Introduction
In late 2018, the novel coronavirus disease 2019 (COVID-19) emerged in China and spread rapidly worldwide, becoming a crucial global public health emergency with an unprecedented strain on health and economic systems [1, 2]. The prevalence of new variants of coronavirus like Delta and Omicron posed more challenges in managing the pandemic due to their high transmissibility, ability to evade detection, and resistance to vaccines [3]. These variants placed additional pressure on limited health resources and led to more hospitalizations and deaths [4]. As of 26 July 2023, a total of 773511195 confirmed cases of COVID-19, including 7023127 deaths, were reported to the World Health Organization (WHO) [5].
A risk prediction model is a mathematical equation that makes use of patient data to assess the likelihood of encountering a healthcare outcome. There are various statistical techniques available for creating a risk prediction model. Some common models include the logistic regression model, Cox proportional hazards model, and classification trees [6, 7]. Logistic regression is a traditional method widely used for binary outcomes and is renowned for its strong explanatory ability. It frequently utilizes a variable selection approach, such as the backward selection method, to identify the strongest predictors. The logistic models were frequently converted into straightforward additive risk scores by assigning weights to the predictors according to the log odds ratios derived from the model to simplify their usability [6]. The risk score models are widely employed in medical practice to forecast the development of diseases, assess treatment effectiveness, predict patient prognosis, or identify individuals at a higher risk of disease progression and mortality [4]. By providing risk estimates, these models play a crucial role in guiding clinical decisions regarding the limited resource and capacity allocation to patients most likely to benefit from early intervention. They aid in preventing disease progression, reducing the risk of complications, and alleviating the burden on healthcare systems [4]. The recent COVID-19 pandemic has underscored the significance of risk prediction models in public health emergencies.
So far, several models have been proposed based on integrating demographic, clinical, and radiological features for early prediction and identification of patients at risk for severe pneumonia, intubation, intensive care unit (ICU) transfer, and patient death [3, 4, 8, 9]. 
However, these models have not generally been used in clinical practice. They are prone to bias [4] due to several reasons, such as a limited selection of samples, the retrospective nature of the study design, and unclear details regarding the development and validation of the model. However, it is essential to note that with new variants of SARS-CoV-2 with distinct characteristics, these models need to be updated accordingly. Iran has been significantly impacted by the COVID-19 pandemic. Nevertheless, limited clinical experiences and research have been conducted on the Iranian population. Only a few studies are available that have retrospectively examined the development of risk score prediction models for COVID-19 in Iranian patients [10-12], which diminishes the reliability and validity of these studies. Therefore, we undertook a prospective study to address this gap and develop a more reliable and validated risk prediction score model to identify Iranian COVID-19 patients at a higher risk of disease progression during hospitalization.

Methods
Study design, participants, and data collection 
This cohort study was conducted at Bohlool Hospital in Gonabad City, Iran, from April 4 to June 5, 2021. The study included COVID-19 patients admitted to the hospital who met the eligibility criteria. The patients’ demographic, clinical, and laboratory information was collected upon admission. The patients were followed over time, and their clinical outcomes were recorded until the end of the study. The inclusion criteria comprised a definitive diagnosis of COVID-19 by reverse transcription-polymerase chain reaction (RT-PCR) test and a minimum age of 18 years. All patients were treated according to the physician’s diagnosis. White blood cells (WBC), neutrophil to lymphocyte ratio (NLR), interleukin-6 (IL-6), C-reactive protein (CRP), lactate dehydrogenase (LDH), and ferritin tests were performed as a supplement for all patients. Other data were extracted using hospital information system (HIS) resources. Patients with incomplete records were excluded from the study. 

Potential predictive variables 
Potential predictor variables were as follows: Age, sex, pregnancy, use of tobacco, use of opium, history of COVID-19, inpatient department, partial pressure of oxygen (PaO2), temperature, computed tomographic (CT) imaging score, signs and symptoms at admission (including fever, cough, muscular pain, respiratory distress, loss of consciousness, decreased sense of smell, reduced sense of taste, seizure, abdominal pain, nausea, vomiting, diarrhea, anorexia, headache, vertigo, paresthesia, paraplegia, chest pain, skin lesion(, comorbidities (liver diseases, diabetes, hematologic diseases, human immunodeficiency virus/acquired immune deficiency syndrome [HIV/AIDS], autoimmune diseases, heart diseases, kidney diseases, asthma, chronic lung diseases, nervous diseases, hypertension, and other), laboratory values (WBC, NLR, CRP, IL-6, LDH, and ferritin), and clinical management and pharmacological treatment (tracheal intubation, O2 therapy, and drugs).

