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSub-pleural Spare (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAtoll Sign (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(9.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAssessing the mortality rate of COVID-19 in pregnant individuals serves as a valuable approach for ascertaining the extent of the disease's impact. The present investigation reveals that the fatality rate among expectant individuals with confirmed positive polymerase chain reaction (PCR) outcomes for COVID-19 was 9.75%. The prevalence of this rate was documented as 2.2% in Mexico in the year 2020(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), but a another study conducted in Mexico in 2021 found a prevalence of 18.5%(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). According to a comprehensive review, the mortality rate among pregnant individuals varied from 8.5% in Kenya to 61.5% in Uganda(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The death rate in the general population exhibited a considerable variation, with reported figures ranging from 0.1% in Burundi to 18.1% in Yemen(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).The observed variations in death rates could potentially be attributed to disparities in the size of the tested population, reporting methodologies employed by healthcare systems, and unidentified variables.\u003c/p\u003e \u003cp\u003ePrior to the advent and widespread adoption of polymerase chain reaction (PCR) assays for the diagnosis of COVID-19, lung computed tomography (CT) scans served as the primary diagnostic modality, particularly in Iran(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As a result of the pronounced ionization effect associated with CT scans, the majority of pregnant patients exhibiting minor symptoms were not subjected to this imaging technique. Instead, CT scans were reserved for those individuals presenting with notable dyspnea or other symptomatic manifestations. Consequently, the death rate among pregnant individuals exhibited a greater incidence compared to that observed across the broader population. One potential factor contributing to the elevated mortality rate among pregnant individuals is the heightened cardiac burden experienced during pregnancy, perhaps rendering the lungs more vulnerable to the development of acute respiratory distress syndrome (ARDS).\u003c/p\u003e \u003cp\u003eIn the present study, it was observed that 25 patients, accounting for 56.75% of the total sample, exhibited normal chest CT scans. Conversely, a smaller proportion of patients, namely 16 individuals (43.24%), displayed abnormal chest CT scans. The most frequently observed radiological abnormalities on chest CT scans were ground glass opacity (GGO) (24.39%) and consolidation (21.95%). Our investigation also revealed the significance of lung involvement in pregnant patients, since the average extent of lung involvement in cases resulting in maternal death was notably greater compared to the surviving groups. The sole radiological indicator that exhibited a substantial correlation with unfavorable prognosis was the existence of consolidation. There was a strong association observed between low oxygen saturation, increased C-reactive protein (CRP) levels, and leukopenia and abnormal chest computed tomography (CT) scans.\u003c/p\u003e \u003cp\u003eIn the present investigation, a total of four instances of maternal mortality were documented. Among these cases, two individuals exhibited near-complete consolidation of the lungs, while one individual displayed around 85% consolidation of the lungs. Additionally, one case shown near-80% ground-glass opacity involvement in the lungs. Each of the four patients had been referred to the hospital more than five days following the initial manifestation of symptoms. The most prevalent symptoms seen among patients were dyspnea and fever. However, it was determined that fever did not exhibit a statistically significant correlation with survival status, as indicated by a p-value greater than 0.05. Several factors were found to be predictive of poor prognosis in pregnant individuals with COVID-19, including advanced maternal age, prolonged gestational age, the presence of underlying medical conditions, low oxygen saturation levels upon admission, experiencing dyspnea (shortness of breath), leukopenia (low white blood cell count), and elevated levels of C-reactive protein (CRP).\u003c/p\u003e \u003cp\u003eBased on the results of our study, it is recommended that pregnant individuals exhibiting admission O2 saturation values below 90%, considerable discomfort, leukopenia, and positive C-reactive protein (CRP) levels should be promptly subjected to early computed tomography (CT) scan imaging upon admission. Early CT scan imaging may be beneficial for patients who meet these criteria, and those who have symptoms lasting for a prolonged duration may also derive benefits from early CT scan imaging upon admission. Additional research is required to establish appropriate guidelines for the utilization of CT scan imaging in pregnant individuals. This intervention has the potential to assist hospital bed managers and physicians in making informed decisions on the allocation of ICU or isolated beds at the time of patient arrival. By doing so, it has the potential to contribute to a reduction in maternal mortality rates and the timely discharge of patients who no longer require hospitalization.