Bilateral Lung Involvement, Low PNI, and Elevated BUN/Albumin Ratio in PCR-Positive Viral Pneumonias: Clinical–Radiological Correlations

Bilateral Lung Involvement, Low PNI, and Elevated BUN/Albumin Ratio in PCR-Positive Viral Pneumonias: Clinical–Radiological Correlations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bilateral Lung Involvement, Low PNI, and Elevated BUN/Albumin Ratio in PCR-Positive Viral Pneumonias: Clinical–Radiological Correlations Filiz Çimen, İrfan Esen, Oğuzhan Özdemir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7426635/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 Objective: The purpose of the present study was to assess how bilateral pulmonary involvement, prognostic nutritional index (PNI), the blood urea nitrogen / albumin (BUN/Alb) ratio, D-dimer levels, and systemic inflammatory indicators are interrelated in patients diagnosed with viral pneumonia confirmed by Polymerase chain reaction (PCR). Methods: The study included 113 patients who had radiologically confirmed pneumonic infiltrations were retrospectively analyzed. Patients were divided into two groups according to PCR results: PCR-positive (n = 56) and PCR-negative (n = 57). Between the two cohorts, demographic information was systematically compared, presenting symptoms, laboratory results, systemic inflammatory indices (Neutrophil / Lymphocyte Ratio (NLR), Platelet / Lymphocyte Ratio (PLR), PNI, Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), CAR (C-Reactive Protein-to-Albumin Ratio), BAR), and radiological findings. Results: Bilateral lung involvement was significantly more frequent in PCR-positive patients compared to PCR-negative patients (78.6% vs. 57.9%, p = 0.003). The PNI levels were markedly lower in the PCR-positive group, while systemic inflammatory indices (NLR, SII, SIRI) showed higher tendencies. In PCR-positive patients, D-dimer levels demonstrated strong positive correlations with PLR, NLR, SII, SIRI, CAR, and most prominently with the BUN/Alb ratio (r = 0.520), as well as with NLR (r = 0.458) and SII (r = 0.439). Conversely, in PCR-negative patients, D-dimer correlated only with PLR and CAR. Conclusion: PCR-positive viral pneumonias are characterized by more extensive bilateral lung involvement, impaired nutritional-immune status reflected by low PNI levels and an elevated BUN/Alb ratio, and a higher systemic inflammatory burden. D-dimer levels appear to reflect not only the inflammatory load but also renal–nutritional imbalance, underscoring their prognostic significance. The combined evaluation of radiological findings and systemic biomarkers, including PNI and the BUN/Alb ratio, may contribute to improved risk stratification and management in critically ill patients. Viral pneumonia COVID-19 Prognostic Nutritional Index systemic inflammation D-dimer BUN/Albumin ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Viral pneumonias are characterized by severe clinical course, high mortality rates, and prolonged intensive care requirements, particularly in elderly patients, immunocompromised individuals, and those with underlying chronic comorbidities [ 1 , 2 ]. These infections may result in severe respiratory failure, multiple organ dysfunction, and extended hospital stays. The emergence of COVID-19 in 2019, which rapidly evolved into a global pandemic, represents the most prominent example of this clinical scenario. Numerous studies have demonstrated that SARS-CoV-2 causes widespread alveolar damage and triggers a systemic inflammatory response [ 3 ]. Polymerase chain reaction (PCR) testing is considered the gold standard method for directly detecting the presence of viral pathogens in the diagnosis of viral pneumonia. However, assessing disease severity based solely on PCR results is insufficient; the integration of clinical findings, radiological imaging, and laboratory parameters provides a more accurate prediction of prognosis [ 4 ]. The presence of cases that are PCR-negative but radiologically and clinically compatible with viral pneumonia underscores the importance of a comprehensive diagnostic approach [ 5 ]. Chest computed tomography (CT) remains a fundamental tool for evaluating the distribution and extent of pneumonic infiltrates. In viral pneumonias, including COVID-19, bilateral lung involvement is frequently observed, and this finding has been associated with disease severity, hypoxemia, the need for mechanical ventilation, and mortality in numerous studies [ 6 , 7 ]. Bilateral involvement is thought to contribute to hypoxemia and intensification of the systemic inflammatory response through widespread alveolar damage and increased capillary permeability. In recent years, several hematological and biochemical biomarkers have been introduced into clinical practice to evaluate systemic inflammatory responses, including the NLR, PLR, SII, SIRI, CAR, and the BAR [ 8 ]. Furthermore, the PNI, calculated using serum albumin and lymphocyte count as components, provides information on a patient’s nutritional status and immune capability; declines in these values are commonly associated with increased risk of poor clinical progression [ 9 – 11 ]. Among coagulation parameters, D-dimer holds particular prognostic significance in diseases such as COVID-19 that are characterized by a widespread inflammatory response and endothelial injury. Elevated D-dimer levels indicate both increased inflammatory burden and heightened risk of coagulopathy, and have been linked to adverse clinical outcomes. However, studies simultaneously examining bilateral lung involvement, low PNI, elevated D-dimer levels, and other inflammatory indices in the same patient group remain limited. Much of the existing literature has focused primarily on COVID-19; therefore, large-scale studies encompassing different viral etiologies are needed [ 12 , 13 ]. The purpose of this investigation was to assess the links involving PCR positivity, the extent of radiological involvement, D-dimer levels, and systemic inflammatory markers (particularly PNI) in Intensive Care Unit (ICU) patients with viral pneumonia, and to place these findings in comparison with cases that were PCR-negative but exhibited similar clinical severity. Methods This investigation was designed as a retrospective, single-center, observational study. This research was carried out following the approval of the institutional Ethics Committee of Health Sciences, Yüksek İhtisas University (Date: 25.06.2025, Decision No: 14/329). Every step of the study adhered to established ethical norms and respected the principles of the Declaration of Helsinki. Between January 1, 2020, and December 31, 2024, a total of 1,049 patients who were evaluated with a preliminary diagnosis of viral pneumonia at VM Medical Park Ankara Hospital were screened. Among them, 113 patients who met the diagnostic criteria for viral pneumonia, demonstrated pneumonic infiltration on chest CT, and underwent PCR testing were included in the study. The patients were categorized into two groups: 56 PCR-positive and 57 PCR-negative. Demographic characteristics, presenting symptoms, chest CT findings, and laboratory-based inflammatory markers were analyzed for all cases. Patient selection was carried out according to predefined inclusion and exclusion criteria. The inclusion criteria consisted of: a PCR-confirmed diagnosis of viral pneumonia, availability of chest CT imaging obtained at ICU admission, and complete laboratory evaluations recorded within the first 24 hours after admission. The exclusion criteria were defined as: presence of bacterial superinfection during the same hospitalization confirmed by positive culture, age under 18 years, and incomplete clinical, radiological, or laboratory records. The patient selection process for the viral pneumonia cohort is presented below as a diagram (Fig. 1 ). The dependent variable of the study was the lung involvement pattern on chest CT, classified as bilateral or unilateral. Independent variables included the PNI, NLR, PLR, SII, SIRI, CAR, and BAR, demographic factors (age and sex), and comorbidities. The evaluated outcomes were ICU length of stay (days), duration of mechanical ventilation (days), and ICU mortality. Patient data were obtained through review of the hospital information management system and digital archives. Variables analyzed included demographic and clinical data (age, sex, comorbidities, ICU stay duration, need for mechanical ventilation, and mortality), as well as symptoms (cough, fever, dyspnea, fatigue, etc.). Laboratory parameters were assessed using complete blood counts and biochemical profiles obtained within the first 24 hours of admission, from which inflammatory indices were calculated. NLR was derived from dividing the absolute neutrophil count by the