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ORIGINAL ARTICLE
Year : 2022  |  Volume : 25  |  Issue : 11  |  Page : 1779-1784

Can scoring systems be used for the triage of COVID-19 patients?


1 Department of Anesthesiology and Reanimation, Ordu University School of Medicine, Ordu, Turkey
2 Department of Family Medicine, Ordu State Hospital, Ordu, Turkey

Date of Submission18-Jun-2021
Date of Acceptance10-Jun-2022
Date of Web Publication18-Nov-2022

Correspondence Address:
Dr. Z Cebeci
Department of Anesthesiology and Reanimation, Training and Research Hospital, Ordu University School of Medicine, Bucak Town, Nefs I Bucak Street, Ordu
Turkey
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/njcp.njcp_1617_21

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   Abstract 


Background and Aim: Whether to send COVID-19 patients home with quarantine measures or to hospitalize and treat them on an inpatient basis is a very important decision in the treatment of COVID-19 patients. This study aimed to introduce a scoring system that will enable making decisions on inpatient or outpatient treatment of patients by scoring their symptoms, clinical, radiological, and laboratory results during the initial assessment. Materials and Methods: Data of patients over 18 years of age, examined for COVID-19 between March 11, 2020, and May 31, 2020, and who had a positive PCR result, and their radiological (computed tomography reports) and blood test (complete blood count, blood gas and laboratory results) results were recorded to develop our scoring system. Results: A comparison of COVID-19 patients, who received outpatient and inpatient treatments by age variable, revealed a significant result (P < 0.001). The comparison of laboratory results showed a significant difference between both groups (P < 0.001). The comparison of the groups by the presence of comorbidity also revealed a significant result (P < 0.001). According to the scoring system that we developed (Cebeci score), a score of 5 points and above had a specificity of 81% and a sensitivity of 88% for indicating the probability of receiving inpatient treatment. Conclusion: We believe that the scoring system we developed will be a simple, practical, and leading guide for physicians to avoid dilemmas regarding the issue of whether to quarantine patients at home or to hospitalize them in order to use medical resources effectively.

Keywords: COVID-19 pandemic, scoring systems, triage


How to cite this article:
Cebeci Z, Cebeci A B, Coskun I, Canakci E. Can scoring systems be used for the triage of COVID-19 patients?. Niger J Clin Pract 2022;25:1779-84

How to cite this URL:
Cebeci Z, Cebeci A B, Coskun I, Canakci E. Can scoring systems be used for the triage of COVID-19 patients?. Niger J Clin Pract [serial online] 2022 [cited 2022 Nov 30];25:1779-84. Available from: https://www.njcponline.com/text.asp?2022/25/11/1779/361445




   Introduction Top


In December 2019, a novel coronavirus infection named coronavirus disease (COVID-19) emerged in the Wuhan province of China.[1] In a short time, the disease spread rapidly all over the world and was declared a pandemic by the World Health Organization as of March 2020.[2] The COVID-19 pandemic associated with the SARS-CoV-2 virus infection has affected more than 180 countries worldwide.[3] The number of new and severe cases is rapidly increasing day by day due to the easy transmission of the virus by patients with only a mild form of the disease or asymptomatic patients.[4] This situation has led to a large number of infected people, enormous unmet medical demands in many countries, and, again the need for personal protective equipment in many countries.[5] Hospital capacities, physical facilities, patient density, and admissions are the issues that need to be organized and well planned during the COVID-19 pandemic. For this reason, it is important to decide whether patients will receive inpatient or outpatient treatment by rapidly diagnosing them to use hospital resources effectively and to avoid a flaw in the healthcare system. Efficient use of hospital resources is also important for the country's economy. Therefore, attempts have been made to develop scoring systems for the triage of patients during the COVID-19 pandemic. It is possible to find various scoring systems in the literature.[5],[6] Based on this idea, we thought of developing a scoring system to contribute to the literature and to facilitate the management of patients. This study aims at introducing a scoring system that will enable making decisions on inpatient or outpatient treatment of COVID-19 patients by scoring their symptoms, clinical, radiological, and laboratory results during the initial assessment.


