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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 12
| Issue : 2 | Page : 58-62 |
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CO-RABS score: A novel bedside scale to predict the severity in COVID patients
Merugolu Finny Theo Joseph, Dabboo Patwari, Kishore Kumar Talukdar, Bhaskar Neog, Sunny Kumar Yadav, Nasimur Riaz
Department of Internal Medicine, Hayat Hospital, Guwahati, Assam, India
| Date of Submission | 28-Jun-2022 |
| Date of Acceptance | 16-Aug-2022 |
| Date of Web Publication | 23-Nov-2022 |
Correspondence Address:
Dr. Merugolu Finny Theo Joseph
House No – 5/201/B, Nagulapalem, Parchur, Prakasam 523169, Andhra Pradesh
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/ajoim.ajoim_9_22
Study Objective: To determine the prognostic significance of a new score (CO-RABS), formulated by our Institute to classify the covid patients into mild, moderate, severe cases and also to compare it with the conversion AIIMS based classification. Patients and Methods: This is a retrospective study in which we have collected data from the medical records of patients who were admitted in our Hospital with covid infection during 2nd and 3rd waves of the pandemic. We have taken Comorbidities (CO), Respiratory rate (R), Age (A), Blood pressure (B) and SpO2 (S) of the patients at the time of admission to calculate an overall score (Abbreviated as CO-RABS). Basing on this score, the patients were classified into mild, moderate and severe cases. We then compared our CO-RABS score based classification with AIIMS classification using a statistical software. Results: We studied 727 patients (440 men, 287 women) and 99 patients died due to covid related complications. The ability to predict the prognosis was higher for our newly formulated CO-RABS score when compared to AIIMS classification. (AUC of CO-RABS 0.88 vs 0.82 of AIIMS; p < 0.05). Conclusion: The ability of CO-RABS score to predict the prognosis of covid infection is higher than that of AIIMS/ICMR classification. Hence it can be used as a supportive tool in the covid management protocol along with all the other conversion modes of treatment. Keywords: Classification, CO-RABS, Covid Pneumonia
How to cite this article:
Merugolu Finny Theo Joseph, Patwari D, Talukdar KK, Neog B, Yadav SK, Riaz N. CO-RABS score: A novel bedside scale to predict the severity in COVID patients. Assam J Intern Med 2022;12:58-62 |
How to cite this URL:
Merugolu Finny Theo Joseph, Patwari D, Talukdar KK, Neog B, Yadav SK, Riaz N. CO-RABS score: A novel bedside scale to predict the severity in COVID patients. Assam J Intern Med [serial online] 2022 [cited 2023 Mar 22];12:58-62. Available from: http://www.ajimedicine.com/text.asp?2022/12/2/58/361830 |
| Introduction | |  |
Covid-19 Infection is a complex and evolving inflammatory disease and critical clinical deterioration can result from various processes: respiratory failure, circulatory failure, destabilization of a pre-existing comorbidity or hospital acquired illnesses. It is not surprising to note that no single clinical rule has enough operating characteristics to be useful in this wide spectrum of clinical outcomes.
Covid-19 Infection presents an important therapeutic challenge to physicians all across the globe and especially in largely populated countries like India, as they have to decide whether the patient needs hospital admission or not. Therefore, it is very crucial to assess the severity of the disease, as it forms the starting point in the management algorithm and helps in achieving favourable outcomes.
Several classifications are available to help clinicians assess the severity of the Covid-19 Infection. While clinical judgment may vary from person to person and place to place, objective assessment based on scoring systems would probably remain same. Hence, this helps to standardize the criteria required to judge the severity of Covid Pneumonia. These guidelines help in severity assessment as well as rationale for antibiotics use. Different regions have adopted different guidelines for the management of Covid Pneumonia. Currently our country follows All Indian Institute of Medical Sciences (AIIMS) based guidelines Table 1][1] to classify the patients into mild moderate and severe cases using two variables; Respiratory Rate (RR) and Oxygen Saturation (SpO2).
The problem with this classification is that it’s not taking into account the other important variables such as age, comorbidities, blood pressure etc which play an important role in the clinical outcome.[2],[3],[4] We have observed numerous patients who were initially classified as mild disease and then rapidly progressing to severe disease (False negative cases). This may not be a major problem if the patients are in hospital settings but the real problem arises in rural parts of India where people are classified as mild disease, just basing upon SpO2 and Respiratory rate. They were being sent for home based covid care and eventually getting deteriorated, later ending up fatally. In order to prevent this, we have formulated a bedside, non-invasive clinical assessment score which also takes into account the other important factors such as age, blood pressure at the time of admission, comorbidities along with respiratory rate and SpO2. Basing on the final score, the patients were classified into mild, moderate and severe cases.
