Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients

Background: Numerous tools, including nutritional and inflammatory markers, have been evaluated as the predictors of poor outcomes in COVID-19 patients. This study aims to verify the predictive role of the prognostic nutritional index (PNI), CONUT Score, and inflammatory markers (monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)) in cases of deep vein thrombosis (DVT) and acute pulmonary embolism (APE) risk, as well as mortality, in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study, and included 899 patients over the age of 18 who had a COVID-19 infection, confirmed through real time-polymerase chain reaction (RT-PCR), and were admitted to the County Emergency Clinical Hospital and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2020 and March 20212. Results: Non-Surviving patients were associated with a higher incidence of chronic kidney disease (p = 0.01), cardiovascular disease (atrial fibrillation (AF) p = 0.01; myocardial infarction (MI) p = 0.02; peripheral arterial disease (PAD) p = 0.0003), malignancy (p = 0.0001), tobacco (p = 0.0001), obesity (p = 0.01), dyslipidemia (p = 0.004), and malnutrition (p < 0.0001). Multivariate analysis showed that both nutritional and inflammatory markers had a high baseline value and were all independent predictors of adverse outcomes for all enrolled patients (for all p < 0.0001). The presence of PAD, malignancy, and tobacco, were also independent predictors of all outcomes. Conclusions: According to our findings, higher MLR, NLR, PLR, SII, SIRI, AISI, CONUT Score, and lower PNI values at admission strongly predict DVT risk, APE risk, and mortality in COVID-19 patients. Moreover, PAD, malignancy, and tobacco, all predicted all outcomes, while CKD predicts APE risk and mortality, but not the DVT risk.

Data analysis was conducted depending on survival during the hospitalization and patients were divided into two groups named "Survivors" and "non-Survivors". The ideal cut-off value for all markers studied was used to calculate the DVT and APE developed, and the mortality rate.

Data Collection
The patient's age, gender, and hospitalization period were extracted from the hospital's electronic database. Regarding comorbidities, the following cardiac pathologies were recorded: arterial hypertension (AH), atrial fibrillation (AF), ischemic heart disease (IHD), history of myocardial infarction (MI), chronic heart failure (CHF), as well as other pathologies: chronic kidney disease (CKD), peripheral arterial disease (PAD), and diabetes mellitus (DM).
The following were extracted from the first laboratory analyses: hemoglobin level, hematocrit level, glucose level, total cholesterol level, triglyceride level, serum albumin, Glomerular filtration rate (GFR), blood urea nitrogen (BUN), creatinine, number of neutrophils, lymphocytes, monocytes, platelets, potassium, and sodium.
All patients received prophylactic anticoagulation during the hospitalization with low-weight molecular heparin.

Nutritional and Inflammatory Markers
Nutritional and inflammatory biomarkers were determined from the first blood test result. The ratio was calculated using the equations as seen in Table 1.

Study Outcomes
The primary endpoints were the occurrence of DVT, APE, and mortality during the hospitalization stay. The number of days spent in the hospital and the combinate endpoint of DVT and APE were recorded as secondary outcomes. The primary outcomes were stratified for the optimal cut-off value of inflammatory and nutritional biomarkers.

Deep Vein Thrombosis and Acute Pulmonary Embolism Diagnostics
The patients who presented symptoms of DVT were evaluated by Doppler ultrasound for both upper or lower extremities, and the level of vein thrombosis was recorded. Moreover, patients with suspected APE during hospitalization were evaluated by using a Computed Tomography Angiogram.