   Materials and Methods Top


After receiving the approval of the Turkish Ministry of Health Clinical Research Committee for our study, an application was submitted to the local ethics committee for approval. Ethical approval for our study was obtained from the Ordu University Clinical Research Local Ethics Committee with the decision number of 2020/113 dated May 28, 2020. No financial support was received for this study. Our study was designed as a retrospective cohort study. The study included PCR (+) patients, who were hospitalized in Ordu province hospitals between March 11, 2020, and May 31, 2020, and received the definitive diagnosis of COVID-19 in accordance with the diagnostic guidelines of our Ministry of Health COVID-19 scientific board. Data of patients over 18 years of age, who were registered in the Public Health Management System (HSYS), examined for COVID-19 between March 11, 2020, and May 31, 2020, and had a positive PCR result, and their radiological (computed tomography reports) and blood test (complete blood count, blood gas and laboratory results) results were recorded to develop our scoring system. Our COVID-19 scoring system is presented in [Table 1]. Age and gender variables regarding the demographic characteristics of the cases were recorded. The recorded laboratory results included aspartate aminotransferase (AST), alanine aminotransferase (ALT), C-reactive protein (CRP), D-dimer, ferritin, lactate dehydrogenase (LDH), lymphocyte, neutrophil, lymphocyte/neutrophil ratio, neutrophil/lymphocyte ratio. The computed tomography findings in the hospital system were reviewed and categorized into two as consistent and inconsistent with COVID-19 disease. The comorbidities were recorded as the presence and absence of comorbidities. Patients were divided into two groups based on their treatment as outpatient and inpatient groups.
Table 1: COVID-19 Scoring System (Cebeci Score)

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Our study included all COVID-19 cases over the age of 18, whose information can be accessed from the hospital information management system of Ordu province. Those under the age of 18, whose sufficient information could not be accessed from the hospital information management system in Ordu province, were excluded from the study.

Statistical method

Data analysis was performed using IBM SPSS version 22 and Jamovi 1.6.16 statistical package software. Compliance of data to normal distribution was determined by the Shapiro-Wilk test and histograms. Parametric continuous variables were expressed as mean and standard deviation, while categorical variables were expressed using the number of patients and percentages. The Chi-squared test was used to compare categorical variables. The Mann-Whitney U test was used to compare non-normally distributed variables. Binomial logistic regression analysis was performed to determine the effects of the variables on the model. The effects of the variables were demonstrated by giving beta value, odds ratio, and 95% confidence intervals. ROC analysis was carried out to determine the diagnostic performance of the scoring test. The cut-off value was expressed as sensitivity, specificity, and area under the curvevalues. Statistical significance level was accepted as P < 0.05 in all comparisons.


   Results Top


The analysis of the patients included in our study revealed that 52.1% of 336 patients were male (n = 175), the number of patients with at least one comorbidity was 183, and the number of patients with infiltrates consistent with viral pneumonia on radiological imaging was 146. The number of outpatients (Group I) was 198, while the number of inpatients (Group II) was 138 [Table 2]. The demographic characteristics of the patients are presented in [Table 2].
Table 2: Laboratory results and treatment modalities of COVID-19 patients

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The comparison of COVID-19 patients who received outpatient and inpatient treatments by age variable revealed a significant result (P < 0.001). The mean age of those who were hospitalized was higher. The comparison of laboratory results showed that all laboratory parameters, except the ALT variable, were significant for both groups (P < 0.001). The laboratory results of our patients and the type of treatment given are presented in [Table 2].