lymphocyte count [ 8 ]. PLR was obtained in a similar way, using platelet count over lymphocyte count [ 14 ]. SII was expressed as the product of platelet and neutrophil counts divided by lymphocytes [ 15 ]. SIRI was expressed as the product of neutrophils and monocytes divided by lymphocytes [ 16 ]. The CAR was obtained by dividing CRP level (mg/L) by albumin level (g/L). The BAR was calculated by dividing blood urea nitrogen (BUN, mg/dL) by albumin (g/dL) [ 17 ]. The BAR has been described as a biosignal closely linked to critical disease and increased risk of death in clinical conditions such as acute pancreatitis, sepsis, congestive heart failure, and pneumonia [ 18 ]. Radiological evaluation was performed independently (blinded assessment) by two experienced radiologists who had no access to clinical information. The Prognostic Nutritional Index (PNI) was derived according to the formula below: PNI = (10 × Albumin [g/dL]) + (0.005 × Lymphocyte count [/mm³]) [ 19 ]. A double-blind method was applied during radiological evaluation. For variables with missing data, records were reviewed, and in cases where missingness could not be resolved, the multiple imputation method was applied. Data gathered within the initial 24 hours of intensive care admission formed the basis for all calculations. Statistical Analysis Data were initially transferred into Microsoft Excel for general evaluation. Data entry errors and inconsistencies were checked. Normality was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Continuous data were analyzed with the Student’s t-test when they followed a normal distribution, and with the Mann–Whitney U test when they did not. Categorical variables were assessed using either the Chi-square test or Fisher’s exact test. For correlation analyses, Pearson’s test was applied to normally distributed variables, while Spearman’s test was used for non-normally distributed variables. The predictive power of D-dimer, PNI, and other inflammatory markers for bilateral lung involvement was evaluated using ROC analysis, and AUC values were presented together with their 95% confidence intervals. Statistical significance was defined as a p-value below 0.05. All statistical analyses were carried out using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp. Armonk, NY, USA). RESULTS In total, 113 patients were enrolled in the study. Of these, 56 (49.5%) were PCR-positive and 57 (50.5%) were PCR-negative. The median age was 55.5 years (min: 21, max: 92) in the PCR-positive group and 47.0 years (min: 20, max: 91) in the PCR-negative group. In the PCR-positive group, 33 patients were female and 23 were male, whereas in the PCR-negative group, 27 were female and 30 were male. Cough was the most frequently reported symptom in both groups, occurring at a significantly higher rate in the PCR-positive group (PCR-positive: 82.1%; PCR-negative: 59.6%; p = 0.02). The second most common symptom in both groups was dyspnea (PCR-positive: 73.2%; PCR-negative: 59.6%; p = 0.18). The least frequent symptoms were sore throat and myalgia in both groups. No meaningful differences in other symptoms were detected across the two groups (p > 0.05). (Table 1 ). Table 1 Symptom Distribution Between PCR Positive and PCR Negative Groups Symptom PCR Positive n (%) PCR Negative n (%) Total n (%) p-value Dyspnea 41 (73.2) 34 (59.7) 75 (66.4) 0.18 Fever 19 (33.9) 16 (28.1) 35 (31.0) 0.64 Cough 46 (82.1) 34 (59.7) 80 (70.8) 0.02 Fatigue 12 (21.4) 10 (17.5) 22 (19.5) 0.78 Headache 6 (10.7) 7 (12.3) 13 (11.5) 1.00 Sore throat 3 (5.4) 2 (3.5) 5 (4.4) 0.68 Myalgia 4 (7.1) 3 (5.3) 7 (6.2) 0.72 Sputum production 9 (16.1) 11 (19.3) 20 (17.7) 0.84 Table 2 summarizes the mean ± standard deviation of the key biochemical and hematological parameters for the two groups (Table 2 ). Table 2 Laboratory Parameters by PCR Status Parameter PCR Positive (Mean ± SD) PCR Negative (Mean ± SD) Creatinine 0.87 ± 0.38 0.88 ± 0.35 Urea 35.73 ± 27.81 30.17 ± 11.20 Albumin 3.80 ± 0.64 4.15 ± 0.50 Leukocyte 7.40 ± 4.87 7.96 ± 3.47 Neutrophil 5.77 ± 4.77 5.79 ± 3.23 Lymphocyte 1.18 ± 0.48 1.55 ± 0.75 Monocyte 0.39 ± 0.18 0.49 ± 0.20 Platelet 230.02 ± 88.87 234.25 ± 91.64 Hematocrit 37.21 ± 6.38 47.34 ± 47.62 Hemoglobin 12.57 ± 2.32 14.03 ± 1.60 Of the inflammatory and nutritional indices analyzed, only the Prognostic Nutritional Index (PNI) demonstrated a statistically significant variation between PCR-positive and PCR-negative patients (p = 0.008). PNI values were lower in the PCR-positive group (35.4 ± 6.1) compared to the PCR-negative group (39.0 ± 6.8). Other indices, including NLR, PLR, SII, SIRI, CAR, and the BUN/Albumin ratio, showed no significant differences (all p > 0.05). (Table 3 ). Table 3 Inflammatory and Nutritional Indices by PCR Status Parameter PCR Positive (Mean ± SD) PCR Negative (Mean ± SD) p Value NLR 9,2 ± 6,5 8,5 ± 5,9 0,492 PLR 211,4 ± 106,1 199,8 ± 96,5 0,517 SII 1834 ± 1194 1671 ± 1150 0,418 PNI 35,4 ± 6,1 39,0 ± 6,8 0,008 SIRI 3,5 ± 3,4 3,1 ± 2,7 0,605 CAR 2,7 ± 2,1 2,3 ± 2,4 0,432 BUN/Alb 5,6 ± 2,9 5,1 ± 2,7 0,318 In the PCR-positive group, D-dimer levels were significantly higher [1069.18 ± 1230.59 ng/mL; median 730.0 (190–6450)] compared to the PCR-negative group [784.88 ± 997.97 ng/mL; median 443.0 (150–5169)] (p = 0.008). In the PCR-positive cohort, D-dimer showed significant positive correlations with PLR (r = 0.366), NLR (r = 0.458), SII (r = 0.439), SIRI (r = 0.297), and CAR (r = 0.319), with the strongest associations observed for NLR and SII. Additionally, D-dimer exhibited the highest correlation with the BUN/Alb ratio (r = 0.520), whereas no meaningful association was found in the PCR-negative group (r = 0.020). In the PCR-negative group, significant positive correlations were found only with PLR (r = 0.340) and CAR (r = 0.373). These findings suggest that the increase in D-dimer levels in PCR-positive cases is more strongly associated with systemic inflammatory burden and, in particular, reflects a combined effect of inflammation, renal function, and nutritional status as captured by the BUN/Alb ratio (Fig. 2 ). In the PCR-positive group, the rate of bilateral lung involvement was 76.8%, whereas it was 57.9% in the PCR-negative group, and this variation was confirmed as statistically significant (p = 0.003). Right lung involvement was observed in 10.7% of the PCR-positive group and 28.1% of the PCR-negative group, while left lung involvement was 12.5% and 14.0%, respectively. Analysis showed that the frequency of unilateral right and left lung involvement was comparable across the groups, with no significant differences detected. These findings indicate that radiological involvement was more extensive in PCR-positive patients, with bilateral patterns being particularly prominent. The following graph presents a comparative distribution of right, left, and bilateral lung involvement rates in both groups (Fig. 3 ). The following black-and-white bar chart illustrates the mean values of systemic inflammatory markers (PLR, NLR, SII, PNI, SIRI, CAR, BUN/Alb) in PCR-positive and PCR-negative patient groups. Upon examination of the graph, it is notable that the SII values were higher in the PCR-positive group, whereas the PNI values were lower. Differences between the groups were limited for the other parameters. This visual representation summarizes the group comparisons of laboratory findings and supports the statistical analysis results (Fig. 4 ). The following graph illustrates the Spearman correlation coefficients between the rate of bilateral lung involvement and systemic inflammatory markers (PNI, CAR, BUN/Alb, PLR, NLR, SII, SIRI). The direction of the bars indicates whether the correlation is positive or negative, while their height reflects the strength of the relationship. The striped bars represent statistically significant correlations (p < 0.05). A negative correlation indicates that as the respective parameter increases, the rate of bilateral involvement decreases, whereas a positive correlation suggests that both increase together (Fig. 5 ). Among the evaluated factors, a statistically significant negative correlation emerged exclusively between bilateral lung involvement and PNI (rho = − 0.232; p = 0.0207). This finding suggests that a decrease in PNI, which reflects nutritional and immune status, may be associated with more extensive bilateral involvement on radiological imaging. In other words, patients with lower PNI levels tend to present with more widespread lung involvement. Discussion In this study, we demonstrated that patients with PCR-positive viral pneumonia exhibited a significantly higher rate of bilateral lung involvement, lower PNI values, and stronger positive correlations between D-dimer and various inflammatory parameters. Our findings are largely