COV-Score predicts mortality in SARS-CoV-2 infection

doi:10.21203/rs.3.rs-1594450/v1

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COV-Score predicts mortality in SARS-CoV-2 infection

https://doi.org/10.21203/rs.3.rs-1594450/v1

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Purpose to present the importance of quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate the lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 include only qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement.

Methods We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors).

Results We found associated with higher mortality a lower percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin, respectively a higher percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine. The most accurate regression model included as predictors: age, lymphocytes, PaO2/FiO2 ratio, and percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score).

Conclusion COV-Score represents a viable alternative to current prediction scores, demonstrating a better sensitivity and specificity in predicting a poor outcome at the time of admission. Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall accuracy prediction.

COVID19 induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan City, China, in December 2019 [1], causing worldwide around 500 million cumulative cases and 6 million deaths by April 2022 [2]. The severe forms of pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The current evolution models and prognosis scores used in the evaluation and management of severe cases include only qualitative or semi-quantitative evaluations of pulmonary lesions [3, 4], which lead to a less accurate prognosis and assessment of pulmonary involvement. Therefore, in the present paper, we aim to present the importance of quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate the lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters.

We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID19 in our department between April 2020 and December 2020. The patients were divided into two groups: the surviving patients were included in group A (76 patients) and the non-survivors in group B (24 patients).The severe form of COVID-19 was defined as at least one of the following criteria: peripheral oxygen saturation (SpO₂) ≤ 93% in ambient air, respiratory rate (RR) > 30/min, arterial oxygen partial pressure to fractional inspired oxygen ratio (PaO2/FiO2 ratio) < 300, or lung infiltrates > 50% of lung parenchyma) [5]. Inclusion criteria were: age over 18 years, confirmed COVID-19 by RT-PCR (Real Time – Polymerase Chain Reaction) or rapid antigen testing, chest CT (computer tomography) scan at admission. Exclusion criteria were: age under 18 years, pregnant or breastfeeding women, and no chest CT evaluation at admission.

Collected data included: demographic and lifestyle parameters (age, sex, smoking status), anthropometric parameters (weight, height, IMC), clinical parameters (heart rate, systolic and diastolic blood pressure, consciousness status), respiratory function parameters (respiratory rate, SpO₂ by pulse oximetry and/or SpO₂ by arterial blood gasses analysis, along with PaO₂ and PaO2/FiO2 ratio), complete blood count, coagulation parameters [D-dimers, plasminogen activator inhibitor-1(PAI-1), international normalized ratio (INR)], inflammation markers (C-reactive protein(CRP), interleukin 6 (IL-6), ferritin), biochemical serum parameters (brain natriuretic peptide, troponin I, creatine kinase including MB isoform, myoglobin, serum albumin, lactate dehydrogenase, alanine transaminase, creatinine). Comorbidities included arterial hypertension, diabetes mellitus, coronary artery disease, congestive heart failure (CHF), chronic kidney disease (CKD), obesity, asthma, chronic obstructive pulmonary disorder (COPD), cancer, and any state of immunosuppression. We assessed the severity of lung involvement by native chest CT examination on a 64 slice CT Somatom Definition As (Siemens), using a pitch of 1.2, collimation of 1.2 mm, 3 mm reconstructions with mediastinal (B31f image filter), and lung window (B80f ultra-sharp image filter). We performed the quantitative lung assessment using SyngoPulmo 3D with the segmentation of the lung parenchyma, excluding the main bronchi and pulmonary vessels from the pulmonary densitometric analysis. We calculated the percent of lung involvement based on density ranges, considering alveolar lesions for densities higher than 0 Hounsfield units (HU), mixt lesions (alveolar and interstitial) for densities between 0 and − 200HU, interstitial lesions for densities between − 200 and − 800HU, normal parenchyma for densities between − 800 and − 1000HU, and emphysema or hyperinflation for densities lower than − 1000HU[6]. We further analyzed the normal lung densities using a cluster analysis method that calculates and displays volumes of connected voxels in a specified density range. For the normal lung densities, we used the following cluster types: cluster1 (C1) between 2 and 10 mm³, cluster2 (C2) between 11 and 60 mm³, cluster3 (C3) between 61 and 200 mm³, and cluster4 (C4) over 201 mm³. We evaluated the impact of all parameters on survival rate, mortality rate, and the overall accuracy of prediction using logistic models. Based on the best logistic model we developed a new prognostic score (COV-Score), to predict mortality in SARS-CoV-2 pneumonia, which was compared to existent scores (MuLBSTA respectively Smart COP) using logistic regression and ROC curve analysis. MuLBSTA represents a predictive tool for assessing the mortality risk of viral pneumonia, based on the following parameters: age, lymphocyte count, smoking status, arterial hypertension, presence of multilobular infiltrates, and presence of bacterial infection [3]. SmartCOP predicts 30-day mortality in community-acquired pneumonia and establishes which patients will require vasopressor or respiratory support [4]. The required features are systolic blood pressure, multilobar involvement in chest radiography, albumin level, respiratory rate, tachycardia, confusion, and low oxygenation, and arterial pH.

