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Rox Index Variation as Predictor of Outcomes in COVID-19 Patients. Calderón Hospital Quito

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22 March 2024

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25 March 2024

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Abstract
Background: During the pandemic, Emergency departments were overcrowded with critically ill patients and confronted ethical dilemmas to assign respiratory support to them due to scarce resources. Quick tools to evaluate patients at admission were needed; many scores were used but inaccurate to predict outcomes. The Rox Index is an easy and fast score that reflects the respiratory status in acute respiratory failure patients, this score could predict the outcome of covid 19 patients. Hypothesis: The 24-hour difference in the Rox Index discriminate accurately the mortality and needs for mechanical ventilation of patients with covid-19. Methods: Study design: Prospective analytic study. Population: 204 Patients admitted to the emergency department from May to August 2020. Data were collected from the clinical records. The Rox Index was calculated at admission and 24h later, the difference was used to establish the outcome, a logistic regression model adjusting for age, sex, presence of comorbidities and disease severity to build and perform ROC analysis. Results: Difference in respiratory ROX Index between admission and 24h is a good predictor for death AUC 0.92 and for mechanic ventilation AUC: 0.75. Each decrease in one unit of the difference at 24h had an Odds Ratio for death risk: 1.48 (95%CI: 1.31-1.67) and for mechanic ventilation: 1.16 (95%IC: 1.1-1.23). Conclusion: The 24-hour variation of Rox Index has good predictive value and allows healthcare professionals to identify which patients will benefit from invasive treatment, especially in low resource settings where emergency physicians deal with survival.
Keywords: 
Subject: Medicine and Pharmacology  -   Emergency Medicine

1. Introduction

Coronavirus disease-2019 (COVID-19) represented a major global health threat. Health Systems were easily overwhelmed [1], The world’s real number of covid 19 deaths are far more than the published data [2]; the WHO states, in their last report, that approximately there is an excess mortality of 66% [3].
In April 2020, during the first and devastating wave of COVID-19, Ecuador reported 10,93 deaths per million habitants, this was one of the highest mortality rates in South America at the time [4] and was underestimated due to limitations in data acquisition and reporting [3].
In low resource settings the number and severity of cases resulted in high mortality due to a lack of mechanical ventilators and many other medical supplies. Prioritization of patients through Triage tools were essential to give opportunity to those who had the best chance of surviving and made easier the decision to refer them to the best equipped hospitals [5]. Triage, using simple and economic methods were warranted to perform in any situation and specially in low-resource settings [5].
The prediction of COVID-19 severity and the initial approach were to be established In the Prehospital field and Emergency Departments as soon as possible to optimize management.
Acute respiratory distress syndrome (ARDS) was present in up to 20% of patients with COVID-19, most of them with an urgent need of mechanical ventilation [6,7]
Under this scenario, the ethical and professional responsibility, forced to identify specific problems, establish protocols, evaluate individual and collective risks to face the pandemic [8]. Without any doubt there was a huge gap between offer and demand in health care and medical supplies, creating ethical dilemmas to assign resources in the best way possible [9], this situation became worse in low-resource countries because there is inequality to access to good quality health care systems unlike in high-income countries [10].
In Covid-19, the main concern was the rapid respiratory decompensation that led to establishing early intubation and mechanical ventilation protocols based on the increased O2 needs. [11,12,13]; recognizing the right moment to intubate is important otherwise delaying this procedure could be the cause of severe complications in short time [14,15,16,17].
Patients with clinical deterioration and significant respiratory distress needed invasive protective mechanical ventilation but there is still substantial controversy about it, Xiao Lu, et al. showed that mortality was higher in Covid-19 intubated patients compared with non-intubated [15,18], nevertheless some authors establish the opposite [19,20]. This controversy maybe impacted mortality after days or weeks of invasive mechanical ventilation [21,22].
The available severity scores had reported underestimation when assessing the risk of death. Fan G. et al., reported in a retrospective study the predictive performance of 7 severity scores for in-hospital mortality (A-DROP, CURB-65, PSI, SMART-COP, NEWS2, CRB-65 and qSOFA) with ROC curves. The A-DROP score shown the highest discrimination in predicting in-hospital death. The NEWS2 score is easy to use and could be helpful in emergency department, but it doesn’t reflect the severity of hypoxemia and lung injury [23,24]
Many score scales have been used in Emergency Departments and Intensive Care Units to estimate the severity of illness and prognosis [25]. One of the challenges Emergency physicians confront is the precision and safety in decision-making of COVID-19 patients that should be admitted to the hospital or discharged home from the ER through the application of simple, precise, and practical scales mostly in rapid and lethal conditions like this pandemic [26].
The Ecuadorian health system was easily overwhelmed because of the high number and the severity of covid 19 patients, emergency departments and intensive care units were overloaded in a few days and could not handle the demand. This exceptional situation motivated clinicians to use clinical scales for the evaluation and management of COVID-19 patients [27].
The necessity of early predictors to identify the clinical condition, the indication of mechanical ventilation and the vital prognosis was of high priority during the pandemic. There are many scales, as mentioned before, that had been used but with limitations in their predictive power, complex to calculate and some with laboratory data that made impossible to use them immediately in prehospital, triage and emergency departments scenarios. Certain predictive tools like the Rox Index (RI), described by Roca [28], which express the relation between pulse oximetry/ Inspired oxygen fraction (FiO2)/ Respiratory Rate, is a useful tool in decision-making and in the immediate therapeutic management of patients [28,29]. Used originally for In-patients with pneumonia with acute respiratory failure treated with High flow nasal cannula (HFNC), RI can help identify those patients with low and those with high risk for intubation [29].
Although the predictive capacity of RI is adequate, it is not high enough to be used as the sole criterion to predict failure of the HFNC, and which patients may require Invasive mechanical ventilation (IMV). Therefore, it is important to add to this tool clinical variables or serial measurements to improve the predictive value.
The objective of the present study was to evaluate the 24-hour RI variation as a noninvasive tool in the Emergency department to define the possible outcome of covid-19 patients [15,16] in the pandemic scenario.

