Abstract
BACKGROUND:
It is crucial to examine and classify patients as soon as possible to save their lives when they display Coronavirus Disease of 2019 (COVID-19) symptoms. The Altered sense of smell/taste, Inflammation, Fever, Elevated Lactate dehydrogenase, and Lymphocytopenia (AIFELL) evaluation tool is quick, easy, and simple for medical professionals.
OBJECTIVES:
Determine the relationship between the COVID-19 patient confirmation and the AIFELL score. Examine any relationships between the AIFELL score and the degree of mortality.
MATERIALS AND METHODS:
A retrospective study was conducted on 970 hospitalized (18 years or older) with a COVID-19 diagnosis in 2021. Patients admitted to the intensive care unit (ICU) as critical cases and moderate cases. The Chi-square test was utilized.
RESULTS:
The scores of the AIFELL tool ranged from 0 to 6 points; the AIFELL score for COVID-19 symptoms with a high score (4–6) made up 41.5% of the patients. More than half of the patients (58.7%) were men; the oldest age group ranged from 40 to 50 years. A very high risk of dying due to a positive COVID-19 virus exists in more than a fifth of patients (21.5%). The ICU (37.5%) received around a third of the patients. The findings showed significant associations between levels of mortality risk and gender and age. There were significant associations between AIFELL scores and mortality risk levels. AIFELL scores, mortality risk levels, and patient admissions to the critical care unit were strongly associated.
CONCLUSION:
The AIFELL scores were excellent for predicting COVID-19 mortality risk levels and ICU admission.
Keywords: 4C mortality scores, AIFELL scores (triage tool), COVID-19, ICU admission, patients
Background
Since March 7, 2021, more than 116 million people have been infected globally with the coronavirus (COVID-19), and about 2.6 million COVID-19-infected patients have died.[1,2] Globally, 6,938,353 deaths and 767,364,883 confirmed cases of COVID-19 had been reported to the World Health Organization (WHO) as of May 31, 2023. Despite efforts to control the spread of the virus, it remains a significant public health challenge in the world, including Jordan and India. Jordan had 1,746,997 cases between January 3, 2020, and May 31, 2023, with 14,122 deaths reported to the WHO. India had 44,990,278 confirmed cases between January 3, 2020, and May 31, 2023, with 531,867 deaths reported to the WHO.[3]
Coronavirus cases can be classified into four levels of severity: mild, common, severe, and critical.[2] The mortality rates resulting from the complications of COVID-19 ranged from 3% in China to 10% in Spain.[4,5] An anticipated 10–15% of mild cases of COVID-19 deteriorated to become severely ill, and 15–20% of severely ill cases deteriorated to critical cases. Many critical cases within the latter stage required intensive care units (ICU) treatments.[6]
Statistics indicated that around 80% of well-defined cases of COVID-19 could be treated as outpatients. Up to 15% of infected patients needed hospitalization, and 5% should have been treated in ICUs because they were critically ill.[7]
The clinical manifestations of COVID-19 at the beginning of the disease are not easily distinguished from other respiratory tract problems. Using a triage tool, healthcare workers (HCWs) can categorize cases of respiratory tract infections and predict the risk of deterioration (i.e., the risk of death) to aid in inappropriately making patient admission and discharge decisions and justifying the choice of medical management.
Altered sense of smell/taste, Inflammation, Fever, Elevated Lactate dehydrogenase, and Lymphocytopenia (AIFELL), a triage tool that can be used easily in emergency rooms (ERs), was used to select possible COVID-19 cases. A score of ≥4 points/criteria was highly associated with possible COVID-19, while scores between 0 and 3 were associated with other respiratory conditions. The score was calculated by counting the number of positive criteria for the following elements: Altered sense of smell/taste, Inflammation (C-reactive protein ≥30 mg/l), fever (≥38.0°C), or history of fever in the last three days, elevated lactate dehydrogenase, and lymphocytopenia.[8] However, it is important to note that a positive score does not necessarily confirm a diagnosis and further testing may be required. This tool can be beneficial when there are not enough resources to treat everyone who needs to be hospitalized. The purpose of the AIFELL hospital's triage process is to determine which patients need emergency care the most urgently.