For the statistical analysis, we used Statistical Package for Social Sciences (SPSS version 25, IBM Corp., Armonk, NY, USA). Patient data are presented as medians and quartiles (Q1, Q3) for continuous variables and as percentages for the categorical variables. We used Mann-Whitney U-test for continuous variables and Pearson Chi-Square for categorical variables. We calculated the relationship between the mortality and the registered patient data using Pearson correlation and ANOVA test for statistical significance. Logistic regression was performed to assess how various parameters impact mortality and establish the best predictive model. The statistical significance of logistic regression was estimated by the Omnibus test of model coefficients and verified by the Hosmer-Lemeshow test. We used the Nagelkerke R Square to determine the weights of the dependent variable in the prediction model. A p-value lower than 0,05 was considered statistically significant.

We included 100 adult patients with severe COVID-19 pneumonia, and we divided them into two groups; In group A (survivors) were included 76 patients with a median age of 57,5 (49,2–65) years. In group B (non-survivors), were included 24 patients with a median age of 66 (62–71) years.

Sixty (78.9%) patients from group A and 23 (95,8%) patients in group B had comorbidities, consisting in obesity (46,1% vs. 45,8%), diabetes mellitus (17,1% vs. 33,3%), arterial hypertension (41,9% vs. 58,3%), CHF (3,9% vs. 12,5%), peripheral vascular disease (1,3% vs. 4,1%), CKD (3,9% vs. 20,8%), COPD (10,5% vs. 16,6%), chronic hepatitis (3,9% vs. 8,3%), history of neoplasia (0% vs. 25%), ischemic stroke (2,6% vs. 20,8%), dementia (1,3% vs. 4,1%), peptic ulcer (1,3%vs. 0%), and other pathologies (32,9% vs. 54,1%) including history of current depressive disorder and rheumatologic diseases. The median Charlson Comorbidity Index (CCI) [7] score was 1,5 [1; 2] in group A and 3 [2; 6] in group B.

All patients required supplemental oxygen at admission, 17 patients (22.3%) in group A received oxygen by nasal cannula/Venturi mask versus 9 patients (37,5%) in group B and 57 patients (77,6%) in group A by non-rebreathing masks versus 12 patients (50% in group B); 3 patients (12.5%) in group B were mechanically ventilated at admission.

Patients required a median oxygen flow of 12 [10–20] L/min in group A and 25 [12,5–30] L/min in group B, with a median FiO₂ of 60% [50–60] vs 60% [48,7–78,7], median PaO₂ of 84 [71–104]mmHg vs 81,5 [61,5–93,7]mmHg, median PaO2/FiO2 ratio of 163,3 [126,6-217,9] vs 110,5 [96,5- 156,9], median pCO₂ of 37 [34,9–40]mmHg vs 36 [32; 39,2] mmHg and a median RR of 35 [20,7–30] PaO2/FiO2 ratio] breaths /minute vs 26,5 [20; 28,5] breaths/minute.

In group B, lower values were detected for PaO2/FiO2 ratio, lymphocytes, platelets, and hemoglobin and higher values for neutrophils, BNP, D-dimers, creatine kinase, lactate dehydrogenase, and serum creatinine compared with group A, as showed in Table 1. For the other measured parameters, we found no statistical difference in value distribution.

