Role of Chest Tomography Combined With Prognostic Indices for Severity Classication in Patients With SARS-CoV2 Pneumonia. Beyond Vaccination There Are Disease.

The initial evaluation of patients with COVID-19 represents a challenge to regulate a decision in preventive and timely management. There are various proposals as support tools that deserve to be evaluated. Aim. Evaluation of Chest CT Score performance and prognostic indices in COVID-19 patients to predict progress to critical illness. Methods. This was a retrospective study run from April to December 2020. Patients of any age and gender and who required hospitalization due to a conrmed diagnosis of COVID-19 by RT-PCR and Chest CT, were included. Demographic, characteristics prognosis indexes and laboratory were analyzed. Patients with acute coronary ischemic syndrome (ACS), acute heart failure, those who developed critical illness in the rst 24 hours, and those with no RT-PCR result were excluded. Critical illness was dened by the need for supplemental oxygen and / or death during the hospitalization. Results. 109 patients were included. The mean age was 53.88 ± 13.51 years. In 75% of them, there was at least one comorbidity and 30% developed critical illness. In 49.5% there was a CORADS-5 on admission, and in 50% there was a peripheral distribution of the interstitial inltrate in the left lower lobe. The risk factors were FiO2, CT Score> 18 and the NRL index. The combination of the High risk qCSI plus CT score> 18 indices was the best prediction index for development of a critical condition. Average mortality was 10%. Conclusion. The combined use of indices in patients infected with SARSCoV2 shows diagnostic accuracy and predicts severity. sensitivity, specicity, OR, positive (PPV) and negative (NPV) predictive value. Results are presented as odds ratios (OR) with 95% condence intervals (95% CI) and p-values. Statistical analysis was performed with STATA version 16.0 software (StataCorp LLC).


Introduction
Chest computed tomography (chest CT) has shown great utility in the diagnosis of infection with SARS-CoV-2. Its complementary use with the reverse transcription polymerase chain reaction (RT-PCR) test to assess subjects infected with SARS-CoV2 allows timely medical intervention and accurate therapeutic decision in COVID- 19. (1) The most common COVID-19 ndings in Chest CT are ground glass opacities (GGO), which are isolated or in combination with areas of focal consolidation. This areas show predominantly a bilateral distribution and subpleural predominance (2)(3)(4). In patients with SARS-CoV2 infection, chest CT provides relevant data that are currently part of the diagnostic tools (5,6) and might be further implied in prognosis (7,8).
In March 2020, the Dutch Society of Radiology developed a standardized evaluation scheme for the lung condition due to COVID-19 whose acronym is CO-RADS. It was derived from the COVID-19 Reporting and Data System. This system proposed a level of suspicion of pulmonary involvement in COVID-19, based on the ndings of the chest CT. It divided the suspicion ranging from very low CO-RADS 1 to very high CO-RADS 5 with two additional categories taking into account a technically de cient study CO-RADS 0 and the existence of a positive reverse transcription polymerase chain reaction (RT-PCR) test for Of all these tools, one of the scores proposed to evaluate the risk of critical illness in hospitalized patients is COVID-GRAM. Its use predicts the risk of a patient to be admitted to the critical care unit (ICU), the requirement of invasive ventilation and of death (8). Similarly, the Quick COVID-19 severity index (qCSI) predicts the risk of critical respiratory disease in 24 hours, in patients admitted from the emergency department (13).
On the other hand, other indexes were created with laboratory parameters obtained after patient admission, such as the neutrophil-lymphocyte ratio (NLR), which predicts the level of physiological stress (14). It allows for the early detection of sepsis and helps in decision making regarding the requirement for admission to the ICU (15). Therefore, many of these indexes allow a comprehensive evaluation of the underlying problem in the patient at the time they seek care. The objective of this work was to evaluate the prognostic performance of clinical indexes and chest CT in patients with COVID-19 who progress to critical illness.

Source of data
This was a retrospective and observational study run between April and December 2020 at the Ignacio Chávez National Institute of Cardiology in Mexico City, Mexico. This study was carried out under the rules of the Declaration of Helsinki. It was evaluated and accepted by the ethics and research committee of the INC (number ) and registered at www.clinicaltrials.gov (NCT04577105). Informed consent was obtained and signed by the patient or his legal representative.

Participants
Patients of any age who required hospitalization at the third level of care due to a con rmed diagnosis of COVID-19 by RT-PCR (16-19) were included. Patients had undergone chest CT on admission. The patients received standard treatment according to the recommendations available at the date of admission by the American Infectious Diseases Society (IDSA) and the panel of the National Institutes of Health (NIH) (20,21). Patients with a diagnosis of acute coronary ischemic syndrome (ACS), acute heart failure, patients who were admitted to another clinical trial, those who developed critical illness in the rst 24 hours after admission or did not have a RT-PCR result were excluded.