According to Siddiqi and Mehra (2020),[9] the patient diagnosed with COVID-19 reported that the course of COVID-19 disease ranged from mild to severe stages. The criteria for these stages were the following: a patient was considered in Stage 1 (i.e., asymptomatic or mild symptoms) when he or she had an early infection/incubation period; a patient was considered in Stage 2a when he or she developed a pulmonary disease (e.g., viral pneumonia without and with hypoxia); a patient was considered in Stage 2b when he or she had moderate symptoms with usual cough, fever, and sometimes dyspnea; a patient was considered in Stage 3 when he or she had severe disease with extra-pulmonary systemic hyper-inflammation acute respiratory distress syndrome (ARDS), shock, progressive respiratory, or cardiac failure possible.[9]
Age and gender were reported to be significant risk factors for COVID-19 deaths early on in the pandemic, with a higher risk for males and an exponentially increasing risk with age.[10,11]
The current study is important because it aids in the classification of cases by medical personnel, which helps save lives, ensure that patients receive the appropriate level of care, support the creation of individualized treatment plans, and enhance the quality of healthcare outcomes. It uses a triage tool based on laboratory findings and clinical examinations conducted on patients in emergency situations, converting the results into a number that can be used to determine the patient's status in terms of risk. Additionally, this can guarantee that patients get the right amount of care.
Due to the COVID-19 pandemic's impact on emergency departments, which resulted in a high volume of patients with mild respiratory symptoms as well as those who had more severe symptoms, it is critical to experiment with and test this tool in order to determine which patients require immediate medical attention and which ones can wait safely. Additionally, separating patients within the hospital can help with some issues related to the epidemic's spread, such as hospital overcrowding and the spread of disease. The virus is present inside the hospital and is spreading to the medical staff.
Clinical prediction scores help medical professionals decide which patients to admit during the COVID-19 pandemic. A triage score, “AIFELL,” was created to combine laboratory, radiological, and clinical data. To the best of our knowledge, there has never been a literature review that looked at how well this tool can predict COVID-19 and the risk of mortality. Therefore, it is essential to investigate its potential use in predicting COVID-19 outcomes.
Objectives
Identify the association between the mortality risk levels [very high risk (critical), high risk (severe), intermediate risk (common), and low risk (mild)], and the selected demographic characteristics (gender and age) of patients infected by COVID-19.
Identify the association between possible COVID-19 cases for hospitalization by using AIFELL scores (≥4 and <3) and the selected demographic characteristics (gender and age) of patients infected by COVID-19.
Identify the association between possible COVID-19 cases for hospitalization using AIFELL scores and the mortality risk levels.
Identify the association between AIFELL scores and ICU admission.
Identify the association between AIFELL scores and the course of COVID-19.
Significance
There is a need in ERs for a triage tool that helps HCWs quickly identify patients with COVID-19 who are at a high risk of dying during current and subsequent waves of the pandemic at the time of hospital admission. Early detection of COVID-19 patients who may develop a critical illness is critical and may help provide correct treatments and optimize resource use.
Triage scores will support patient assessment and help determine the need for further diagnostic prevention and treatment decision steps. Predicting the risk of deterioration (i.e., mortality risk) will assist in patient admission and discharge decisions and justify the selection of medical management.
Our purpose in this current study is to investigate the association between the mortality risk levels and AIFELL score, thus helping to bring the attention of HCWs to offer earlier treatment before the mild disease progresses to a severe stage.
Materials and Methods
Study design and setting
A retrospective design was utilized in the current study. The researchers reviewed the clinical history, laboratory, and instrumental variables of all patients aged ≥18 years old diagnosed with COVID-19 who were admitted to a large hospital in Amman, Jordan.