consistent with previously reported radiological and biochemical profiles in COVID-19 and other viral pneumonias [ 20 , 21 ]. A low PNI reflects not only nutritional deficiencies but also an increased inflammatory burden. The inverse correlation observed between bilateral involvement and PNI in our study suggests that extensive disease may disrupt the immune–nutritional balance. This finding is consistent with the results of Miniksar et al. [ 10 ], who demonstrated that reduced PNI values were linked to a higher risk of mortality in ICU patients diagnosed with pneumonia. When clinical, laboratory, and radiological features were compared according to PCR status, patients in the PCR-positive cohort showed a higher frequency of bilateral pulmonary infiltrates, lower PNI values, and marked elevations in inflammatory markers including SII, NLR, and SIRI. The pronounced correlations between D-dimer and both NLR (r = 0.458) and SII (r = 0.439) indicate that changes in coagulation pathways are closely intertwined with systemic inflammation rather than reflecting thrombosis alone. Consistent with this, Wang et al. [ 22 ] reported that diffuse parenchymal disease in PCR-positive pneumonia could contribute to adverse outcomes. In our data, bilateral infiltration was recorded in 78.6% of PCR-positive cases, compared with 57.9% of PCR-negative cases (p = 0.003). Unlike the PCR-positive cohort, correlations in PCR-negative patients were limited to PLR and CAR. Importantly, the most prominent association in PCR-positive patients emerged between D-dimer and the BUN/Alb ratio (r = 0.520), while this relationship was virtually absent in PCR-negative individuals (r = 0.020). This novel observation suggests that rising D-dimer levels in PCR-positive pneumonia may mirror not only systemic inflammatory activation but also the combined impact of renal impairment and nutritional depletion. Collectively, these findings support the view that PCR-positive pneumonia is characterized by more widespread radiological involvement, amplified inflammatory drive, deterioration of immune–nutritional balance, and increased organ vulnerability. The utility of systemic inflammatory indicators such as NLR, PLR, SII, SIRI, and CAR to reflect disease severity and prognosis has been emphasized in previous research [ 20 , 23 , 24 ]. In our study, especially in PCR-positive patients, D-dimer levels were strongly correlated with these parameters. This suggests that D-dimer may serve in clinical practice not only as a marker of thrombotic complications but also as an important marker reflecting systemic inflammation. The weaker associations observed in the PCR-negative group may indicate that, although inflammation is present, the burden is less intense than in the PCR-positive group. Furthermore, composite indices reflecting both nutritional and inflammatory status (e.g. PNI, SIRI, PAN Immune Inflammation Value) have been shown in the literature to predict mortality and prognosis. These results support the finding that the negative link between PNI and bilateral involvement observed in our study suggests that early nutritional support and inflammation control may improve outcomes in ICU practice. The existence of patients with PCR-negative but radiologically compatible COVID-19 underscores the insufficiency of basing conclusions solely on PCR testing for diagnosis. Earlier research has likewise demonstrated that PCR negativity may occur in advanced stages, and that combining thoracic CT findings with laboratory parameters can improve diagnostic accuracy. In conclusion, PCR-positive viral pneumonias are characterized by more extensive bilateral lung involvement, lower PNI levels, and heightened inflammatory responses; among these, D-dimer emerges as an additional biomarker reinforcing this clinical picture. Our findings suggest that integrating radiological features with biomarker-based parameters may offer clinically meaningful support for diagnosis, risk assessment, and therapeutic decision-making in intensive care units. Conclusion This study demonstrated that extensive bilateral lung involvement in PCR-positive viral pneumonia is associated with lower PNI values and an intensified inflammatory response. In this cohort, D-dimer correlated positively with multiple inflammatory indices, most notably NLR and SII, whereas the PCR-negative group exhibited only limited associations. Importantly, the strongest correlation was observed with the BUN/Alb ratio, suggesting that D-dimer elevation in PCR-positive patients reflects not only systemic inflammation but also the combined impact of renal dysfunction and nutritional imbalance. The coexistence of low PNI, high BUN/Alb ratio, and bilateral radiological involvement highlights impaired immune–nutritional reserves and an amplified inflammatory burden, conditions that may predispose to adverse clinical outcomes. Integrating radiological assessment with PNI, the BUN/Alb ratio, and key laboratory markers could therefore provide a more comprehensive strategy for early risk stratification and individualized management in critically ill patients. Abbreviations • ICU Intensive Care Unit • PCR Polymerase Chain Reaction • CT Computed Tomography • NLR Neutrophil-to-Lymphocyte Ratio • PLR Platelet-to-Lymphocyte Ratio • SII Systemic Immune-Inflammation Index • SIRI Systemic Inflammation Response Index • CAR C-Reactive Protein-to-Albumin Ratio • BAR Blood Urea Nitrogen-to-Albumin Ratio • PNI Prognostic Nutritional Index • CRP C-Reactive Protein • BUN Blood Urea Nitrogen Declarations Data availability Patient data used in this study were obtained from the hospital information management system and institutional archives. In accordance with ethical approval and privacy regulations, these data are not publicly available. De-identified datasets may be provided by the corresponding author upon reasonable request and with appropriate institutional permissions. Conflict of Interest Statement The authors have no conflicts of interest to declare. Financial Disclosure The authors declared that this study has received no financial support. ETHICAL DECLARATIONS Ethics Committee Approval This study was conducted with the approval of the Health Sciences Research Ethics Committee of Yuksek Ihtisas University (Date: 25.06.2025, Decision No: 329-Scientific Studies Ethics Committee /14) Informed Consent All patients signed and free and informed consent form. Referee Evaluation Process Externally peer-reviewed. Author Contribution Author A (FC): Study conception and design, data collection, data analysis, and drafting of the manuscript.Author B (IE): Supervision, critical revision of the manuscript for important intellectual content, and guidance on methodology.Author C (OO): Radiological evaluation, interpretation of imaging findings, and contribution to data interpretation. References Kalanuria AA, Ziai W, Mirski M (2014). Ventilator-associated pneumonia in the ICU. Crit Care. 18(2):208 doi: 10.1186/cc13775 Kalil AC, Metersky ML, Klompas M, et al (2016). Management of adults with hospital-acquired and ventilator-associated pneumonia: 2016 IDSA/ATS guidelines. Clin Infect Dis. 63(5):e61–111 doi: 10.1093/cid/ciw353 Polidoro RB, Hagan RS, de Santis Santiago R, Schmidt NW (2020). Overview: systemic inflammatory response in COVID-19. Front Immunol. 11:580 doi: 10.3389/fimmu.2020.00580 Rodríguez-Pérez H, Ciuffreda L, Hernández-Beeftink T, et al (2024). Tracheal aspirate metagenomics reveals association of antibiotic resistance with nonpulmonary sepsis mortality. Am J Respir Cell Mol Biol. 70(1):50–60 doi: 10.1165/rcmb.2023-0293OC Adukauskiene D, Ciginskiene A, Adukauskaite A, et al (2023). Polymicrobial multidrug-resistant Klebsiella pneumoniae ventilator-associated pneumonia outcomes. Antibiotics (Basel). 12(6):1056 doi: 10.3390/antibiotics12061056 Coccolini F, Sartelli M, Kluger Y, et al (2020). COVID-19: the showdown for mass casualty preparedness. World J Emerg Surg. 15(1):26 doi: 10.1186/s13017-020-00304-5 Zhou F, Yu T, Du R, et al (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 395(10229):1054–62 doi: 10.1016/S0140-6736(20)30566-3 Lagunas-Rangel FA (2020). Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe COVID-19. Int Immunopharmacol. 85:106507 doi: 10.1016/j.intimp.2020.106507 Onodera T, Goseki N, Kosaki G (1984). Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 85(9):1001–5 Miniksar ÖH, Er Ö, Polat N (2024). The prognostic value of inflammation indices in predicting ICU admission and mortality. J Intensive Care Emerg. 21(3):155–62 Liu Y, Liu D, Zhang L, et al (2020). Prognostic nutritional index and mortality in patients with COVID-19: a retrospective cohort study. J Infect. 81(3):e20–2 doi: 10.1016/j.jinf.2020.05.007 Hu R, Han C, Pei S, et al (2021). Procalcitonin levels in COVID-19 patients. Int J Antimicrob Agents. 