Outcome
Critical illness was de ned as the need for supplemental oxygen (> 10 L / min by low-ow device, high-ow device, noninvasive, or invasive ventilation) and / or death during patient hospitalization (8,22).

Predictors
Data from the electronic medical record were collected. Upon admission to the emergency service, a clinical questioning and a physical examination were performed. Oxygen saturation was quanti ed by pulse oximetry (%) in ambient air and the FiO2 administered was calculated using the O2 ow rate, L / min. The device used varied from a nasal cannula, a mask with or without reservoir and a high-ow cannula (23,24). Non-serious patients met early-onset epidemiological characteristics (27) epidemiological history, (28) fever or other respiratory symptoms, (29) and typical abnormalities of the CT scan of pneumonia virus. (30) Critically ill patients also met at least one of the following conditions: Di culty breathing, RR ≥ 30 times / min, (2) Oxygen saturation (resting state) ≤ 93%, (3) PaO2 / FiO2 ≤ 300 mmHg. COVID-19 patients were con rmed by a positive result of high-throughput sequencing or real-time reverse transcriptase polymerase, positive RT-PCR result for SARS-CoV RNA-from nasal and pharyngeal swab samples (1). Only laboratory con rmed cases were included in the analysis. The imperative of informed consent was waived in light of anonymity.

TOMOGRAPHY
Images were acquired with a Siemens 256-slice multidetector tomograph (Somatom® De nition Flash 128x2, Siemens Healthcare, Forchheim, Germany) following the recommended parameters for low-dose simple chest tomography. The chest scout was acquired using 35 mA, 100 Kv and 6 mm slices, and then the chest tomographic slices maintaining inspiration in a cephalocaudal direction with 80 mA, 100 Kv, a duration of 2.24 seconds, a pitch of 1 and slices of 1 mm, with a total of 110 DLP, with which a total of 1.5 mSv is calculated with the conversion factor for thorax. Multiplanar reconstructions were performed with Kernel lters B26f and B50f for the mediastinum and lung, respectively, at 1 mm slices.
The CT score was calculated by 2 experts as follows: a value of 0 to 5 was assigned in each lung lobe for a total of 5 lobes (assigning 0 points with involvement was 0%, 1 point with involvement less than <5 %, 2 points with involvement of 5-25%, 3 points with involvement of 26-50%, 4 points with involvement of 51-75% and 5 points with involvement of > 75%) obtaining a severity score by tomography of 0 -25 points (11). There was a strong agreement between raters (0.89).

Sample Size and Missing Data
A sample size of 30 patients was estimated with an alpha of 0.05, a power of 0.80, a R2 0.35 and 5 tested covariates.
Variables with more than 5% missing values were excluded.

Statistical Analysis Methods
The categorical variables were expressed in frequencies and percentages, the continuous ones as mean ± SD or mean or interquartile range, depending on their distribution. Comparisons of proportions between groups were made with the chi-square test or Fisher's exact test and Student's T test or Mann-Whitney U according to the number and distribution. A logistic regression analysis was performed by the backward elimination approach (36) to select and estimate the association between the severity of patients with COVID-19 and the variables (clinical or Chest CT) that were relevant based on prior knowledge and statistically signi cant in univariate binary logistic regression analysis. Variables with p <0.10 were included in the multivariate model in the univariate analysis to select the predictors. We report the area under the receptor operating characteristic (ROC), sensitivity, speci city, OR, positive (PPV) and negative (NPV) predictive value. Results are presented as odds ratios (OR) with 95% con dence intervals (95% CI) and p-values. Statistical analysis was performed with STATA version 16.0 software (StataCorp LLC).