\u003c/p\u003e \u003cp\u003eThe study conducted by Kuzan et al. revealed that fever and dry cough were the prevailing symptoms observed in pregnant individuals affected with COVID-19. Likewise, in the research conducted by Liu et al., the primary clinical manifestations encompassed cough, fever, and nasal congestion(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The study conducted by Kuzan et al. involved a retrospective analysis of clinical, biochemical, and radiological characteristics in a group of fifty-five pregnant women who exhibited symptoms suggestive of COVID-19. Among these patients, 9.1% required admission to the intensive care unit (ICU), with three individuals developing acute respiratory distress syndrome (ARDS) and one unfortunate fatality occurring(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The findings of chest CT scans frequently revealed bilateral involvement (88.2%), multilobe distribution (100%) with both peripheral and central involvement (70.6%), patchy morphology (94.1%), and ground-glass opacity (94.1%)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCertain medical professionals have posited that the clinical trajectory and ultimate result of COVID-19 in expectant individuals may not be more severe compared to the broader populace, a hypothesis that has been deliberated upon in the majority of analogous investigations(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Prior studies have indicated that the majority of pregnant individuals diagnosed with COVID-19 were in the third trimester of pregnancy and experienced symptoms of mild to moderate severity(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Nevertheless, our research demonstrated a positive correlation between advancing maternal age and an elevated rate of mortality.\u003c/p\u003e \u003cp\u003eThere is evidence to suggest that the presence of COVID-19 during pregnancy is linked to worse maternal outcomes, such as increased morbidity, preterm birth, and a heightened likelihood of requiring critical care. In the present investigation, the most prevalent neonatal and fetal problems seen were intrauterine growth restriction (IUGR) with a frequency of 19.51% and pre-term birth with a frequency of 9.75%. Notably, no instances of stillbirths, miscarriages, or neonate fatalities were recorded. The study conducted by Liu et al. examined clinical and CT data from a sample of 21 pregnant women and 19 non-pregnant women of similar age who were diagnosed with COVID-19 pneumonia. The findings revealed that the rate of preterm birth among pregnant women was 41.2%. Out of the total sample size of 55 pregnant women, a proportion of five individuals (9.1%) required admission to the critical care unit. Among these cases, three individuals experienced the onset of acute respiratory distress syndrome, while one unfortunate individual succumbed to the condition(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The study involved 23 pregnant patients who were hospitalized and diagnosed with COVID-19, as conducted by Wu et al. The retrospective collection of clinical presentations encompassed many components, such as laboratory testing, chest CT imaging, and symptoms, as documented in the records. In this particular study, it is noteworthy that all participants had been discharged from the hospital. However, it is important to highlight that the median duration of hospitalization was found to be 17 days, with a range spanning from 6 to 31 days(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother significant point of worry is the vertical spread of infection. In the present investigation, all infants subjected to polymerase chain reaction (PCR) testing exhibited negative results for the SARS-CoV-2 virus, hence indicating a diminished occurrence of vertical transmission. This observation aligns with prior research, wherein a small proportion of babies exhibited positive results for SARS-CoV-2 using PCR testing. The origins of these infections are not well established, as they could have arisen during the process of vaginal delivery or subsequent to birth through transmission from an asymptomatic infected mother or hospital personnel. The presence of heightened concentrations of immunoglobulin (Ig) M and IgG specific to SARS-CoV-2 in neonates born to women with confirmed infection indicates the potential occurrence of vertical transmission(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of our investigation indicate that the administration of hydroxychloroquine did not result in a decrease in the length of hospitalization for pregnant individuals diagnosed with COVID-19. The inclusion or exclusion of this medicine in worldwide therapeutic guidelines has been subject to occasional changes since the initial identification of the condition(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Nevertheless, subsequent clinical trials have provided evidence contradicting the purported clinical advantages of hydroxychloroquine in treating patients with COVID-19(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Numerous clinical trials have been undertaken thus far in the pursuit of identifying an efficacious intervention for COVID-19, with the majority of these studies omitting individuals who are pregnant(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additional investigation is required in order to furnish comprehensive data pertaining to therapeutic alternatives for expectant individuals.