56(2):106051 doi: 10.1016/j.ijantimicag.2020.106051 Sîrbu AC, Dragomirescu RF, Arghir OC, Arghir IA (2025). Clinical and laboratory features correlated with CT scan lung injury in COVID-19 Delta and Omicron variants. Medicina (Kaunas). 61(5):931 doi: 10.3390/medicina61050931 Qu R, Ling Y, Zhang YHZ, et al (2020). Platelet-to-lymphocyte ratio is associated with prognosis in patients with coronavirus disease-19. J Med Virol. 92(9):1533–41 doi: 10.1002/jmv.25767 Hu B, Yang XR, Xu Y, et al (2014). Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 20(23):6212–22 doi: 10.1158/1078-0432.CCR-14-0442 Qi Q, Zhuang L, Shen Y, et al (2016). A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 122(14):2158–67 doi: 10.1002/cncr.30057 Ranzani OT, Zampieri FG, Forte DN, et al (2013). C-reactive protein/albumin ratio predicts 90-day mortality of septic patients. PLoS One. 8(3):e59321 doi: 10.1371/ Li J, Zhang Y, Chen W, et al (2024). Blood urea nitrogen to albumin ratio as a predictor of mortality in sepsis patients: a retrospective cohort study. BMC Nephrol. 25(1):42 doi: 10.1186/s12882-025-04214-z Mohri Y, Inoue Y, Tanaka K, et al (2013). Prognostic nutritional index predicts postoperative outcome in colorectal cancer. World J Surg. 37(11):2688–92 doi: 10.1007/s00268-013-2156-9 Yao Y, Cao J, Wang Q, et al (2020). D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case-control study. J Intensive Care. 8:49 doi: 10.1186/s40560-020-00466-z Wang G, Wang N, Liu T, et al (2024). Association between prognostic nutritional index and mortality risk in patients with community acquired pneumonia: a retrospective study. BMC Pulm Med. 24(1):555 doi: 10.1186/s12890-024-03373-3 Wang D, Hu B, Hu C, et al (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA. 323(11):1061–9 doi: 10.1001/jama.2020.1585 Ugajin M, Yamaki K, Iwamura N, et al (2012). Prognostic value of blood urea nitrogen to serum albumin ratio in community-acquired pneumonia. Int J Gen Med. 5:583–9 doi: 10.2147/IJGM.S33674 Baran B, Yetkin NA, Rabahoğlu B, et al (2025). Assessment of mortality risk in patients with community-acquired pneumonia: role of novel inflammatory biomarkers. J Clin Lab Anal. (Online ahead of print) doi: 10.1002/jcla.70081 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7426635","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512573373,"identity":"daa4d404-1c32-49d0-ba60-da264395faf5","order_by":0,"name":"Filiz Çimen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBAC9hkMDAeAtBwDAw+RWnhuQLQYk6YFBBIbiNci3fzw0I2KO+kbjp89+OADg52cbgMhLTLHDA7nnHmWu+FMXrLhDIZkY7MDBLTYS+QwHM5tO5y74UCOmTQPw4HEbYS08IC1/DucbnD+DUlaGg4nGNwg3pY0oF+OHTaceeONseEMAyL8wiOR/PhzTs1heb7zOYYPPlTYyRHUAgcKYJUGxCoHAfkGUlSPglEwCkbBiAIAbbtFtzD+ODgAAAAASUVORK5CYII=","orcid":"","institution":"VM Medical Park Ankara Hospital","correspondingAuthor":true,"prefix":"","firstName":"Filiz","middleName":"","lastName":"Çimen","suffix":""},{"id":512573374,"identity":"bdcfcdcc-2ec6-4bcf-8660-d3b73315bba6","order_by":1,"name":"İrfan Esen","email":"","orcid":"","institution":"VM Medical Park Ankara Hospital","correspondingAuthor":false,"prefix":"","firstName":"İrfan","middleName":"","lastName":"Esen","suffix":""},{"id":512573375,"identity":"7658b8f1-941a-41bf-ab84-c647c59399e3","order_by":2,"name":"Oğuzhan Özdemir","email":"","orcid":"","institution":"VM Medical Park Ankara Hospital","correspondingAuthor":false,"prefix":"","firstName":"Oğuzhan","middleName":"","lastName":"Özdemir","suffix":""}],"badges":[],"createdAt":"2025-08-21 13:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7426635/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7426635/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91188681,"identity":"918ed78a-d331-4867-bb06-be65aae6b3a2","added_by":"auto","created_at":"2025-09-12 14:24:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient Selection Flowchart for Viral Pneumonia Cohort\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/68d0e4550797e4a551934173.jpeg"},{"id":91188684,"identity":"71ac79b3-f177-4f54-bf58-be565316bcc4","added_by":"auto","created_at":"2025-09-12 14:24:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":464558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between D-dimer and Inflammatory Parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/5b26e497c622c20b88df6aa3.jpeg"},{"id":91188677,"identity":"9f1e20d7-0bea-4b72-b56b-82bee87f735c","added_by":"auto","created_at":"2025-09-12 14:24:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparison of radiological involvement patterns between PCR positive and PCR negative groups.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/c3d77cf64e23d12bdee0d7d5.jpeg"},{"id":91188678,"identity":"0a128888-e4d0-42a3-a70e-7b6454424ed3","added_by":"auto","created_at":"2025-09-12 14:24:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGroup means of inflammatory and nutritional indices in PCR positive and PCR negative patients.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/640f99fdd6d49d9772846e5c.jpeg"},{"id":91188688,"identity":"2c4a52af-0985-4a44-8708-ba7ac27ca774","added_by":"auto","created_at":"2025-09-12 14:24:28","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":150637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Between Inflammatory Indices and Radiological Extent\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/3c94aea7c21e05bf264ee81f.jpeg"},{"id":102297636,"identity":"73821d98-4410-45b8-8d5f-eb6cc91a6e5a","added_by":"auto","created_at":"2026-02-10 10:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1800701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7426635/v1/1cdd624d-b68c-41a8-b883-42a09defa370.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBilateral Lung Involvement, Low PNI, and Elevated BUN/Albumin Ratio in PCR-Positive Viral Pneumonias: Clinical–Radiological Correlations\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eViral pneumonias are characterized by severe clinical course, high mortality rates, and prolonged intensive care requirements, particularly in elderly patients, immunocompromised individuals, and those with underlying chronic comorbidities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These infections may result in severe respiratory failure, multiple organ dysfunction, and extended hospital stays. The emergence of COVID-19 in 2019, which rapidly evolved into a global pandemic, represents the most prominent example of this clinical scenario. Numerous studies have demonstrated that SARS-CoV-2 causes widespread alveolar damage and triggers a systemic inflammatory response [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePolymerase chain reaction (PCR) testing is considered the gold standard method for directly detecting the presence of viral pathogens in the diagnosis of viral pneumonia. However, assessing disease severity based solely on PCR results is insufficient; the integration of clinical findings, radiological imaging, and laboratory parameters provides a more accurate prediction of prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The presence of cases that are PCR-negative but radiologically and clinically compatible with viral pneumonia underscores the importance of a comprehensive diagnostic approach [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChest computed tomography (CT) remains a fundamental tool for evaluating the distribution and extent of pneumonic infiltrates. In viral pneumonias, including COVID-19, bilateral lung involvement is frequently observed, and this finding has been associated with disease severity, hypoxemia, the need for mechanical ventilation, and mortality in numerous studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Bilateral involvement is thought to contribute to hypoxemia and intensification of the systemic inflammatory response through widespread alveolar damage and increased capillary permeability. In recent years, several hematological and biochemical biomarkers have been introduced into clinical practice to evaluate systemic inflammatory responses, including the NLR, PLR, SII, SIRI, CAR, and the BAR [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, the PNI, calculated using serum albumin and lymphocyte count as components, provides information on a patient\u0026rsquo;s nutritional status and immune capability; declines in these values are commonly associated with increased risk of poor clinical progression [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong coagulation parameters, D-dimer holds particular prognostic significance in diseases such as COVID-19 that are characterized by a widespread inflammatory response and endothelial injury. Elevated D-dimer levels indicate both increased inflammatory burden and heightened risk