Prince Hamzah Hospital is a facility equipped to receive patients who have been diagnosed and tested positive for COVID-19. Patients have been isolated in this hospital to cover Jordan's capital. The hospital can accommodate 60 patients and has staff members trained to manage COVID-19 cases. It was created as a part of Jordan's efforts to stop the virus' spread and offer those infected quality medical care. The center is prepared to prevent the transmission of the virus to the rest of the hospital's wards, assuring that all patients in the hospital are within the safety procedures. The center is sterilized and fully equipped for quarantine. The isolation wards in the hospital have been equipped with 50 beds for COVID-19. The number of beds dedicated to COVID-19 patients progressively increased with the diffusion of the epidemic to a peak capacity of about 436 beds.
Study participants and sampling
The researchers assumed that the level of significance alpha is equal to 0.05, which represents the probability that the observed relationship could result from chance. Based on the G power (i.e., a software for calculating statistical power, determining sample size, and offering accurate analysis for various study designs), the sample size should equal 870. To exclude files that did not meet the criteria below or missing data from the patients' medical records, we reviewed 1,100 files. The final sample size was equal to 970.
The inclusion criteria were: age more than 18 years old and admitted to the hospital as a case of COVID-19 infection; a positive SARS-CoV-2 swab result at admission; blood sample results of at least two of the three of the following laboratory tests: C-Reactive Protein (CRP), Lactate Dehydrogenase (LDH), and total lymphocyte count; existing chest images (X-ray or computed tomography of the thorax); and body temperature.
Exclusion criteria were: patients with a tumor history and complained of digestive system surgery history, patient records with fewer than two laboratory tests (CRP, LDH, and total lymphocyte count), missing chest images, or missing documentation of body temperature. In addition, patients' data were excluded from the analyzes if COVID-19 was not the main reason for hospital admission.
Data collection tools and technique
Three tools were used in the current study to achieve the study objectives. The first one assessed the demographic information about the patients (i.e., participants). The researchers developed this tool to understand better patients' specific background characteristics, such as age, income, work situation, marital status, and chronic diseases. Furthermore, epidemiological, clinical laboratory, radiological characteristics, treatments, and outcomes data were obtained using standardized data collection forms (i.e., modified case record form for severe acute respiratory infection clinical characteristics shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium).
The second tool was the 4C (Clinical Characterization Consortium for Coronavirus) mortality score. This tool was built based on the outcome of the pneumonia risk stratification scores, the International Federation for Emergencies and Acute Respiratory Infection, and the WHOs clinical characterization protocol.[12,13]
The 4C mortality score is a reliable predictor of mortality in COVID-19 patients and can help healthcare providers make informed decisions about patient care. It considers various clinical factors such as age, sex, and comorbidities to assess a patient's risk of death accurately.[12,13]
The purpose of the 4C tool was to convert complicated medical pictures into actual numerical values to help HCWs to identify the risk of dying for the patient. The validity of the 4C tool was 0.79.[6,12,13] The final 4C mortality score which contained the patient demographics, clinical observations, and blood parameters (routinely collected and available at the initial visit to ER), allowed for a calculation score to be generated early in the patient's course. The range of the 4C mortality score was from 0 to 21 points. The mortality risk was classified into four classifications: a) low mortality risk (score 0–3), b) intermediate risk (score 4–8), c) high risk (score 9–14), and d) very high risk (score 15–21).