Participants
Eighty-six patients were excluded due to lack of clinical data, delayed RT-PCR or Chest CT results, and 3 patients were also excluded who required endotracheal intubation within 24 hours of admission. Therefore, 109 patients were included. Table 1 shows the characteristics and demographics of the included patients. The mean age was 53.88 ± 13.51 years, 69 (63.3%) were men. 74 patients (75%) had at least one comorbidity, 32 (29.9%) patients developed critical disease and the average mortality was 10.14%.  Table 2 shows the laboratory ndings and the differences observed according to the severity of the disease. Values are expressed in number (percentage) and median (interquartile range).
AST = aspartate aminotransferase, ALT = alanine aminotransferase, CK-MB = creatine kinase-MB, eGFR = estimated glomerular ltration rate, FiO2 = fraction of inspired oxygen, hsTnI = high sensitive Troponin-I, INR = international normalized ratio, LDH = lactate dehydrogenase, NT-proBNP = NT-pro-brain natriuretic peptide, PaO2 = partial pressure of oxygen * media (Standard deviation), † Student's t-test, § Mann-Whitney U test   Table 5 shows the Sensitivity (Se), Speci city (Sp), Positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR +), negative likelihood ratio (-) and the OR of each predictor in simple and combined form. * total severity score greater than 18 AU-ROC = area under the ROC, CT = computed tomography, COVID-GRAM = Critical Illness Risk Score, LR = likelihood ratio, NLR = Neutrophil-lymphocyte ratio, NPV = negative predictive value, OR = odds ratio, PPV = positive predictive value, qCSI = quick COVID-19 severity index The patients in critical condition were older, the respiratory rate, breaths / min higher and the tomography showed that consolidation and pericardial effusion were more frequent. In the univariate analysis, risk factors for developing severe disease were found, such as an increase in FIO2% and an increase in the Neutrophil-Lymphocyte ratio. The COVID-GRAM and qCSI score was seven times higher in the critically ill patients. Figure 1 shows tomography images of patients in different severity conditions.

Discussion
During the COVID-19 pandemic, the provision of optimal treatment for patients emerged as a priority which led to the implementation of measures with multiple therapies. At the same time, protecting health workers involved in the management and care of these patients also became a priority. (37) Additionally, several clinical indexes were proposed, aiming at the evaluation and selection of the patients that might receive therapy at home, those that required hospitalization and those that were in need of ventilatory support.
An index that was already in use was the CURB-65 Score for Pneumonia Severity. It calculates mortality from communityacquired pneumonia and was proposed to de ne subjects that might be treated as outpatients and those that were in need of hospitalization. This score includes Respiratory Rate ≥ 30, within its parameters (38).
A comparative analysis of the precision of the CURB-65 Score, the Pneumonia severity score (PSI) and COVID-GRAM A was performed in patients with COVID-19. These indexes were proposed to predict mortality and to determine the need for invasive mechanical ventilation. It was found that the COVID-GRAM index score was more accurate in identifying patients with higher mortality from SARS-CoV-2 pneumonia (39). Neither of these scores accurately predicted the need for invasive mechanical ventilation upon admission to an intensive care unit. (40). Among the parameters that COVID-GRAM the abnormality of the Xray study, the presence of dyspnea and the NLR index are included.
On the other hand, other indexes directed to determine the need for anticoagulation in hospitalized patients due to the risk of venous thrombosis, VTE or bleeding were suggested. However they include heart and respiratory failure within their scoring parameters (41). In all of them, the authors found usefulness and suggested their employment during the classi cation and follow-up of patients with COVID-19. Some other indexes, such as the qSOFA (Quick SOFA) Score for Sepsis, were also proposed for the prediction of mortality; however, these are more frequently used for the diagnosis of sepsis, which occurs in critically ill COVID patients.
Among the indexes that evaluate respiratory compromise the NEWS is found. This score was analyzed in 35,585 medical admissions and includes respiratory rate, oxygen saturations, any supplemental oxygen, temperature and systolic blood pressure. Therefore, it could classify the degree of disease and the intervention required for intensive care. The authors suggested that prospective studies were needed to con rm the reliability of their scales. (42) Moreover, the Berlin study for the diagnosis of acute respiratory distress syndrome (ARDS) includes the deterioration of oxygenation, de ned by the relationship between (PaO2 / FiO2) or by the relationship between peripheral O2 saturation (pulse oximetry ) and FiO2 (SpO2 / FiO2). (REF) Chest CT, an imaging parameter, is useful in the evaluation of patients with lung damage or serious complication of viral pneumonia, mainly when the nding on the chest radiograph is normal or inconclusive (43). In this study, we found that CT provides us with a quick evaluation and predicts critical severity in the short term (4,11). Through the use of the CT score, we could predicted which patient would require priority ventilatory support and hospitalization. The use of the indexes, alone or combined, even when evaluated retrospectively in this study, allowed us to recognize that these parameters make the difference between a health condition without seriousness and a condition that evolves to a serious condition. However, patients have an important respiratory compromise in which care must be de ned in a timely manner both for ventilatory support and for the comprehensive initial therapy most useful in the intensive care unit.
We found that the use of CT alone as a diagnostic test has a good speci city and a moderate sensitivity; however, when combining its use with other indexes such as qCSI, both speci city and sensitivity are increased and the PPV and NPV increase. In a study by Liang and Cols (44), the authors sought the validation of a clinical score to predict the occurrence of critical illness and found that the associated factors were chest radiograph with abnormality, hemoptysis, dyspnea, unconscious state, number of comorbidities, previous cancer, NLR index, lactic dehydrogenase and direct bilirubin. The area under the curve was 0.88, and the authors concluded that the scale predicts which patients will develop critical disease. The ndings in our study are very similar in all the medical parameters to those reported in that study, but we also show statistical robustness when comparing these parameters between critically ill and non-serious patients. In this study the tomographic status of the patient was analyzed together with the compromised ventilatory status.
These results make us consider that all laboratory parameters are essential to evaluate a patient with COVID-19 at the time of admission; however, it is relevant that the degree of respiratory compromise is of special importance since it is included in most of the indexes. In the study by Yuan et al. the utility of a clinical computed tomography-based receiver with operating characteristic and a curve model for the diagnosis of COVID-19 was assessed (12). These authors demonstrated that mortality can be predicted with a sensitivity of 86% and speci city of 85%, and by means of a regression model of the ROC curve based on chest CT signs according to lung involvement. They showed a high diagnostic value of this method.
Furthermore, other indexes such as the Quick COVID-19 Severity Index (qCSI), include for their evaluation 3 variables among which the respiratory rate, pulse oximetry and nasal cannula ow rate are mentioned. Respiratory rate is also a parameter that that is included in the scores of other indexes.
The qCSI index was widely used by some countries during the pandemic. Studies comments that its results surpassed other models, including the evaluation of rapid sequential organ failure related to sepsis and that of the CURB-65. Therefore, this index was proposed as a useful clinical tool to help make decisions about the level of care required in patients admitted to a hospital (13).
In the present study we found that the qCSI index showed good sensitivity and low speci city; however, its usefulness showed greater utility when combined with the CT evaluation, reaching a better speci city and a high percentage of NPV. This indicates that it predicts the probability for the individual to reach a severity condition when the score is not met.
One of the tools that is most often accessible when evaluating any condition in a patient are the laboratory determinations. In We evaluated the combined use of the LRN index with CT and found that sensitivity and speci city increased, and in relation to CT. The average CT Score in critically ill patients was 11.01.
Of all the indexes used in critically ill patients, the combination High risk qCSI plus CT score > 18 was the most useful. It should be noted that the High risk qCSI includes the respiratory parameters, breaths / min rate, pulse oximetry an O2 glow rate, L / min, which emphasizes the importance of ventilatory care.
The multivariate analysis showed that the variables FiO2, CT Score > 18 and the NRL index are the main risk factors. This shows biological coherence, since the respiratory rate, breaths / min was clearly increased and the Horowitz Index (P / F ratio) decreased in critically ill patients.
On the other hand, the correlation between the CT Score and the respiratory rate, breaths / min was moderate (40%) p = 0.0001, which indicates that the parameters observed in the increase in respiratory work correlate with the damage found at the lung level reported by the CT Score