Statistical Analysis
SPSS for Mac OS version 28.0.1.0 was used for statistical analysis (SPSS, Inc., Chicago, IL, USA). Chi-square tests were used to assess the associations of the ratios with category factors, while t-Student or Mann-Whitney tests were used to assess differences in continuous variables. To analyze the predictive power and to establish the cut-off values of inflammatory biomarkers, the receiver operating characteristic (ROC) curve analysis was utilized. The ROC curve analysis was used to determine the appropriate MLR, NLR, PLR, SII, SIRI, AISI, PNI, and CONUT Score cut-off values based on the Youden index (Youden Index = Sensitivity + Specificity -1, ranging from 0 to 1). To identify independent predictors of DVT and APE risk, and mortality in COVID-19 patients, a multivariate logistic regression analysis using variables with p < 0.1 was undertaken.
Regarding the comorbidities and risk factors, the non-survivor patients had a higher incidence of AF (p = 0.01), MI (p = 0.02), CKD (p = 0.01), PAD (p = 0.001), Tobacco (p = 0.0001), Obesity (p = 0.01), and dyslipidemia (p = 0.004), as seen in Table 2. In terms of Nutritional status, lower PNI score (p < 0.0001), higher CONUT Score (p < 0.0001), as well as moderate (p < 0.0001) and severe (p < 0.0001) malnutrition were present in the non-survivors group. Moreover, regarding the laboratory findings, non-survivor patients had lower hemoglobin levels (p = 0.001), hematocrit (p = 0.0002), cholesterol (p < 0.0001), albumin (p < 0.0001), and lymphocyte (p < 0.0001), and higher glucose (p = 0.006), bun (p < 0.0001), creatinine (p < 0.0001), and neutrophils (p < 0.0001). All systemic inflammatory markers were higher in the second group (for all p < 0.0001). Moreover, all outcomes studied were higher in the poor outcome group (for all p < 0.0001). The ROC curves of all inflammatory and nutritional markers were created to determine whether the baseline of these markers was predictive of DVT risk, APE risk, and mortality during the hospitalization stay (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 3.
The DVT, APE risk, and mortality were further analyzed after dividing the patients into paired groups, according to the optimal cut-off value of all studied markers. Moreover, there was a higher incidence of all outcomes for all the markers as seen in Table 4.
A multivariate analysis was used to determine the association between all markers, underlying risk factors, DVT, APE development risk, and mortality during the hospitalization. A high baseline value of all systemic inflammatory markers and CONUT Score was a strong independent predictor of all outcomes (for all p < 0.0001), as well as a lower baseline value of PNI (p < 0.0001). Moreover, as shown in Table 5 (Table 5).

Discussion
The main result of this study is that inflammatory and nutritional indicators might predict the risk of DVT and APE, as well as death during hospitalization in COVID-19 patients. Moreover, BAP, malignancy, and tobacco were strong predictors of the three outcomes, and CKD was a predictor of APE risk and mortality. To our knowledge, this is the first study that analyzes all hematological markers (MLR, NLR, PLR, SII, SIRI, and AISI), and nutritional markers (PNI and CONUT Score), in the prediction of DVT, APE, and mortality, on 889 COVID-19 patients.
In regard to the predictive role of nutritional markers, in the meta-analysis published by Hung et al. [52], in which 13 studies were included, and a total number of 4204 COVID-19 patients, it was demonstrated that PNI is a predictor of mortality. Moreover, research by Bodolea et al. [53] resulted in PNI > 28.05 (HR:0.91; p = 0.01), and a CONUT Score > 7.5 (HR:1.15; p = 0.01) becoming associated with mortality, in the case of 90 COVID-19 patients with the severe form.
Numerous recent studies have also shown the superior role of superficial and deep learning-based intelligence in the diagnosis of COVID-19 patients using X-rays and CT scans with higher accuracy than the standard diagnostic approach [65][66][67][68][69][70].
This work supplements the previous two studies published by our group of researchers, Arbanasi et al. and Halmaciu et al., which revealed the role of inflammatory markers in the prediction of ICU admission, IMV necessity, ALI risk, and mortality [45,46]. The hypercoagulability status of COVID-19 patients is well known, and the unpredictable evolution of these fragile patients, as well as the presence of comorbidities and risk factors, requires the establishment of prognostic tools and stratification of patients for better management of their health.
As established in our two previous studies, systemic inflammatory biomarkers based on the total number of neutrophils, monocytes, lymphocytes, and platelets outperform traditional inflammatory indicators including D-Dimers, interleukin-6, and fibrinogen. They are also commonly tested and have a low cost when compared to other inflammatory markers.
Among the study's strengths are the inclusion of all hematological markers based on red cell blood, as well as nutritional markers and the inclusion of 889 patients. Regarding the study's limitations, it should be noted that it is a retrospective study, without the possibility of knowing the antiviral medication received by the patients during hospitalization. Furthermore, the pre-admission medication was not accessible for inclusion in the statistical analysis. Another limitation is the inability to monitor outcomes recorded during hospitalization. As a result, in the future, we recommend undertaking prospective studies in which the rate of thromboembolic events is assessed both at 30 days and three months following discharge.

Conclusions
Higher MLR, NLR, PLR, SII, SIRI, AISI, CONUT Score, and lower PNI values at admission highly predict DVT risk, APE risk, and mortality in COVID-19 patients, according to our data. Furthermore, PAD, malignancy, and tobacco, all predicted all outcomes, while CKD predicts APE risk and mortality but not DVT risk.
Given the high risk of thromboembolic events in COVID-19 patients and the inexpensive cost of these inflammatory and nutritional indicators, they can be used to classify admission risk groups, improve patient care, and establish predictive patterns. However, regarding the study's limitations (being a retrospective study, without the possibility of knowing the antiviral medication received by the patients or the pre-admission medication), we recommend undertaking prospective studies in which the rate of thromboembolic events is assessed both at 30 days and three months following discharge.