The third tool was AIFELL, a simple triage instrument for an ER setting. The score was calculated by counting the number of positive criteria for the following elements: Altered sense of smell/taste, inflammation (C-reactive protein ≥30 mg/l), fever (≥38.0°C), or history of fever in the last three days, elevated lactate dehydrogenase, and lymphocytopenia.[8] The scores range from 0 to 6 points. A score of ≥4 points/criteria is highly associated with possible COVID-19, while scores between 0 and 3 were associated with other respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD), bronchial asthma, bacterial pneumonia, aspiration pneumonitis, and other viral infections. The strengths of the AIFELL score were its straightforwardness, immediate availability, and broad applicability due to its simple components.[8]
To ensure the validity and reliability of the tools, the researchers define the research questions, build the instruments on valid and published data, and then perform construct validity, which is concerned with whether the tools measure what they are supposed to measure. The evaluators of the tools were HCWs in clinical and academic fields. Researchers also conducted a pilot study to test the tools and make necessary modifications before administering them to the evaluators. The rigorous process ensured that the tools were reliable and valid for use in healthcare settings.
Two researchers collected data from the electronic medical records of Prince Hamza Hospital Center located in Amman, Jordan, between February 2021 and October 2021. A clarification was requested from HCWs when data were missing from the medical records. Data were collected by direct communication with attending physicians and other HCWs.
Ethical considerations
All the data were coded and analyzed. The results were offered as group data. Privacy of all information was maintained throughout the study, and patients' identity was anonymous. For security purposes, the data were kept in a locked cabinet. Only the researchers had access to this cabinet.
Pilot study
The pilot study was conducted before the data collection using 10% of the sample size, equal to 90 patient records. The pilot study aimed to know the clarity of the tools and to assess the feasibility of the data collection procedure. The psychometric characteristics of the tools were examined using the Statistical Package for the Social Sciences (SPSS) program version 26.[14]
To determine whether the tools were reliable, the internal consistency of the tools Cronbach's Alpha was used. The Cronbach's Alpha showed high levels for the AIFELL and the 4C mortality risk tools. The results indicated that Cronbach's Alpha of the AIFELL scores and the 4C mortality risk tools were 0.87 and 0.91, respectively, which means a high internal consistency.
Data analyzes
Data were received from research assistants daily and cleaned and coded by the researchers. Data were reviewed to identify incomplete and incorrect parts of the data. Each file was assigned a specific code number. The SPSS version 26.0 for Windows[14] was used for the data analyzes. All the study variables were clearly labeled and recognized in one SPSS computer file (data set) according to the level of measurement. It is essential to document that all the information regarding variables' names, coding values, and other related information was saved digitally in a comprehensive manner.
Before the official analyzes, preliminary analyzes were conducted to summarize the data statistically, examine the trends, and check for input data errors. The first stage of analysis involved checking the distributions and frequencies of variables. These were reported in terms of the numbers and percentages (for nominal measures). The Chi-square test was utilized to assess the relationships between dependent and independent variables.
Results
Demographic characteristics
The current study sample consisted of 970 Jordanian patients with positive COVID-19. The demographic/independent variables (gender and age) and the dependent variables (AIFELL scores, mortality risk level, and admission) of the study sample are summarized in Table 1. More than half of the patients were male (n = 569, 58.7%), the largest age group ranged from 40 to 50 years (n = 316, 32.6%), and the AIFELL scores (i.e., triage tool) for symptomatic COVID-19 who had a high score (4–6) were (n = 403, 41.5%).
Table 1.
Distribution of the numbers and percentages of the demographic (Independent) and dependent variables of the sample (n=970)
| Demographic (Independent) and Dependent Variables | |||
|---|---|---|---|
| Demographic (Independent) Variables | n | Percentages (%) | |
| Gender | Male | 569 | 58.7 |
| Female | 401 | 41.3 | |
| Age | 18–28 years | 28 | 2.9 |
| 29–39 years | 166 | 17.1 | |
| 40–50 years | 316 | 32.6 | |
| 51–61 years | 246 | 25.4 | |
| 62–72 years | 108 | 11.1 | |
| More than 72 years | 106 | 10.9 | |
|
| |||
| Dependent Variables | |||
| AIFELL Scores | 0–3 (respiratory problems like COPD, bronchial asthma, bacterial pneumonia, and other viral infections) | 567 | 58.5 |
| 4–6 (highly associated with positive COVID-19 thus justifying hospitalization) | 403 | 41.5 | |
| Mortality Risk Levels | Very high risk (Critical) | 209 | 21.5 |
| High risk (Severe) | 145 | 15 | |
| Intermediate risk (Common) | 211 | 21.8 | |
| Low risk (Mild) | 405 | 41.7 | |
| Admission | ICU | 364 | 37.5 |
| Ward | 606 | 62.5 | |
More than one-fifth of the patients (n = 209, 21.5%) had a very high mortality risk from positive COVID-19. Around one-third of all patients (n = 364, 37.5%) were admitted to the ICU, of whom 50% needed invasive mechanical ventilation. The most common complications were acute kidney injury and ARDS.