Aim of the work
To compare the prognostic value of the new classification which we formulated through CO-RABS score with the conversion AIIMS/ICMR classification, currently being used in India to predict the need for ICU admission and overall mortality in covid infected patients. We have primarily focussed on how accurately the “Severe” group helps in predicting the mortality.
| Patients and Methods | | |
In this study, medical records of 727 Covid Infected patients of 18+ age group admitted during 2nd and 3rd waves of pandemic in the period between April 2021 to March 2022 were reviewed. Only patients in whom Covid infection was the main reason for admission were included. We have excluded the patients of road traffic accidents with fatal injuries who were incidentally found to be covid positive. The following data at the time of admission were obtained from the medical records: Blood Pressure, Respiratory Rate, SpO2, Age and Comorbidities. All these variables were given individual scores basing on the mortality rate in each group [Table 2]. Overall score was calculated for each patient and they were classified based upon the score into mild, moderate and severe groups [Table 3]. The main focus of our study is to compare how accurate the two classifications predict mortality among the “Severe” group
Study protocol
The medical records of those 727 patients were reviewed. Data is collected including Comorbidities, Age, Sex, Blood pressure, Respiratory rate and SpO2 at the time of admission. Basing on that data, patients were classified into mild, moderate and severe cases using AIIMS guidelines and CO-RABS score. The accuracy of predicting mortality among the two classifications was compared using Statistical software [Table 4]. | Table 4: Comparison of Sensitivity, Specificity, AUC of AIIMS classification and CO-RABS score in predicting the outcome among the Severe group
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Statistical analysis
Statistical analysis was done using the MedCalc® Statistical Software version 20.106 (MedCalc Software Ltd, Ostend, Belgium; http://www.medcalc.org; 2022). The mean ± SD were used for quantitative variables and number and % were used for qualitative variables. To assess the differences in frequency of qualitative variables, Chi-square test or Fisher’s exact test were applied. In order to assess differences in means of quantitative variables, Independent samples t-test was applied and Mann–Whitney test was used for non-parametric statistics. To determine the ability of different classifications to predict mortality, ROC curves weres generated. The statistical methods were verified, assuming a significance level of p < 0.05 and a highly significance level of p < 0.001.
| Results | | |
Out of 727 patients included in this study, 99 patients died. The mean ± SD age of the non survivors was 63 ± 15 years while it was 50 ± 17 years among the survivors, with a statistically significant difference between the two groups (p < 0.001). As regards gender, the non-survivors group comprised 69 males (69.70%) and 30 females (30.30%) while in the survivors group 371 were males (59.08%) and 257 were females (40.92%), with statistically significant difference between the two groups (p < 0.05). As regards clinical parameters on admission there was statistically significant difference between the two groups in all the parameters, SpO2, respiratory rate, systolic and diastolic blood pressures. The distribution of patients into mild, moderate and severe groups and the respective mortality rate in each group is compared between the two classifications and there was significant difference [Table 5][Table 6][Table 7]. We have taken into account seven comorbidities which are known to cause deterioration in covid infection including coronary artery disease, chronic liver disease, chronic kidney disease, diabetes mellitus, hypertension, COPD / Asthma, cerebrovascular disorders.[4] The ability to predict the outcome was higher for CO-RABS score when compared to AIIMS classification (AUC of CO-RABS 0.879 vs 0.824 of AIIMS; p < 0.05) [Graph 1] and [Graph 2].  | Graph 1: Comparison of ROC curves of AIIMS classification and CO-RABS score in predicting the outcome among the Severe group
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 | Graph 2: Comparison of ROC curves of AIIMS classification and CO-RABS score in predicting the overall outcome
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| Discussion | | |
Covid Pneumonia, unlike the other medical conditions have highly unpredictable pathogenesis[5] where even a completely stable person can deteriorate and die within few hours. Delayed hospital admission is associated with increased mortality,[6] and therefore early recognition of these patients is important. Currently AIIMS guidelines are being used to classify covid patients. Based on the severity, patients were managed either at home or at hospital. As discussed earlier, elimination of false negativity is the main theme of our classification because many patients were being falsely classified as mild and moderate disease while in-fact they were having severe disease. To overcome this, we included Age, Comorbidities, Blood pressure (At the time of admission) which were proven to play a crucial role in severity and outcome.[2] The mean SBP in survivors was 126 ± 14 when compared to 131 ± 21 in the non-survivors (p < 0.001). We have observed that patients who presented with hypertension had very high mortality rate 90.90% (n = 11). Gasping respiration or agonal breathing is a distinct abnormal pattern of breathing and brainstem reflex characterized by gasping, labored breathing, accompanied by strange vocalizations and myoclonus[7]: Possible causes include cerebral ischemia, extreme hypoxia (inadequate oxygen supply to tissue),[8] or even anoxia (total depletion of oxygen). We have observed 100% mortality in those who presented with gasping state. Comorbidities were present in 87.88% of non-survivors with a mean value of 2.17 ± 1.43 and 48.09% of survivors with mean value 0.77 ± 0.95 (p < 0.001). The mean age of the non-survivors was 63 ± 15 years and 50 ± 17 years in the survivors (p < 0.001). The above data clearly explains how important the comorbidities, age and BP were in predicting the severity and outcome in covid pneumonia. Also, there were many studies which confirm the same. Ran, J., Song, Y., Zhuang, Z. et al. in 2020 studied Blood pressure control and adverse outcomes of COVID-19 infection in patients with concomitant hypertension in Wuhan, China. In this study, they found that high average SBP and high SBP/DBP variability during hospitalization were independently associated with in-hospital mortality, ICU admission, and heart failure.[2] Hu C and co-workers studied the effect of age on the clinical and immune characteristics of critically ill patients with COVID-19 and they concluded that a high proportion of critically ill patients were 60 or older. Furthermore, rapid disease progression was noted in elderly patients.[3] Honardoost M et al. studied the association between presence of comorbidities and COVID-19 severity and they have found that the presence of comorbidities is associated with worse outcome in COVID-19 infection.[4] The incidence of severe covid pneumonia and adverse events increases with age. For example, 58.58% of total deaths in our study population occurred in patients over the age of 60.