CT imaging scoring
An experienced radiologist classified CT images into four categories based on the extent of pulmonary parenchymal involvement: Mild, moderate, severe, and critical. The mild stage included CT images with several ground-glass opacities (GGOs) and less than 25% lung tissue involvement. The moderate stage included CT images with GGOs and between 25% and 50% of lung tissue involvement. The severe stage showed both GGOs and areas of consolidation, with lung tissue involvement ranging from 50% to 75%. The critical stage included diffuse GGOs, consolidation, and reticular changes in the lungs, with lung tissue involvement exceeding 75% [13].

Outcomes
Patients were divided into two groups based on the outcomes: 1) Without disease progression, patients who fully recovered and discharged or showed stable symptomatic improvement, and 2) With disease progression, patients who had progressed to severe illness and stayed in the intensive care unit or died.

Ethical considerations
The study was approved by the Ethics Committee of Gonabad University of Medical Sciences. The ethical principles of human medical research (Helsinki Declaration) were observed in all study phases. All patients’ data were extracted confidentially and encrypted from the hospital information system (HIS). The patient’s diagnosis process was carried out under the guidelines published by the WHO and the Iranian Ministry of Health and Medical Education.

Statistical analysis
Descriptive statistics and bivariate analysis
The Kolmogorov-Smirnov test was used to examine the normal distribution for quantitative variables, including age, PaO2, temperature, duration of hospitalization, and laboratory values. The age distribution was reported as the Mean±SD. The two groups with and without disease progression were compared using the independent t-test. The median (25th percentile, 75th percentile) and the Mann-Whitney test were used to describe and compare other quantitative variables between the two groups, respectively. Describing and comparing qualitative variables were carried out using number (percentage) and the chi-square test, respectively. A two-sided P<0.05 was considered significant.

Development and validation of risk score prediction model 
After randomly dividing data into two parts (the discovery dataset with 80% of data and the validation dataset with 20%), the prediction model was developed using a logistic regression model on the discovery dataset. The variables with a P<0.2 in the simple logistic regression model were entered into a multiple logistic regression model. We considered a backward removal method with P<0.05 for entering variables and P<0.1 for removing variables into and from the multiple logistic regression model. Then, the coefficients obtained from the model were converted to an integer risk score. The highest sensitivity and specificity values were used to determine the optimum cut-off point for the risk score model [14]. Model calibration was assessed using the Hosmer-Lemeshow test with a P>0.05, indicating acceptable goodness of fit to the data [15]. The area under the receiver operator curve (AUC), with a minimum value of 0.70 as a desirable discrimination ability [14], was used to evaluate the discrimination ability of the risk score model in both discovery and validation datasets. The performance of the risk score prediction model was compared with each of the predictors used in the model, namely IL-6, NLR, and lung involvement. Additionally, the performance of the risk score was compared to two other laboratory variables, CRP and LDH, which were statistically significant in the bivariate analyses. The significance level was considered 0.05. All data analysis was performed using SPSS software, version 21.

Results
Individual and clinical characteristics of patients
Data were analyzed from 171 patients with COVID-19, of which 149(87.13%) were hospitalized in the isolation ward and 22(12.86%) in the ICU. Of 171 patients, 141(82.5%) were recovered or showed symptomatic improvement. Nine patients (5.2%) had progressed to severe disease, while 21(12.3%) died. The individual and clinical characteristics of the patients are shown in Table 1.











Bivariate analysis
The findings of bivariate analysis showed several distinguishing characteristics between patients whose COVID-19 progressed and those who were discharged or experienced symptomatic improvement. These characteristics included a higher age (P<0.001), lower median PaO2 values upon admission (P<0.001), lower prevalence of fever (P=0.018), and higher prevalence of respiratory distress upon admission (P=0.018). Additionally, there was a higher prevalence of preexisting lung diseases (P=0.004) and hypertension (P=0.005), more severe lung involvement (P<0.001), elevated levels of NLR (P=0.032), CRP (P=0.005), IL-6 (P<0.001), and LDH (P=0.027), reduced administration of Remdesivir (P=0.042), and increased administration of hydrocortisone (P=0.019) among those whose condition progressed. 

Development and validation of risk score prediction model 
Sixteen variables, including age, sex, use of opium, inpatient department, fever, respiratory distress, loss of consciousness, lung involvement, hypertension, chronic lung disease, Remdesivir, hydrocortisone, IL-6, CRP, LDH, and NLR in the simple logistic regression model with P<0.2 were selected as potential predictor variables to enter the multiple logistic regression model. Three variables, including lung involvement, NLR, and IL-6, were significant in the multiple logistic regression model and were set in the risk score formula (Table 2).