Association between the mortality risk levels and the selected demographic characteristics of patients infected by COVID-19
The results presented in Table 2 revealed the association between the mortality risk levels and the patients' demographic characteristics. The findings indicated that gender and age were significantly associated with the mortality risk levels (P = 0.00).
Table 2.
Association between the mortality risk levels and the demographic characteristics of patients infected by COVID-19 using Chi-square (n=970)
| Demographic variables | Mortality risk levels |
Total | P | ||||
|---|---|---|---|---|---|---|---|
| Low risk | Intermediate risk | High risk | Very high risk | ||||
| Gender | Male | 183 | 108 | 113 | 165 | 569 | **0.000 |
| Female | 222 | 103 | 32 | 44 | 401 | ||
| Total | 405 | 211 | 145 | 209 | 970 | ||
| Age | 18-28 years | 13 | 8 | 4 | 4 | 29 | **0.000 |
| 29-39 years | 66 | 39 | 26 | 35 | 166 | ||
| 40-50 years | 151 | 71 | 39 | 54 | 315 | ||
| 51-61 years | 78 | 45 | 49 | 74 | 246 | ||
| 62-72 years | 48 | 19 | 19 | 22 | 108 | ||
| More than 72 years | 49 | 29 | 8 | 20 | 106 | ||
| Total | 405 | 211 | 145 | 209 | 970 | ||
Note: **P<0.05
Association between possible COVID-19 cases for hospitalization by using AIFELL scores and the selected demographic characteristics of patients infected by COVID-19
The results presented in Table 3 indicated a significant association between AIFELL scores and gender and age (P = 0.00).
Table 3.
Association between possible COVID-19 cases for hospitalization by using AIFELL scores and the demographic characteristics of patients infected by COVID-19 using Chi-square (n=970)
| Demographic variables | AIFELL scores |
Total | P | ||
|---|---|---|---|---|---|
| 0–3 Associated with other respiratory conditions | 4–6 Associated with COVID-19 | ||||
| Gender | Male | 251 | 316 | 567 | **0.000 |
| Female | 318 | 85 | 403 | ||
| Total | 569 | 401 | 970 | ||
| Age | 18–28 years | 19 | 9 | 28 | **0.000 |
| 29–39 years | 92 | 74 | 166 | ||
| 40–50 years | 211 | 105 | 316 | ||
| 51–61 years | 114 | 132 | 246 | ||
| 62–72 years | 60 | 48 | 108 | ||
| More than 72 years | 71 | 35 | 106 | ||
| Total | 567 | 403 | 970 | ||
Note: **P<0.05
Association between possible COVID-19 cases for hospitalization using AIFELL scores and the mortality risk levels
The results presented in Table 4 revealed a significant association between the AIFELL scores and the mortality risk levels (P = 0.00).
Table 4.
Association between possible COVID-19 cases for hospitalization using AIFELL scores and the mortality risk levels using Chi-square (n=970)
| Mortality risk levels | AIFELL scores |
Total | P | |
|---|---|---|---|---|
| 0–3 Associated with other respiratory conditions | 4–6 Associated with COVID-19 | |||
| Low (Mild) | 398 | 7 | 405 | **0.000 |
| Intermediate (Common) | 162 | 49 | 211 | |
| High (Severe) | 4 | 141 | 145 | |
| Very High (Critical) | 3 | 206 | 209 | |
| Total | 567 | 403 | 970 | |
Note: **P<0.05
Association between AIFELL scores and ICU admission
The results presented in Table 5 showed a significant association between the AIFELL scores and the admission of patients to the ICU (P = 0.00).