The gender distribution, presence of comorbidity, and whether the patients have consistent radiological findings are presented in [Table 3]. While the comparison by gender variable showed no difference between the groups (P = 0.071), the comparison of radiological imaging and the presence of at least one comorbidity showed a significant difference (P < 0.001) [Table 3].
Table 3: Results of patients in terms of gender distribution, presence of comorbidity and consistent radiological findings

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With our modeling, the predictive power of outpatient or inpatient treatment was found to be 96.1%. The modeling we created with the data used in the study has a predictive value of 96.1%. Our scoring correctly predicted 97.4% of outpatients and 94.1% inpatient treatment. When adapted to all patients, it predicted 96.1% of the patients correctly [Table 4].
Table 4: Numerical confirmation table of our scoring system

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According to the logistic regression analysis, it was observed that the increase in age (OR = 1.16, 95% CI = 1.07–1.25), neutrophil/lymphocyte (OR = 4.11, 95% CI = 1.34–12.57), LDH (OR = 1.01, 95% CI = 1–1.02), and CRP (OR = 0.97 95% CI = 0.95–0.99) values increased the probability of hospitalization of the patients [Table 5].
Table 5: Binominal logistic regression analysis results of patients

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When the cut-off value was taken as 5, the specificity and sensitivity of our scoring system were 81% and 88%, respectively. In other words, according to the scoring system we performed, a score of 5 points and above has a specificity of 81% and a sensitivity of 88% for indicating the probability of receiving inpatient treatment [Table 6], [Graph 1].
Table 6: Specificity and sensitivity level of our scoring system

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   Discussion Top


We found that the scoring system we performed for COVID-19 makes accurate predictions with high sensitivity and specificity. We believe that using practical scoring systems will be very guiding for clinicians in such a pandemic disease with high mortality. Because both in our country and all over the world, physicians from every branch can be employed in pandemic outpatient clinics and pandemic wards. Even if they are laboratory branch physicians or clinicians who have worked in the laboratory for years, physicians from many different branches can compulsorily work in pandemic services and outpatient clinics. Considering this, it is obvious that it is very important to use practical scoring systems that can guide physicians correctly. Our scoring score will be very useful in the clinical practice of physicians for the triage of COVID-19 patients. In other words, it will help the clinician to make the right decision while deciding on giving inpatient treatment or sending patients home with quarantine measures. Scoring systems were also used for the pandemics experienced in the past. Practical scoring systems have also been used in the H1N1 outbreak recently. Adeniji et al.[7] used the Simple Triage Scoring System to predict the mortality of intensive care patients. In their retrospective cohort studies, they also provided the validation of intensive care mortality scores with SOFA scores. They stated that the STSS score with high specificity and high sensitivity was as useful as the SOFA score in predicting the mortality of H1N1 intensive care patients and the requirement for mechanical ventilation. In our study, we found that our scoring we used similarly gave accurate results in COVID-19 patients with high specificity and sensitivity. Although our study population is not the same, our study results are like the study results of Adeniji et al.