2. Materials and Methods

2.1. Study Population

Between May 1 to August 31 of 2020 a total of 204 consecutive patients were admitted to the Covid-19 treatment area in a reference second-level hospital in Quito (2850 m) altitude. Patients were admitted to the study if they had the following inclusion criteria: age 18 or older, met any of the clinical WHO criteria for Covid-19, a positive PCR-rt test, or chest CT scan result highly suspicious (CoRads 4-5) of Covid-19.
The protocol was approved by the ethics committee MSP-CZ9HGDC-2021-0700-O.
No intervention was made in this research, there is no individual identification data shown, data were obtained from the clinical records. There was no need for informed consent.

2.2. Information Collected

In 204 patients with confirmed Covid-19 infection, respiratory parameters like inspired O2 fraction, Respiratory frequency, O2 Saturation were registered, and RI calculated as the relation between O2 (Saturation/Fi O2) / Respiratory frequency, in real-time at admission and 24 hours later were calculated and registered. Other analysed variables were presence of comorbidities, radiologic and tomographic compromise, disease duration, and hospitalization time. The main outcomes were death and mechanic ventilation.
The information was prospectively collected and registered in an excel data base.

2.3. Analysis Plan:

Description of respiratory parameters was made presenting means and standard deviations of each parameter at entry and 24 hours later, differences in respiratory parameters between those whosurvived and those who deceased or those who need and did not need mechanic ventilation were tested using T-test with correction for unequal standard deviation when necessary. ROC curves analysis for each respiratory parameter and the Rox-index difference were settled to calculate under the curve AUC either to explore association with death and mechanic ventilation. Difference between entry and 24h respiratory ROX index was explored by subtracting entry minus 24h values. Cut offs for parameters were obtained by calculating the Youden index for each analysed parameter. A ROC analysis was performed to built up a logistic regression model adjusting for age, sex, presence of comorbidities and disease severity.
To analyse if the improving or waning in 24 hours RI difference, the RI scores were categorized as: >20 = normal respiratory function; 15-19 = mild respiratory deterioration; 10-14.9 = moderate respiratory deterioration; and <10 severe respiratory function deterioration. This arbitrary classification is based on normal values for Quito altitude. Logistic regression analysis was used to explore association between the change of RI using the improving status (change from a worse to better category) as basal compared with those who did not change of category, or with those who deteriorate the RI from one category to the next worse level, or with those who deteriorate more than 1 category. The logistic regression model was adjusted by age, sex, comorbidities, and radiologic severity. The results were also, explored to investigate if the change of RI category was modified by sex, age, presence or comorbidity and radiologic severity.

3. Results

3.1. Characteristics of the Population

A total of 204 patients were admitted to the Hospital with a Covid-19 diagnosis. The mean age was 57 years and 60% were male. Eighty-eight patients (43%) had comorbidities, the most frequent were hypertension and diabetes mellitus. Fever, cough, dyspnea, and malaise-myalgia were the most common symptoms. Most patients (93%) had a tomographic highly suspicious pulmonary affectation according to the imagen Co-Rads classification. Approximately one quarter of patients required mechanical ventilation, and 56 patients (27%) died (Table 1).