Table 5.
Association between AIFELL scores and ICU admission using Chi-square (n=970)
| ICU admission | AIFELL scores |
Total | P | |
|---|---|---|---|---|
| 0–3 Associated with other respiratory conditions | 4–6 Associated with COVID-19 | |||
| Yes | 32 | 332 | 364 | **0.000 |
| No | 535 | 71 | 606 | |
| Total | 567 | 403 | 970 | |
Note: **P<0.05
Association between AIFELL scores and the course of COVID-19
The results presented in Table 6 revealed an association between AIFELL scores and the course of COVID-19 (P = 0.00). This association indicated that AIFELL scores between 4 and 6 at admission were linked with developing high-risk/severe and very high-risk/critical disease stages (i.e., Stages 2b and 3) during the hospital stay.
Table 6.
Association between AIFELL scores and the course of COVID-19 using Chi-square (n=970)
| AIFELL scores | Course of COVID-19 |
Total | P | |||
|---|---|---|---|---|---|---|
| Stage 1 | Stage 2a | Stage 2b | Stage 3 | |||
| 0-3 Associated with Other Respiratory Conditions | 240 | 250 | 21 | 56 | 567 | **0.000 |
| 4-6 Associated with COVID-19 | 1 | 0 | 178 | 224 | 403 | |
| Total | 241 | 250 | 199 | 280 | 970 | |
Note: **P<0.05
Discussion
The current study's findings reported a significant association between COVID-19 and the mortality of elderly males (i.e., above 60 years). This result is consistent with previously published results.[15,16] Older age is a risk factor for COVID-19 mortality because of the decrease in the immune system efficiency in older patients.[16]
Furthermore, the results of the current study showed that compared with female patients, male patients had a higher mortality rate. This result is consistent with other studies[17,18,19,20] that suggested a possible protective effect of estrogen.
Respiratory complications such as respiratory failure have been found clinically as one of the significant mortality causes of COVID-19.[21] Clinical presentations of COVID-19, especially among outpatients, can vary greatly.[22]
Mass gatherings and crowds are common in contemporary societies, increasing the possibility of spreading COVID-19 diseases. Strategies to stop the spread of COVID-19 and provide comprehensive medical care are essential. Inadequate infrastructure and population control can cause harm or even death, especially in developing countries. It is essential to educate the public about the importance of adhering to safety protocols during events and gatherings, such as limiting the number of attendees, ensuring proper ventilation, and providing easy access to emergency exits.[23] According to the current study results, a score of AIFELL from 4 to 6 points at admission or ER would need more medical care and admission to ICU. In contrast, patients with AIFELL scores that range from 0 to 3 may need standard floor care or may be discharged due to a better prognosis.
Patients receiving care in the emergency department have the right to have an accurate diagnosis as quickly as possible and at the lowest cost. The COVID-19 triage tool is necessary to recognize possible COVID-19 cases that will progress to more critical stages (2a, 2b, and 3). The variables of the AIFELL tool are usually available in both the ER and general practice settings. During the COVID-19 pandemic, the AIFELL score may help select symptomatic COVID-19 cases from patients with respiratory symptoms in the ER or general practice setting.
A study was conducted to investigate the patients' rights in the emergency department of a public hospital in a Middle Eastern country. It found that patients' rights were often violated in some ways, including a lack of privacy and confidentiality and poor communication. Healthcare professionals should receive adequate training about the patient's rights regarding a correct and rapid diagnosis. Clear communication between healthcare professionals and patients is crucial to establishing trust and providing effective care.[24]
A significant association between the course of COVID-19 and AIFELL scores means that patients need more intensive care. Patients need oxygen supplementation due to hypoxia during hospitalization (COVID-19, Stages 2b and 3).