The risk score for disease progression was developed based on the regression coefficients of the multiple logistic regression model (Equations 1 and 2). 

1. Risk score=(0.433×IL-6)+(0.205×NLR)+(0.072 ×Long involvement)

The Equation 2 was obtained to calculate the probability of disease progression.

2. Probability=Exp(Risk score)/1+Exp(Risk score)
The Hosmer - Lemeshow test statistic indicated a well-fitting risk score model to the data (P=0.681).

The performance and validation of risk score
The AUC was 97.0 (95% CI, 93.5%, 1.00%) in the discovery dataset and 97.3 (95% CI, 92.3%, 100.0%) in the validation dataset, indicating a good performance in both discovery and validation datasets. The performance of combined variables of IL-6, NLR, and lung involvement as predictors of disease progression in the risk score prediction model was significantly higher than IL-6, NLR, lung involvement, CRP, and LDH alone (Figure 1). Table 3 listed the cut-off values of sensitivity, specificity, positive and negative predictive values of each of the mentioned variables, and AUC of the established prediction model.



Discussion
In this study, a risk score prediction model was established to aid in the identification of Iranian COVID-19 patients who are at a higher risk of disease progression at the time of hospitalization. The risk score model comprised three variables, IL-6, NLR, and lung involvement, and demonstrated an AUC of 97.0 (95% CI, 93.5%, 1.00%) in the discovery dataset and 97.3 (95% CI, 92.3%, 100.0%) in the validation dataset. These results indicate good performance in discovery and validation datasets, comparable to other models in the literature. A study has shown that predictive scores based on IL-6 and NLR, in combination with respiratory rate, SpO2/FiO2 ratio, and LDH, exhibit superior capability (AUC over 0.80) compared to other similar scores developed for the prediction of adverse outcomes in COVID-19, specifically in the prediction of invasive mechanical ventilation [16]. Another study has demonstrated that three models containing one of the parameters of NLR or IL-6, combined with three clinical parameters (age, sex, and SpO2), exhibit promising discrimination in predicting adverse outcomes in COVID-19 patients [17]. In another study, IL-6 has been identified as an independent predictor of COVID-19 mortality in a six-point prediction score model [18]. Additionally, a study has verified the predictive role of chest CT severity score in the need for invasive mechanical ventilation and mortality in COVID-19 patients, with an AUC of 0.759 [19]. It is said that the increase in NLR values is related to the decrease in lymphocytes, which have a crucial role in battling SARS-infected cells. The direct effect of SARS-CoV-2 on lymphocytes causing cell death, increasing lactic acid and inhibiting lymphocyte proliferation, and directly invading and destroying the lymphatic organs are the main reasons for decreasing lymphocytes in patients with severe COVID-19 [20]. Cytokines play critical roles in regulating immunological and inflammatory responses. IL-6 is a major inflammatory cytokine with pleiotropic effects implicated in coronavirus-induced storms [21, 22]. The cytokine storm, ie, high and uncontrolled levels of cytokines, triggers following the inflammatory responses induced in immune, epithelial, and endothelial cells due to SARS-CoV-2 invasion in the body [22]. Cardiovascular collapse and dysfunction syndromes of various organs, including renal and liver failure, are the consequences of high concentrations of the cytokine storm [23]. 
In this study, the bivariate analyses showed that disease progression was significantly associated with older age, consistent with other studies [24-26]. Older adults are more likely to develop more severe COVID-19 complications due to comorbidities, reduced physical functioning, poor body resistance, and a decline in angiotensin-converting enzyme-2 (ACE2) expression levels and anti-inflammatory response [4, 26].
Fever and respiratory distress were also associated with the risk factors of disease progression. Fever is the most frequent symptom in COVID-19 patients, which occurs in the immune system response to virus infections in the body [27]. Delay in seeking medical attention and the rapid disease progression in patients with COVID-19 could lead to an excessive inflammatory condition called a cytokine storm, which appears with an unlimited fever [28], promoting further inflammation and further immune activation with undesired effects [27]. Respiratory symptoms are also important indicators of the severity of the infection.
Similar to other studies [29, 30], patients with preexisting hypertension or chronic lung disease comorbidities were more susceptible to COVID-19 progression in the present study. Severe COVID-19 outcomes in patients with preexisting hypertension could be related to endothelial dysfunction and renin-angiotensin system (RAS) imbalance that favors a pro-inflammatory state, causing a higher level of IL-6 and tumor necrosis factor-α (TNF-α) [31, 32]. Elevating respiratory problems in COVID-19 patients with preexisting chronic lung diseases could worsen the condition and increase mortality [33].
Bivariate analyses demonstrated a significant association between elevated CRP and LDH values and disease progression in patients with COVID-19. In line with the findings of our study, numerous research [34-36] proposed that increased CRP and LDH values are related to higher levels of severity of COVID-19 and poorer outcomes. CRP is a plasma protein clinically used as a biomarker to identify disease severity in various inflammatory conditions [37]. COVID-19 progression may be accompanied by a cytokine storm, activation of the complement system, and amplifying inflammatory insults because of increasing CRP production through stimulating hepatocytes by cytokines such as IL-6 and TNFα. However, it is difficult to effectively state adaptive immunity in severe or critically ill COVID-19 patients due to severe damage to the integrity of the alveolar epithelial and endothelial barrier and significant decreases in lymphocyte counts with T cell-mediated immunosuppression. Therefore, it leads to severe macrophage infiltration and worsens acute lung injury [38]. LDH is an intracellular enzyme found in almost all body cells, and increasing its concentration may indicate damage to tissue/cells and viral infections or lung damage [39]. Nevertheless, the progress and prognosis of the pathogenic mechanism of LDH on COVID-19 remains unclear [40].
To date, corticosteroids and Remdesivir are the two most promising treatments for COVID-19 [41]. Nevertheless, as a result of the scarcity of randomized trials and inconclusive observational studies, the efficacy and safety of corticosteroids in viral pneumonia patients and Remdesivir’s risk and benefit for COVID-19 patients who require high-flow oxygen or mechanical ventilation are not certain [42, 43]. In this study, taking Remdesivir in patients with poorer outcomes was statistically significantly less, and hydrocortisone was more.
Based on the multivariate logistic regression model results, IL-6, NLR, and lung involvement on admission were the only independent biomarkers associated with COVID-19 progression. Our risk prediction model with these three variables achieved an acceptable receiver operating characteristic (ROC)-AUC of 0.90 in the discovery dataset and 0.89 in the validation dataset for disease progression prediction. However, for the validation dataset, confidence intervals were wider for indices, especially for sensitivity, which may be related to the smaller sample size in the validation dataset. Therefore, a larger sample size is needed to assess its applicability in future research.