\u003c/p\u003e \u003cp\u003eThe current investigation exhibits a number of constraints. The retrospective nature of the study led to the exclusion of valuable data, and adherence to the ALARA principle resulted in a limited number of CT scans being performed on pregnant women, hence reducing the sample size. Furthermore, the presence of diverse geographical areas, distinct subtypes of COVID-19, and variations in the timeframe of the investigation constituted additional constraints.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eour study revealed that the degree of lung involvement and the identification of consolidation on computed tomography (CT) scans can serve as prognostic indicators for pregnant individuals with COVID-19. Pregnant individuals exhibiting admission oxygen saturation levels below 90%, experiencing notable distress, displaying leukopenia, and exhibiting positive C-reactive protein (CRP) levels were found to have a higher probability of presenting abnormal chest computed tomography (CT) scans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors state no conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors received no funds for this project.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials.\u003c/p\u003e\n\u003cp\u003eThe data supporting this manuscript\u0026apos;s findings are not publicly available due to Ethical restrictions. Data are, however, available from the authors upon reasonable request and with permission of Shiraz University of Sciences.\u003c/p\u003e\n\u003cp\u003eEthics Statements\u003c/p\u003e\n\u003cp\u003eEthical approval to report this study was obtained from the Ethics Committee of Shiraz University of Sciences. (Approval No. IR.SUMS.REC.1399.042).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShenavandeh S, Sefidbakht S, Iranpour P, Teimouri A, Hooshmandi S, Hooshmandi E, et al. COVID-19 and granulomatosis with polyangiitis (GPA): a diagnostic challenge. Rheumatology. 2020;59(8):2170\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Yu Y, Xu J, Shu H, Liu H, Wu Y, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine. 2020;8(5):475\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRac H, Gould AP, Eiland LS, Griffin B, McLaughlin M, Stover KR, et al. Common bacterial and viral infections: review of management in the pregnant patient. Annals of Pharmacotherapy. 2019;53(6):639\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonney EA. Alternative theories: Pregnancy and immune tolerance. Journal of reproductive immunology. 2017;123:65\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Mascio D, Khalil A, Saccone G, Rizzo G, Buca D, Liberati M, et al. Outcome of Coronavirus spectrum infections (SARS, MERS, COVID 1\u0026ndash;19) during pregnancy: a systematic review and meta-analysis. American journal of obstetrics \u0026amp; gynecology MFM. 2020:100107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasraeian M, Zare M, Vafaei H, Asadi N, Faraji A, Bazrafshan K, et al. COVID-19 pneumonia and pregnancy; a systematic review and meta-analysis. The Journal of Maternal-Fetal \u0026amp; Neonatal Medicine. 2020:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Toro F, Gjoka M, Di Lorenzo G, De Seta F, Maso G, Risso FM, et al. Impact of COVID-19 on maternal and neonatal outcomes: a systematic review and meta-analysis. Clinical Microbiology and Infection. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumbreras-Marquez MI, Campos-Zamora M, Lizaola-Diaz de Leon H, Farber MK. Maternal mortality from COVID-19 in Mexico. Int J Gynaecol Obstet. 2020;150(2):266\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOchoa FI, Plascencia JL, Sep\u0026uacute;lveda C. Maternal mortality from COVID-19 in Mexico. Ginecolog\u0026iacute;a y Obstetricia de M\u0026eacute;xico. 2021;89(09):748-.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalvert C, John J, Nzvere FP, Cresswell JA, Fawcus S, Fottrell E, et al. Maternal mortality in the covid-19 pandemic: findings from a rapid systematic review. Global health action. 2021;14(sup1):1974677.