of coagulopathy, and have been linked to adverse clinical outcomes. However, studies simultaneously examining bilateral lung involvement, low PNI, elevated D-dimer levels, and other inflammatory indices in the same patient group remain limited. Much of the existing literature has focused primarily on COVID-19; therefore, large-scale studies encompassing different viral etiologies are needed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe purpose of this investigation was to assess the links involving PCR positivity, the extent of radiological involvement, D-dimer levels, and systemic inflammatory markers (particularly PNI) in Intensive Care Unit (ICU) patients with viral pneumonia, and to place these findings in comparison with cases that were PCR-negative but exhibited similar clinical severity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis investigation was designed as a retrospective, single-center, observational study. This research was carried out following the approval of the institutional Ethics Committee of Health Sciences, Y\u0026uuml;ksek İhtisas University (Date: 25.06.2025, Decision No: 14/329). Every step of the study adhered to established ethical norms and respected the principles of the Declaration of Helsinki. Between January 1, 2020, and December 31, 2024, a total of 1,049 patients who were evaluated with a preliminary diagnosis of viral pneumonia at VM Medical Park Ankara Hospital were screened. Among them, 113 patients who met the diagnostic criteria for viral pneumonia, demonstrated pneumonic infiltration on chest CT, and underwent PCR testing were included in the study. The patients were categorized into two groups: 56 PCR-positive and 57 PCR-negative. Demographic characteristics, presenting symptoms, chest CT findings, and laboratory-based inflammatory markers were analyzed for all cases.\u003c/p\u003e\u003cp\u003ePatient selection was carried out according to predefined inclusion and exclusion criteria. The inclusion criteria consisted of: a PCR-confirmed diagnosis of viral pneumonia, availability of chest CT imaging obtained at ICU admission, and complete laboratory evaluations recorded within the first 24 hours after admission. The exclusion criteria were defined as: presence of bacterial superinfection during the same hospitalization confirmed by positive culture, age under 18 years, and incomplete clinical, radiological, or laboratory records. The patient selection process for the viral pneumonia cohort is presented below as a diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe dependent variable of the study was the lung involvement pattern on chest CT, classified as bilateral or unilateral. Independent variables included the PNI, NLR, PLR, SII, SIRI, CAR, and BAR, demographic factors (age and sex), and comorbidities. The evaluated outcomes were ICU length of stay (days), duration of mechanical ventilation (days), and ICU mortality. Patient data were obtained through review of the hospital information management system and digital archives. Variables analyzed included demographic and clinical data (age, sex, comorbidities, ICU stay duration, need for mechanical ventilation, and mortality), as well as symptoms (cough, fever, dyspnea, fatigue, etc.). Laboratory parameters were assessed using complete blood counts and biochemical profiles obtained within the first 24 hours of admission, from which inflammatory indices were calculated.\u003c/p\u003e\u003cp\u003eNLR was derived from dividing the absolute neutrophil count by the lymphocyte count [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. PLR was obtained in a similar way, using platelet count over lymphocyte count [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SII was expressed as the product of platelet and neutrophil counts divided by lymphocytes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. SIRI was expressed as the product of neutrophils and monocytes divided by lymphocytes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The CAR was obtained by dividing CRP level (mg/L) by albumin level (g/L). The BAR was calculated by dividing blood urea nitrogen (BUN, mg/dL) by albumin (g/dL) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The BAR has been described as a biosignal closely linked to critical disease and increased risk of death in clinical conditions such as acute pancreatitis, sepsis, congestive heart failure, and pneumonia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRadiological evaluation was performed independently (blinded assessment) by two experienced radiologists who had no access to clinical information. The Prognostic Nutritional Index (PNI) was derived according to the formula below:\u003c/p\u003e\u003cp\u003ePNI = (10 \u0026times; Albumin [g/dL]) + (0.005 \u0026times; Lymphocyte count [/mm\u0026sup3;]) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA double-blind method was applied during radiological evaluation. For variables with missing data, records were reviewed, and in cases where missingness could not be resolved, the multiple imputation method was applied. Data gathered within the initial 24 hours of intensive care admission formed the basis for all calculations.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were initially transferred into Microsoft Excel for general evaluation. Data entry errors and inconsistencies were checked. Normality was assessed using the Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests. Continuous data were analyzed with the Student\u0026rsquo;s t-test when they followed a normal distribution, and with the Mann\u0026ndash;Whitney U test when they did not. Categorical variables were assessed using either the Chi-square test or Fisher\u0026rsquo;s exact test. For correlation analyses, Pearson\u0026rsquo;s test was applied to normally distributed variables, while Spearman\u0026rsquo;s test was used for non-normally distributed variables.\u003c/p\u003e\u003cp\u003eThe predictive power of D-dimer, PNI, and other inflammatory markers for bilateral lung involvement was evaluated using ROC analysis, and AUC values were presented together with their 95% confidence intervals. Statistical significance was defined as a p-value below 0.05. All statistical analyses were carried out using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp. Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn total, 113 patients were enrolled in the study. Of these, 56 (49.5%) were PCR-positive and 57 (50.5%) were PCR-negative. The median age was 55.5 years (min: 21, max: 92) in the PCR-positive group and 47.0 years (min: 20, max: 91) in the PCR-negative group. In the PCR-positive group, 33 patients were female and 23 were male, whereas in the PCR-negative group, 27 were female and 30 were male.\u003c/p\u003e\u003cp\u003eCough was the most frequently reported symptom in both groups, occurring at a significantly higher rate in the PCR-positive group (PCR-positive: 82.1%; PCR-negative: 59.6%; p\u0026thinsp;=\u0026thinsp;0.02). The second most common symptom in both groups was dyspnea (PCR-positive: 73.2%; PCR-negative: 59.6%; p\u0026thinsp;=\u0026thinsp;0.18). The least frequent symptoms were sore throat and myalgia in both groups. No meaningful differences in other symptoms were detected across the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSymptom Distribution Between PCR Positive and PCR Negative Groups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR Positive n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCR Negative n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (73.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (82.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80 (70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeadache\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSore throat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.68\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSputum production\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation of the key biochemical and hematological parameters for the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eLaboratory Parameters by PCR Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR Positive (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCR Negative (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e35.73\u0026thinsp;\u0026plusmn;\u0026thinsp;27.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e30.17\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e230.02\u0026thinsp;\u0026plusmn;\u0026thinsp;88.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e234.25\u0026thinsp;\u0026plusmn;\u0026thinsp;91.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e37.21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e47.34\u0026thinsp;\u0026plusmn;\u0026thinsp;47.