Gerotziafas et al.[6] carried out a scoring system to predict disease progression in COVID-19 patients. While 330 patients were included in the derivation cohort of the study, 120 patients who were admitted directly from the emergency department to the intensive care unit constituted the validation cohort. They created a scoring system to predict the worsening of the disease according to comorbidities and hematological parameters. They stated that if the score is 18 and above, COVID-19 patients have a high risk of worsening, and if the score is less than 18, they have a low risk of worsening. The authors stated that the scoring system they developed would make a significant contribution to even Phase III studies of antiviral agents. Similarly, we stated that those who score 5 points and above with our scoring should be treated in an inpatient manner. It is a fact that the conditions of patients who go home with quarantine measures deteriorate, and they are readmitted to hospital emergency departments. It will be useful to use our scoring in order to avoid this possible bad scenario. In their study, Matsumoto et al.[8] found high CRP levels in severe pneumonia, like our study. CRP levels are elevated in COVID-19 disease. However, it is of note that a high CRP level is not specific to the infection and its level will increase in other inflammatory conditions as well.[9] It is suggested that it may be beneficial to use CRP together with other clinical parameters such as procalcitonin and with serial measurements. In our study, we found that each 0.004 unit increase in CRP parameter increased the probability of inpatient treatment 0.97 times. A study conducted by Lu et al.[10] on intensive care patients with sepsis found significantly higher LDH values in patients who died compared to survived patients. Cut-off values determined as >488 U/L were found to have high specificity and low sensitivity. Similarly, each 0.02 unit increase in LDH parameter (OR = 1.01, 95% CI = 1–1.02) increased the probability of inpatient treatment 1.01 times in our study. LDH is found in all cells as an enzyme of the glycolysis pathway. Its predictive value as an indicator of altered glucose metabolism has been investigated in patients with sepsis.[9] There is evidence in the literature that though slightly, serum LDH level is associated with mortality. However, according to our statistical results, it seems difficult to state that a high LDH level is the independent predictor of a negative clinical outcome. We believe that more detailed studies should be conducted for the correlation of high LDH levels with lung injury and tissue damage in severe COVID-19 patients and its potential mechanisms. As determined by many studies in the literature, advanced age increases the mortality of COVID-19 disease and increases the severity of the disease.[10],[11],[12],[13],[14] In our study, the mean age was high in the inpatient group, and it was statistically significant when compared with the outpatient group. In our scoring system, we gave 1 point if the age group was 60–79, and 2 points if the age was 80 and above, considering it a negative risk factor. We found that every 0.18 unit increase in age variable increased the probability of inpatient treatment 1.16 times. Our results are consistent with the literature results. Increased neutrophil to lymphocyte ratio is caused by increased neutrophil count and/or decreased lymphocyte count. While the increase in neutrophil count may be secondary to inflammation, the decreased lymphocyte count may develop secondary to lymphocyte accumulation in the lungs, immunological lymphocyte destruction, thymus and bone marrow suppression, or cell death.[15] A study by Xia et al. reported that a high neutrophil to lymphocyte ratio in COVID 19 patients could be a preliminary sign that the clinical course of the disease would be poor.[16] Similarly, the study of Qin et al.[12] reported that a high neutrophil to lymphocyte ratio was a mortality predictor. In our study, we found that a high neutrophil to lymphocyte ratio was a predictor of inpatient treatment. Our results are consistent with the literature results. In their study, Harbalioglu et al. stated that high neutrophil ratio, high LDH level, and advanced age factor could be used as predictive criteria for inpatient treatment planning in COVID-19 patients. Our study results are totally in line with the study results of Harbalioglu et al. All three factors were our prediction criteria as well.[17] Ji et al.[5] stated that when clinicians make decisions using the “CALL score” they carried out, their medical resources would be used more accurately and efficiently, and they could reduce the mortality rate. We agree with Ji et al., and we think that we offer a good model to clinicians with the score we carried out. Because it is a very important decision to send patients home with quarantine measures or to hospitalize them and treat them. We believe that the score model we carried out in this regard will be beneficial for clinicians. Our study has some limitations. The retrospective design of the study is the first limiting factor. The second limiting factor is that we were able to access data from a relatively narrow population during file reviews. In conclusion, COVID-19 treatment is a difficult and laborious process for both the patient and the physician. Since it is a mortal disease, physicians may experience difficulties while providing a treatment plan. All our colleagues must be very careful in every COVID-19 patient in order to avoid conscientious and legal issues. We believe that the scoring system we carried out will be a simple, practical, and leading guide for physicians to avoid dilemmas regarding the issue of whether to quarantine patients at home or to hospitalize them. We believe that our study will shed light on future studies to be conducted with larger populations.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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Matsumoto H, Kasai T, Sato A, Ishiwata S, Yatsu S, Shitara J, et al. Association between C-reactive protein levels at hospital admission and long-term mortality in patients with acute decompensated heart failure. Heart Vessels 2019;34:1961-8.  Back to cited text no. 8
    
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Xia X, Wen M, Zhan S, He J, Chen W. An increased neutrophil/lymphocyteratio is an early warning signal of severe COVID-19. Nan Fang Yi Ke Da Xue Xue Bao 2020;40:333-6.  Back to cited text no. 16
    
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Harbalıoğlu H, Genç O, Yıldırım A. Three predictors for predicting inpatient treatment in patients diagnosed with COVID 19: Age, lactatedehydegenase, and neutrophil/ lymphocyte ratio. Pam Med J 2021;14:57-62.  Back to cited text no. 17
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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