3.2. Respiratory Associated Factors for Death and Mechanical Ventilation

The saturation O2 levels were statistically higher at entry and 24h later in those patients who survived than those who did not, and in those who required mechanical ventilation when compared with those who did not. Oxygen inspired fraction (FiO2) administered levels and respiratory frequency were statistically higher in deceased patients and in those requiring mechanical ventilation both at entry and after 24h. RI values were significantly higher at entry and 24 later in patients who survived and those who did not need mechanical ventilation. Difference in RI values were negative in survivors and in those who did not require mechanic ventilation. These differences were statistically higher, showing deterioration of RI, in those who did not survive and those who had mechanic ventilation (Table 2).

3.3. Prediction Analysis of Respiratory Parameters and Difference between RI at Entry versus 24h later for Risk of Death and Mechanical Ventilation

Difference in respiratory RI at entry versus 24h is a better predictor for death AUC 0.92 than for a mechanical ventilation predictor AUC: 0.75 (Table 3) (Figure 1 and Figure 2). Each decrease in one unit of difference of RI means increasing in 48% and 16% the risk of death and mechanical ventilation respectively. Odds ratio for death risk: 1.48 (95%CI: 1.31-1.67) and for mechanical ventilation: 1.16 (95%IC: 1.1-1.23), after adjustment for age, sex, comorbidities, and severity of the disease.

3.4. Association between 24 hs Changes in Respiratory ROX Index and Death

A greater proportion of patients were admitted with moderate to severe respiratory deterioration (62%), and 24 hs later around 39% of patients were in those categories. In the same way, a minority of patients were admitted with normal respiratory function, but this increased 24 hs later (Table 4).
The higher the deterioration in 24 hs in RI score, the higher the likelihood of death or need for mechanic support. Even, in the absence of change in the first 24 hs, there is a statistically increase in possibility of death and ventilatory support, compared with those who improve RI scores in the first 24 hs. The possibility of death and mechanical ventilation increase when the deterioration of RI is even higher. This association was independent of age, sex, disease severity and presence of comorbidities, there was no modification effect in this association due to these confounders (Table 5).

4. Discussion

The Rox Index is a valuable tool for quick evaluation of the condition of Covid 19 patients, at admission it has a good prognostic value AUC of 78.6 to predict mortality and 76.1 for mechanical ventilation. Once stabilization and initial treatment are set up, a 24h ROX index variation became an even better prognostic tool with an AUC of 92 and 75 for mortality and mechanical ventilation respectively; this also shows that a 24h improvement in functional respiratory category is associated with survival and no need for mechanical ventilation.
In a health disaster like this pandemic [30], conceptually, the healthcare provider’s efforts should be directed to those with the better chance to survive [31], especially in low resource settings or countries where infrastructure is poor and access to respiratory equipment and supplies like HFNC and NIV was very difficult. As far as we have searched the literature there are few publications about the value of the RI to predict mortality or mechanical ventilation in COVID-19 patients [32,33]. Vega et al., establish that RI is a useful tool to guide intubation specially in moderate respiratory categories and Basoulis et al., also shows that a 12h RI is useful to predict mortality. The 24 h RI difference seems to be a good tool to differentiate between those who will survive and allows to direct resources and therapy in the right way, but maybe this is a long interval to improve or change therapeutic measures, maybe trying to calculate the RI difference in shorter periods could have a prognostic benefit for individuals.
This research was conducted in Quito, 204 consecutive patients were enrolled during the critical period of the pandemic. We identified some limitations, first this was a monocentric study, and although it was a public center patients characteristics might not be similar as in other healthcare facilities. Second Quito is a high-altitude city (2850 m) it represents a special environmental situation, the habitants of high altitude usually develop hypocapnic hypoxia and have respiratory rates higher than the sea level. The equation to calculate the RI has three components: Oxygen saturation, oxygen inspired fraction and respiratory rate, the component that keeps the same between high altitude and sea level is inspired oxygen fraction (0.21) [34,35], due to this we arbitrarily introduced four groups of the Rox index based on the normal parameters for Quito (Table 4) this may become a source of bias during the analysis, as a reference Gianstefany et al., describe sea level values higher than those in Quito [26] and are useful to contrast the data shown in our analysis, for instance the cut off for ambulatory care in Gianstefany research 26 different from 20 in our study.
We describe 4 categories that translate different stages of respiratory compromise in patients with covid-19 pneumonia, from normal to severely compromised respiratory function, if patients don’t improve the index value in 24h after implementing all the initial treatment and, even worse, if they deteriorate one or two categories (adjusted OR for deaths was 38 y 674, respectively) the outcome is worse increasing mechanical ventilation needs and mortality. The greater OR values were explained by the small numbers of analyzed patients in that category, however, even in the small numbers, the trend for association between respiratory deterioration and death or ventilator support were consistent.
Patients with Rox Index at admission superior to 20 or those who improved their Rox Index in 24h were assigned to observation or discharged for ambulatory treatment.
The in-hospital mortality was 27% (56/204) with a LOS of 7.27 days on average, the Rox index in this group deteriorated from 9.8 to 4.9 showing the rapid progress and severity of the disease. Twelve of the discharged patients died at home after being discharged from the hospital 10 of them improve the 24h Rox Index difference and only 2 deteriorated, maybe this group of patients has died because of complications of the disease but this analysis is out of the scope of this report.