Limitations
This was an observational, retrospective study. Being a third-level hospital, admitted patients do not represent the behavior of mild cases of COVID-19. There is loss of follow-up after their discharge and further evaluations are di cult. The FiO2 (%) provided to the patient had uctuations due to the administration method. It was dynamic and therefore, the calculation had limitations.

Interpretation
The use of clinical risk indexes (COVID-19 and qCSI) in combination with a CT score> 18 obtained by Chest CT could represent a good option in the risk strati cation of critical disease development.

Implications
The importance of imaging methods and their appropriate clinical use is relevant. The studied indexes show predictive support with good sensitivity and speci city, which could be considered in the context of the initial evaluation for a SARS-COV-2 infection. The data shown could also be considered in the current state of relapses. These indexes should be considered for the initial evaluation of patients infected with SARSCoV2 and may help in taking accurate clinical decisions that prevent the patient from progressing to seriousness. They may prove useful even asymptomatic subjects, who may have lung damage not detectable by symptoms or due to minor signs in the laboratory.

Conclusion
The combined use of indexes such as the CT Score, Neutrophil Consent for publication. This was obtained within the ethical approval, the participants of the study gave their consent to publish the data collected during the study period.
Availability of data and materials. The basic data are part of the Mexican cohort of patients treated for SARS CoV2 infection with suspected lung disease. The head of the Computed Tomography area is Dr. Sergio Criales who has data available if someone requires it. order. The procedure would be through your email scriales@gmail.com. All the results to which the manuscript refers are duly documented in the text, gures and tables.

Con ict of interest statement
No party having a direct interest in the results of the research supporting this article has or Will confer a bene t on the author(s) or on any organization with which the author(s) is/are Associated.

Funding
This research received no grant from any funding agency in public, commercial, or non-pro t sectors.