In this study, the sensitivity of “severe” class of AIIMS in predicting mortality was 62.63% and specificity was 91.24% with AUC 0.769. CO-RABS score on the other hand had sensitivity of 75.76%, specificity of 91.72% and AUC 0.837. [Table 4] (p < 0.05). The mortality rate in this study was 13.62% and as discussed, mortality was higher in the elderly patients with comorbidities. Those patients who died had mean SpO2 of 82 ± 16%, mean Respiratory rate 23 ± 4.
CO-RABS scoring as previously discussed was given basing on the mortality rate in each group and we have found that people who had score ≥ 7 (n = 45) had 95.58% mortality, which is a stunning reveal and sadly 15.55% of these people were classified as mild or moderate disease by AIIMS classification. Thus, we also plan to consider score ≥ 7 as “Very Severe” disease and such patients need to be admitted in ICU immediately irrespective of SpO2 and Respiratory rate.
Even though CO-RABS score had higher sensitivity and specificity than AIIMS classification, it is important to emphasize that this study is done on a relatively small group of patients (n = 727). One issue with the current study was that the generalizability of findings to other populations is unclear because it is a single centre retrospective study and published reports being restricted to the patients admitted in our institute only. Hence, large scale multicentric prospective studies need to be conducted before finally including CO-RABS score into the management guidelines.
| Conclusion | | |
The ability of CO-RABS score to predict the prognosis of covid infection is higher than that of AIIMS classification. Hence, it can be used as a supportive tool in the covid management protocol along with all the other conversion modes of treatment.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Data availability statement
The data set used in the current study is available on request from Dr Finny Theo Joseph Merugolu.
| References | | |
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| 2. | Ran J, Song Y, Zhuang Z, Han L, Zhao S, Cao P, et al. Blood pressure control and adverse outcomes of COVID-19 infection in patients with concomitant hypertension in Wuhan, China. Hypertens Res 2020;43:1267-76.  |
| 3. | Hu C, Li J, Xing X, Gao J, Zhao S, Xing L . The effect of age on the clinical and immune characteristics of critically ill patients with COVID-19: A preliminary report. PLoS One 2021;16:e0248675. http://doi.org/10.1371/journal.pone.0248675 |
| 4. | Honardoost M, Janani L, Aghili R, Emami Z, Khamseh ME. The association between presence of comorbidities and COVID-19 severity: A systematic review and meta-analysis. Cerebrovasc Dis 2021;50:132-40. |
| 5. | Safadi MAP, Silva CAAD. The challenging and unpredictable spectrum of COVID-19 in children and adolescents. Rev Paul Pediatr 2020;39:e2020192. doi: 10.1590/1984-0462/2020/38/ 2020192. PMID: 32901700; PMCID: PMC7477941. |
| 6. | Hu Z, Li S, Yang A, Li W, Xiong X, Hu J, et al. Delayed hospital admission and high-dose corticosteroids potentially prolong SARS-cov-2 RNA detection duration of patients with COVID-19. Eur J Clin Microbiol Infect Dis 2021;40:841-8. doi: 10.1007/s10096-020-04085-2. Epub 2020 Oct 29. PMID: 33123934; PMCID: PMC7594939. |
| 7. | Perkin RM, Resnik DB. The agony of agonal respiration: Is the last gasp necessary? J Med Ethics 2002;28:164-9. doi:10.1136/jme.28.3.164. PMC 1733591. PMID 12042401 |
| 8. | Islam SA, Lussier AA, Kobor MS. Chapter 17 - Epigenetic analysis of human postmortem brain tissue. In: Huitinga I, Webster MJ, editors. Handbook of Clinical Neurology, Brain Banking. Vol. 150. Amsterdam: Elsevier. 2018 p. 2-430. |
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]
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