5. Conclusions

Rox Index at the admission of covid 19 pneumonia patients, is an easy and quick to apply scale that has same or more accurate prediction than other scales used to predict the severity and outcome when used in Emergency departments. Interestingly, even more accurately, the 24h difference of RI has the potential to predict the outcome of patients and who benefit from intensive measures in a scenario of a healthcare disaster like this pandemic.
More research on this topic is needed to understand and take the best decisions for patients specially in low-resource settings where equipment is scarce and infrastructure is poor.

Limitations

This research is monocentric, conducted in a high-altitude city like Quito and probably introduces some bias in the analysis.

Author Contributions

Augusto Maldonado: Research Idea and design, recruitment individuals, data analysis, bibliographic search, composition, and final text revision. Pablo Endara: Research design, statistical analysis, bibliography search, composition of the final text. Patricio Abril: Bibliographic search, composition, and final text revision, recruitment individuals. Henry Carrion: Bibliographic search, composition, and final text revision. Carolina Largo: Bibliographic search, composition. Patricia Benavides: Administrative coordination, Bibliographic search, final text revision.

Conflicts of Interest

The authors declare that there is no conflict of interest in this research.

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Preprints 102077 g001
Figure 1. ROC curves for Death prediction. Adjusted for age, sex, comorbidities, and disease severity. Sat O2. B. FiO2. C. Respiratory Frequency. D. ROX Index difference.
Figure 1. ROC curves for Death prediction. Adjusted for age, sex, comorbidities, and disease severity. Sat O2. B. FiO2. C. Respiratory Frequency. D. ROX Index difference.
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Figure 2. ROC curves for Mechanic Ventilation prediction. Adjusted for age, sex, comorbidities. A. Sat O2. B. FiO2. C. Respiratory Frequency. D. Respiratory ROX index difference.
Figure 2. ROC curves for Mechanic Ventilation prediction. Adjusted for age, sex, comorbidities. A. Sat O2. B. FiO2. C. Respiratory Frequency. D. Respiratory ROX index difference.
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Table 1. Characteristics of the Study population.
Table 1. Characteristics of the Study population.
Age Mean; DS (RIQ) 57,1; 16,4; (44-69)
n (%)
Sex Female 81 (39,7)
Male 123 (60.29)
Comorbidities Diabetes mellitus 28 (13.73)
Hypertension 44 (21.6)
Lung Disease 9 (4.4)
Hipotiroidism 9 (4.4)
Renal disease 9 (4.4)
Surgical Dg 5 (2.4)
Cerebral vascular disease 3 (1.47)
Obesity 5 (2.4)
Symptoms Fever 98 (48)
Cough 128 (62.75)
Dispnea 146 (71.6)
Malasia, myalgias etc 93 (45.6)
Sore trhoath 25 (12.25)
Diarrohea 13 (6.4)
Thoracic pain 8 (3.9)
Neurologic symptoms 4 (1.96)
Anosmia/disgeusia 6 (2.94)
cephalea 21 (10.3)
Co-Rads Grade 1 - 2 3 (1.47)
Grade 3 - 4 11 (5.39)
Grade 5 - 6 190 (93.14)
Mechanical Ventilation Needed 47 (23.04)
Death 56 (27.45)
Table 2. Associated factors for death and mechanical ventilation.