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMORTALITY ANALYSES (Mortality in the most affected countries): Johns Hopkins University \u0026amp; Medicine; 2023 [updated 23 January 2023. Mortality in the most affected countries]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coronavirus.jhu.edu/data/mortality\u003c/span\u003e\u003cspan address=\"https://coronavirus.jhu.edu/data/mortality\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasekhi A, Hoseinyazdi M, Esmaeilian S, Teimouri A, Safaei A, Rafiee F. COVID-19 pneumonia presenting as a single pulmonary nodule in a kidney transplant recipient: A case report and literature review. Radiology Case Reports. 2020;15(9):1587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuzan TY, Altıntoprak KM, \u0026Ccedil;ift\u0026ccedil;i H\u0026Ouml;, Kuzan BN, Yassa M, Tuğ N, et al. Clinical and radiologic characteristics of symptomatic pregnant women with COVID-19 pneumonia. Journal of the Turkish German Gynecological Association. 2021;22(3):196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWastnedge EA, Reynolds RM, van Boeckel SR, Stock SJ, Denison FC, Maybin JA, et al. Pregnancy and COVID-19. Physiological reviews. 2020;101(1):303\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong L, Tian J, He S, Zhu C, Wang J, Liu C, et al. Possible vertical transmission of SARS-CoV-2 from an infected mother to her newborn. Jama. 2020;323(18):1846\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamontagne F, Agoritsas T, Macdonald H, Leo Y-S, Diaz J, Agarwal A, et al. A living WHO guideline on drugs for covid-19. bmj. 2020;370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmrani AS, Pathan SA, Thomas SA, Harris TR, Coyle PV, Thomas CE, et al. Randomized double-blinded placebo-controlled trial of hydroxychloroquine with or without azithromycin for virologic cure of non-severe Covid-19. EClinicalMedicine. 2020;29:100645.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitehead CL, Walker SP. Consider pregnancy in COVID-19 therapeutic drug and vaccine trials. The Lancet. 2020;395(10237):e92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Shiraz University of Medical Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung CT scan, Imaging, COVID-19, pregnancy, outcome","lastPublishedDoi":"10.21203/rs.3.rs-3840586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3840586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e COVID-19 is a novel infectious disease that poses significant challenges for the management of pregnant patients, especially regarding the use and interpretation of chest CT scans. This study aimed to evaluate the clinical significance and radiological features of chest CT scans in pregnant women with COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The authors conducted a cross-sectional, multi-center study of 53 pregnant women with confirmed COVID-19 by RT-PCR testing who underwent chest CT scans in three hospitals in Iran. They collected and analyzed data on demographic, clinical, laboratory, and radiological variables, as well as maternal and neonatal outcomes. They used low-dose chest CT protocol and abdominal shields to minimize radiation exposure and assessed the interobserver reliability of the chest CT diagnosis using Cohen’s kappa coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study included 41 pregnant women with COVID-19 and available chest CT scans. The mean maternal age was 34.6±7.60 years and the mean gestational age was 28.73±5.66 weeks. Dyspnea was the most common symptom (85.4%), followed by fever (68.3%) and cough (51.2%). Asthma (p=0.001) and diabetes (p=0.001) were significantly associated with worse outcomes and abnormal chest CT scans. Myalgia (p=0.001), respiratory distress (p=0.001), admission O2 saturation (p=0.001), leukopenia (p=0.001), lymphocytosis (p=0.001), and high CRP levels (p=0.001) were also correlated with adverse outcomes and abnormal chest CT scans. Normal chest scans were observed in 25 patients (61%), while 16 patients (39%) had abnormal CT scan findings. Ground glass opacity (81.3%) and consolidation (68.8%) were the most common radiological features on chest CT scans. Four maternal deaths occurred due to delayed referral and underlying comorbidities such as heart failure or renal failure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The percentage of lung involvement and the presence of consolidation on chest CT scans could predict the prognosis of pregnant women with COVID-19. Chest CT scans could be a useful tool for the diagnosis and management of COVID-19 in pregnancy, especially in cases of severe symptoms or comorbidities.\u003c/p\u003e","manuscriptTitle":"Do chest CT findings predict the outcome in pregnant patients with COVID-19 pneumonia? A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-12 17:16:11","doi":"10.21203/rs.3.rs-3840586/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"163a4972-ef6b-4bba-9170-e0e04cdfd3ea","owner":[],"postedDate":"January 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27990809,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2024-01-12T17:16:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-12 17:16:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3840586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3840586","identity":"rs-3840586","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}