Table 1

Biologic changes in survivors (group A) and non-survivors (group B)
Parameter Median (IQR)	Group A	Group B	p-value
PaO2/FiO2 ratio	163,3 [126,6; 217,9]	110,5 [96,5; 156,8]	0,008
Neutrophils (x10³/µL)	6200 [4650; 8350]	8750 [5400; 13675]	0,016
Lymphocytes (x10³/µL)	950 [700; 1200]	525 [400; 800]	< 0,001
Neutrophils/lymphocytes ratio	6,8 [4,1; 11,4]	14,6 [8,7; 30,8]	< 0,001
Platelets (x10³/µL)	229,5 [191,7; 318,5]	160 [1110,2; 275,7]	0,004
Hemoglobin (g/dL)	14 [12,8; 14,5]	12,5 [11,3; 13,7]	0,001
BNP (ng/L)	86 [17; 301]	754 [144,7; 2428,7]	0,003
D-dimers (ng/mL)	246 [156; 354]	555 [213; 1141]	0,001
Creatine kinase (U/L)	61,5 [34,2; 127]	191,5 [76; 307,5]	0,004
Myoglobin (µg/L)	120 [83,5; 225,2]	236 [128,3; 332,4]	0,007
Lactate dehydrogenase (U/L)	354,5 [287; 454,2]	570 [457; 680]	0,001
Serum creatinine (mg/dL)	0,9 [0,7; 1,1]	1,4 [1,1; 1,5]	< 0.001
Serum albumin (g/L)	3,8 [3,4; 4]	3,35 [3,1; 3,6]	< 0.001

A higher percentage of lung involvement was found in group B, including alveolar, interstitial and mixt lesions, with a dominant interstitial component. The distribution of normal lung densities differs between the two groups of patients, with a higher percentage of normal lung densities surrounded by inflammatory changes in group B.

The characterization of lung lesions using semi-quantitative assessment tools (number of pulmonary lobes with pathologic changes) had no statistical significance between Group A and Group B (Table 2).

Table 2

CT lung involvement in survivors (group A) and non-survivors (group B) at admission
Parameter Median [IQR]	Group A	Group B	p-value
alveolar lesions (%)	1,8 [1; 3,2]	3,1 [1,5; 5,1]	0,062
mixt lesions (%)	4,6 [2,5; 7,3]	6,9 [4,1; 12]	0,036
interstitial lesions (%)	39,4 [31,7; 47,8]	49,2 [44,3; 60,1]	0,001
total lung involvement (%)	47,2 [35,9; 63]	64,9 [48,1; 74,1]	0,003
normal lung densities (%)	52,7 [37; 64,1]	35,1 [25,9;51,9]	0,003
Cluster 1 (2-10mmc) (%)	0,4 [0,2; 0,9]	1,1 [0,3; 1,5]	0,029
Cluster 2 (10-60mmc) (%)	0,45 [0,2; 0,9]	0,9 [0,3; 1,7]	0,011
Cluster 3 (60-200mmc) (%)	0,1 [0; 0,3]	0,4 [0,2; 0,6]	0,001
Cluster 4 (over200mmc) (%)	51,6 [35,5; 63,7]	33 [20,7; 51,7]	0,002
lobes with interstitial lesions (n)	5 [5; 5]	5 [5; 5]	0,931
lobes with alveolar lesions (n)	0 [1; 2]	0 [0; 2]	0,306
lobes with atelectatic changes (n)	3,5 [2; 5]	2 [2; 4]	0,253

A lower percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin, respectively a higher percentage of interstitial lesions, oxygen flow, FiO₂, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine are associated with higher mortality. (Table 3).