Table 2. Associated factors for death and mechanical ventilation.
Death Mechanic Ventilation
Survived Deceased Absent Present
N=148 N=56 P value n=157 n=47 P value
Age: mean; SD 55.8; 16.5 60.5; 15.9 0.06 57.6; 17.3 55.6; 12.8 0.47
Disease time: mean; SD 7.3; 4.7 7.5; 5.3 0.72 7.15;5.1 7.9; 4.26 0.34
Hospitalization time: mean; SD 9.1; 9.8 10.3; 10 0.46 6.9 (5.8) 18.3; 14.7 <0.0001
Sat O2 entry: mean; SD 79.7; 0.95 67.7; 19.8 0.0001 78.8; 13.3 68.4; 18.3 <0.0001
Sat O2 24h: mean; SD 91.6; 2.2 82.4; 10.4 <0.0001 89.9; 6.7 86.2; 7.6 0.0016
Difference sat O2: mean; SD -11.9; 11.3 -14.6; 22.1 0.37 -11.1; 13.2 -17.8; 19.2 0.007
FiO2 entry, mean; SD 0.22; 0.05 0.27; 0.17 0.04 0.22; 0.07 0.26; 0.16 0.02
FiO2 24h, mean; SD 0.24; 0.1 0.70; 0.23 <0.0001 0.29; 0.18 0.62; 0.27 <0.0001
Resp. Freq. entry 27.6; 7.6 31.6; 8.9 0.004 27.9; 7.7 31.3; 9.1 0.01
Resp. Freq. 24h 22.7; 6.2 30.2; 7.8 <0.0001 23.5; 5.7 28.8; 10.6 <0.0001
ROX Index entry 14.3; 4.5 9.8: 4.8 <0.0001 14; 4.9 10; 4.2 <0.0001
ROX Index 24h 18.54; 4.2 4.99; 2.99 <0.0001 17.1; 6 7.4; 5.8 <0.0001
Difference ROX Index mean; SD -4.3; 5.3 4.8: 4.9 <0.0001 -3.06; 6.13 2.6; 6.2 <0,0001
Table 3. Predictors of death and mechanical ventilation.
Table 3. Predictors of death and mechanical ventilation.
Death Mechanic Ventilation
Parameter AUC Sensitivity Specificity AUC Sensitivity Specificity
O2 sat entry 0.7410 60.71 77.70 0.7352 72.34 69.42
FiO2 entry 0.6761 62.5 64.19 0.6237 76.59 53.5
Resp. Freq entry 0.6792 91.07 39.86 0.6434 76.59 53.5
ROX Index 0.7868 75 72.97 0.7616 74.47 70.06
Difference in Rox Index 0.92 0.89 0.80 0.75 57 83
Table 4. Frequency of respiratory severity categories at entry and 24 hs later.
Table 4. Frequency of respiratory severity categories at entry and 24 hs later.
Functional respiratory category Admmision 24 hs
n (%) n (%)
≥ 20 Normal 17 (8.3) 54 (26.5)
15 to 19 Mild 60 (29.4) 71 (34.8)
10 to 14,9 Moderate 64 (31.4) 24 (11.8)
< 10 Severe 63 (30.9) 55 (26.9)
Table 5. Association between 24 hours Rox index change and death or mechanic ventilation.
Table 5. Association between 24 hours Rox index change and death or mechanic ventilation.
DEATH MECHANIC VENTILATION
Rox Index 24hs. Change Survive Death Crude OR (95%CI) Adj.OR (95%CI) no MV MV Crude OR (95%CI) Adj.OR (95%CI)
Improve 87 (97.7) 2 (2.3) 1 1 81 (91) 8 (9) 1 1
No change 46 (58.2) 33 (41.8) 31.2 (7.2-135.9) 46 (9.8-216.2) 55 (69.6) 24 (30.4) 4.4 (1.9-10.5) 4.4 (1.8-10.7)
Deterioring 1 category 14 (53.8) 12 (46.2) 37.3 (7.5-184.6) 38 (7.1-199.8) 17 (65.4) 9 (34.6) 5.4 (1.8-15.9) 5.2 (1.71-15.6)
Deterioring 2 category 1 (10) 9 (90) 391 (32.2-4753.4) 674 (47.5-9564) 4 (40) 6 (60) 15.2 (3.5-65.3) 12.9 (2.9-56.5)
Basal category, improve means a change from worse to a better functional respiratory category. No change means no 24 hs. change in functional respiratory category whichever the initial category was. Deterioring 1 category means change normal to mild, mild to moderate, or moderate to severe respiratory deterioration in 24 hs. Deterioring 2 category means change from mild to severe, or normal to moderate respiratory function in 24 hs.
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