Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients’ Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.


Introduction
Due to the rapid spread of the SARS-Cov-2 (severe respiratory syndrome coronavirus 2) infection, the World Health Organization (WHO) declared the outbreak of the COVID-19 pandemic on 11 March 2020, which has become a global phenomenon and public health problem, which in the last two years had a negative impact on current medical practice [1][2][3]. Despite the development of antiviral therapies, severe forms of the disease require intensive therapy and have a high mortality rate [4,5]. Real-Time PCR (RT-PCR) is the preferred technique since it provides the most accurate disease diagnosis [6,7].
Chest computer tomography (CT) is a non-invasive, quick imaging tool that plays an essential role in the diagnosis and progression of COVID-19 patients [27][28][29][30]. Numerous scores have been established for evaluating the degree of pulmonary damage and standardized radiological interpretation [31][32][33][34]. Among these is the Total Score System (TSS), introduced by Chung et al. [35], whose prognostic role was studied and established for the negative progression, as well as the degree of severity and mortality, of COVID-19 patients [31,36,37].
This study aims to verify the predictive role of inflammatory biomarkers (MLR, NLR, SII, SIRI, AISI, and IL-6) and the TSS in need of invasive mechanical ventilation (IMV) and mortality in COVID-19 patients.

Study Design
The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed by real-time-polymerase chain reaction (RT-PCR) and radiological chest CT findings, admitted to County Emergency Clinical Hospital of Targu-Mures , , Romania, and Modular Intensive Care Unit of UMFST "George Emil Palade" of Targu Mures, Romania between January 2021 and December 2021. Exclusion criteria were as follows: patients who died and need invasive mechanical ventilation in the first 24 h from admission, patients with end-stage kidney disease and dialysis, recent malignancy diagnosed within a maximum of six months prior to our studied period, and any leukemia or other hematological disorders, major surgery: any major resection or reconstruction of any digestive organ, cardiovascular reconstruction/revascularizations (major heart/aortic surgeries), major surgery of the lungs or kidneys, autoimmune diseases, and patients without a chest CT scan in the first 24 h.
Patients included in the study were initially divided into two groups depending on their poor outcome during the hospitalization named "Survivors" and "non-Survivors." The ideal cut-off value for MLR, NLR, SII, SIRI, AISI, IL-6 and TSS was used to calculate the need for IMV and mortality.

Data Collection
The patients' demographic data were extracted from the hospital's electronic database. We searched for the following comorbidities in the medical history: arterial hypertension (AH), ischemic heart disease (IHD), atrial fibrillation (AF), chronic heart failure (CHF), myocardial infarction (MI), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), peripheral arterial disease (PAD), chronic kidney disease (CKD), cerebrovascular accident (CVA), dyslipidemia, tobacco use, obesity, and length of hospital stay. in addition, we collected data from the first blood test result (hemoglobin, hematocrit, neutrophil count, lymphocyte count, monocyte count, platelet count, IL-6, glucose level, cholesterol level, triglyceride level, potassium level, blood urea nitrogen level, and creatinine level).

Systemic Inflammatory Markers
The systemic inflammation index was determined from the first blood test result. The MLR, NLR, SII, SIRI, and AISI were calculated using the equations below:

Chest CT Severity Score
Chest CT exams were performed in the first 24 h from admission. Image analysis was performed using a PACS (Picture Archiving and Communication System) workstation (IN-FINITT Healthcare Co., Ltd., Seoul, Korea). Chest CT images were assessed to evaluate the extent of pulmonary parenchymal involvement for the presence of ground-glass opacities (GGOs), consolidation, and pleural effusion.
TSS was calculated by quantifying the disease-affected areas for each lobe to evaluate pulmonary parenchymal involvement. Each of the five lobes was given a score ranging from 0 to 4, based on the percentage of the affected area as none (0%), minimal (1-25%), mild (26-50%), moderate (51-75%), or severe (76-100%). TSS was calculated by adding the values for five lobes ranging from 0 to 20.

Vaccination Status
During the studied period in Romania, four different vaccines were used Pfizer (BioN-Tech, Mainz, Germany), AstraZeneca (Oxford University, Oxford, UK), Moderna (National Institute of Allergy and Infectious Diseases and Biomedical Advanced Research and Development Authority, Cambridge, MA, USA), and Janssen (Johnson and Johnson, New Brunswick, NJ, USA). Depending on the number of doses for each type of vaccine, patients were registered as unvaccinated, partially vaccinated, and fully vaccinated.

Study Outcomes
The primary endpoints were the need for IMV, in-hospital mortality rate, and a composite endpoint of IMV need and mortality. Outcomes were stratified for the baseline's optimal MLR, NLR, SII, SIRI, AISI, IL-6, and TSS cut-off value.

Statistical Analysis
SPSS for Mac OS version 28.0.1.0 was used for statistical analysis (SPSS, Inc., Chicago, IL). Chi-square tests were used to assess the associations of MLR, NLR, SII, SIRI, AISI, IL-6, and TSS with category factors, while t-Student or Mann-Whitney tests were used to assess differences in continuous variables. To assess the predictive power and establish cut-off MLR, NLR, SII, SIRI, AISI, IL-6, and TSS, the receiver operating characteristic (ROC) curve analysis was utilized. The receiver operating characteristic (ROC) curve analysis was used to determine the appropriate MLR, NLR, SII, SIRI, AISI, IL-6, and TSS cut-off values based on the Youden index (Youden Index = Sensitivity + Specificity − 1, ranging from 0 to 1). To identify independent predictors of IMV need, mortality, and a composite endpoint of IMV need and mortality, a multivariate logistic regression analysis using variables with p < 0.1 was undertaken.