Table 3

Risk factors associated with poor outcome
Parameter	Pearson correlation	p-value	Exp B (95% CI for Exp B)	Change in mortality (%)
Interstitial lesions (%)	0,320	0,001	1,065 (1,022; 1,109)	6,5 *
Total lung involvement (%)	0,335	0,001	1,049 (1,018; 12,08)	4,9 *
Normal lung densities (%)	-0,335	0,001	0,953 (0,926; 0,982)	4,7% #
Cluster1 (2-10mmc) (%)	0,209	0,047	1,798 (1,015; 3,183)	79,8 *
Cluster2 (10-60mmc) (%)	0,233	0,019	1,936 (1,081; 3,467)	93,6 *
Cluster 3 (60-200mmc) (%)	0,241	0,016	4,92 (1,262; 19,181)	392 *
Cluster 4 (over200mmc) (%)	-0,341	0,001	0,956 (0,93; 0,98)	4,4 #
Age (years)	0,260	0,009	1,052 (1,011; 1,094)	5,2 *
O₂ flow ( L/min)	0,306	0,002	1,092 (1,029; 1,159)	9,2 *
FiO₂(%)	0,260	0,009	1,045 (1,009; 1,083)	4,5 *
PaO2/FiO2 ratio	-0,235	0,02	0,99 (0,982; 0,999)	1 #
Lymphocytes( x 10³/µL)	-0,360	< 0,001	0,997 (0,995; 0,999)	0,3 #
Neutrophils/lymphocytes ratio	0,341	0,001	1,067 (1,023; 1,113)	6,7 *
Platelets( x 10³/µL)	-0,302	0,002	0,99 (0,984; 0,997)	1 #
Hemoglobin (g/dL)	-0,362	< 0,001	0,623 (0,47; 0,83)	38#
CKMB (U/L)	0,258	0,01	1,047 (1,004; 1,091)	4,7 *
LDH (U/L)	0,371	< 0,001	1,005 (1,002; 1,008)	0,5 *
Serum albumin (g/L)	-0,450	< 0,001	0,062 (0,012; 0,32)	93,8 #
Myoglobin(µg/L)	0,282	0,013	1,006 (1,001; 1,011)	0,6 *
*for 1 unit increase in the parameter; # for 1 unit decrease in the parameter

Logistic regression was used to assess the OR for each parameter correlated with the risk of a poor outcome.

The mortality rate doubled for every additional 19,2 years of age, 10,8 L/min oxygen requirement, 22,2% FiO₂, 14,9 value of Neu/Ly, 21,3 U/L of CKMB, 200 U/L of LDH, 166,6 µg/L increase in myoglobin. The mortality rate also doubled for every decrease with 100 of PaO2/FiO2 ratio, 333 (x10³/µL) in lymphocytes count, 100 (x10³/µL) in platelets count, and 1,06 (g/L) in serum albumin.

A 100% increase in mortality rate was observed for every 15,3% additional interstitial lesions, 20,4% additional total lung involvement, and every 21,2% decrease in normal lung densities.

Evaluating the cluster densities, the mortality rate raised by 100% for every increase with 1,2% in Cluster 1, 1,07% in Cluster 2, 0,25% in Cluster 3 and for every decrease with 22,7% in Cluster 4.

The impact of demographic, hematologic, biologic, radiologic, and respiratory variables on survival rate, mortality rate, and the overall accuracy of prediction are shown in (Table 4). The overall accuracy prediction was similar in all logistic models (over 81%) with a higher corrected survival percentage (over 90%) and lower corrected mortality percentage (14,3–47,8%).

Table 4

Regression models to evaluate survival/mortality rate in patients with severe COVID-19 pneumonia
Regression model type	Omnibus test of model coefficients	Nagelkerke R Square	Hosmer Lemeshow test	Corrected survival rate (%)	Corrected mortality rate (%)	Overall accuracy prediction (%)
Radiologic	< 0,001	0,260	0,404	94,7	37,5 (45,8)	81 (83)
Clinical	< 0,001	0,347	0,255	95,9	40,9	83,3
Respiratory assistance	0,003	0,280	0,199	94,6	36,4	81,3
Hematologic	< 0,001	0,466	0,356	92,1	54,2	83
Biologic	0,027	0,380	0,742	90,3	36,4	76,2
Coagulation	0,109	0,222	0,499	97,1	25	78,3
Mixt	< 0,001	0,699	0,877	94,3 (97,1)	78,6 (85,7)	89,8 (93,9)

Predictor variables used for radiological model: percentage of total lung involvement, normal lung densities, number of pulmonary lobes with alveolar involvement, and atelectatic lesions. The description of normal lung densities using the cluster evaluation algorithm generates an increase of 8,3% for the corrected deceased percentage and 2% for the overall accuracy prediction. For a radiological model with only qualitative (presence of pulmonary lesions yes/no) and semi-quantitative parameters (number of lobes with interstitial lesions/alveolar lesions/atelectatic changes), we found no statistical significance (p = 0,166 in Omnibus test of model coefficients and 0,023 in Hosmer-Lemeshow test).

Predictor variables used for clinical model: heart rate, systolic blood pressure, diastolic blood pressure, respiratory rate, CCI, consciousness status (1- oriented, 2-confused, 3 - stupor, 4 -coma).