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e12.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\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\u003eOf the inflammatory and nutritional indices analyzed, only the Prognostic Nutritional Index (PNI) demonstrated a statistically significant variation between PCR-positive and PCR-negative patients (p\u0026thinsp;=\u0026thinsp;0.008). PNI values were lower in the PCR-positive group (35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1) compared to the PCR-negative group (39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8). Other indices, including NLR, PLR, SII, SIRI, CAR, and the BUN/Albumin ratio, showed no significant differences (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eInflammatory and Nutritional Indices by PCR Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCR Positive (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCR Negative (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9,2\u0026thinsp;\u0026plusmn;\u0026thinsp;6,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e8,5\u0026thinsp;\u0026plusmn;\u0026thinsp;5,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,492\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e211,4\u0026thinsp;\u0026plusmn;\u0026thinsp;106,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e199,8\u0026thinsp;\u0026plusmn;\u0026thinsp;96,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1834\u0026thinsp;\u0026plusmn;\u0026thinsp;1194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1671\u0026thinsp;\u0026plusmn;\u0026thinsp;1150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e35,4\u0026thinsp;\u0026plusmn;\u0026thinsp;6,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e39,0\u0026thinsp;\u0026plusmn;\u0026thinsp;6,8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3,5\u0026thinsp;\u0026plusmn;\u0026thinsp;3,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3,1\u0026thinsp;\u0026plusmn;\u0026thinsp;2,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2,7\u0026thinsp;\u0026plusmn;\u0026thinsp;2,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2,3\u0026thinsp;\u0026plusmn;\u0026thinsp;2,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN/Alb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5,6\u0026thinsp;\u0026plusmn;\u0026thinsp;2,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5,1\u0026thinsp;\u0026plusmn;\u0026thinsp;2,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,318\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\u003eIn the PCR-positive group, D-dimer levels were significantly higher [1069.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1230.59 ng/mL; median 730.0 (190\u0026ndash;6450)] compared to the PCR-negative group [784.88\u0026thinsp;\u0026plusmn;\u0026thinsp;997.97 ng/mL; median 443.0 (150\u0026ndash;5169)] (p\u0026thinsp;=\u0026thinsp;0.008). In the PCR-positive cohort, D-dimer showed significant positive correlations with PLR (r\u0026thinsp;=\u0026thinsp;0.366), NLR (r\u0026thinsp;=\u0026thinsp;0.458), SII (r\u0026thinsp;=\u0026thinsp;0.439), SIRI (r\u0026thinsp;=\u0026thinsp;0.297), and CAR (r\u0026thinsp;=\u0026thinsp;0.319), with the strongest associations observed for NLR and SII. Additionally, D-dimer exhibited the highest correlation with the BUN/Alb ratio (r\u0026thinsp;=\u0026thinsp;0.520), whereas no meaningful association was found in the PCR-negative group (r\u0026thinsp;=\u0026thinsp;0.020). In the PCR-negative group, significant positive correlations were found only with PLR (r\u0026thinsp;=\u0026thinsp;0.340) and CAR (r\u0026thinsp;=\u0026thinsp;0.373). These findings suggest that the increase in D-dimer levels in PCR-positive cases is more strongly associated with systemic inflammatory burden and, in particular, reflects a combined effect of inflammation, renal function, and nutritional status as captured by the BUN/Alb ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the PCR-positive group, the rate of bilateral lung involvement was 76.8%, whereas it was 57.9% in the PCR-negative group, and this variation was confirmed as statistically significant (p\u0026thinsp;=\u0026thinsp;0.003). Right lung involvement was observed in 10.7% of the PCR-positive group and 28.1% of the PCR-negative group, while left lung involvement was 12.5% and 14.0%, respectively. Analysis showed that the frequency of unilateral right and left lung involvement was comparable across the groups, with no significant differences detected. These findings indicate that radiological involvement was more extensive in PCR-positive patients, with bilateral patterns being particularly prominent. The following graph presents a comparative distribution of right, left, and bilateral lung involvement rates in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe following black-and-white bar chart illustrates the mean values of systemic inflammatory markers (PLR, NLR, SII, PNI, SIRI, CAR, BUN/Alb) in PCR-positive and PCR-negative patient groups. Upon examination of the graph, it is notable that the SII values were higher in the PCR-positive group, whereas the PNI values were lower. Differences between the groups were limited for the other parameters. This visual representation summarizes the group comparisons of laboratory findings and supports the statistical analysis results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe following graph illustrates the Spearman correlation coefficients between the rate of bilateral lung involvement and systemic inflammatory markers (PNI, CAR, BUN/Alb, PLR, NLR, SII, SIRI). The direction of the bars indicates whether the correlation is positive or negative, while their height reflects the strength of the relationship. The striped bars represent statistically significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A negative correlation indicates that as the respective parameter increases, the rate of bilateral involvement decreases, whereas a positive correlation suggests that both increase together (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the evaluated factors, a statistically significant negative correlation emerged exclusively between bilateral lung involvement and PNI (rho = \u0026minus;\u0026thinsp;0.232; p\u0026thinsp;=\u0026thinsp;0.0207). This finding suggests that a decrease in PNI, which reflects nutritional and immune status, may be associated with more extensive bilateral involvement on radiological imaging. In other words, patients with lower PNI levels tend to present with more widespread lung involvement.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that patients with PCR-positive viral pneumonia exhibited a significantly higher rate of bilateral lung involvement, lower PNI values, and stronger positive correlations between D-dimer and various inflammatory parameters. Our findings are largely consistent with previously reported radiological and biochemical profiles in COVID-19 and other viral pneumonias [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA low PNI reflects not only nutritional deficiencies but also an increased inflammatory burden. The inverse correlation observed between bilateral involvement and PNI in our study suggests that extensive disease may disrupt the immune\u0026ndash;nutritional balance. This finding is consistent with the results of Miniksar et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], who demonstrated that reduced PNI values were linked to a higher risk of mortality in ICU patients diagnosed with pneumonia.\u003c/p\u003e\u003cp\u003eWhen clinical, laboratory, and radiological features were compared according to PCR status, patients in the PCR-positive cohort showed a higher frequency of bilateral pulmonary infiltrates, lower PNI values, and marked elevations in inflammatory markers including SII, NLR, and SIRI. The pronounced correlations between D-dimer and both NLR (r\u0026thinsp;=\u0026thinsp;0.458) and SII (r\u0026thinsp;=\u0026thinsp;0.439) indicate that changes in coagulation pathways are closely intertwined with systemic inflammation rather than reflecting thrombosis alone. Consistent with this, Wang et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that diffuse parenchymal disease in PCR-positive pneumonia could contribute to adverse outcomes. In our data, bilateral infiltration was recorded in 78.6% of PCR-positive cases, compared with 57.9% of PCR-negative cases (p\u0026thinsp;=\u0026thinsp;0.003). Unlike the PCR-positive cohort, correlations in PCR-negative patients were limited to PLR and CAR. Importantly, the most prominent association in PCR-positive patients emerged between D-dimer and the BUN/Alb ratio (r\u0026thinsp;=\u0026thinsp;0.520), while this relationship was virtually absent in PCR-negative individuals (r\u0026thinsp;=\u0026thinsp;0.020). This novel observation suggests that rising D-dimer levels in PCR-positive pneumonia may mirror not only systemic inflammatory activation but also the combined impact of renal impairment and nutritional depletion. Collectively, these findings support the view that PCR-positive pneumonia is characterized by more widespread radiological involvement, amplified inflammatory drive, deterioration of immune\u0026ndash;nutritional balance, and increased organ vulnerability.