Results
During the study period, 267 patients diagnosed with COVID-19 met the inclusion criteria and followed up during hospitalization. The mean age was 71.19 ± 10.25 , and 159 patients were male (59.55%) ( Table 1). During the hospitalization, 60 patients (22.47%) needed IMV, 82 patients died (30.71%), and 45 patients (16.85%) needed IMV and deceased later, respectively. Depending on the survival status during the hospitalization, the patients were enrolled in two groups: Survivors and Non-Survivors. Mean age was statistically higher in the second group (p = 0.01). In terms of comorbidities and risk factors, in the non-Survivors group was a higher incidence of AF (p = 0.0008), CHF (p = 0.01), dyslipidemia (p = 0.01), and CKD (p = 0.002). Regarding the Pulmonary CT scan findings, in the second group, all five pulmonary lobes were affected in a higher proportion (p < 0.0001), and the TSS was higher (p < 0.0001). Regarding vaccination status, the non-Survivors group had a higher incidence of unvaccinated (p = 0.001) and a lower incidence of fully vaccinated (p = 0.0005). Moreover, several variables from Laboratory data were associated with poor outcomes: non-Survivors had lower lymphocyte (p < 0.0001) and potassium level (p < 0.0001), and higher neutrophils (p < 0.0001), monocyte (p = 0.0006), glucose (p < 0.0001), MLR (p < 0.0001), NLR (p < 0.0001), SII (p < 0.0001), SIRI (p < 0.0001), AISI (p < 0.0001), and IL-6 (p < 0.0001). In addition, the non-Survivors patients had a higher incidence of IMV need (p < 0.0001) and a long hospital stay (p = 0.0005). The rest of the comorbidities and laboratory data are presented in Table 1.  Receiver operating characteristic curves of MLR, NLR, SII, SIRI, AISI, IL-6, and TSS were created to determine whether the baseline of these markers was predictive of IMV need, mortality, and common endpoint in patients with COVID-19 (Figures 1-3). The optimal cut-off value obtained from Youden's index, areas under the curve (AUC), and the predictive accuracy of the markers are listed in Table 2.       Depending on the optimal cut-off value according to the ROC, the outcomes were further analyzed after dividing the patients into paired groups. There was a higher incidence of all adverse outcomes for all the markers analyzed, as seen in Table 3.   Multivariate analysis showed that a high baseline value for all the analyzed markers was an independent predictor of adverse outcomes for all recruited patients. Furthermore, for all hospitalized patients, an age over 70 (p = 0.02; p = 0.001; p = 0.005), AF (p = 0.009; p < 0.0001; p = 0.01), dyslipidemia (p = 0.01; p = 0.01; p = 0.02), and unvaccinated (p = 0.04; p < 0.001; p = 0.002) was an independent predictor of a poor prognosis for all the outcomes. CHF and CKD were independent predictors for mortality (p = 0.01) and composite endpoint (p = 0.02) but not for IMV need (Table 4).

Discussion
This study included 267 individuals diagnosed with COVID-19 pneumonia. We determined the preoperative values for all patients for inflammatory biomarkers and TSS and monitored IMV requirement, mortality rate, and a composite endpoint of IMV need and mortality. The most important finding of our study is that a high baseline value for MLR, NLR, SII, SIRI, AISI, and TSS (p < 0.0001) is a strong predictor of all outcomes.
Moreover, to the best of our knowledge, our study demonstrates for the first time that patients with higher MLR, NLR, SII, SIRI, AISI, and TSS showed a higher risk of disease progression to IMV need and intra-hospital mortality.
Similar to our research, Bellos et al. [36] discovered that a TSS higher than 10.5 (75% Sensitivity, 70% Specificity; AUC:0.811) is a prognostic factor for ICU admission ( [22]. Similarly, Kudlinsky et al. published an article in which they proved the prognostic impact of NLR > 11.57 (p = 0.0008) and SII > 2058 (p = 0.02) in COVID-19 patients' death [65]. In a cohort study involving 411 COVID-19 patients, Regolo et al. discovered a correlation between baseline NLR values greater than 11.38 and the necessity for ICU admission (p < 0.0001) [19]. Moreover, in the papers conducted by Hamad et al. [65], Nalbant et al. [66], and Fois et al. [55], high values of SIRI and AISI were related to the severe form of the disease, necessity of ICU, and increased mortality [55]. Given the findings of our study, which support the work published in the literature during the last two years, as well as the low cost and ease of use of hematological markers and the lung damage score, their use in medical practice allows for better stratification of risk groups and the establishment of appropriate therapeutic management, thus improving the progression of patients with COVID-19.
Our study has certain limitations, despite the statistically significant results. First and foremost, it is a retrospective, monocentric research with patient follow-up during hospitalization. Prospective multicenter trials with long-term follow-ups are recommended in the future. Furthermore, due to the study's retrospective nature, we could not access data about chronic medications used before admission (such as corticosteroids and antiinflammatories meds). Therefore, we could not establish the effect of other medications on inflammatory biomarkers. Furthermore, additional research is necessary to support our findings.

Conclusions
According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality. Given the ease of access and low cost of these ratios and chest CT severity score, they can be used for admission risk group categorization, improved patient care, and the development of predictive patterns. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.