Predictor variables used for respiratory assistance model: SpO₂, oxygen flow requirement (L/min), PaO2/FiO2 ratio, CO₂, oxygen delivery (facial mask/nasal mask/non-breathing mask/high flow O₂, non-invasive ventilation/orotracheal intubation/extracorporeal membrane oxygenation)

Predictor variables used for the hematologic model: Leukocytes, neutrophils, lymphocytes, platelets, hemoglobin

Predictor variables used for biological model: Lactate dehydrogenase, myoglobin, serum albumin, serum creatinine, ferritin

Predictor variables used for coagulation model: PAI-1, INR, D-dimers, platelets (no significance by Omnibus test of model coefficients)

Predictor variables used for the mixt model: age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Replacing the parameter “lung involvement” with the parameters presented in the cluster analysis leads to an increase of 2,8% for the corrected survival rate, 7,1% for the corrected mortality rate, and 4,1% for the overall accuracy prediction.

A ROC curve analysis was performed for every variable in the mixt prediction model to identify the cut-off value with optimal specificity and sensitivity; the obtained data were used to create a prognosis score (COV-Score) for the enrolled patients. We awarded 2 points for “percent of lung involvement”>60% and 1 point for each of the following parameters: age > 65 years, lymphocytes < 775 ( x 10³/µL), PaO2/FiO2 ratio < 140, LDH > 450(U/L), serum albumin < 3,6 g/L, D-dimers > 290ng/mL, oxygen flow > 14,5L/min, and myoglobin > 235µg/L.

We compared the obtained COV-Score using logistic regression with similar scores with radiological evaluation (MuLBSTA and Smart COP). COV-Score showed better overall performance: higher Nagelkerke R Square (0,5 vs. 0,18 respectively 0,09 ) and higher p-value for Hosmer-Lemeshow test (0,73 vs. 0,24 respectively 0,06) in estimating the outcome (survivors vs. non-survivors), with p-values < 0,001 in Omnibus test. We also obtained a better prediction of corrected mortality rate (47,8% vs. 17,4% respectively 13%), higher overall accuracy prediction (85,9% vs. 78,8% respectively 79,8%), and superior OR (Table 5).

Table 5

Logistic regression analysis for prediction scores
Regresion model type	B	S.E.	Wald	Sig.	Exp(B)	95% CI for Exp(B)
Regresion model type	B	S.E.	Wald	Sig.	Exp(B)	Lower	Upper
COV-Score Constant	0,808	0,182	19,749	< 0,001	2,243	1,571	3,203
COV-Score Constant	-4,963	0,983	25,490	< 0,001	0,007
MuLBSTA Constant	0,370	0,116	10,265	0,001	1,448	1,155	1,817
MuLBSTA Constant	-4,244	1,037	16,748	< 0,001	0,014
Smart COP Constant	0,430	1,171	6,320	0,012	1,537	1,099	2,150
Smart COP Constant	-3,498	0,978	12,791	< 0,001	0,03

Based on the data in Table 5, we can also calculate the probability of death according to COV-Score using the following formula: EXP (Constant + 0,808*COV-Score) / [1 + EXP (Constant + 0,808*COV-Score)]. For a COV-Score of 6, we obtain P = EXP (-4,963 + 0,808*6) / [1 + EXP(-4,963 + 0,808*6)] = 0,89/1,89 = 47%.

In Table 6 can be found the death probabilities for all values of COV-Score from 1 to 10.

Table 6

Probability of death for COV-Score
COV-Score value	0	1	2	3	4	5	6	7	8	9	10
Probability of death	0%	1,5%	3,3%	7,3%	15%	28,4%	47%	66,6%	81,7%	90,1%	95,7%

We performed an additional ROC curve analysis to verify the obtained results by logistic regression and the prediction accuracy for each evolution score. COV-Score presented a larger AUC and was able to provide better prediction: for similar specificities (0,75 − 0,78) we obtained a better sensitivity (0,87 vs 0,7 and 0,52) respectively for similar sensitivities (0,87 − 0,9) we obtained a better specificity (0,77 vs 0,28 and 0,18) (Table 7).