\u003c/p\u003e\u003cp\u003eThe utility of systemic inflammatory indicators such as NLR, PLR, SII, SIRI, and CAR to reflect disease severity and prognosis has been emphasized in previous research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our study, especially in PCR-positive patients, D-dimer levels were strongly correlated with these parameters. This suggests that D-dimer may serve in clinical practice not only as a marker of thrombotic complications but also as an important marker reflecting systemic inflammation. The weaker associations observed in the PCR-negative group may indicate that, although inflammation is present, the burden is less intense than in the PCR-positive group. Furthermore, composite indices reflecting both nutritional and inflammatory status (e.g. PNI, SIRI, PAN Immune Inflammation Value) have been shown in the literature to predict mortality and prognosis. These results support the finding that the negative link between PNI and bilateral involvement observed in our study suggests that early nutritional support and inflammation control may improve outcomes in ICU practice.\u003c/p\u003e\u003cp\u003eThe existence of patients with PCR-negative but radiologically compatible COVID-19 underscores the insufficiency of basing conclusions solely on PCR testing for diagnosis. Earlier research has likewise demonstrated that PCR negativity may occur in advanced stages, and that combining thoracic CT findings with laboratory parameters can improve diagnostic accuracy. In conclusion, PCR-positive viral pneumonias are characterized by more extensive bilateral lung involvement, lower PNI levels, and heightened inflammatory responses; among these, D-dimer emerges as an additional biomarker reinforcing this clinical picture. Our findings suggest that integrating radiological features with biomarker-based parameters may offer clinically meaningful support for diagnosis, risk assessment, and therapeutic decision-making in intensive care units.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that extensive bilateral lung involvement in PCR-positive viral pneumonia is associated with lower PNI values and an intensified inflammatory response. In this cohort, D-dimer correlated positively with multiple inflammatory indices, most notably NLR and SII, whereas the PCR-negative group exhibited only limited associations. Importantly, the strongest correlation was observed with the BUN/Alb ratio, suggesting that D-dimer elevation in PCR-positive patients reflects not only systemic inflammation but also the combined impact of renal dysfunction and nutritional imbalance. The coexistence of low PNI, high BUN/Alb ratio, and bilateral radiological involvement highlights impaired immune\u0026ndash;nutritional reserves and an amplified inflammatory burden, conditions that may predispose to adverse clinical outcomes. Integrating radiological assessment with PNI, the BUN/Alb ratio, and key laboratory markers could therefore provide a more comprehensive strategy for early risk stratification and individualized management in critically ill patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eICU\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive Care Unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePCR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCT\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComputed Tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePLR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePlatelet-to-Lymphocyte Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSII\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic Immune-Inflammation Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSIRI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic Inflammation Response Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCAR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-Reactive Protein-to-Albumin Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eBAR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlood Urea Nitrogen-to-Albumin Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePNI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrognostic Nutritional Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-Reactive Protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eBUN\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlood Urea Nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient data used in this study were obtained from the hospital information management system and institutional archives. In accordance with ethical approval and privacy regulations, these data are not publicly available. De-identified datasets may be provided by the corresponding author upon reasonable request and with appropriate institutional permissions.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eFinancial Disclosure\u003c/h2\u003e\u003cp\u003eThe authors declared that this study has received no financial support.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e\u003cb\u003eETHICAL DECLARATIONS\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEthics Committee Approval\u003c/strong\u003e\u003cp\u003e This study was conducted with the approval of the Health Sciences Research Ethics Committee of Yuksek Ihtisas University (Date: 25.06.2025, Decision No: 329-Scientific Studies Ethics Committee /14)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eInformed Consent\u003c/h2\u003e\u003cp\u003e All patients signed and free and informed consent form. Referee Evaluation Process Externally peer-reviewed.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor A (FC): Study conception and design, data collection, data analysis, and drafting of the manuscript.Author B (IE): Supervision, critical revision of the manuscript for important intellectual content, and guidance on methodology.Author C (OO): Radiological evaluation, interpretation of imaging findings, and contribution to data interpretation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKalanuria AA, Ziai W, Mirski M (2014). Ventilator-associated pneumonia in the ICU. Crit Care. 18(2):208 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/cc13775\u003c/span\u003e\u003cspan address=\"10.1186/cc13775\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalil AC, Metersky ML, Klompas M, et al (2016). Management of adults with hospital-acquired and ventilator-associated pneumonia: 2016 IDSA/ATS guidelines. Clin Infect Dis. 63(5):e61\u0026ndash;111 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cid/ciw353\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciw353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolidoro RB, Hagan RS, de Santis Santiago R, Schmidt NW (2020). Overview: systemic inflammatory response in COVID-19. Front Immunol. 11:580 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.00580\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.00580\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-P\u0026eacute;rez H, Ciuffreda L, Hern\u0026aacute;ndez-Beeftink T, et al (2024). Tracheal aspirate metagenomics reveals association of antibiotic resistance with nonpulmonary sepsis mortality. Am J Respir Cell Mol Biol. 70(1):50\u0026ndash;60 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1165/rcmb.2023-0293OC\u003c/span\u003e\u003cspan address=\"10.1165/rcmb.2023-0293OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdukauskiene D, Ciginskiene A, Adukauskaite A, et al (2023). Polymicrobial multidrug-resistant Klebsiella pneumoniae ventilator-associated pneumonia outcomes. Antibiotics (Basel). 12(6):1056 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/antibiotics12061056\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics12061056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoccolini F, Sartelli M, Kluger Y, et al (2020). COVID-19: the showdown for mass casualty preparedness. World J Emerg Surg. 15(1):26 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13017-020-00304-5\u003c/span\u003e\u003cspan address=\"10.1186/s13017-020-00304-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, et al (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 395(10229):1054\u0026ndash;62 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30566-3\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30566-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLagunas-Rangel FA (2020). Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe COVID-19. Int Immunopharmacol. 85:106507 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.intimp.2020.106507\u003c/span\u003e\u003cspan address=\"10.1016/j.intimp.2020.106507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOnodera T, Goseki N, Kosaki G (1984). Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi. 85(9):1001\u0026ndash;5\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiniksar \u0026Ouml;H, Er \u0026Ouml;, Polat N (2024). The prognostic value of inflammation indices in predicting ICU admission and mortality. J Intensive Care Emerg. 21(3):155\u0026ndash;62\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Liu D, Zhang L, et al (2020). Prognostic nutritional index and mortality in patients with COVID-19: a retrospective cohort study. J Infect. 81(3):e20\u0026ndash;2 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jinf.2020.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jinf.2020.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu R, Han C, Pei S, et al (2021). Procalcitonin levels in COVID-19 patients. Int J Antimicrob Agents. 56(2):106051 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijantimicag.2020.106051\u003c/span\u003e\u003cspan address=\"10.1016/j.ijantimicag.2020.106051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026icirc;rbu AC, Dragomirescu RF, Arghir OC, Arghir IA (2025). Clinical and laboratory features correlated with CT scan lung injury in COVID-19 Delta and Omicron variants. Medicina (Kaunas). 61(5):931 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina61050931\u003c/span\u003e\u003cspan address=\"10.3390/medicina61050931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQu R, Ling Y, Zhang YHZ, et al (2020). Platelet-to-lymphocyte ratio is associated with prognosis in patients with coronavirus disease-19. J Med Virol. 92(9):1533\u0026ndash;41 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.25767\u003c/span\u003e\u003cspan address=\"10.1002/jmv.25767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu B, Yang XR, Xu Y, et al (2014). Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 20(23):6212\u0026ndash;22 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-14-0442\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-14-0442\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi Q, Zhuang L, Shen Y, et al (2016). A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer. 122(14):2158\u0026ndash;67 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.30057\u003c/span\u003e\u003cspan address=\"10.1002/cncr.30057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanzani OT, Zampieri FG, Forte DN, et al (2013). C-reactive protein/albumin ratio predicts 90-day mortality of septic patients. PLoS One. 8(3):e59321 doi:\u003cdiv class=\"ExternalRefDOI\"\u003e10.1371/\u003c/div\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Zhang Y, Chen W, et al (2024). Blood urea nitrogen to albumin ratio as a predictor of mortality in sepsis patients: a retrospective cohort study. BMC Nephrol. 25(1):42 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12882-025-04214-z\u003c/span\u003e\u003cspan address=\"10.1186/s12882-025-04214-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohri Y, Inoue Y, Tanaka K, et al (2013). Prognostic nutritional index predicts postoperative outcome in colorectal cancer. World J Surg. 37(11):2688\u0026ndash;92 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00268-013-2156-9\u003c/span\u003e\u003cspan address=\"10.1007/s00268-013-2156-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao Y, Cao J, Wang Q, et al (2020). D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case-control study. J Intensive Care. 8:49 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40560-020-00466-z\u003c/span\u003e\u003cspan address=\"10.1186/s40560-020-00466-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang G, Wang N, Liu T, et al (2024). Association between prognostic nutritional index and mortality risk in patients with community acquired pneumonia: a retrospective study. BMC Pulm Med. 24(1):555 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12890-024-03373-3\u003c/span\u003e\u003cspan address=\"10.1186/s12890-024-03373-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Hu B, Hu C, et al (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus\u0026ndash;infected pneumonia in Wuhan, China. JAMA. 323(11):1061\u0026ndash;9 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2020.1585\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.1585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUgajin M, Yamaki K, Iwamura N, et al (2012). Prognostic value of blood urea nitrogen to serum albumin ratio in community-acquired pneumonia. Int J Gen Med. 5:583\u0026ndash;9 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/IJGM.S33674\u003c/span\u003e\u003cspan address=\"10.2147/IJGM.S33674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaran B, Yetkin NA, Rabahoğlu B, et al (2025). Assessment of mortality risk in patients with community-acquired pneumonia: role of novel inflammatory biomarkers. J Clin Lab Anal. (Online ahead of print) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcla.70081\u003c/span\u003e\u003cspan address=\"10.1002/jcla.70081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Viral pneumonia, COVID-19, Prognostic Nutritional Index, systemic inflammation, D-dimer, BUN/Albumin ratio","lastPublishedDoi":"10.21203/rs.3.rs-7426635/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7426635/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e\u003cp\u003eThe purpose of the present study was to assess how bilateral pulmonary involvement, prognostic nutritional index (PNI), the blood urea nitrogen / albumin (BUN/Alb) ratio, D-dimer levels, and systemic inflammatory indicators are interrelated in patients diagnosed with viral pneumonia confirmed by Polymerase chain reaction (PCR).\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThe study included 113 patients who had radiologically confirmed pneumonic infiltrations were retrospectively analyzed. Patients were divided into two groups according to PCR results: PCR-positive (n\u0026thinsp;=\u0026thinsp;56) and PCR-negative (n\u0026thinsp;=\u0026thinsp;57). Between the two cohorts, demographic information was systematically compared, presenting symptoms, laboratory results, systemic inflammatory indices (Neutrophil / Lymphocyte Ratio (NLR), Platelet / Lymphocyte Ratio (PLR), PNI, Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), CAR (C-Reactive Protein-to-Albumin Ratio), BAR), and radiological findings.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eBilateral lung involvement was significantly more frequent in PCR-positive patients compared to PCR-negative patients (78.6% vs. 57.9%, p\u0026thinsp;=\u0026thinsp;0.003). The PNI levels were markedly lower in the PCR-positive group, while systemic inflammatory indices (NLR, SII, SIRI) showed higher tendencies. In PCR-positive patients, D-dimer levels demonstrated strong positive correlations with PLR, NLR, SII, SIRI, CAR, and most prominently with the BUN/Alb ratio (r\u0026thinsp;=\u0026thinsp;0.520), as well as with NLR (r\u0026thinsp;=\u0026thinsp;0.458) and SII (r\u0026thinsp;=\u0026thinsp;0.439). Conversely, in PCR-negative patients, D-dimer correlated only with PLR and CAR.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003ePCR-positive viral pneumonias are characterized by more extensive bilateral lung involvement, impaired nutritional-immune status reflected by low PNI levels and an elevated BUN/Alb ratio, and a higher systemic inflammatory burden. D-dimer levels appear to reflect not only the inflammatory load but also renal\u0026ndash;nutritional imbalance, underscoring their prognostic significance. The combined evaluation of radiological findings and systemic biomarkers, including PNI and the BUN/Alb ratio, may contribute to improved risk stratification and management in critically ill patients.\u003c/p\u003e","manuscriptTitle":"Bilateral Lung Involvement, Low PNI, and Elevated BUN/Albumin Ratio in PCR-Positive Viral Pneumonias: Clinical–Radiological Correlations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 14:24:22","doi":"10.21203/rs.3.rs-7426635/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":"6503f7d1-ff7d-4124-aac9-c525a96627e6","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T05:25:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 14:24:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7426635","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7426635","identity":"rs-7426635","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}