Conclusion 
We identified a risk score that may represent a potential diagnostic tool in the clinical setting to identify COVID-19 patients at a higher risk of disease progression. This risk score model might have significant potential in public health. It can aid in making informed decisions and providing targeted interventions while maximizing efficiency and utilizing available resources. Considering other changes in biomarkers and clinical and diagnostic symptoms of patients with COVID-19 can help us determine the exact indicators of the disease progression.

Strengths and limitations
This study had some strengths. One of this study’s strengths was the study’s prospective nature, which added to the validity of data and findings. This study validated the risk score, and all indices for assessing the risk score performance were reported with a confidence interval. An additional strength was the simplicity of the achieved risk score. The study had some limitations, too. First, it was conducted in a single center. Second, in this study, the risk score model had an acceptable ROC-AUC in the discovery and validation datasets for disease progression prediction. However, for the validation dataset, confidence intervals were wider for indices, especially for sensitivity, which may be related to the smaller sample size in the validation dataset. Therefore, a single-center study and a small sample size weakened the generalizability of the study. Thus, future studies with national data and larger sample sizes are needed.

Ethical Considerations
Compliance with ethical guidelines
This study was approved by the Ethics Committee of Gonabad University of Medical Sciences (Code: IR.GMU.REC.1399.079). All phases of this study were based on the ethical principles of human medical research (Helsinki Declaration). Each patient or their respective families provided informed consent.

Funding
This study was conducted with support from the Social Development & Health Promotion Research Center, Gonabad University of Medical Sciences (No.: 139932).

Authors' contributions
Idea presentation: Mohsen Sahebanmaleki, Sina Sabeti Bilondi and Ali Delshad Noughabi; Study design and conceptualization: Fatemeh Mohammadzadeh, Mitra Tavakolizadeh, and Jafar Hajavi; Data collection: Mohsen Sahebanmaleki, Sina Sabeti Bilondi, Ali Delshad Noughabi and Hosein Aalami; Data analysis and writing the original draft: Fatemeh Mohammadzadeh; Review, editing and final Approval: All authors.

Conflict of interest
The authors declared no conflict of interest.

Acknowledgments
The authors gratefully acknowledge the Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, and all the people who participated in the study.


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