Table 7

ROC curve analysis for prediction scores
Regresion model type	AUC	Std error	p-value	Cut off value 1	Se	Sp	Cut off value 2	Se	Sp
COV-Score	0,884	0,036	< 0,001	4,5	0,87	0,77	4,5	0,87	0,77
MuLBSTA	0,731	0,061	0,001	8,5	0,7	0,75	6	0,9	0,28
Smart COP	0,639	0,077	0,045	5,5	0,52	0,78	3,5	0,87	0,18

The analyzed data regarding the radiologic evaluation of patients with severe forms of COVID-19 pneumonia demonstrate a limited value of qualitative and semi-quantitative parameters in establishing an accurate prognosis model. In our study, all patients presented involvement of all five pulmonary lobes in case of interstitial lesions and similar lesion extent in case of alveolar lesions and atelectatic changes, leading to similar distribution of values in both group A and group B and to lack of correlation between the qualitative and semi-quantitative parameters and the risk of death. The data obtained from the quantitative pulmonary evaluation showed independent correlation with the risk of death for both the differentiated evaluation of pulmonary lesions (alveolar, mixt, and interstitial) and in the global assessment of lung involvement, with statistical significance and good overall accuracy prediction in the prognosis logistic models.

The quantitative lung assessment is obtained using dedicated medical software which allows the evaluation of pulmonary lesions based on mathematical algorithms, taking into consideration all voxels from the lung volume included in a density interval defined by the software user, providing an objective result, quantifiable, reproducible and accurate. We want to emphasize that not all pathologic density voxels are grouped together; there are areas with normal density voxels surrounded by areas with pathological voxels, thus inside the same pulmonary lobe we have areas with inflammatory changes alternating with areas with normal lung parenchyma. When normal lung areas are isolated/surrounded by areas with inflammatory changes, there is a high probability that the inflammatory process will rapidly extend to those normal lung areas, estimating the isolated normal lung volumes will lead to an improvement in the assessment of lung involvement. A larger percent of isolated normal lung areas (C1, C2, and C3) was correlated with a poor outcome in the study group while a larger percent of normal lung areas connected with similar normal areas and not surrounded by inflammatory changes (C4) was correlated with lower risk of death. The cluster analysis method for normal pulmonary densities represents an important element to consider at patient admission, improving the prediction models. The limitation of this method is represented by the availability of medical software with the option to calculate specified customizable density clusters. Therefore, for a larger addressability, in the evaluation score introduced by our work (COV-Score), we used as quantitative lung assessment parameter “percent of lung involvement”.

COV-Score represents a new prognostic score for the outcome of severe SARS-CoV-2 pneumonia, based on predictor variables significantly associated with mortality: percent of lung involvement, age, lymphocyte count, PaO2/FiO2 ratio, LDH, serum albumin, oxygen flow requirement, and myoglobin. This new score introduces an objective evaluation of pneumonia by CT scan which allows both the differentiation of mild cases from moderate or severe cases and an accurate quantitative assessment of each type of lung involvement. Use of dedicated software as a diagnostic tool allows for an objective lung evaluation, providing a useful tool to initially assess the death risk and adequately monitor the evolution and therapeutic response of the patient. Similar with our findings, elderly patients have generally a greater prevalence of positive laboratory tests, such as leukocytosis, lymphopenia, decreased platelet count and anemia, lower albumin, increased D-dimers, and prolonged INR, but old age itself represents an independent risk factor for death of COVID-19 [8], in concordance with results found in our study. Wagner J and al. communicated that lymphocytopenia can be used as an early prognostic factor in determining the clinical evolution and disease severity of a hospitalized COVID-19 patient, with an OR of 3.40 (95% CI 1.06–10.96; P = 0.04) in patients with severe forms of COVID-19 [9]. Although a lower PaO2/FiO2 ratio and higher O₂ flow represent independent risk factors associated with the risk of death, the use of high-flow nasal oxygen upon admission in adult patients with COVID-19 may lead to an increase in ventilator-free days and a reduction in ICU length of stay, when compared to early initiation of invasive mechanical ventilation [10]. Elevated LDH could indicate a 44% probability, and non-elevated LDH an 11% probability for poor prognosis and increased mortality (OR 4.22 (95% CI 2.49 to 7.14) [11], therefore the association of LDH with other parameters in prognostic models is indicated to increase its prognosis value. Serum albumin demonstrated the capacity to bind SARS-CoV-2 spike protein S1 subunit, reducing its interactions with the cell receptors, the patients with reduced or glycated albumin presented a reduced binding [12], which can explain the capacity of albumin to independently predict the mortality in cases of SARS-Cov-2 infection. Patients with COVID-19 and elevated myoglobin levels are at significantly higher risk of severe disease and mortality, the presence of elevated myoglobin being more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3–52.1%) vs.30.7% (24.7–37.1%)] and preferred as a cardiac biomarker in case of severe patients [13]. In our study a myoglobin increase of 166,6 µg/L raised the odds of death with 100%. D-dimer elevations are a frequent laboratory finding in COVID-19 patients requiring hospitalization, representing a good biomarker indicative of disease severity which could independently predict in-hospital mortality for a value higher than 2.0µg/mL. A hypercoagulable state in patients with COVID-19 could be explained by dysfunction of endothelial cells resulting in excess thrombin generation, increased blood viscosity, activation of hypoxia-inducible transcription factor-dependent signaling pathway, or the long-term bed rest and invasive treatment in patients with severe forms of pneumonia [14].