The results of the current study indicated that the AIFELL tool can predict the course of COVID-19 disease during hospitalization. The AIFELL tool can predict the COVID-19 disease severity and its progression in hospitalized patients. Therefore, the AIFELL tool can be used for different purposes, such as COVID-19 triage and prediction of patient health status.
A study conducted by Molavi-Taleghani et al. (2020) evaluated the risks of procedures in the Department of Pediatric Surgery to raise the standard of care it found that technical issues, organizational issues, human factors, and other factors increase the degree of risk. Frequent training sessions and workshops should be conducted to ensure all employees are knowledgeable about the most recent protocols to improve the classification of emergency department cases at risk of death and those with COVID-19 infection.[25]
Healthcare workers' training and ongoing learning are essential for new assessment tools or clinical interventions. A study found that field massage can help with nursing interventions and has a calming effect on premature babies. The AIFELL triage tool should be taught to all HCWs in emergency departments and in simulation-based training programs. These interventions can improve patient outcomes and the overall quality of care in emergency departments.[26]
A study examined the relationship between the quality of working life and organizational commitment of prehospital paramedic personnel in emergency medical systems affiliated with the Kerman University of Medical Sciences, Iran, during the COVID-19 pandemic. 200 participants were randomly selected according to Morgan's table and Meyer and Allen's organizational commitment questionnaires and Bolton's quality of working life questionnaires were used. Normative commitment had the highest mean score, while emotional commitment had the lowest average. Skills development opportunities and continuous learning had the highest mean score, while wage and material benefits had the lowest average. There was a significant relationship between organizational commitment and the quality of working life. To increase the quality of working life and organizational commitment of emergency medical personnel, more emphasis should be placed on employee participation in decision-making, skill development opportunities, continuous learning, and job security. Additionally, organizations should foster a supportive and positive work environment that values employee well-being and recognizes their contributions.[27]
The AIFELL tool must be used in ER because detecting critical patients in the ER for ICU admission is a challenge for HCWs since finding a positive COVID-19 patient requires many hours (>6 hours in our setting).
Coronavirus was one of the disasters that negatively affected the global health system. Disaster risk management and training of health personnel are essential for the healthcare system to be prepared for future health crises. This management includes providing and developing disaster risk management plans, upgrading infrastructure, conducting evaluations to identify areas for improvement, conducting regular drills and simulations to test the effectiveness of the plans, and establishing partnerships with other organizations and agencies to enhance coordination and collaboration during emergency situations.[28]
Limitation and recommendation
A large sample size was used in the current study to provide a high statistical power to investigate the associations between dependent (AIFELL scores, mortality risk level, and admission) and independent (gender and age) variables. To the best of our knowledge, the current study is the first one to date that has been conducted to test a triage and prediction tool for COVID-19 in clinical settings in Jordan.
The main limitation of the current study is that data were obtained from one center established in a hospital with a limited patient count (n = 970). Due to its retrospective nature, some values were only obtained from some patient records.
Implications for public health and research
Healthcare workers in ER should be encouraged to use the AIFELL tool because of its simplicity, availability, and extensive applicability due to its clear components. Furthermore, we recommend other researchers test the AIFELL scores in larger cohorts of patients to expand more dependable data regarding its diagnostic accuracy.
The probability of COVID-19 death in all age groups was approximately similar to that for death from other respiratory causes combined during the period of this study, but lower than the probability of death from cancer and cardiovascular diseases. Therefore, we recommend conducting further research to compare the mortality risk levels among communicable diseases (e.g., COVID-19) and non-communicable diseases (e.g., diabetes). Conducting further research will play a significant role in clarifying and increasing the perception of the society in Jordan about the necessity of COVID-19 preventive procedures to keep and promote health in Jordan.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
We want to express our special thanks to the Hashemite University located in Zarqa, Jordan for supporting us in completing this study.
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