While the corrected survival rate was similar to COV-Score (97,4%), MuLBSTA score had a corrected mortality rate significantly lower (17,4% vs. 47,8%) with a lower overall accuracy percentage of 78,8% (vs. 85,9%) and a lower AUC (0,731 vs. 0,884). In our study, all patients with severe COVID-19 pneumonia presented multilobular infiltrates, and therefore a semi-quantitative evaluation has a limited use in establishing the prognosis of severe patients. Another aspect to consider is that while a bacterial co-infection represents a risk factor, the majority of patients in our study presented no documented bacterial infection at admission, and the case distribution for bacterial co-infections was not significantly associated with the non-survivors group.

The corrected survival rate of SmartCOP was 100%, slightly higher than COV-Score (97,4%), and the corrected mortality rate was significantly lower (13% vs. 47,8%) with a lower overall accuracy percentage of 79,8% (vs. 85,9%) and a lower AUC (0,639 vs. 0,884). The main cardiovascular risk factor associated with severe SARS-CoV-2 infection is represented by high blood pressure [15], confirmed in our study, in which 22% of the patients presented high systolic blood pressure, and none of them had a value lower than 90mmHg. Patients in Group A (survivors) had a higher respiratory rate (35) than the patients in Group B (non-survivors) – 26,5. A third predictor variable - multilobar chest radiography involvement also has no prediction relevance because, in the case of severe forms of pneumonia, all patients (survivors and nonsurvivors) presented multilobar chest involvement - in this case, a quantitative assessment is mandatory to evaluate the extent of lung involvement. Therefore, we can emphasize that three of the predictor variables of SmartCOPscore don’t fit very well in the profile of SARS-CoV-2 patients with severe pneumonia and cannot be used accurately as predictors: the low systolic blood pressure, high respiratory rate, and multilobar chest radiography involvement.

There are many comorbidities associated with the patient risk of developing a severe form of COVID-19 pneumonia or complicating its evolution, such as obesity, diabetes mellitus, cardiovascular pathologies, chronic kidney disease, chronic pulmonary disease, chronic hepatitis, oncologic pathologies, and immune diseases [16–24]. Therefore the association of comorbidities represents an important prognostic factor to consider when evaluating the risk of death at admission, before performing biologic and radiologic investigations.

Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters, and we recommend its use as a standard radiological parameter in evaluating COVID-19 pneumonia. The characterization of normal lung densities using the cluster evaluation algorithm increases the non-survivor and overall accuracy prediction when assessing the prognosis of severe patients. COV-Score represents a viable alternative to current prediction scores (MuLBSTA and SmartCOP), demonstrating a better sensitivity and specificity in predicting a poor outcome at the time of admission. Serum albumin, LDH, lymphocyte count, and degree of lung involvement showed the best correlation with the risk of death.

Competing interests: The authors have no relevant financial or non-financial interests to disclose.

Ethical consent: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the local Ethical Committee.

Informed consent was obtained from all individual participants included in the study.

Data availability: data is available upon reasonable request from the corresponding author

Author contributions: All of the authors have all contributed equally to this work, in conception and design, material preparation, data collection and analysis, manuscript writing, and final approval of manuscript.

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No competing interests reported.

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COV-Score predicts mortality in SARS-CoV-2 infection

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